© 2011

Aditya U. Vanarase

ALL RIGHTS RESERVED

DESIGN, MODELING AND REAL-TIME MONITORING OF CONTINUOUS

POWDER MIXING PROCESSES

by

ADITYA U. VANARASE

A dissertation submitted to the

Graduate School – New Brunswick

Rutgers, The State University of New Jersey

in partial fulfillment of the requirements

for the degree of

Doctor of Philosophy

Graduate Program in Chemical and Biochemical Engineering

written under the direction of

Prof. Fernando J. Muzzio and Prof. Marianthi Ierapetritou

And approved by

____________________________

____________________________

____________________________

____________________________

____________________________

New Brunswick, New Jersey

October 2011

ii

ABSTRACT OF THE DISSERTATION

DESIGN, MODELING AND REAL-TIME MONITORING OF CONTINUOUS

POWDER MIXING PROCESSES

By Aditya U. Vanarase

Dissertation Directors

Prof. Fernando J. Muzzio and Prof. Marianthi G. Ierapetritou

Continuous processing is an advantageous alternative for the current methods used in the

pharmaceutical manufacturing. Important advantages that it offers include smaller

equipment footprint, reduced efforts in the scale-up work, and the potential to utilize

already continuous processes to make the entire manufacturing more efficient. In the

current pharmaceutical manufacturing environment, powder mixing process is carried out

in the batch mode. The necessary methods and guidelines to design an equivalent

continuous process are not well established.

The work presented in this dissertation focuses on the characterization, design and

optimization of a continuous powder mixing process for pharmaceutical powders. A

systematic study was performed of the effects of process and design variables, and

material properties involved in the continuous powder mixing process. The bulk powder

flow behavior was characterized using the residence time distribution (RTD)

measurement approach. Impeller speed, material bulk density and impeller design greatly

influenced the mean residence time. With increasing impeller speed, mechanical

fluidization was observed, which significantly affected axial dispersion coefficients.

Intermediate rotation rates exerted maximum strain on the material, which leads to

maximum homogenization. The strain measurements correlated well with the properties

iii

of tablets including content uniformity and tablet hardness. Mixing performance was

largely dominated by the material properties of the mixture, and the blend uniformity

measurement was affected by the sample size analyzed. An experimental protocol was

developed to measure the blend uniformity in the in-line mode, and a methodology was

further built to quantitatively relate the in-line NIR measurements with the off-line wet

chemistry measurements. Considering the shear limitations of the continuous bladed

mixer, alternative blending strategies, suitable for blending of cohesive materials were

also demonstrated. A combination of a high-shear mixing followed by a low-shear

mixing process provided the optimal mixing performance.

The predictive understanding of the continuous powder mixing process developed in this

dissertation can assist towards the design and development of a fully controlled

continuous manufacturing process.

iv

Acknowledgements

First of all, I thank my research advisors, Prof. Fernando Muzzio and Prof. Marianthi

Ierapetritou for their guidance, continuous motivation and support throughout the course

of my PhD. I especially thank Prof. Muzzio for helping me improve my writing and

presentation skills. I also thank the Engineering Research Center (ERC) for proving

funding for my research and travel, and for providing a great number of opportunities to

interact with the folks from industry. I thank Prof. Benjamin Glasser for assessing my

PhD proposal, and helping me build my dissertation. I also thank my external committee

members Prof Rajesh Dave and Dr. Ralf Weinekötter for becoming referees for my

dissertation defense.

I extend my sincere gratitude and appreciation to my collaborators Prof. Rodolfo

Romañach, Prof. Rohit Ramachandran and Dr. Janne Paaso. Furthermore, a special vote

of appreciation to Prof. Rodolfo Romañach and Dr. Janne Paaso for the technical

discussions on the NIR spectroscopy and their assistance in chemometric modeling. I

thank all my undergraduate students and summer interns, Albert Gasser, Sabin Mathew,

Rizwan Aslam and Rocio Arroyave for assisting me in all the experimental work.

Many thanks for the support of my fellow graduate researchers, Amit Mehrotra, Patricia

Portillo, Marcos Llusa, Bill Engisch, Juan Osorio, Alisa Vasilenko, Fani Boukouvala,

Yijie Gao, Matt Metzger, Brenda Remy, and post-docs Atul Dubey, Eric Jayjock,

Athanas Koynov, Kalyana Pingali, and Rafael Mendez. Finally I dedicate my PhD to my

parents, and my sister Isha who always supported me and encouraged me to get a

doctorate degree. Thank you!

v

Table of Contents

ABSTRACT OF THE DISSERTATION ....................................................................... ii ACKNOWLEDGEMENTS ............................................................................................ iv

TABLE OF CONTENTS ................................................................................................. v LIST OF TABLES .......................................................................................................... vii LIST OF FIGURES ....................................................................................................... viii CHAPTER 1 INTRODUCTION................................................................................. 1

1.1 Motivation ...................................................................................................................... 1 1.2 Mathematical modeling in continuous powder mixing .................................................. 4

1.2.1 Theoretical developments .............................................................................................. 4 1.2.2 RTD modeling ............................................................................................................... 6

1.3 Experimental characterization of continuous powder blending process ........................ 8 1.3.1 Experimental studies on the performance and RTDs of in continuous powder mixers . 8 1.3.2 PEPT and PIV studies on the bladed mixers ............................................................... 11 1.3.3 Lubricant blending ....................................................................................................... 12 1.3.4 Blending of cohesive powders in continuous mixing systems ..................................... 14

1.4 On-line process monitoring of powder blending processes ......................................... 14 CHAPTER 2 CHARACTERIZATION OF POWDER FLOW BEHAVIOR IN

THE CONTINUOUS MIXER ........................................................... 18 2.1 Equipment and experimental set-up ............................................................................. 19 2.2 Materials and methods ................................................................................................. 20

2.2.1 Materials ...................................................................................................................... 20 2.2.2 Methods ....................................................................................................................... 20

2.3 RTD, Hold-up and number of Blade passes measurement........................................... 21 2.3.1 RTD measurement ....................................................................................................... 21 2.3.2 Hold-up measurement .................................................................................................. 22 2.3.3 Strain measurement ...................................................................................................... 23

2.4 RTD modeling methodology ....................................................................................... 24 2.5 Experimental conditions .............................................................................................. 25 2.6 Results .......................................................................................................................... 25

2.6.1 Effects of process parameters on flow behavior .......................................................... 25 2.6.2 Effects of design parameters on flow behavior ............................................................ 28 2.6.3 Effects of material properties on flow behavior ........................................................... 30 2.6.4 Predictive model for blend uniformity suitable for control purposes ........................... 35 2.6.5 Conclusions .................................................................................................................. 36

2.7 Figures for Chapter 2 ................................................................................................... 39 2.8 Tables for Chapter 2 ..................................................................................................... 51

CHAPTER 3 CHARACTERIZATION OF THE POWDER FLOW BEHAVIOR

IN THE CONTINUOUS BLENDER USING DEM ........................ 53 3.1 Methods ....................................................................................................................... 53

3.1.1 Simulation set-up ......................................................................................................... 53 3.1.2 The Discrete Element Method (DEM) ......................................................................... 54

3.2 Results .......................................................................................................................... 58 3.2.1 Data Acquisition and processing .................................................................................. 58 3.2.2 Mean residence time .................................................................................................... 59 3.2.3 Number of blade passes ............................................................................................... 60 3.2.4 Mean centered variance ............................................................................................... 61

3.3 Conclusions .................................................................................................................. 62 3.4 Figures for Chapter 3 ................................................................................................... 63 3.5 Tables for Chapter 3 ..................................................................................................... 68

vi

CHAPTER 4 CHARACTERIZATION OF THE MIXING PERFORMANCE OF

THE CONTINUOUS MIXER ........................................................... 69 4.1 Methods ....................................................................................................................... 69

4.1.1 NIR Spectroscopy ........................................................................................................ 69 4.1.2 LIBS (Laser Induced Breakdown Spectroscopy) ......................................................... 70 4.1.3 Washburn‘s method ..................................................................................................... 71

4.2 Results .......................................................................................................................... 72 4.2.1 APAP mixing ............................................................................................................... 72 4.2.2 Lubricant mixing .......................................................................................................... 77

4.3 Conclusions .................................................................................................................. 81 4.4 Figures for Chapter 4 ................................................................................................... 84 4.5 Tables for Chapter 4 ..................................................................................................... 91

CHAPTER 5 CONTINUOUS MONITORING OF POWDER MIXING

PROCESS BY NIR SPECTROSCOPY ............................................ 92 5.1 Chemometric calibration model development using on-line NIR spectral data ........... 93

5.1.1 Equipment and experimental set-up ............................................................................. 93 5.1.2 Materials and pre-blend preparation ............................................................................ 93 5.1.3 NIR Spectroscopy ........................................................................................................ 94 5.1.4 Results .......................................................................................................................... 97 5.1.5 Conclusions ................................................................................................................ 102

5.2 Continuous monitoring using VTT Spectrometer ...................................................... 102 5.2.1 Equipment and experimental set-up ........................................................................... 102 5.2.2 Methods ..................................................................................................................... 104 5.2.3 Results ........................................................................................................................ 108 5.2.4 Conclusions ................................................................................................................ 114

5.3 Figures for Chapter 5 ................................................................................................. 116 5.4 Tables for Chapter 5 ................................................................................................... 124

CHAPTER 6 DEVELOPMENT OF INTEGRATED CONTINUOUS MIXING

AND DE-LUMPING PROCESS ..................................................... 127 6.1 Mixing effects in low shear (Gericke mixer) and high shear mixing (Quadro - Comil)

continuous mixing equipment ................................................................................................................. 128 6.1.1 Equipment .................................................................................................................. 128 6.1.2 Results ........................................................................................................................ 129 6.1.3 Conclusions ................................................................................................................ 133

6.2 Figures for Chapter 6 ................................................................................................. 134 CHAPTER 7 CONCLUSIONS AND RECOMMENDATIONS .......................... 137

7.1 Conclusions ................................................................................................................ 137 7.2 Recommendations for future work ............................................................................. 141

7.2.1 New Blender designs: ................................................................................................ 142 7.2.2 Development of an integrated feeder-mixer system with a recirculation tank ........... 142

7.3 Figures for Chapter 7 ................................................................................................. 146 REFERENCES 148 CURRICULUM VITA ................................................................................................. 158

vii

LIST OF TABLES

Table 2-1: Feeder configurations used in the experiments ............................................................................ 51 Table 2-2: Materials, supplier and particle size ............................................................................................. 51 Table 2-3: Experimental conditions .............................................................................................................. 51 Table 2-4: Bulk density, Carr Index, dilation, particle size of excipients ..................................................... 51 Table 2-5: Predictive model for Mean Residence Time ................................................................................ 52 Table 2-6: Predictive models for Axial Dispersion Coefficient ................................................................ 52 Table 3-1: Impeller blade configurations ...................................................................................................... 68 Table 3-2: DEM Simulation Parameters ....................................................................................................... 68 Table 4-1: Analysis of variance (ANOVA) for the NV (Normalized Variance). ......................................... 91 Table 5-1: Experimental conditions for continuous mixing experiments .................................................... 124 Table 5-2: Development of calibration models and its initial evaluation .................................................... 124 Table 5-3: Evaluation of model precision (standard deviation) and model accuracy (RMSEP) at each

calibration concentration. ............................................................................................................................ 124 Table 5-4: Evaluation of continuous mixer experiments at various APAP concentrations. ........................ 125 Table 5-5: Off-line calibration samples ....................................................................................................... 125 Table 5-6: Off-line (UV absorption) data of sample size, mean concentration, variance, RSD and

confidence intervals ..................................................................................................................................... 126 Table 5-7: Model fitting results for in-line and off-line data ....................................................................... 126

viii

LIST OF FIGURES

Figure 2-1: (a) Experimental set-up (b) Continuous powder mixer (Gericke GCM-250) ............................. 39 Figure 2-2: Effect of rotation rate on RTD. Other parameters: Flow rate — 30 kg/h, and blade configuration

— All forward ............................................................................................................................................... 39 Figure 2-3: Effect of rotation rate on (a) mean residence time (b) mean centered variance (c) hold-up and

(d) number of blade passes. Other parameters: flow rate — 30 kg/h, and blade configuration — All

Forward. ........................................................................................................................................................ 40 Figure 2-4: Effect of flow rate on RTD at (a) 39 RPM (b) 100 RPM (c) 162 RPM and (d) 254 RPM. Other

parameters: Blade configuration — All Forward. ......................................................................................... 41 Figure 2-5: Effect of flow rate on (a) mean residence time (b) mean centered variance (c) hold-up and (d)

number of blade passes. Other parameters: blade configuration — All Forward. ......................................... 42 Figure 2-6: Effect of flow rate on (a) hold-up and (b) bulk residence time. .................................................. 43 Figure 2-7: Effect of weir position on hold-up. ............................................................................................. 43 Figure 2-8: Effect of blade configuration on RTD at (a) 39 RPM (b) 100 RPM (c) 162 RPM and (d) 254

RPM. Other parameters: flow rate: 30 kg/h. ................................................................................................. 44 Figure 2-9: Effect of blade configuration on (a) mean residence time (b) mean centered variance (c) hold-

up and (d) number of blade passes. Other parameters: Flow rate — 30 kg/h. ............................................... 45 Figure 2-10: PLS model for Output variable - Mean Residence Time .......................................................... 46 Figure 2-11: Loading plot for the PLS model of Output variable - Mean Residence Time, and Input

variables - Impeller Speed, Flow rate, Bulk density and Cohesion ............................................................... 46 Figure 2-12: Variable Importance Plot (VIP) of the PLS model of output variable - Mean Residence Time

....................................................................................................................................................................... 47 Figure 2-13: Effect of Bulk density on mean residence time at 30 kg/hr ...................................................... 47 Figure 2-14: Effect of impeller speed on the number of blade passes for different excipients ..................... 48 Figure 2-15: PLS Model for Output Variable - Axial Dispersion Coefficient .............................................. 48 Figure 2-16: Loading plot for the PLS model of output variable – Axial Dispersion Coefficient, and input

variables – impeller speed, flow rate, bulk density and cohesion .................................................................. 49 Figure 2-17: Variable Importance Plot (VIP) for the PLS model of output variable - Axial Dispersion

Coefficient ..................................................................................................................................................... 49 Figure 2-18: Effect of cohesion on the axial dispersion coefficient .............................................................. 50 Figure 3-1: Computer aided drawing of a continuous blender made at the actual scale. Two feeders

continuously provide particles in two streams on either side of the impeller which rotates in the direction

shown by the curved arrow. A D-shaped semicircular weir was placed at the outlet such that its flat edge

was at 45° with the horizontal. ...................................................................................................................... 63 Figure 3-2: Blade patterns used in DEM simulations and experimental validation studies. A) Forward blade

pattern with two 20° blades shown; B) Alternate pattern with one forward facing and one backward facing

blade, both at 20°. .......................................................................................................................................... 63 Figure 3-3: Simulation snapshots at a) 40rpm, b) 100rpm, c) 160rpm and d) 250rpm. The red and blue

particles are fed as two parallel streams of same mean particle size with a normal particle size distribution.

The particle bed fluidization begins at approximately 160rpm. .................................................................... 64 Figure 3-4: Effect of process parameters on mean residence time (DEM Simulations) ................................ 65 Figure 3-5: Comparison between experimental and DEM simulation results for the mean residence time .. 65 Figure 3-6: Effect of operational parameters on the number of blade passes (DEM simulations) ................ 66 Figure 3-7: Effect of operational parameters on the number of blade passes (Experimental) ....................... 66 Figure 3-8: Effect of operational parameters on the mean centered variance (DEM simulations) ................ 67 Figure 3-9: Comparison between the DEM simulations and experimental results for the mean centered

variance (MCV) ............................................................................................................................................. 67 Figure 4-1: Schematic of the experimental set-up for LIBS .......................................................................... 84 Figure 4-2: Experimental set-up for Washburn's method .............................................................................. 84 Figure 4-3: Comparison between flow rates: (a) VRR vs. rotation rate (‗All Forward‘ Blade configuration)

(b) RSD vs. rotation rate (‗All Forward‘ Blade configuration) (c) VRR vs. rotation rate (‗Alternate‘ blade

configuration) (d) RSD vs. rotation rate (‗Alternate‘ Blade configuration). Comparison between Blade

configurations: (e) RSD vs. rotation rate (30 kg/hr), (f) RSD vs. rotation rate (45 kg/hr) (Note: Comparison

ix

between the blade configurations is shown only with the RSD, plots of VRR are not shown here in order to

avoid redundancy) ......................................................................................................................................... 85 Figure 4-4: Effect of MgSt concentration: (a) RSDNIR vs. Rotation rate (b) RSDLIBS vs. Rotation rate (c)

Tablet hardness vs. Rotation rate (d) Hydrophobicity vs. Rotation rate. ....................................................... 87 Figure 4-5: (a) Effect of design parameters on RSDNIR (b) Effect if design parameters on hydrophobicity

(c) Effect of blade configuration on tablet hardness ...................................................................................... 88 Figure 4-6: (a) Feeding positions for MgSt (b) Effect of feed position on blend uniformity at the blender

discahrge ....................................................................................................................................................... 89 Figure 4-7: Feed position at the blender inlet (a) RSD vs. blender length (a) Mean concentration vs. blender

length ............................................................................................................................................................. 90 Figure 4-8: Feed position - Center of the blender (a) RSD vs. blender length (b) Mean concentration vs.

blender length ................................................................................................................................................ 90 Figure 5-1: (a) CDI Spectrometer installed on a powder conveying chute at the mixer discharge (b) Chute

..................................................................................................................................................................... 116 Figure 5-2: NIR spectra for acetaminophen and for 0% and 15% (w/w) powder blends ............................ 117 Figure 5-3: Scores plot from principal component of analysis of calibration set spectra in 1100–1390 nm

spectral range............................................................................................................................................... 117 Figure 5-4: API content predicted by NIR for cross validation and external validation samples................ 118 Figure 5-5: NIR predictions from monitoring the continuous mixing process for three representative blends

..................................................................................................................................................................... 118 Figure 5-6: Schematic of the multipoint NIR measurement equipment, it consists of a fiber-optic light

source, fiber-optic probes and a fiber-optic spectral camera ....................................................................... 119 Figure 5-7: (a) Schematic picture (left) and photograph (right) of the multipoint fiber-optic light source. It

has 24 output fibers with a ST connector (b) The fiber-optic spectral camera (Spectral camera NIR, Specim

Ltd., Oulu, Finland) ..................................................................................................................................... 119 Figure 5-8: Experimental set-up – (a) Above-the-chute configuration (b) Below-the-chute configuration 120 Figure 5-9: The unprocessed calibration set spectra (left) and the spectra after the baseline correction (right)

..................................................................................................................................................................... 120 Figure 5-10: The scatter plot of using the PLS model with the calibration set (left) and after cross-validation

(right)........................................................................................................................................................... 121 Figure 5-11: The scatter plot of cross-validation after averaging the results of each sample over the 5

measurement points of probe number 1 (left) and probe number 2 (right).................................................. 121 Figure 5-12: Blend uniformity (RSD) as a function of sample size (NIR Spectroscopy) ........................... 122 Figure 5-13: Blend uniformity (RSD) as a function of sample size (UV Absorption) ................................ 122 Figure 5-14: (a) RSD

2 as a function of sample size (Comparison between NIR data and mathematical

model) (b) Linear regression for the best case ( 02

0RSD ) .................................................................... 123

Figure 5-15: (a) RSD2 as a function of sample size (Comparison between UV absorption data and

mathematical model) (b) Linear regression for the best case ( 02

0RSD ) ............................................... 123

Figure 6-1: (a) Conical mill (Comil - Quadro Model # 197) (b) Milling chamber with conical round

impeller ....................................................................................................................................................... 134 Figure 6-2: (a) Schematic of the experimental set-up for mixing in Gericke Continuous mixer (b) Effect of

impeller speed on blend uniformity (RSD) ................................................................................................. 135 Figure 6-3: (a) Schematic of the experimental set-up for mixing in Comil (b) Effect of impeller speed and

screen size ................................................................................................................................................... 135 Figure 6-4: Effect of operational parameters of mill on residence time ...................................................... 135 Figure 6-5: (a) Schematic of the experimental set-up for integrated low and high shear mixing (Low-shear

mixing first) (b) Mixing performance after low and high shear mixing ...................................................... 136 Figure 6-6: (a) Schematic of the experimental set-up for integrated low and high shear mixing (High-shear

mixing first) (b) Mixing performance after high and low shear mixing ...................................................... 136 Figure 7-1: Schematic of the continuous processing line with a recirculation tank .................................... 146 Figure 7-2: Schematic of an integrated feeder, mixer and recirculation tank system .................................. 146 Figure 7-3: (a) Dynamic response of the continuous mixer for feeder refills (b) Mixer response for recycle

flow rates 10 and 50 times of input flow rate .............................................................................................. 147

1

Chapter 1 Introduction

1.1 Motivation

Continuous processing is considered as an advantageous choice in many industries,

including chemicals, food, household goods, microelectronics, and many others. Its main

advantages are better controllability, and, for sufficiently large volumes, lower

manufacturing cost by decreased footprint and labor. The pharmaceutical industry,

however, due to the rigid nature of its regulatory framework, has remained largely

focused on conventional batch manufacturing. However, since the inception of the

Process Analytical Technologies initiative (PAT [1]), and more recent, the Quality by

Design (QbD) initiative [2], significant efforts in designing new manufacturing strategies

are underway. Continuous manufacturing for solid dose pharmaceutical products is aimed

at improving product quality, reducing manufacturing cost, and essentially provide safer

products to the patients.

Continuous processing for secondary pharmaceutical manufacturing is an attractive

option because processes such as tableting, roller compaction, and capsule filling are

already carried out in the continuous mode [3], while mixing, wet granulation, drying,

and coating are performed in batch mode; this mixture of batch and continuous steps is a

frequent source of inefficiencies. Also, continuous processes can be scaled up simply by

time extension, as opposed to batch processes, which require size scale up and which

often do not scale-up easily or well. Continuous processing offers other advantages over

batch mixing, including smaller equipment size, reduced in-process inventory, less solid

handling such as filling and emptying of blenders (potentially reducing undesirable

2

effects like segregation), better control around a well-defined steady state, and higher

uniformity of shear application. However, continuous processing has some limitations,

including higher initial cost, difficult implementation for low volume products, and

reduced process flexibility. Although continuous manufacturing has been heavily

implemented in the bulk chemical industry, including processes that involve powder

handling applications such as catalysts manufacturing [4], mineral processing [5], and

food manufacturing [6], the understanding of continuous powder mixing processes is still

limited and only a handful of papers have been published on this topic.

Continuous mixing is important in many processes in pharmaceutical manufacturing,

including some obvious ones such as API and lubricant mixing, and some less apparent,

such as wet granulation, coating, extrusion, and drying, where mixing often plays a

critical role. Design of continuous mixing operations requires evaluation of a large

parametric space, including selection and design of mixing and feeding equipment;

evaluation of operating parameters such as impeller rotation rate and flow rate;

characterization of the effects of material properties such as particle size distribution and

powder cohesion, and controlling environmental variables such as relative humidity and

temperature. This large number of variables (and their interactions) makes it difficult to

implement the process for a new entity without detailed studies. Thus, identifying critical

process parameters is a key step towards effective implementation of continuous

manufacturing.

The work presented in this dissertation is focused towards implementing continuous

secondary manufacturing pharmaceutical processes, with specific concentration on

continuous mixing of powders. Although the work presented here is based on a

3

pharmaceutical product manufacturing case, the methods used are general and they apply

to other industries as well.

The dissertation work is focused on performing a systematic evaluation of the process

and design parameters related to the continuous mixing process and also the

interconnection between different material properties and continuous mixing

performance. The approach used is primarily experimental in nature. DEM simulations of

the powder flow behavior in the mixer were also performed, and compared against the

experimental results. Formation of undesirable agglomerates [7,8] in powder blends is a

commonly faced problem in powder mixing processes. The evaluation of continuous

mixers for their potential to mitigate these problems possibly through a combination of

different processing approaches is also addressed.

The four specific aims of this dissertation are as follows:

Specific Aim I: Understand the powder flow behavior in the continuous mixer

(Convection, shear and dispersion) (Chapter 2, Chapter 3)

Specific Aim II: Understand effects of process and design parameters and material

properties on blend homogeneity and blend properties (Chapter 4)

Specific Aim III: Develop fast, effective on-line sensing methods for monitoring blend

uniformity in continuous powder blending systems (Chapter 5)

Specific Aim IV: Characterization and optimization of an integrated process consisting

of continuous mixing and de-lumping to achieve desired blend properties (Chapter 6)

In the next section, important theoretical and experimental literature is discussed and a

conceptual framework of the dissertation will be established.

4

1.2 Mathematical modeling in continuous powder mixing

1.2.1 Theoretical developments

Detailed information on continuous mixing can be found in reviews such as Pernenkil et

al. [9] and Williams and Rahman [10]. Only a few important theoretical developments

and experimental investigations related to this dissertation are cited here. The

performance measure of continuous blenders was first defined by Beaudry [11] using the

Variance Reduction Ratio (VRR), defined as the ratio of the input and output variance in

concentration of the key component. Thus, the larger the VRR is, the better the

performance of the continuous blender. Theoretical developments on continuous blenders

began when Danckwerts [12] introduced the concept of residence time distribution

(RTD). Using the RTD function , Danckwerts defined the dynamic output

concentration as a function of input concentration . The

relationship is given in equation (1-1).

0

0 )()()( dEtCtC i (1-1)

With some mathematical manipulations, the final form of VRR was expressed as given in

equation (1-2).

r

ro

i

Fluid

drrIrRVRR

0

2

2

)()(21

(1-2)

Where 2

'' )()()(

i

ii rttrR and

0

)()()( drrEErI .

5

The term is the autocorrelation coefficient of for the time interval . Using the

autocorrelation, the time scale of segregation for the incoming feed rate variability can be

calculated. William and Rahman [13,14] proposed a numerical method for predicting the

VRR using the RTD for both ideal and non-ideal blenders. Thus, both Danckwerts‘ and

William and Rahamans‘ definitions express the variability in output concentration as a

function of input variability. Danckwerts‘ definition for VRR describes the macro-mixing

process but does not contain information about the micro-mixing, which is especially

important for granular materials. Weinekötter and Reh (1995) [15] modified Danckwerts‘

definition by adding a term to account for the variance of the ideal random powder

mixture. Weinekötter‘s modified definition of VRR is given in equation (1-3).

2

2,11

input

idealout

fluidsolid VRRVRR (1-3)

Weinekötter and Reh [15] also used the concept of scale of segregation introduced by

Danckwerts [16] to extract structural information about mixtures. By calculating the

power density spectrum of the in-line measurements, the scale of segregation of the

mixture can be calculated as defined in equation (1-4). In equation (1-4), is the

autocorrelation coefficient.

0

0 )( dI xx (1-4)

Thus, the RTD along with the knowledge of input variability seems to be sufficient to

predict mixing performance.

6

1.2.2 RTD modeling

Several methods have been proposed for modeling RTDs. For continuous powder

blenders, RTDs have been modeled using a fractional tubularity model [17] (which

consists of a CSTR in series with a PFR), a delay and a dead volume, tanks in series [17]

and dispersion models [5,18].

The axial dispersion model has been one of the most classical approaches to model axial

mixing in both continuous and batch mixers or rotating drums. The one-dimensional

diffusion equation used to describe mixing in the axial direction is given by equation (1-

5).

2

2

z

cD

z

cu

t

czz (1-5)

This equation has been solved for various systems [18] using appropriate initial and

boundary conditions. The most common cases concerned with continuous flow systems

include Dankwerts‘ closed-closed and open-open boundary conditions [19]. The

important approximation made here is that the particle bed in the system was treated to be

a continuum. Fokker Planck Equations (FPEs) were introduced for the case of continuous

mixers by Sommer [19] and were solved for each component in the mixture. Kehlenbeck

and Sommer [20] later showed the applicability of FPEs for particle streams with

identical physical properties.

Other methods used to model continuous mixers are Markov Chain models [21,22],

population balance models [23] and discrete element models [24,25]. Berthiaux et al.

[22] proposed Markov chain models to calculate all the parameters necessary to

characterize RTD in a continuous blender. Using this approach, they showed that the

ratio of mean residence time and period of fluctuation of the inflow streams is the main

7

factor governing VRR in a continuous mixer rather than the variance of RTD. Berthiaux

[26] again by using the same methodology of Markov chain modeling simulated the RTD

in a continuous mixer that was capable of incorporating effects of different flow

conditions as well as tracer properties.

The methodology of using the RTD to model the continuous powder blending process

accounts only for the axial mixing component. This approach is valid for the case of free

flowing (cohesionless) powders. For the case of cohesive powders, a minimum required

level of shear stress, and a minimum amount of radial mixing are also required. The RTD

modeling approach, as presented in the literature is often not sufficient. In this

dissertation, the role of radial mixing is identified, and the analytical method error

associated with the blend uniformity measurement is also incorporated into the

mathematical model. Correlations which relate the RTD parameters (dispersion

coefficient, axial velocity) with the input parameters including material properties, flow

rate, shear rate and blade pattern/mixer geometry are in their infancy. To fill this gap,

relationships between the process parameters and RTD were developed, and the role of

critical material properties was also addressed in this dissertation.

Two promising future directions of modeling work include DEM modeling of the

continuous mixer geometry and predictive/data driven modeling of RTD as a parallel

effort with experimental investigations. The DEM models have been used in the past to

simulate industrial mixers such as the double cone blender [27-29], the V blender [30-

32], the tote blender [33,34], and also continuous mixers [24,25] in some recent

publications. Although the DEM simulation is an attractive option to study such systems

with flexible choice of geometry and operating parameters, they suffer some important

8

limitations. One of the major difficulties in DEM simulations is the huge computing

power required to simulate real case scenarios involving > 105 particles. Also, identifying

the correct parameters to simulate real material properties is a big challenge. DEM

simulations have so far used spherical particles, but in reality particles have shapes other

than spherical. Despite these limitations, DEM is one of the best available tools to

simulate granular flows [30]. In this dissertation DEM simulations of the powder flow

behavior in the continuous mixer were performed, and compared with experimental RTD

measurements.

1.3 Experimental characterization of continuous powder blending process

Blender geometries reported in the literature, relevant to the continuous blender design

used in this dissertation include bladed mixers (batch and continuous) and continuous

rotary calciners. The experimental approaches used to investigate these geometries,

reported in the literature can be classified in two categories. The first approach consists of

measurement of the RTD and the variance of the mixture at the outlet. The other

approach consists of characterizing the flow behavior in the mixer using radioactive

particle tracking methods such as PEPT (Positron Emission Particle Tracking), or

imaging techniques such as the PIV (Particle Image Velocimetry).

1.3.1 Experimental studies on the performance and RTDs of in continuous powder

mixers

Studies of continuous mixing for practical applications have been reported for materials

such as zeolite pellets [4], foods (semolina and couscous, chocolate mixtures) [35],

Aluminum hydroxide and Irgalite [16], sand and salt [13], etc. William and Rahman [14]

9

measured RTDs in a continuous mixer for free flowing granular materials salt and sand.

Their proposed model for predicting VRR matched the experimental results very well.

Harwood et al. [36] examined seven different continuous mixers and categorized them

according to their performance for both cohesive and free-flowing powders. Weinekotter

and Reh [15] measured RTDs in a continuous mixer using Al(OH)3 as a bulk material

and SiC or Irgalite as tracers. The main parameter examined in their studies was the

effect of feeder fluctuations and the type of mixer. The ability of the continuous mixer to

reduce input fluctuations was dependent on both the Peclet number (Pe) and the time

period of fluctuation of the feed rate. The continuous mixer was found to be acting as a

low pass filter, filtering only the high frequency noise. Sudah et al. [4] conducted RTD

experiments in a rotary calciner using zeolite pellets to study the effect of rotation rate,

flow rate and angle of inclination on mean residence time, hold-up and axial dispersion

coefficient. A dispersion model was used to fit the experimental data to extract the

dispersion coefficient and the axial velocity. Mean residence time was related to the angle

of inclination and rotation rate but was independent of the flow rate up to 10% fill level.

The axial dispersion coefficient also was a function of the speed and the angle of

inclination (at high rotation rates), but was weakly dependent on the flow rate. Ziegler

and Aguilar [17] studied RTDs in the continuous processing of chocolate in a twin-screw

co-rotating mixer and modeled them using a combination of PFR and CSTR (fractional

tubularity), and CSTR with delay model. The fractional tubularity model was found to fit

the data better than the CSTR with delay model. The mean residence time was found to

be directly proportional to the rotation rate and inversely proportional to the feed rate.

Sherritt et al. [37] reviewed a significant amount of experimental data for rotary calciners

10

and proposed a new design equation for the axial dispersion coefficient in both batch and

continuous systems in terms of the rotation rate, fill level, drum diameter and particle

diameter. Additionally they examined rotation rate and fill level for a horizontal rotary

drum using radioactive tracer material. The axial dispersion coefficient that was

measured followed their design equation. However, this correlation takes into account

only the particle diameter as the material property. Other material properties such as

cohesion and particle density were not included. Also, this correlation applies only for

rotary calciners. Generalized correlations for bladed mixers are not well established in the

literature.

Experimental studies for pharmaceutical powders are recent and include materials such as

lactose and acetaminophen mixtures [38], and a mixture involving nine ingredients

including three different actives [39]. The important process parameters examined were

rotation rate, flow rate, and angel of inclination of the mixer or rotary drum. Portillo et al.

[38] reported that lower rotation rates and an upward inclination resulted in better mixing

performance. Marikh et al. [40] examined the effect of rotation rate and flow rate on

hold-up in a continuous powder mixer using semolina and couscous material. They

developed a correlation between mean residence time and rotation rate which could be

used as a basis for scale-up criteria. Marikh et al. [35] compared two different types of

stirrers for a pharmaceutical mixture, and proposed a more generalized correlation for

hold-up as a function of rotation rate and flow rate. They reported that the mixing

performance becomes better with increase in the rotation rate. However, at excessively

high rotation rates, mixing performance become worse. They also reported the choice of

one stirrer over another in producing better quality of mixture. Pernenkil et al. [41]

11

examined the Zigzag blender for caffeine and lactose mixture. Operational parameters

examined were the shell rotation rate and the intensifier bar rotation rate. They reported

the effect of the intensifier bar rotation rate on VRR to be the highest, while the shell

rotation rate showed a non-linear effect. Experimental studies for pharmaceutical

powders reported so far have been specific to a particular material and the mixer studied.

Very few studies have attempted to explain the physics behind the results.

1.3.2 PEPT and PIV studies on the bladed mixers

Several studies are reported in the literature on bladed mixers used for the granular

mixing applications such as wet granulation and agitated drying. These mixers are similar

to the equipment used in the present case of the continuous blender. Recent experimental

studies using PEPT have provided deeper insights into the detailed flow patterns in

powder mixers and rotating drums.

Stewart et al. [42] compared experimental measurements performed using PEPT and

DEM simulations of a granular flow in a vertical bladed mixer. They were able to

simulate the overall motion of the bed very well. However they indicated a need for the

proper selection of material properties in DEM for a realistic simulation. Laurent et al.

[43] performed experiments in a horizontal batch mixer using glass ballotini. They

utilized PEPT to track a single particle. Using the PEPT measurements they calculated

the RMS velocity and the dispersion coefficient at various positions in the mixer. They

also studied the effect of different sizes of tracer particles. The size of tracer particles has

negligible effect on the radial flow structure, but the axial motion was significantly

affected. The design of the agitator in both continuous and batch mixers is largely based

on empiricism or, at best, follows heuristic rules. Laurent and Bridgwater [44] utilized the

12

PEPT technique to compare two impeller designs. A design with short paddles showed

increase in the dispersion coefficient with the increase in the fill level, as opposed to the

decrease observed for the case with long flat blade impeller. Both impellers showed a

cellular structure created by radial supports but the short paddle device showed more

chaotic behavior. Conway et al. [45] used PIV to measure velocity fields on free exposed

surfaces (top and the wall). They characterized the flow and segregation behavior at low

as well as high shear rates.

The studies that are reported in the literature so far are focused on the operating

parameters or the design of mixer. Little attention has been given to the effect of material

properties. In a real industrial environment, manufacturing processes needs to be tuned

according to the raw material properties. Thus, developing a methodology for selecting

the optimum process parameters for a given material property is addressed in this

dissertation.

1.3.3 Lubricant blending

One of the important and widely used materials in the pharmaceutical industry is

Magnesium Stearate (MgSt) [46]. This material has not been reported so far in the

literature in the context of continuous mixing. Lubricant blending is a required step in

continuous as well as batch processing scenario, and it needs to be characterized in

continuous powder blending systems. A brief literature review on lubricant mixing and

its importance in pharmaceutical industry is provided below.

Lubricant mixing is a very important and necessary step in pharmaceutical

manufacturing. Lubricants are added to the formulation for four primary purposes: 1- It

facilitates ejection of the tablet from the die, 2- It provides internal failure points for the

13

tablet during compression, reducing accumulation of stored elastic stress, 3- It acts as an

anti-adherent [47] reducing the sticking between the tablet and the punch, and 4. It

improves the flow properties of powders [48]. Although they facilitate the manufacturing

processes, lubricants introduce some complexities in the product. Lubricants affect the

compaction process (Force Displacement curve) as well as post-compact properties

including porosity [49] and hardness [50]. They have also been shown to affect tablet

disintegration [51,52] and dissolution rate [53], which often are directly related to the

bioavailability of the drug. Podczeck and Miah [54] conducted experiments to study the

effect of particle size and shape on the angle of friction and the flow function for both

lubricated and un-lubricated blends. Yamatoma et al. [51] conducted experiments to

relate the disintegration time to the bulk density of the formulation, which is influenced

by the presence of lubricant. Van der Watt and de Villiers [55] studied the scale-up of V-

mixer by relating the mixer size with crushing hardness of tablets. Ragnarsson et al. [56]

related mixing time to the tablet strength. All of the studies mentioned here so far are

specific to the system in place and their results do not extend to different materials or

mixers. Mehrotra et al. [57] identified the two important parameters, shear rate and strain,

irrespective of the system and correlated them with properties of tablets and powder.

They conducted experiments in a modified Couette shear cell exposing powder to a

controlled and uniform shear environment. Strain was found to be the parameter

maximally affecting blend homogeneity, bulk density, flowability of powders and tablet

hardness.

In this dissertation, measurement of strain (in terms of blade passes) conducted for a

continuous blender as a function of different operating conditions was used to establish a

14

link between the operating conditions of the blender and the resulting blend/tablet

properties.

1.3.4 Blending of cohesive powders in continuous mixing systems

APIs (Active Pharmaceutical Ingredients) which often have an average particle size of

less than 20 µm in diameter are highly cohesive, and tend to form agglomerates because

of overwhelming Van der Waals forces. Cohesion in powder/granular systems is also

caused by the capillary forces [58] which exist due to the presence of moisture in the

system. In the present case of dry powders the primary source of cohesion are Van der

Waals forces and/or electrostatic forces [59]. Powders which tend to agglomerate are

typically mixed under high shear environment such that the agglomerates are de-lumped

and an ordered mixture is sometimes created. High shear blending is typically performed

using a V-blender with a high-speed intensifier bar or by introducing a mill [8]. In this

study, a co-mill was selected as a suitable continuous de-lumping device. In order to

qualify the feasibility of a co-mill for the continuous process, its efficiency for mixing

and de-lumping needs to be examined. In this dissertation, the effect of co-mill process

parameters on mixing performance and its position in the overall continuous processing

scheme is examined.

1.4 On-line process monitoring of powder blending processes

A typical continuous process for pharmaceutical manufacturing consists of unit

operations such as powder feeding, blending, granulation and compaction or capsule

filling. In a continuous process, monitoring and ensuring blend uniformity in real time at

the blender discharge point are highly critical. Unlike batch manufacturing, it is difficult

15

to rework the blending process in a continuous manufacturing scenario. Continuous

monitoring of blend uniformity is the first step towards implementing process control for

continuous blending operations, or to facilitate rejection of non-uniform powder from the

blending operation.

Typically in the pharmaceutical industry, following a blending operation, a batch of

powder passes inspection only if the variability in the sample concentrations is under a

specified limit. This limit is defined in the blend uniformity guidance by FDA [60] as the

relative standard deviation between the samples concentrations extracted in the end of a

blending process to be under 6%. PAT (Process Analytical Technology) [1] has recently

been introduced by FDA as a tool for building predictive understanding of

pharmaceutical manufacturing processes. Examples of powder PAT applied to blending

processes are abundant in literature; they typically include Near Infra-Red NIR

spectroscopy [61], Raman spectroscopy [61,62] and Laser Induced Fluorescence LIF

[63]. Most of the PAT work for blend uniformity monitoring exists for batch blending,

which includes commonly used blenders such as V-blender [64], Bin blender [63], Y-

mixer [64] and Nauta mixer [65]. Examples of PAT applied to continuous blending

processes are scare in the literature.

Near infrared (NIR) spectroscopy is one of the most commonly used analytical

techniques for monitoring pharmaceutical processes [66]. The NIR spectral region

extends from 780 to 2500 nm, and NIR spectra consist of absorbance bands

corresponding to overtones and combinations of fundamental C–H, N–H, S–H and O–H

molecular vibrations. NIR methods have been developed to monitor a number of

pharmaceutical unit operations including granulation [67], drying [68] and crystallization

16

[69]. The NIR spectra after following an adequate pre-processing provide both the

physical as well as chemical signature of the material. Some studies include quantitative

methods for the determination of drug concentration during the blending process

[70][71,72], while other studies include qualitative approaches to evaluate the end-point

as the changes in the spectra are reduced during the blending process [73]. A series of

studies [74-76], describing the use of NIR spectroscopy as a PAT tool in the design and

implementation of a blending process are available in the literature. The use of NIR

spectra to develop a control chart based on Hotelling's T2 statistic was also demonstrated

[77].

Methods reported in the literature to monitor blend uniformity are mostly generic, which

include quantitative methods such as a PLS (Partial Least Squares) modeling, or

qualitative methods such as Principal Component Analysis (PCA) of the spectra acquired

during blending process, monitoring the pooled standard deviation between spectra,

monitoring the dissimilarity between the process spectra and spectrum of an uniform

mixture or individual components.

PAT for blending has been reported primarily as a tool for monitoring evolution of RSD

during the blending process and detect the blending end–point. The final blend

uniformity measured using a PAT method, and the blend uniformity measured using an

off-line method based on wet-chemistry are often poorly correlated, rather the methods to

directly link off-line and on-line blend uniformity measurements are not very well

established in the literature. In order to develop a relationship between the off-line and

on-line measurements, it is necessary to quantify the error in the measurement method

and the sample size being analyzed, and relate that with equivalent offline measurements.

17

Sekulic et al. [78] demonstrated a methodology to estimate the effective sample mass

being analyzed by a fiber optic probe in a blending process, by comparing the spectral

variance of fiber optic measurements against a priory relationship developed between

spectral variance and sample mass using offline micro-sample cups. Very few studies

have considered this aspect while applying PAT to the blending processes.

PAT applications for continuous blending include some recent studies using NIR

spectroscopy [79] and LIF [71,78]. In the continuous blending process, typically powder

is in a state of motion, and inherently there is always a certain degree of spectral

averaging involved in the measurement. Blend uniformity, quantified as the RSD

(Relative Standard Deviation) between the in-line measurements, is dependent on the

degree of averaging. It is necessary to select the averaging window depending on the

sample size of interest (one unit dose), which is often not the case in the current industrial

practice.

In this dissertation a methodology is presented using a case study based on

pharmaceutical powders, allowing quantification of the error associated with the in-line

measurements, as well as the relationship between in-line and off-line blend uniformity

measurements. The methodology was developed using a multi-point fiber optic probe

NIR monitoring system.

18

Chapter 2 Characterization of powder flow behavior in the

continuous mixer

Powder mixing processes are primarily governed by intrinsic powder flow mechanisms

such as convection, shear and dispersion [80]. RTDs have been used extensively in the

past to characterize macroscopic flow behavior in non-ideal reactors for liquid systems

[18]. In this chapter, RTDs in a continuous powder mixer were measured to characterize

the bulk powder flow behavior. Effect of process variables (impeller speed, flow rate),

design variables (impeller design, mixer outlet (weir) design) and material properties

(bulk density, particle size, and cohesion) on the RTD were examined. RTDs were

measured experimentally by providing a tracer impulse and measuring the concentration

of the tracer at the mixer discharge. The effects of aforementioned variables were

compared using the statistical RTD parameters (mean residence time and mean centered

variance) and strain exerted on the powder. RTDs were also characterized

computationally using DEM simulations of the powder flow behavior in the continuous

mixer. The comparison between experimental and computational measurement of RTDs

is presented in Chapter 3. RTDs can also be used for predicting the axial mixing behavior

of the system. A reduced order model for the powder flow behavior in the continuous

mixer was developed by applying 1-D Fokker Plank Equations (FPEs). In this chapter,

the RTD data was fitted using an analytical solution of the FPE solved for appropriate

boundary conditions for the case of continuous powder mixer. The methodology for

model development and data-fitting is also described in this chapter.

19

2.1 Equipment and experimental set-up

The experimental set-up is shown in Figure 2-1. A commercial continuous mixer

manufactured by Gericke was examined. The design of the mixer is illustrated in Figure

2-1(b). The continuous mixer (Model GCM-250) is 0.3 m long and 0.1 m in diameter.

The impeller consists of 12 triangular shaped blades, equally spaced along the axis of

rotation. The first and the last blades of the impeller are designed differently due to their

position close to the end walls of the mixer vessel. The angle of the blade with the shaft

can be changed. In the present study, blade angles both in the forward and backward

directions were examined. Forward direction means that the blade imposes a forward

flow on the powder along the axis of the mixer; backward direction implies flow in the

reverse direction. The mixer is equipped with a weir (Figure 2-1 (b)), which is a

semicircular disc placed at the exit of the mixer to control powder hold-up. The weir can

be rotated to change the fill level of the powder in the mixer.

Additional details are shown in Figure 2-1 (a). Loss-In-Weight (LIW) feeders

manufactured by Schenck AccuRate were used in the experiments. Performance of LIW

feeders, which could affect the blend homogeneity at the mixer discharge, is dependent

on the type tooling used. In the present study, the size of the discharge opening, screw

design, and hopper design were selected based on manufacturer's recommendation. The

particular settings used for feeding Acetaminophen (APAP) and Avicel are presented

in Table 2-1.

20

2.2 Materials and methods

2.2.1 Materials

The materials used in this study, their mean particle size and the supplier are listed in

Table 2-2. Excipients, including micro-crystalline cellulose (Avicel PH-200, Avicel PH-

101), Fast Flo Lactose and Dicalcium Phosphate were used as model materials to study

the effect of material properties on the powder flow behavior in the mixer.

Acetaminophen was used as a tracer for measuring RTDs for micro-crystalline cellulose

and lactose, whereas Caffeine was used as a tracer for Dicalcium phosphate.

2.2.2 Methods

Chemical composition of the powder samples was analyzed using NIR spectroscopy.

Chemometric calibration models were built for individual tracer-excipient combinations.

A general protocol for building calibration models involves following steps. Calibration

samples with known concentration of tracer are prepared by accurately massing the

individual components. Calibration samples are then scanned multiple times to acquire

representative spectra. The NIR analyzer Antaris (Thermo Fisher) was used in this study.

‗Ominc‘ software was used to acquire the calibration spectra, and ‗TQ Analyst‘ was used

to build chemo-metric models. A PLS algorithm was then applied to build the calibration

curve. Each calibration model was validated using a cross-validation program (leave one

spectra out at a time). More information on calibration model development using NIR is

presented in Chapter 5.

21

2.3 RTD, Hold-up and number of Blade passes measurement

2.3.1 RTD measurement

Residence time distributions in the mixer were measured by the impulse response

method. Initially, the bulk material (Avicel) was fed in the mixer until steady state flow

was reached. Tracer (APAP or Caffeine) was inserted manually in the inflow stream as

an ―instantaneous‖ pulse. Approximately 11 g of the tracer material was used in the

experiments. The amount of tracer was selected such that the concentration of APAP at

the exit of the mixer would be over several orders of magnitude above the detection

limits of NIR method, in order to resolve the long tail of the residence time distribution.

Samples were subsequently collected at various times from the outlet of the mixer. The

samples collected were analyzed by NIR spectroscopy to determine the concentration of

tracer in them. Thus, for each experiment, a dataset of concentration vs. time was

collected. Using this data, the residence time distribution function ))(( tE , the mean

residence time )( and the mean centered variance )( 2 were calculated. As established

in prior literature [81] these parameters completely describe axial mixing in a continuous

flow system. Mathematical relationships for each of these terms are given below.

Residence Time Distribution (RTD) ))(( tE :

0

)(

)()(

dttc

tctE

(2-1)

Mean Residence Time (MRT) )( :

0

)( dtttE

(2-2)

22

Mean Centered Variance (MCV) )( 2 :

2

0

2

2

)()( dttEt

(2-3)

RTD parameters, mean residence time (MRT) and mean centered variance (MCV) were

used to quantify the flow behavior in the continuous mixer. For a perfectly mixed system,

which is often described as a perfectly mixed CSTR (Continuously Stirred Tank

Reactor), the residence time distribution function )(tE is given by /te . For such a

system, the MCV is equal to one. For a perfectly unmixed system, also described as a

perfect PFR (Plug Flow Reactor) )(tE is given by a Dirac delta function which is zero

everywhere except at t . At this condition the MCV is equal to zero. Flow behavior of

real cases is expected to be in between these two extreme conditions. Thus, a higher

value of MCV indicates better mixing condition. Since the RTD arises from velocity

differentials in the axial direction, MCV calculated here refers to the mixing in axial

direction.

Mixing behavior in the continuous blender can be described as a combination of axial and

radial mixing. Axial mixing is important in order to mitigate the variability introduced by

the feeding process. Radial mixing is necessary to mix the initially unmixed ingredients

to the required degree of homogeneity.

2.3.2 Hold-up measurement

Powder hold-up in the mixer is important because it determines the average residence

time, and thus the total average strain experienced by the powder as it travels through the

mixer. Hold-up was measured by simultaneously monitoring the weight of the powder

23

collected at the outlet of the mixer and that of the powder being fed. Powder was

collected in a collection bucket resting on a scale at the exit of the mixer. The weight of

the powder being fed was monitored by the built-in scale present in loss-in-weight

feeders. The difference between the two weights (at the outlet and at the inlet) at a given

time gives the hold-up. In preliminary measurements, hold-up is initially zero; it

increases with time, and finally reaches a plateau. The mixer operating under constant

hold-up was considered to be operating at steady state. In the absence of stagnant regions,

hold-up measurement is complementary to the measurement of mean residence time

calculated from the RTD curve. As mentioned, hold-up measurements can be used to

calculate the bulk residence time [hold-up (kg) / flow rate (kg/h)] of the powder in the

mixer.

2.3.3 Strain measurement

In the continuous mixer, energy input is provided by rotating the impeller. This energy is

dissipated in the convective transport of the powder, random fluctuations of the velocity

of the particles (granular temperature), friction between the impeller and the powder and

the mean strain (velocity gradients in the powder). Since the impeller rotation rate has an

effect on the residence time (which will be explained in the results section), the strain is

proportional to the product of the shear rate and the residence time, which in turn is

proportional to the number of blade passes in the mixer. Using the residence time, the

number of blade passes at various experimental conditions was calculated as follows:

Number of blade passes = Rotation rate (RPM) × Residence time (s)/60.

24

2.4 RTD modeling methodology

Reduced order model of the continuous mixing process was developed by applying the 1-

D FPE. One dimensional diffusion equation that describes mixing in axial direction is

given by equation 2-4.

2

2

z

cD

z

cU

t

cz

(2-4)

For the present case, following boundary conditions were used to solve the PDE.

Danckwerts‘ open-open vessel boundary condition [18] is given in equation 2-5 and 2-6.

At the entrance:

),0(),0(

),0(),0(00

tctc

tUcz

cDtUc

z

cD

z

z

z

z

(2-5)

At the exit:

),(),(

),(),(

tLctLc

tLUcz

cDtLUc

z

cD

Lz

z

Lz

z

(2-6)

After applying the boundary conditions, the analytical solution of the above PDE is given

by equation 2-7.

Pe

LD

tt

PePe

C

z.

,

/4

)1(exp

/2),1(

20

20

(2-7)

In equation (2-7), is the normalized time, 0t where is the dead time of the system and

is the mean residence time. All the RTD datasets were fitted using equation (2-7). The

fitting parameters in equation (2-7) are mean residence time )( , dead time )( 0t , pulse

25

strength )( 0C and the Peclet number )(Pe . A non-linear regression program from

MATLAB was used for the data fitting exercise. The methodology used for model fitting

is can also be found in Gao et al. [82]. Once the parameters are estimated, the total

residence time )( 0tTotal , and axial dispersion coefficient )( zD can be calculated.

2.5 Experimental conditions

In the first set of experiments, the effect of process parameters and mixer design

parameters was examined using Avicel PH-200 as the model material. The parametric

space for the continuous mixer consists of manipulated (independent) process parameters

(impeller rotation rate and flow rate) and manipulated (independent) design parameters

(blade configuration, angle of weir). The experimental conditions examined are presented

in Table 2-3.

2.6 Results

2.6.1 Effects of process parameters on flow behavior

The following sections focus on the effect of rotation rate and flow rate on the powder

flow behavior in the continuous mixer.

2.6.1.1 Effect of rotation rate on flow behavior

Figure 2-2 shows the effect of rotation rate on the residence time distribution. As the

rotation rate increases, the mean residence time (τ) decreases (Figure 2-3 (a)). The mean

centered variance was found to increase with increasing impeller rotation rate (Figure 2-3

(b)). Inverting the mean centered variance gives the equivalent number of ideal stirred

tanks in series that would give a similar RTD. For the mixer of interest here, the number

26

of ideal stirred tanks decreased from about 6 to 3 as the rotation rate increased. The

number of stirred tanks in series indicates the degree of dispersion of the input pulse;

fewer tanks indicate faster dispersion, which also means faster axial mixing. Thus,

increasing rotation rate gives rise to better axial mixing. However, faster rotation rate also

decreases the time available for mixing (lower residence time) and increases the

variability in the residence time distribution (less uniform shear treatment). These are

opposing effects in achieving good mixing. In fact, for the present case, at 254 RPM,

mean residence time was found to be extremely low. Therefore, the 254 RPM rotation

rate may not be the best operating condition even though the degree of dispersion is the

highest. Hold-up in the mixer (Figure 2-3(c)) decreases from 0.57 to 0.04 kg as the

rotation rate increases from 39 to 254 RPM (Fr = 0.07–3.3). For rotation rates of 162 and

254 RPM, the powder bed was fluidized in the mixer. In order to estimate the amount of

strain the powder experiences in the mixer, the number of blade passes was calculated as

a function of rotation rate. The relationship between these variables is depicted in Figure

2-3 (d). The number of blade passes was found to be at maximum at the intermediate

rotation rates, entirely due to the significant change in hold up at higher speeds. This

indicates that between 100 and 162 RPM, the powder undergoes maximum amount of

mechanical work (total strain). It is important to estimate total strain applied during

processing since it may affect powder flow behavior, bulk density of the powder and also

the blend uniformity [57]. All these variables are important attributes of powder blends.

2.6.1.2 Effect of flow rate on flow behavior

Flow rate is a key process parameter that is directly related to the capacity of the

manufacturing system. Although it was stated that continuous processes can be scaled up

27

simply by time extension, in certain cases where higher or lower production rates are

required, throughput also needs to be changed. Residence time distributions under

different flow rate and rotation rates are compared in Figure 2-4. RTD curves seem to

overlap with each other as the flow rate increases. Figure 2-5 shows the effect of flow

rate on the RTD parameters, hold-up and the number of blade passes. The effect of flow

rate on the mean residence time is influenced also by the impeller rotation rate. As shown

in Figure 2-5 (a), at low rotation rates, the increase in the flow rate decreases the mean

residence time. With increasing rotation rate, this effect diminishes. For the two flow

rates examined (30 and 45 kg/h), mean centered variance (Figure 2-5 (b)) did not show

any particular trend. This indicates that in the range of 30–45 kg/h, similar degree of axial

mixing was obtained. Figure 2-5 (c) shows the effect of flow rate on hold-up for our

experiments. Increasing the flow rate increases hold-up. Since the hold-up depends on

mixer capacity, at different rotation rates, the dependence of bulk residence time on flow

rate varies.

To clarify the effect of flow rate on residence time, hold-up was measured for a wider

range of flow rates (5–60 kg/h) at three rotation rates (39, 162 and 254 RPM). The bulk

residence time was also calculated from the hold-up measurements. The results are

presented in Figure 2-6 (a - b). Remarkably, bulk residence time is not affected by the

flow rate at very high rotation rates (254 RPM), which means that under these conditions,

mixing performance (such as it might be) is independent of throughput. However,

residence time decreases with increasing flow rate at lower rotation rates (39 RPM and

162 RPM). Observations from Figure 2-5 (a - c) and Figure 2-6 reveal the relationship

between the process parameters and mixer capacity. At the highest rotation rate, hold-up

28

increases linearly with increase in flow rate. As the impeller rotation rate decreases,

mixer capacity becomes the limiting parameter, which leads to the non-linear behavior of

hold-up as a function of flow rate. Similar explanation follows for the trends of residence

time or number of blade passes. At higher rotation rates, the number of blade passes is

not affected by the change in flow rate; at lower rotation rates, increase in flow rate

decreases the number of blade passes (Figure 2-5(d)).

Our results can be compared with studies from the literature. Sudah et al. [4] showed

similar results for rotary calciners; mean residence time was found to be independent of

feed rates up-to 10% fill levels. In the present case, at high rotation rates (254 RPM), fill

level is well below the capacity of the mixer, which yields similar results. For a

continuous mixer with a similar impeller design, Berthiaux et al. [26] has shown that the

mean residence time increases with increases in flow rate. The differences can be

attributed to the different size of the mixer and also to differences in material properties.

A design criterion for scale-up could perhaps be developed from these observations,

recognizing that hold-up should increase with increase in flow rate, but that this effect

interacts with rotation rate and mixer size.

2.6.2 Effects of design parameters on flow behavior

The following sections focus on the effect of weir angle and blade configuration on the

powder flow behavior in the continuous mixer.

2.6.2.1 Effect of weir angle on flow behavior

The weir angle is an additional design parameter that can be used to change the residence

time of the powder in the mixer. Powder hold-up in the continuous mixer was measured

29

for different weir angles. Figure 2-7 shows the effect of the weir angle on the hold-up. In

the interest of clarity, the results are presented only for the ‗Alternate‘ blade

configuration at 39 RPM; trends are similar for other blade patterns and speeds. While

the impeller is rotating in the mixer and the blender is operating, the powder bed fill level

exhibits a gradient around the axis of the mixer. Rotating the weir to the same angle at

which powder bed is tilted gives the maximum hold-up. At 39 RPM, hold up was found

to be the maximum at a weir angle of 20°. At high rotation rates (100 RPM and

162 RPM) and for other blade configurations, a rotated weir also achieved the maximum

hold-up. However, the angle of rotation of the weir corresponding to maximum hold-up

varied between 20° and 45° for different cases. At the highest rotation rate (254 RPM)

the powder bed is at least partially fluidized in the mixer, and the tilted powder bed was

not directly observable, but again a rotated weir showed the highest hold-up in the mixer.

In further experimental investigations to study the effect of other parameters, the weir

position was kept constant at 20°.

2.6.2.2 Effect of blade configuration on flow behavior

Two blade configurations (Table 2-3), ‗All Forward‘ and ‗Alternate‘ were examined to

determine whether mixing performance could be enhanced. Figure 2-8 shows the

residence time distribution functions for the two blade configurations at different rotation

rates. All the results presented in Figure 2-8 and Figure 2-9 corresponds to the 30 kg/h

flow rate. At high rotation rates (162 RPM and 254 RPM), the mean residence time

(Figure 2-9(a)) is greater for the ‗Alternate‘ blade configuration than for the ‗All

Forward‘ configuration. However, at low rotation rates (39 RPM and 100 RPM), this

effect diminishes. This indicates that the mixer capacity again becomes the limiting

30

parameter at lower rotation rate, leading to the observed behavior. Interestingly, hold-up

was found to be higher for the ‗Alternate‘ blade configuration than the ‗All Forward‘

configuration at all the rotation rates (Figure 2-9(c)), indicating higher bulk residence

time. This suggests that ‗Alternate‘ blade configuration creates recirculating zones in the

mixer.

The mean centered variance (MCV) showed a complex dependence between the two

blade configurations. As shown in Figure 2-9 (b), the ‗Alternate‘ blade configuration

showed a higher MCV value at 254 RPM than the ‗All Forward‘ configuration. At lower

rotation rates (39–162 RPM), MCV was not distinguishable between the two blade

configurations. Although the designs of the two blade configurations were significantly

different, a similar degree of dispersion was obtained. Only the rotation rate was found to

affect the axial dispersion. At this point, more work is necessary to study the effect of

blade configuration on RTD. As shown in Figure 2-9 (d), the number of blade passes was

higher for the ‗Alternate‘ blade configuration than for the ‗All Forward‘ configuration,

indicating that a higher amount of total shear is being applied by the ‗Alternate‘ blade

configuration.

2.6.3 Effects of material properties on flow behavior

RTDs in continuous mixers are dependent on the mixer geometry, impeller speed, flow

rate and material properties of the powder. In the previous sections relationships between

RTD and process (flow rate, impeller speed) and design parameters (impeller design,

weir angle) are reported. In this case the original parametric space of process parameters

(flow rate, impeller speed) was extended to include different materials. The materials

31

used in this DoE are presented in Table 2-4. Essentially two bulk material properties

(bulk density and cohesion) were correlated with the observed RTD parameters.

2.6.3.1 Material properties

Material flow properties were characterized using two methods, namely Carr Index (C.I.)

and dilation.

2.6.3.1.1 Carr Index

Carr Index is an indicator of the compressibility of the powder; higher the C.I. is, more

compressible is the powder. C.I. was calculated using equation (2-8) by measuring the

bulk and tapped densities of powder. C.I. measurements are listed in Table 2-4.

T

BC 1100

2-8

2.6.3.1.2 Dilation

Dilation is a complementary measurement to the C.I. which is also an indicator of the

compressibility of the powder. Dilation was measured using the GDR (Gravitational

Displacement Rheometer [83, 84]). Dilation is calculated as the percent change in volume

of the powder bed as function of time. Details on the experimental set-up of dilation

measurement can be found in Faquih et al. [84]. The procedure for dilation measurement

can be briefly described as follows. Powder was filled upto 40% volume in a transparent

acrylic cylinder, the cylinder was tapped on a tapping machine such that powder is

compacted to its tapped density. The cylinder was then mounted on the GDR, which

rotates the cylinder such that powder tumbles inside the cylinder. The tumbling or

avalanching motion of the powder was monitored in the cross-sectional direction using a

32

camera. The images captured by the camera were subsequently analyzed using an image

analysis program to measure the change in the cross sectional area of the powder. By

making an assumption of uniform cross sectional area across the length of the cylinder,

powder dilation can be measured. Dilation measurement, although complementary to the

C.I., is more accurate; because it is measured under fully dilated state of the powder.

Common errors involved in the bulk density measurement, such as powder compaction

while filling the cylinder or inaccurate volume measurement due to uneven powder

surface can be avoided in the dilation measurement. Dilation is an indicator of the level

of cohesion present in the powder; greater the dilation, more cohesive the powder. This

fact has been proven through experimental as well as computational studies [84]. The

correlation between the Flow Index (F.I.) measured by the GDR, and dilation is highly

linear, and it has been demonstrated that the F.I. is proportional to cohesion when

consolidation state of the powder approaches to zero [85]. Thus in our experimental

studies dilation was used as the material property that represent cohesion in the system.

Dilation values of the materials used our study are listed in Table 2-4.

2.6.3.2 Effect of material properties on mean residence time

In the previous sections (2.6.1.1 and 2.6.1.2) it was shown that residence time is a strong

function of impeller speed. Flow rate also affects residence time, but it shows an

interaction with the impeller speed. At lower impeller speeds, higher flow rates show

lower residence times, whereas at higher impeller speeds effect of flow rate is negligible.

In order to quantify the effects of process variables and material properties, a PLS

analysis was performed on the dataset consisting of an output variable – mean residence

time and four input variables – impeller speed, flow rate, bulk density and cohesion. PLS

33

analysis was performed using multi-variate data analysis software - ‗ProSensus

Multivariate‘. PLS analysis showed that 85% of the total variance was captured using just

the first principal component (Figure 2-10). Addition of another principal component did

not increase the R2 value of the model. The relative trends between all the input and

output variables are presented using a loading plot (Figure 2-11). Residence time was

found to increase with decrease in impeller speed, decrease flow rate, increase in bulk

density and decrease in cohesion. The relative importance of input variables is shown

using a VIP (Variable Importance Plot) in Figure 2-12. Impeller speed and bulk density

were found to be the two most important variables that affect mean residence time,

whereas flow rate and cohesion were relatively less important. Bulk density was found to

be the key material property that affected mean residence time; cohesion did not show a

clear trend. The relationship between bulk density and residence time is shown separately

in Figure 2-13. Increases in the bulk density lead to an increase in the mass hold-up in the

continuous mixer which increases residence time. This relationship is a strong function of

impeller speed. At lower impeller speeds (40, 100 RPM) where the flow regime in the

mixer is quasi-static, bulk density has a strong influence. At higher speeds (160,250

RPM) while powder is fluidized in the mixer, bulk density has minimal effect on the

residence time. At higher speeds, the forces exerted by the impeller exceed the

gravitational settling forces.

It is shown in Chapter 4 that the number of blade passes greatly influences the mixing

performance. As shown previously the number of blade passes goes through a maximum

as the impeller speed increases. The optimum speed that provides the maximum number

of blade passes was found to be dependent on the relationship between residence time

34

and impeller speed. It was observed that for powders with higher bulk density, the

optimal speed was smaller. As shown in Figure 2-14, for Avicel PH-101 and Avicel PH-

200 the optimal speed is ~ 160 RPM, whereas for Fast Flo Lactose and Dicalcium

Phosphate the optimal speed is ~ 100 RPM. This result can be used as a guideline to

select impeller speed based on the bulk density of the material.

2.6.3.3 Effect of material properties on axial dispersion coefficient

The second important fitted parameter in the RTD model (Equation (2-7)) is the axial

dispersion coefficient. In the previous section, the effect of process parameters (flow rate

and impeller speed) on the axial dispersion coefficient was studied for the case of Avicel

PH-200. It was shown that the axial dispersion coefficient increases with increase in

speed.However, flow rate did not show any clear effect. In this case study, another PLS

model was developed considering axial dispersion coefficient as the output variable and

the same set of input parameters (impeller speed, flow rate, bulk density and cohesion).

As shown in the bar plot (Figure 2-15), only 50% of the total variance was captured using

this model. The relative trends between input and output variables are shown in Figure

2-16. Axial dispersion coefficient was found to increase with increase in speed, increase

in cohesion, decrease in bulk density and increase in flow rate. The relative importance of

input variables was analyzed by using a VIP plot. As shown in the VIP plot (Figure

2-17), impeller speed and cohesion were found to be the most important variables

affecting axial dispersion coefficient, and bulk density and flow rate had the least

influence. In this DoE, bulk density did not show a clear effect on the axial dispersion

coefficient, because variation in bulk density was primarily driven by the variation in true

density (Dicalcium Phosphate = 2.31 g/cm3, Lactose =1.5 g/cm3, MCC= 1.6 g/cm3).

35

Since true density has the least significance on the axial dispersion coefficient, the effect

of bulk density is not clearly seen. If the materials have similar true densities, bulk

density and cohesion are typically inversely related.

The relationship between dilation and axial dispersion coefficient is shown in Figure

2-18. Speed and cohesion were found to interact significantly. At lower speeds (40,100

RPM), cohesion did not affect the axial dispersion coefficient; whereas at higher speeds

(160, 250 RPM), higher cohesion lead to higher values of the axial dispersion coefficient.

At higher speeds, the movement of powder is in the form of agglomerates as opposed to

individual particles, which essentially increases the axial dispersion coefficient.

2.6.4 Predictive model for blend uniformity suitable for control purposes

In our previous publication [82], a methodology was developed to predict blend

uniformity (RSD) as a function of incoming feed rate variability and process parameters.

As shown in equation (2-9), the total variance )( 2

,solidstotal in concentration observed at the

mixer discharge can be decomposed as the variance due to incomplete axial mixing

)( 2

fluid and the variance due to the non-ideal behavior of the powder mixing process

)( 2

_ feedIdeal .

2_

22, feedidealfluidsolidtotal (2-9)

The variance due to the non-ideal powder mixing process can be due to several sources

including incomplete transverse mixing, sample size, segregation, agglomeration, or

measurement method errors. Usually, the second component of variance is empirical and

needs to be characterized individually for each mixture. However the variability due to

incomplete axial mixing can be computed by predicting the concentration of the output

36

stream as a function of incoming concentrations and process parameters. The

mathematical equation proposed by Danckwerts is provided in equation (2-10).

dEtCtC influidout )()()(,

(2-10)

In order to use equation (2-10) as a predictive model for control purposes, a mathematical

model for the RTD, and a separate empirical model for RTD parameters as a function of

process parameters is necessary. As described in the section 2.4, RTDs were modeled

using FPEs, and RTD parameters (mean residence time, axial dispersion coefficient)

were estimated. Empirical relationships were developed between the RTD parameters

and the impeller speed. The model parameters and the coefficient of correlation for

different pharmaceutical excipients are listed in Table 2-5and Table 2-6. As shown in

Table 2-6, a linear model worked reasonably well for predicting mean residence time as a

function of impeller speed; and an exponential model showed the best possible fit for

predicting the axial dispersion coefficient (Table 2-6). These models are suitable for

constant flow rate conditions, in real situations the dynamics of flow rate also need to be

incorporated. A promising way to develop a dynamic predictive model that includes both

the process parameters (flow rate and impeller speed) is to use an approach similar to

Response Surface Modeling (RSM). In the present case, due to lack of sufficient data, the

feasibility of RSM was not investigated.

2.6.5 Conclusions

The mean residence time decreases with increase in the rotation rate, but the degree of

dispersion increases. Intermediate rotation rates exert the maximum number of blade

passes on the powder, thus maximizing strain and homogenization.

37

Increasing the flow rate also decreases the mean residence time, but this effect diminishes

with increases in rotation rate. At the highest rotation rate (254 RPM), flow rate did not

affect mean residence time. This result indicates that high RPMs could be suitable for

high flow rates since they do not lead to significant decrease in density (which was

observed for low flow rates).

Out of the two blade configurations examined, the ‗Alternate‘ blade configuration

showed greater powder hold-up than the ‗All Forward‘ blade configuration. The degree

of dispersion (MCV) did not show any particular trend between the two blade

configurations since it was also dependent on rotation rate and flow rate.

Bulk density was found to be the key material property that affects mean residence time;

cohesion did not show a clear effect. The effect of cohesion was not seen because the true

densities of the materials included in the DoE were significantly different. The effect of

cohesion needs to be studied using materials of nearly equal true densities, either by

varying the particle size or adding minor ingredients that affect cohesion. The optimal

speed, which exhibits the highest number of blade passes was found to be lower for

materials with greater bulk densities. This result provides a design rule for selecting

impeller speed based on the material bulk density.

The axial dispersion coefficient was affected by cohesion (or dilation), and not by the

bulk density of the material. Cohesion and impeller speed showed a significant

interaction. At lower impeller speeds, cohesion hardly affected the axial dispersion

coefficient, however at higher impeller speeds, greater cohesion lead to higher axial

dispersion coefficients. At lower impeller speeds, while the flow conditions in the mixer

are quasi-static, convection is the main transport mechanism and the dispersive transport

38

due to particle-particle interactions is relatively less significant. At higher impeller

speeds, while the powder is fluidized in the mixer, particles travel in the form of

agglomerates. An increase in cohesion leads to increase in agglomeration behavior in the

powder, which essentially affects the axial dispersion coefficient.

39

2.7 Figures for Chapter 2

Figure 2-1: (a) Experimental set-up (b) Continuous powder mixer (Gericke GCM-

250)

Figure 2-2: Effect of rotation rate on RTD. Other parameters: Flow rate — 30 kg/h,

and blade configuration — All forward

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0 50 100 150 200 250

E(t

) (s

ec-1

)

Time (sec)

39 RPM 100 RPM 162 RPM 254 RPM

40

Figure 2-3: Effect of rotation rate on (a) mean residence time (b) mean centered

variance (c) hold-up and (d) number of blade passes. Other parameters: flow rate —

30 kg/h, and blade configuration — All Forward.

0

10

20

30

40

50

60

70

80

90

0 100 200 300

τ (

sec)

Rotation rate (RPM)

(a)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 100 200 300

σ2τ

Rotation rate (RPM)

(b)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 100 200 300

Ho

ld-u

p (

kg

)

Rotation rate (RPM)

(c)

0

20

40

60

80

100

120

0 100 200 300

# B

lad

e p

ass

es

Rotation rate (RPM)

(d)

N=3

N=5.5

41

Figure 2-4: Effect of flow rate on RTD at (a) 39 RPM (b) 100 RPM (c) 162 RPM and

(d) 254 RPM. Other parameters: Blade configuration — All Forward.

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0 100 200

E(t

) (s

ec-1

)

Time (sec)

30 Kg/hr 45 kg/hr

(a)

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0 100 200

E(t

) (s

ec-1

)

Time (sec)

30 kg/hr 45 kg/hr

(b)

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0 100 200

E(t

) (s

ec-1

)

Time (sec)

30 kg/hr 45 kg/hr

(c)

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0 100 200

E(t

) (s

ec-1

)

Time (sec)

30 kg/hr 45 kg/hr

(d)

42

Figure 2-5: Effect of flow rate on (a) mean residence time (b) mean centered

variance (c) hold-up and (d) number of blade passes. Other parameters: blade

configuration — All Forward.

0

10

20

30

40

50

60

70

80

90

0 100 200 300

τ (

sec)

Rotation rate (RPM)

30 kg/hr 45 kg/hr

(a)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 100 200 300

σ2τ

Rotation rate (RPM)

30 kg/hr 45 kg/hr

(b)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 100 200 300

Ho

ld-u

p (

kg

)

Rotation rate (RPM)

30 kg/hr 45 kg/hr

(c)

0

20

40

60

80

100

120

0 100 200 300

# B

lad

e p

ass

es

Rotation rate (RPM)

30 kg/hr 45 kg/hr

(d)

43

Figure 2-6: Effect of flow rate on (a) hold-up and (b) bulk residence time.

Figure 2-7: Effect of weir position on hold-up.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

0 20 40 60 80

Ho

ld-u

p (

kg

)

Flow rate (kg/hr)

39 RPM 162 RPM 254 RPM

(a)

0

50

100

150

200

250

300

0 20 40 60 80

Bu

lk r

es.

tim

e (s

ec)

Flow rate (kg/hr)

39 RPM 162 RPM 254 RPM

(b)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0 Deg 20 Deg 45 Deg No Weir

Ho

ld-u

p (

kg

)

Weir position

44

Figure 2-8: Effect of blade configuration on RTD at (a) 39 RPM (b) 100 RPM (c)

162 RPM and (d) 254 RPM. Other parameters: flow rate: 30 kg/h.

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0 100 200 300

E(t

) (s

ec-1

)

Time (sec)

All Forward Alternate

(a)

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0 100 200 300

E(t

) (s

ec-1

)

Time (sec)

All Forward Alternate

(b)

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0 100 200 300

E(t

) (s

ec-1

)

Time (sec)

All Forward Alternate

(c)

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0 50 100 150

E(t

) (s

ec-1

)

Time(sec)

All Forward Alternate

(d)

45

Figure 2-9: Effect of blade configuration on (a) mean residence time (b) mean

centered variance (c) hold-up and (d) number of blade passes. Other parameters:

Flow rate — 30 kg/h.

0

10

20

30

40

50

60

70

80

90

0 100 200 300

τ (s

ec)

Rotation rate (RPM)

All Forward Alternate

(a)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0 100 200 300

σ2τ

Rotation rate (RPM)

All Forward Alternate

(b)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 100 200 300

Ho

ld-u

p (

kg

)

Rotation rate (RPM)

All Forward Alternate

(c)

0

20

40

60

80

100

120

140

0 100 200 300

# B

lad

e p

asse

s

Rotation rate (RPM)

All Forward Alternate

(d)

46

Figure 2-10: PLS model for Output variable - Mean Residence Time

Figure 2-11: Loading plot for the PLS model of Output variable - Mean Residence

Time, and Input variables - Impeller Speed, Flow rate, Bulk density and Cohesion

47

Figure 2-12: Variable Importance Plot (VIP) of the PLS model of output variable -

Mean Residence Time

Figure 2-13: Effect of Bulk density on mean residence time at 30 kg/hr

y = 71.476x + 28.537

R² = 0.9786

y = 69.138x + 12.934

R² = 0.9334

y = 27.837x + 18.486

R² = 0.4482

y = 21.594x - 0.3178

R² = 0.684 0

10

20

30

40

50

60

70

80

90

0 0.2 0.4 0.6 0.8 1

Mea

n r

esi

den

e ti

me

(sec

)

Bulk density (g/cc)

40 RPM, 30 kg/hr 100 RPM, 30 kg/hr

160 RPM, 30 kg/hr 250 RPM, 30 kg/hr

Linear (40 RPM, 30 kg/hr) Linear (100 RPM, 30 kg/hr)

Linear (160 RPM, 30 kg/hr) Linear (250 RPM, 30 kg/hr)

48

Figure 2-14: Effect of impeller speed on the number of blade passes for different

excipients

Figure 2-15: PLS Model for Output Variable - Axial Dispersion Coefficient

0

20

40

60

80

100

120

0 50 100 150 200 250 300

# B

lad

e p

ass

es

Impeller Speed (RPM)

Avicel PH101 Avicel PH200

Fast Flo Lactose Dicalcium Phosphate

49

Figure 2-16: Loading plot for the PLS model of output variable – Axial Dispersion

Coefficient, and input variables – impeller speed, flow rate, bulk density and

cohesion

Figure 2-17: Variable Importance Plot (VIP) for the PLS model of output variable -

Axial Dispersion Coefficient

50

Figure 2-18: Effect of cohesion on the axial dispersion coefficient

0.1

1

10

100

1000

0 10 20 30 40 50 60

(Dz)

Axia

l d

isp

ersi

on

co

effi

cien

t (c

m2/s

ec)

% Dilation

40 RPM, 45 kg/hr 100 RPM, 45 kg/hr 160 RPM, 45 kg/hr 250 RPM, 45 kg/hr

51

2.8 Tables for Chapter 2

Table 2-1: Feeder configurations used in the experiments

Material Feeder Flow rates Nozzle-size (mm) Screw type

Avicel PH-200 Schenck

AccuRate

5 - 60 kg/hr 22 Auger

APAP Schenck

AccuRate

0.9 - 1.35 kg/hr 36 Helix

Table 2-2: Materials, supplier and particle size

Material Supplier Particle size (µm)

Avicel PH-200 FMC Biopolymer 90

Avicel PH-101 FMC Biopolymer 234.1

Fast Flo Lactose Foremost Farms USA 120.01

Dicalcium Phosphate TLC Ingredients 186.2

Acetaminophen (APAP)

(micronized)

Mallinckrodt 30

Caffeine TLC Ingredients 36

Table 2-3: Experimental conditions

Process parameters

Flow rate 30, 45 kg/hr

Rotation rate 39 RPM, 100 RPM, 162 RPM, 254 RPM

Mixer design parameters

Blade configuration name: Blade direction,

blade angle (Angle with the shaft)

1. All Forward – All blades directing

forward, blade angle – 20 deg

2. Alternate – Alternate blades

directing in forward and backward,

blade angle – 20 deg

Weir angle 20 deg

Table 2-4: Bulk density, Carr Index, dilation, particle size of excipients

Material Bulk

Density

(g/cm3)

Carr

Index

(C.I.)

%

Dilation

d50

(µm)

Avicel101 0.33 22.25 48.67 90

Avicel200 0.38 10.97 16.2 234.1

Fast Flo Lactose 0.59 9.67 22.05 120.0

1

Dicalcium Phosphate

(CaHPO4)

0.77 15.27 29.47 186.2

52

Table 2-5: Predictive model for Mean Residence Time

Flow rate

(kg/hr)

Material Model Coeff. of

Correlation

(R2)

30 Avicel PH-101 -0.2216N+58.868 0.989

30 Avicel PH-200 -0.2113N+66.336 0.9795

30 Fast Flo Lactose -0.2772N+77.541 0.9872

30 Dicalcium Phosphate -0.3306N+98.599 0.9936

45 Avicel PH-101 -0.155N+43.306 0.9442

45 Avicel PH-200 -0.155N+50.248 0.8321

45 Fast Flo Lactose -0.2247N+61.848 0.9969

45 Dicalcium Phosphate -0.3232N+90.779 0.9955

Table 2-6: Predictive models for Axial Dispersion Coefficient

Flow rate

(kg/hr)

Material Model Coeff. of

Correlation

(R2)

30 Avicel PH-101 N.e. .023904440 0.9778

30 Avicel PH-200 N.e .0090282.2 0.8362

30 Fast Flo Lactose N.e. .011608710 0.9973

30 Dicalcium Phosphate N.e. .020804150 0.9984

45 Avicel PH-101 N.e. .028503140 0.9664

45 Avicel PH-200 N.e .01440223.1 0.9222

45 Fast Flo Lactose N.e. .014607270 0.9383

45 Dicalcium Phosphate N.e. .020406670 0.927

53

Chapter 3 Characterization of the powder flow behavior in the

continuous blender using DEM

This chapter presents a study of the powder flow behavior in a continuous mixer using

DEM modeling. The aim is to establish and employ a predictive model and validate it

against experiments. Once a relationship between a real case scenario and DEM

simulations is established, DEM can be used as to optimize and design continuous

powder mixing processes. The comparison between simulations and experiments was

made using RTD as the main response.

3.1 Methods

3.1.1 Simulation set-up

Computer simulations of the solids mixing process were performed using DEM, a

method by Cundall and Strack [86]. For maximum accuracy, computer aided drawings of

the blender with 1:1 size ratio were created using Pro/Engineer software. The drawings

were imported into a commercial DEM-based simulation program called EDEM™ (a

commercial software package marketed by DEM Solutions Inc) (Figure 3-1). At the inlet

of the blender two feeders were providing a continuous supply of particles on either side

of the impeller at a uniform feed rate. The two streams were completely segregated from

each other. At the other end of the blender, a semicircular weir was placed in order to

increase the hold-up and facilitate back mixing. The weir was placed such that its straight

edge made a 45° angle with the horizontal. The impeller rotation was counterclockwise

when viewed along the axis of rotation from the outlet end. Two impeller blade patterns

were designed using the blade angles shown in Table 3-1. The first pattern has twelve

54

blades with all forward facing (in the direction of flow) angles except for the last blade,

and the second pattern has twelve blades with alternating forward-reverse angles. The

impeller blade patterns are shown in Figure 3-2.

A full factorial experimental design consisting of two feed rates (of 30 kg/h and 45 kg/h),

four impeller speeds (40rpm, 100rpm, 160rpm and 250rpm) and two blade configurations

was examined in this study. The blender was fed with particles continuously in the form

of two streams on either side of the blade at the inlet. Table 3-2 shows the simulation

parameters used in this study.

3.1.2 The Discrete Element Method (DEM)

DEM is a technique for simulating the behavior of granular materials with each particle

treated as a discrete unit as opposed to continuum models where the material is treated as

a featureless medium with smooth properties defined by continuous functions. In the

DEM method, the motion of each particle is tracked based on the calculated positions and

velocities, which are a result of the forces present in the system. Forces on particles are of

two types – contact forces and body forces (equation (3-1)). The contact forces are due to

inter-particle or particle-boundary collisions. The boundary can be any physical object in

the system, such as walls, impellers, and baffles. Forces are resolved into normal and

tangential components that are independent of each other.

bodycontactTotal FFF (3-1)

In equation (3-1) is the resultant force on a given particle due to its interactions

with other particles and/or boundary elements as well as due to the effect of external

force fields such as the gravitational field, cohesive or electrostatic interactions. The term

accounts for all the normal and tangential contact forces and denotes

55

the sum of all body forces. This resultant force is computed for each particle at a high

frequency (a time-step being typically of the order of 10 µsec.) and the new particle

position is computed by numerically solving the equations of motion. Using Newton‘s

law the position of a particle i that has j number of contacts with its surroundings is

related to the resultant force by equation (3-2).

j lbody

tijiii

j kbody

tij

nijii

FRI

FFFxm

(3-2)

In equation (3-2), is the mass of the particle of radius , is its position, its

acceleration, and is its moment of intertia. and are the normal and tangential

components of the contact force on the particle due to its th

contact respectively. The

term accounts for all body forces acting on the particle using a summation

index k. In this study, gravity is considered to be the only body force acting on the

particles (k = 1, ). The rotational components of motion are the angular

displacement θ, angular acceleration , and sum of all torques due to body

forces using summation index .

The contact forces in DEM are calculated using a suitable contact model. There are

several types of contact models available for use in DEM simulations [87-89]. They can

be broadly classified into the following categories - molecular dynamics like potential-

based contact models [90] and the more commonly used linear viscoelastic [91], non-

linear viscoelastic [92,93] and hysteretic [94-96]contact models. This study uses a

contact model introduced by Tsuji et.al.[93] which is based on Hertzian contact theory

[86,87,96,97]. This model utilizes a soft particle approach. The distance between the

centers of each pair of particles or particle-boundary is computed at every time step. A

56

contact is detected if the distance between the centers of particles (in case of a particle-

particle contact) is less than sum of the particle radii or the distance between a boundary

and the center of a particle (in case of a particle-boundary contact) is less than the

particle‘s radius. A very small overlap is allowed in each of the normal and tangential

direction.

Normal Forces

The normal force due to a contact that resulted in a normal overlap is given by:

4/1'2/3nnnnn

n kF

(3-3)

In the above equation, refers to the normal stiffness coefficient, is the normal

damping coefficient, and is the rate of deformation.

The normal stiffness coefficient is obtained by equation (3-4) for particle-particle collision

)1(3

)R2(

2

eqv Ekn

(3-4)

In the above equation, E is the particle‘s Young‘s modulus and is the material Poisson

ratio. is defined as the effective radius of the contacting particles. If two contacting

particles have radii and the effective radius is obtained by

ji

ji

RR

RReqvR (3-5)

In case of a particle-boundary collision, the Poisson ratio of the boundary and the

Elastic modulus of the boundary are also required. For a particle of radius colliding

with the boundary, the stiffness coefficient is then calculated as:

57

bb

i

nEE

Rk

)1()1(

34

22 (3-6)

With the knowledge of the normal stiffness coefficient and a chosen coefficient of

restitution e, the normal damping coefficient is calculated as:

22lnln

e

mke

n

n (3-7)

Tangential Forces

The tangential force is calculated in a similar fashion as its normal counterpart. The

tangential contact force also consists of elastic and damping components. When a

tangential overlap of has been detected and there is a corresponding normal overlap of

due to the same contact, the tangential force is calculated as:

4/1'ntttt

t kkF (3-8)

In the above equation, is the tangential stiffness coefficient, and is the tangential

damping coefficient. The tangential stiffness coefficient is calculated [98] by:

2/1eqv

2

2R2

nn

Gk (3-9)

where G is the particle‘s Shear modulus. It is related to the elastic modulus E as:

)1(2

EG (3-10)

The tangential displacement (or overlap) is calculated by time-integrating the relative

velocity of tangential impact, between two colliding entities (interparticle or particle-

wall contact):

dtv trelt (3-11)

between two entities having velocities and is calculated by resolving the

absolute relative velocity [ using , the tangential component of the unit vector

58

connecting the centers of the colliding particles (or center of a particle and its contact

point with the geometry for particle-geometry contact) and adding the effect of angular

velocities as:

jjiitjitrel RRnvvv ˆ].[ (3-12)

The tangential force is limited by the Coulomb condition, which states that the tangential

force should be less than the normal force scaled by the coefficient of static friction as

. If, in the simulation, the tangential force obtained from equation (3-8)

exceeds the Coulomb limit for any pair-interaction, the slip is accounted for by resetting

the tangential displacement is to .

3.2 Results

3.2.1 Data Acquisition and processing

The continuous blending simulation was performed by rotating the impeller at a constant

speed and feeding two streams of particles at the inlet, simulating two feeders. The

blender was allowed to fill up until the mass hold-up reaches a near-constant value,

indicating that a steady state is achieved. The simulation was then run further for another

50-300 seconds at the steady state to allow for a time-window for data collection. Figure

3-3 shows the simulation snapshots at 40, 100, 160 and 250rpm while the blender is

operating under steady state. An increasing fluidization was observed in the 160 to

250rpm range.

The RTDs were computed using the following methodology: The particles were

continuously created at the inlet and two equal streams (meaning same mean particle size,

size distribution and feed rate). After the steady stage was achieved, the particles that

59

were created at in a 1-second window were tagged. Those particles were tracked until

they crossed the weir at the outlet of the mixer. The time taken by each tagged particle to

cross the weir was recorded as the residence time of that particle. The simulations were

run at steady state for long enough time such that at least 95% of the tagged particles

were retrieved at the outlet. A histogram was then created using 1-second interval bins.

Thus a curve of frequency (number of particles) vs. time was obtained, which was used

further to calculate the RTD, and the necessary RTD parameters.

3.2.2 Mean residence time

Computational results showing the effect of operational parameters on the mean

residence time are presented in Figure 3-4. A good qualitative agreement was achieved

between the experimental results (Chapter 2) and DEM simulations. The relative effects

of impeller speed, flow rate, blade configuration and their respective interactions were

also captured reasonably well in the simulations. However, the quantitative comparison

between the two sets of results showed some interesting differences. Experimental

measurements of the mean residence times were higher than those computed from DEM

simulations in the lower range of residence times. An opposite behavior was observed

under higher residence times (Figure 3-5) where experimental values were lower than the

simulations. These differences can be attributed to the differences between the material

properties of real powders, and the particle properties assigned in DEM simulations.

Lower values of residence times (0- 40sec, Experimental) belong to the impeller speed of

250 RPM. At such a high speed, fluidization of particles is created in the blender. Under

fluidization, powder hold-up in the continuous blender significantly decreases. Although

the total flow rates are kept equal in the experimental studies and the simulations, the

60

number of particles present in each system differs significantly. The mean particle size

used in DEM simulations (2 mm) is much greater than the particle size of the powder

used in experiments (Avicel PH-200, d50~200µm). The difference in the particle size

leads to much greater differences in the number of particles present between two systems

(109 orders of magnitude). Under fluidization conditions, while the hold-up is very low,

in the simulation case, the number of particles present in the blender is extremely low. In

that case individual particles might be getting impacted by the impellers (rather than

groups of particles, which is usually the case in real situations). While the particles are

impacted individually by the impeller, they experience much greater forces, which lead to

lower residence times than observed experimentally. Despite of all these differences

between experiments and simulations, DEM seems to a good qualitative tool to

characterize the relative effects of operating and design parameters.

3.2.3 Number of blade passes

As described in Chapter 4, the number of blade passes, which is proportional to the total

strain applied on the material during blending, correlates well with the observed mixing

performance (RSD), hydrophobicity of the blend and tablet hardness. Using the residence

time measurements from DEM simulations, the number of blade passes was calculated.

The results from simulations and experiments are shown in the figures Figure 3-6 and

Figure 3-7 respectively. In this case also, qualitatively, the relative effects of operating

and design parameters are captured well in DEM simulations. However in DEM

simulations, the maximum number of blade passes was observed to be at 100 RPM, while

in experiments it was at 160 RPM. The differences between the two cases are a direct

result of the residence time measurements described in the previous section. The nature

61

of the relationship between residence time and impeller speed is relatively linear in

experiments, while in the simulations two distinct regions can be identified.

3.2.4 Mean centered variance

The effect of operational parameters on the MCV, captured from the DEM simulations is

shown in Figure 3-8. The impeller speed showed a strong effect on the MCV. Except for

the ‗Alternate‘ blade and 45 kg/h case, the MCV increased with increase in speed and

exhibited plateau between 160-250 RPM. At lower impeller speeds (40, 100 RPM), when

the particles are not fluidized, the impeller speed seems to be the only important variable;

effects of flow rate and blade configuration are minimal. Under fluidization (160, and

250 RPM), except for the ‗Alternate‘ blade and 45 kg/h case, the MCV shows a slight

decrease with increase in speed. In this range, other variables show non-monotonic

effects.

These results indicate that the MCV depends primarily on the flow condition in the mixer

(Fluidization or quasi-static flow). In the experimental studies, powder is not completely

fluidized at 160 RPM. The transition regime for fluidization lies between 160 -250 RPM.

This essentially leads to an increase in the MCV with increase in impeller speed until 250

RPM (Figure 2-3). In simulations, while the fluidization occurs early between 100-160

RPM, a plateau in the MCV values was observed between 160 – 250 RPM. A

comparison between the experimental and simulation MCV values is shown in Figure

3-9. As a result of the differences in the onset of fluidization, a poor match (R2=0.43) was

obtained between the experimental and the simulation results.

62

3.3 Conclusions

A good qualitative agreement was achieved between the experimental and simulation

results. The relative trends between the operational (flow rate, speed) and design

variables were faithfully captured in DEM. A different fluidization behavior was

observed in DEM simulations compared to experimental results. The onset of fluidization

in real case scenarios was observed between 160-250 RPM, whereas in DEM simulations

it was observed between 100-160 RPM. The primary reason for this behavior seems to be

the presence of fewer particles present in DEM simulations than the real case scenarios.

The differences in fluidization behavior lead to differences in the optimal operating zone

between the two cases. In DEM simulations, at 100 RPM, the maximum number of blade

passes was observed as opposed to 160 RPM in experimental cases. The mean residence

time in experimental cases was found to be higher than the simulations under higher

impeller speeds, while it was lower than simulations under lower impeller speeds. At

lower speeds, while the particles are not fluidized in the mixer, tend to slip between the

particle-particle and particle-wall contacts. This behavior leads to higher residence times

than observed in the simulations.

63

3.4 Figures for Chapter 3

Figure 3-1: Computer aided drawing of a continuous blender made at the actual

scale. Two feeders continuously provide particles in two streams on either side of the

impeller which rotates in the direction shown by the curved arrow. A D-shaped

semicircular weir was placed at the outlet such that its flat edge was at 45° with the

horizontal.

Figure 3-2: Blade patterns used in DEM simulations and experimental validation

studies. A) Forward blade pattern with two 20° blades shown; B) Alternate pattern

with one forward facing and one backward facing blade, both at 20°.

(a)

(b)

64

Figure 3-3: Simulation snapshots at a) 40rpm, b) 100rpm, c) 160rpm and d)

250rpm. The red and blue particles are fed as two parallel streams of same mean

particle size with a normal particle size distribution. The particle bed fluidization

begins at approximately 160rpm.

(a) (b)

(c) (d)

65

Figure 3-4: Effect of process parameters on mean residence time (DEM

Simulations)

Figure 3-5: Comparison between experimental and DEM simulation results for the

mean residence time

0

20

40

60

80

100

120

0 50 100 150 200 250 300

Mea

n R

esid

ence

tim

e (s

)

Impeller Speed (RPM)

All Forward 30 kg/hr Alternate 30 kg/hr

All Forward 45 kg/hr Alternate 45 kg/hr

0

20

40

60

80

100

120

0 20 40 60 80 100 120

DE

M S

imu

lati

on

Experimental

All Forward 30 kg/hr Alternate 30 kg/hr

All Forward 45 kg/hr Alternate 45 kg/hr

66

Figure 3-6: Effect of operational parameters on the number of blade passes (DEM

simulations)

Figure 3-7: Effect of operational parameters on the number of blade passes

(Experimental)

0

20

40

60

80

100

120

140

160

0 100 200 300

Nu

mb

er o

f b

lad

e p

ass

es

Impeller Speed (RPM)

All Forward 30 kg/hr Alternate 30 kg/hr

All Forward 45 kg/hr Alternate 45 kg/hr

0

20

40

60

80

100

120

140

0 50 100 150 200 250 300

Nu

mb

er

of

bla

de

pas

ses

Impeller Speed (RPM)

All Forward 30 kg/hr Alternate 30 kg/hr

All Forward 45 kg/hr Alternate 45 kg/hr

67

Figure 3-8: Effect of operational parameters on the mean centered variance (DEM

simulations)

Figure 3-9: Comparison between the DEM simulations and experimental results for

the mean centered variance (MCV)

0

0.1

0.2

0.3

0.4

0.5

0.6

0 50 100 150 200 250 300

Mea

n C

ente

red

Va

ria

nce

(-)

Impeller Speed (RPM)

All Forward, 30 kg/hr Alternate, 30 kg/hr

All Forward, 45 kg/hr Alternate, 45 kg/hr

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

MC

V (

DE

M S

imu

lati

on

s)

MCV (Experimental)

All Forward, 30 kg/hr Alternate, 30 kg/hr

All Forward, 45 kg/hr Alternate, 45 kg/hr

Linear Regression

68

3.5 Tables for Chapter 3

Table 3-1: Impeller blade configurations

Impeller blade configuration Name Blade Angles (deg.)

Pattern-1 (11 Forward Blades) 5, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, -30

Pattern-2 (4 Alternate blades) 5, 20, 20, -20, 20, 20, 20, -20, 20, -20, 20, -

30

Table 3-2: DEM Simulation Parameters

Particle Properties Shear Modulus: 2e+06 N/m2

Poisson‘s Ratio: 0.25

Density: 1500 Kg/m3

Diameter: 2 mm

Normal Size distribution with S.D. = 0.2

(Truncated at lower limit of 70% and a

higher limit of 130%)

Particle-Particle Interactions Coefficient of Static Friction : 0.5

Coefficient of Rolling friction : 0.01

Coefficient of Restitution: 0.1

Blender Walls Material: Glass

Shear Modulus: 26 GPa

Density: 2200 Kg/m3

Poisson‘s Ratio: 0.25

Blades Material: Steel

Shear Modulus: 80 GPa

Density: 7800 Kg/m3

Poisson‘s Ratio: 0.29

Particle-Blade Interactions Coefficient of Static friction: 0.5

Coefficient of Rolling friction: 0.01

Coefficient of Restitution: 0.2

Particle-Wall Interactions Coefficient of Static friction: 0.5

Coefficient of Rolling friction: 0.01

Coefficient of Restitution: 0.1

69

Chapter 4 Characterization of the mixing performance of the

continuous mixer

Chapter 2 was focused on the characterization of macroscopic flow behavior in the

continuous mixer using RTD. However, RTD does not provide the complete description

of the system. Micro-scale properties of mixtures, including the scale of segregation of

the mixture and blend homogeneity, are not directly captured in the RTD.

This chapter will focus on the characterization of the powder blend homogeneity as a

function of process and design parameters of the continuous mixer. Two cases, involving

blending studies for APAP and MgSt are presented. A third case study of blending of

highly cohesive powders, which involves mixing and de-lumping, is presented in Chapter

6. In lubricant blending, along with the blend homogeneity, other blend properties such as

hydrophobicity and post-blending flow properties, and tablet properties such as tablet

hardness and dissolution profile, are important variables. The hydrophobicity of the blend

is affected by the total strain applied in blending, shear rate and lubricant concentration

[99]. Over-lubrication often leads to reduced tablet hardness [100] and poor dissolution

[101,102]. In this case study of lubricant blending, effects of process and design

parameters of the continuous blending process on the blend uniformity, hydrophobicity,

content uniformity in tablets and tablet hardness were examined.

4.1 Methods

4.1.1 NIR Spectroscopy

A Nicolet Antaris NIR spectrometer (Thermo Fisher) was used to quantify APAP in the

samples. Spectral data was collected using the software ―Omnic‖ and ―TQ Analyst‖ was

70

used for calibration model development. The instrument measures the spectrum in the

range of 4000 cm− 1

to 10,000 cm− 1

wave numbers. The regression method used was

partial least square (PLS). A Norris derivative filter was used to treat the spectral data.

The coefficient of correlation (R2) for the model obtained was 0.9831 and root mean

squared error of prediction (RMSEP) was 0.239, which indicates good fitting of the

spectral data.

4.1.2 LIBS (Laser Induced Breakdown Spectroscopy)

A schematic for the experimental set-up for LIBS is shown in Figure 4-1. LIBS is based

on the principle of atomic emission spectroscopy of laser induced plasma. LIBS utilizes a

highly energized pulsed laser beam which is focused on a very small area of the sample.

In a single shot of the laser pulse, a tiny amount of material is ablated, which generates

plasma at very high temperatures. At such a temperature, material is broken down into

excited atomic or ionic species. Such excited states decay by emitting radiation in UV,

visible and NIR region. By resolving the white light by an optical spectrometer, a

spectrum consisting of atomic and molecular bands is obtained. In recent years, LIBS has

been applied for quantitative analysis of pharmaceutical products for measuring content

uniformity [103-105] as well as coating thickness uniformity [29,105]. Tablets used in

this study contain APAP, MgSt and micro-crystalline cellulose. Three spectral lines

corresponding to Mg were identified. The peak height at the wavelength 518.36 nm was

used to quantify MgSt.

Spectral data was collected at 13 sites and 11 shots per site in each tablet. The intensity

between different sites and shots was averaged for each tablet. Uniformity of MgSt

distribution can be characterized as the RSD between different sites in each tablet or the

71

RSD between average intensities for different tablets. In the present experimental work,

inter-tablet variability was calculated by computing the RSD between average intensities

measured for 20 tablets at each experimental condition. Micro-distribution of MgSt has

been shown to affect bulk density of the powder, tablet strength etc. LIBS signal intensity

is known to be affected by the matrix changes [106]. The effect of matrix changes as a

function of process parameters on LIBS signal intensity was also measured.

4.1.3 Washburn’s method

According to Washburn theory [107] when a porous solid is brought into contact with a

liquid the rise of the liquid into the pores of the solid obeys the following relationship:

2

2 cosM

CT

(4-1)

The terms are defined as follows:

T = time after contact, η = viscosity of liquid, C = material constant characteristic of solid

sample, ρ = density of liquid, γ = surface tension of liquid, θ = contact angle, M = mass

of liquid adsorbed on solid

If an experiment is performed where the mass of the adsorbed liquid is measured with

time, provided that the powder is uniformly packed and does not dissolve during the

experiment, a graph of Time vs. Mass2 should yield a straight line whose slope is (η / C ρ

2 γ cosθ), is defined as the hydrophobicity. As the slope increases, hydrophobicity

increases, in other words cosθ decreases and contact angle increases.

The experimental set-up is as shown in Figure 4-2. This method measures the amount of

a water-based solution that permeates through a powder bed with respect to time.

Experiments are conducted as follows:

72

1. The powder bed is packed in a chromatographic column (the bottom is made of

sintered glass), which is tapped to obtain a consistent density throughout the

column.

2. The bottom of the column is immersed into the solution, which rises through the

powder bed by the action of capillary forces. As the powder becomes

hydrophobic, the penetration by the solution slows down or stops altogether.

3. The weight of the column increases as the solution penetrates the powder bed.

The weigh is monitored and frequently recorded.

4. The curves obtained should reproduce accurately.

4.2 Results

4.2.1 APAP mixing

To determine the homogeneity of the stream coming out of the mixer, samples were

retrieved from the outlet stream. The relative standard deviation (RSD, Equation (4-2),

which is also known as the coefficient of variability and is the most common mixing

index used in industry, was computed. For each experimental run, 20 samples were

collected from the outlet. Scintillation glass vials were used to store and analyze the

samples. Concentration of acetaminophen in each sample was measured using a NIR

spectroscopy analytical method. RSD between the acetaminophen concentrations was

calculated using the usual relationship.

73

1

)(1

2

N

CC

C

sRSD

N

i

(4-2)

In equation (4-2), C is the average concentration of the total samples (N) collected in

each mixing run and Ci is the concentration of each sample; s is the estimate of the

standard deviation obtained using the sample concentrations. When RSD is smaller,

concentrations of the individual samples are closer to the mean concentration, which

indicates better blend uniformity. In continuous blending systems, blend uniformity is

often represented in the form of VRR (Variance Reduction Ratio). The term VRR, as

given by equation (4-3), was introduced by Danckwerts [12], and is a ratio of the

variance in concentration at the mixer inlet to the variance in concentration at the outlet.

VRR represents the efficiency of the mixer to reduce incoming fluctuations in the feed

rate.

2

2

o

i

s

sVRR

(4-3)

In equation (4-3), 2is is the variance in concentration at the inlet and so

2 is the variance at

the outlet. A smaller value of 2os (or higher value of VRR) indicates better mixing

performance. In the present case si2 changes only when the total input flow rate is

changed. For each of the mixing runs, both indices RSD and VRR were calculated.

The first set of experiments were performed using a low API dose formulation (3%

APAP, 97% Avicel PH-200). In order to determine the statistical significance of each

parameter for blend homogeneity, analysis of variance (ANOVA) was performed. In

74

doing this, we are treating the variance as an averageable response, which in a practical

sense, it is. An alternative method would be to do pairwise comparisons using an F-test

(or similar) but this approach is impractical for multiple levels because proper definition

of an overall α level for the entire data set is unclear. A full factorial design of three

parameters, rotation rate, flow rate and blade configuration was used. The response used

in the analysis was the normalized variance (NV), NV = RSD2. The results from ANOVA

are presented in Table 4-1. Rotation rate was the most statistically significant variable

affecting mixing performance (p = 1.4 × 10− 6

). Following that, the effect of blade

configuration was statistically significant (p = 7.96 × 10− 4

). Flow rate was found to be

statistically not significant (p = 0.85) variable. The interaction between Rotation rate and

blade configuration was also statistically significant (p = 1.75 × 10− 4

). At 254 RPM, the

‗All Forward‘ blade configuration shows higher NV than the ‗Alternate‘ blade

configuration; at 39 RPM, NV nearly coincides for both of the blade configurations.

4.2.1.1 Effect of impeller rotation rate

The rotation rate is one of the important process variables. In a continuous process, for a

given throughput capacity, the rotation rate is the only manipulated variable that can be

easily changed online to control the blend homogeneity. Four rotation rates (39, 100, 162

and 254 RPM) were examined for the ‗All Forward‘ blade configuration. As depicted in

Figure 4-3, the highest VRR (Figure 4-3 (a)) and smallest RSD (Figure 4-3 (b)) were

observed at intermediate rotation rates (100–162 RPM). For ‗Alternate‘ blade

configuration as well, intermediate rotation rates (100 RPM–162 RPM) exhibited lowest

values of the RSDs (Figure 4-3 (d)). The effect of rotation rate was found to be

statistically significant (p = 1.4 × 10− 6

). At intermediate rotation rates, the maximum

75

number of blade passes (maximum strain) is exerted on the powder, which leads to better

mixing performance. The effect of rotation rate on the number of blade passes is shown

in Figure 4-3 (d).

4.2.1.2 Effect of flow rate

The other critical operational variable is the flow rate through the system. During

operation, this variable is typically determined by the capacity of other process

components (for example, the tablet press), thus it is critical during process design to

determine that the mixer is properly sized to achieve optimum performance at the

intended flow rate. As shown in Figure 4-3 (d), for the ‗Alternate‘ blade configuration,

mixing performance was better at the lower flow rate (30 kg/h). The difference in RSD

between the two flow rates was not statistically significant at very high and low rotation

rates. Such relative differences between RSDs are somewhat made clear by the

measurement of strain. For the ‗All Forward‘ condition, both flow rates show similar

mixing performance at higher rotation rates (162 RPM and 254 RPM) (Figure 4-3 (b)).

At low rotation rates (39 RPM and 100 RPM), experiments conducted at higher flow rate

exhibited lower RSD (Figure 4-3 (b)). However, over the entire range of RPM, the effect

of flow rate was statistically not significant with p = 0.85. This result is actually quite

useful, indicating that for the range of flow rates studied here, mixing performance is

robust with respect to flow rate.

However, increase in total flow rate has an impact on fill level in the mixer and also on

the input variability, because feeders operate more accurately at larger flow rates.

Increasing the flow rate improves feeder performance, which leads to lower variance in

76

the input concentration. Variance at the input decreases from si2 = 1.12 to si

2 = 0.41 for

the increase in flow rate from 30 kg/h to 45 kg/h, which essentially leads to decrease in

the VRR (Figure 4-3 (a) and (c)). However, the RSD at the discharge is relatively less

affected by this large change in input variability (Figure 4-3 (b) and (d)). Thus, the

conclusion, for this particular case, is that the variability contributed from feeding is

almost completely filtered out by the continuous mixer, provided that enough residence

time is available. The final RSD of the mixture is largely dominated by the sample size

and the inherent material properties, and how exposure to shear in the mixer affects them.

Considering that acetaminophen is a cohesive material with large electrostatic response,

the high variability in the final blend could be due to agglomeration [8,108] or

electrostatic effects [109], both of which can worsen mixing performance.

4.2.1.3 Effect of blade configuration

A clear effect of the blade configuration was not observable for the conditions examined

here, possibly because the blade configuration effect interacted with the other parameters.

For the lower flow rate (30 kg/h) case, the ‗Alternate‘ blade configuration showed a

better mixing performance than the ‗All Forward‘ configuration at 100, 162 and

254 RPM (Figure 4-3 (e)). For the higher flow rate (45 kg/h) case, the ‗Alternate‘ blade

configuration showed better mixing performance at 162 and 254 RPM; however at 39

and 100 RPM, the ‗All Forward‘ blade showed better mixing performance (Figure 4-3

(f)). Statistically, the effect of the blade configuration on the blend uniformity was

significant (p = 8 × 10− 4

) p = 0.0008. Experimental results at this point are not sufficient

to provide a mechanistic explanation for the observed effect of blade configuration. The

entire experimental investigation performed to assess mixing performance showed that

77

the lowest achievable RSD was about 0.08 (Figure 4-3 (b)). In this case study the

analytical method used for quantification of APAP was NIR spectroscopy, which utilizes

very small sample size (~ 10–20 mg). As explained in section 4.1.1, the prediction error

for NIR calibration model was 0.239, which further indicates that for a 3% APAP case,

the lowest achievable RSD would be 0.239/3 = ~ 0.08. In conclusion, the observed

mixing performance was the best possible mixing performance that could be achieved at

the sa mple size used in the study, and its value was entirely due the inherent material

properties of APAP.

4.2.2 Lubricant mixing

Lubricant mixing experiments were conducted using MgSt as a lubricant (and tracer) and

a pre-blend of Avicel and APAP as a bulk material. The experimental protocol is similar

to that of API blending except for a few additions. The parameters investigated in this

case study include blade configuration, rotation rate, % MgSt and the feed position for

MgSt. The lubricant homogeneity in the powder was analyzed by NIR Spectroscopy. The

extent of lubrication in the powder blend was also characterized by conducting

wettability measurements using Washburn‘s technique. In addition, powder blends

collected after lubricant blending were compressed using a Carver press by applying a

constant pressure. The micro-distribution of MgSt in the tablet was measured using LIBS.

Finally, crushing tablet hardness was also measured to characterize the bonding strength

of tablets as a function of blending protocols.

78

4.2.2.1 Effect of impeller rotation rate and MgSt concentration

The effect of rotation rate and % MgSt on blend uniformity, uniformity in the distribution

of MgSt in tablets, tablet hardness and hydrophobicity is shown in Figure 4-4. The blend

uniformity measured using NIR (RSDNIR) does not change significantly between 39 to

160 RPM. However at 250 RPM, mixing performance becomes worse (Figure 4-4 (a)).

The uniformity of MgSt distribution in tablets was characterized using LIBS (Figure 4-4

(b)). While doing LIBS measurements, all the intensity measurements corresponding to

different sites and shots for a tablet were averaged. The RSD was calculated between the

average intensities of 20 such tablets. For 1% and 2% MgSt concentration levels, RSD

measured using LIBS (RSDLIBS) was the lowest at the intermediate rotation rates. For the

0.5% MgSt case, RSD increased with increase in rotation rate. These results suggest that

the uniformity of MgSt distribution is dependent on sample size as well as operational

parameters. The sample size in the NIR measurement is larger compared to the LIBS

measurement (1cm×0.5cm spot size in NIR vs. 500 µm diameter spot in LIBS). For the

case of 1% and 2% MgSt, at 39 and 160 RPM, similar level of macro-mixing is achieved,

however micro-mixing is better at 160 RPM. At 250 RPM, due to lower residence times,

mixing is poor on both macro as well as micro level. For the case of 0.5% MgSt, similar

phenomenon on the macro level is observed. At micro-level a different behavior than the

other two cases was observed. This can possibly be related to a sampling problem

considering the very concentration of MgSt.

As shown in Figure 4-4(c), the tablet hardness decreased at intermediate rotation rates for

at levels of MgSt. This observation correlates very well with the relationship between the

number of blade passes and impeller speed. At 160 RPM, where the number of blade

79

passes is maximum, the lowest tablet hardness is observed. The relationship between

hydrophobicity and rotation rate was a function of MgSt concentration (Figure 4-4(d)).

At 2% MgSt concentration, hydrophobicity was maximum at the intermediate rotation

rate; otherwise it decreased with increase in rotation rate. Except for the 2% MgSt case,

the hydrophobicity measurement did not show any correlation with the number of blade

passes. This leads to the conclusion that for this particular formulation, hydrophobicity is

not significantly affected by the blending parameters.

4.2.2.2 Effect of design parameters

In order to further understand the mixing behavior of MgSt, mixing experiments with

different blade configurations and weir positions were conducted. The blend uniformity

measurements under different conditions are shown in Figure 3-5. The ‗Alternate‘ and

the ‗All forward‘ blade configuration showed similar mixing performance at rotation

rates 39 and 160 RPM. However, at higher RPMs, alternate blade showed better mixing

performance, which was attributed to higher residence time. A few experiments were also

conducted without the presence of weir in order to expose the blend to a shear level as

low as possible. Removing the weir increased the RSD approximately by a factor of two.

The hydrophobicity tests were conducted on these blends; results are presented in Figure

4-5(b). Although the mixing performance was adversely affected by the absence of the

weir, hydrophobicity of the blends was not affected to a significant degree. This

observation again confirms that for the conditions examined here, hydrophobicity of the

blend is relatively insensitive to blending parameters. Tablet hardness was measured for

different blade configurations. The ‗Alternate‘ blade configuration exhibited lower tablet

80

hardness than the ‗All Forward‘ blade configuration. In this case also, residence time or

the number of blade passes measurements show good correlation with tablet lubrication.

4.2.2.3 Effect of feed position

As indicated earlier, MgSt needs to be blended under the lowest possible shear. Feeding

MgSt farther along the axis of the blender was examined as a possible design variable.

Three different feeding positions as shown in Figure 4-6(a) were examined. The blend

uniformity for MgSt blending was measured under these conditions. As shown in Figure

4-6(b), the RSD increases as the feed position gets farther from the blender inlet. At a

feed position at the blender inlet, and at the center of the blender, RSD is not significantly

different, which indicate complete mixing of MgSt. However, feeding MgSt near the exit

of the blender produced a blend with very high RSD which showed presence of MgSt

agglomerates. Clearly, feeding position close to the blender outlet is not desirable for

blending MgSt.

In order to further clarify the effect of the feed position, the RSD profile along the

blender length was measured. The blender was stopped while it was operating at steady

state, and samples along the length of the blender were collected. At each axial position,

5-10 samples were collected. Figure 4-7(a) shows, RSD profile for the case of the feed

position at the blender inlet, which exhibits a plateau at approximately 50% of the

blender length. The mean concentration of MgSt became steady at 75% of the blender

length (Figure 4-7 (b)). Since the mean concentration of MgSt is not uniform, there seems

to be a sampling problem, possible caused by the fact that the samples retrieved were

very few. For the case of the feed position at the center of the blender, as shown in Figure

4-8(a), the plateau in the RSD is observed at approximately 75% of the length. Given that

81

these RSD profiles are under 39 and 160 RPM, while the feed position for MgSt is at the

center of the blender, a further increase in blender RPM might shift the position of the

plateau. It was presented in Chapter 2 that the number of blade passes significantly

decrease down when powder is completely fluidized in the mixer. Therefore, it is likely

that the feed position at the center of the blender would not be suitable at high impeller

speeds. Given that feeding at the inlet of the blender does not lead to over-lubrication,

there seems to be no further advantage feeding MgSt at the center of the blender.

4.3 Conclusions

Mixing performance was largely dominated by the material properties of the mixture and

the extent of total shear (strain) applied in the mixer. Rotation rate was found to be the

most significant process parameter affecting mixing performance. Intermediate rotation

rates showed the best mixing performance.

The effect of blade configuration on the blend homogeneity was statistically significant.

This effect was also interacted with the rotation rate and the flow rate. More investigation

is required for better understanding of the physical phenomenon.

Some insights were also gained regarding the mixing mechanisms in the continuous

mixer. For cohesive powders, a certain minimum shear rate is often required to break

large clumps or agglomerates present in the mixer. In a batch mixer, this is usually

achieved by applying shear using an intensifier bar. Although the uniformity of shear in a

batch mixer is questionable, by providing the appropriate mixing time, total strain can be

controlled.

In batch mixing, the shear rate depends both on blender rotation speed and blender size,

further complicating scale-up of batch processes. In a continuous mixer, shear rate and

82

total shear are also difficult to control independently, since other parameters such as hold-

up and bulk density also change by changing the shear rate (RPM). In the present case,

when the shear rate is increased, the hold-up decreases. Increasing the shear rate changes

flow conditions inside the mixer from a ‗dense powder bed stirred by the impeller‘

regime to a ‗fluidized bed‘ regime. Once the powder is fluidized, bulk density drops

significantly, which decreases the total strain applied to the powder; as a result of

fluidization, void regions are formed in the powder bed. Increasing the flow rate reduces

such void regions, which in turn increases the strain applied on the powder.

In the work reported here, relationships between hold-up, strain and process/design

parameters were identified. In conclusion, the lowest RSD which represents the best

possible mixing performance was achieved at the intermediate rotation rates and

‗Alternate‘ blade configuration. The lowest value of RSD was a result of the sample size

analyzed and due to the inherent material properties of APAP. Measurement of strain

partially helps in understanding the mixing performance at various experimental

conditions.

In the second case study, blending of MgSt was studied. Four responses including blend

uniformity for MgSt, uniformity for MgSt distribution in tablets, tablet hardness and

blend hydrophobicity were measured at each of the operating conditions. Blend

uniformity, tablet hardness and MgSt distribution in tablets were strongly affected by the

impeller speed and design variables (impeller design, weir position). However,

hydrophobicity was found to be less sensitive to the blender parameters. The strain

measurement (number of blade passes) showed good correlation with tablet hardness and

MgSt uniformity in tablets. The sample size being analyzed in the NIR and LIBS

83

measurement also showed its effects on the RSD trends. The micro-distribution of MgSt

measured by LIBS was found to be the best at 160 RPM. A reasonably good macro

mixing however was achieved between 39-160 RPM. At the highest impeller speed (250

RPM), the residence time drops down to a significantly low value which leads to poor

macro as well as micro mixing performance.

84

4.4 Figures for Chapter 4

Figure 4-1: Schematic of the experimental set-up for LIBS

Figure 4-2: Experimental set-up for Washburn's method

85

Figure 4-3: Comparison between flow rates: (a) VRR vs. rotation rate (‘All

Forward’ Blade configuration) (b) RSD vs. rotation rate (‘All Forward’ Blade

configuration) (c) VRR vs. rotation rate (‘Alternate’ blade configuration) (d) RSD

vs. rotation rate (‘Alternate’ Blade configuration). Comparison between Blade

configurations: (e) RSD vs. rotation rate (30 kg/hr), (f) RSD vs. rotation rate (45

kg/hr) (Note: Comparison between the blade configurations is shown only with the

RSD, plots of VRR are not shown here in order to avoid redundancy)

0

2

4

6

8

10

12

14

16

18

0 100 200 300

VR

R

Rotation rate (RPM)

30 kg/hr - All Forward

45 kg/hr - All Forward

(a)

00.020.040.060.08

0.10.120.140.160.18

0.2

0 100 200 300

RS

D

Rotation rate (RPM)

30 kg/hr - All Forward

45 kg/hr - All Forward

(b)

0

2

4

6

8

10

12

14

16

18

0 100 200 300

VR

R

Rotation rate (RPM)

30 kg/hr - Alternate

45 kg/hr - Alternate

(c)

00.020.040.060.08

0.10.120.140.160.18

0.2

0 100 200 300

RS

D

Rotation rate (RPM)

30 kg/hr - Alternate

45 kg/hr - Alternate

(d)

86

Figure 4-3 (Continued)

00.020.040.060.08

0.10.120.140.160.18

0.2

0 100 200 300

RS

D

Rotation rate (RPM)

30 kg/hr - All Forward

30 kg/hr - Alternate

(e)

00.020.040.060.08

0.10.120.140.160.18

0.2

0 100 200 300

RS

D

Rotation rate (RPM)

45 kg/hr - All Forward

45 kg/hr - Alternate

(f)

87

Figure 4-4: Effect of MgSt concentration: (a) RSDNIR vs. Rotation rate (b)

RSDLIBS vs. Rotation rate (c) Tablet hardness vs. Rotation rate (d) Hydrophobicity

vs. Rotation rate.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 100 200 300

RS

D N

IR

Rotation rate (RPM)

2% MgSt 1% MgSt

0.5% MgSt

0

0.05

0.1

0.15

0.2

0.25

0 100 200 300

LIB

S R

SD

Rotation rate (RPM)

1% Mg 2% Mg 0.5% Mg

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

20.00

0 100 200 300

Ha

rdn

ess

(kP

a)

Rotation rate (RPM)

2% Mg 1% Mg

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 100 200 300

Hy

dro

ph

ob

icit

y (

min

2/g

m)

Rotation rate (RPM)

1% MgSt 2% MgSt 0.5 %MgSt

(a) (b)

(c) (d)

88

Figure 4-5: (a) Effect of design parameters on RSDNIR (b) Effect if design

parameters on hydrophobicity (c) Effect of blade configuration on tablet hardness

0

0.05

0.1

0.15

0.2

0.25

0 100 200 300

RS

D

Rotation rate (RPM)

Alternate blade (No Weir)

Forward blade 20 W

Alternate blade 20 W

0

0.05

0.1

0.15

0.2

0.25

0 100 200 300

Hy

dro

ph

ob

icit

y (

min

/g2)

Rotation rate (RPM)

Alternate blade (No Weir)

Alternate Blade (20 W)

Preblend (before Lubrication)

Forward blade (20 W)

(b)

0

2

4

6

8

10

12

14

16

18

0 100 200 300

Ha

rdn

ess

(kP

a)

RPM

1% Fwd Blade

1% Alternate Blade

(c)

(a)

89

Figure 4-6: (a) Feeding positions for MgSt (b) Effect of feed position on blend

uniformity at the blender discahrge

0

0.1

0.2

0.3

0.4

0.5

0.6

In Mid Outlet

RS

D

MgSt Feed Position

Preblend

(Avicel + API)

Lubricant

(a) (b)

90

Figure 4-7: Feed position at the blender inlet (a) RSD vs. blender length (a) Mean

concentration vs. blender length

Figure 4-8: Feed position - Center of the blender (a) RSD vs. blender length (b)

Mean concentration vs. blender length

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 0.5 1

RS

D

Axial position

39 RPM 160 RPM

0

0.1

0.2

0.3

0.4

0.5

0.6

0 0.5 1

% M

gS

t

Axial position

39 RPM 160 RPM

0

0.5

1

1.5

2

2.5

0 0.5 1 1.5

RS

D

Axial position

39 RPM Inside the blender

160 RPM Inside the Blender

39 RPM - At the exit

160 RPM - At the exit

0

0.5

1

1.5

0 0.5 1 1.5

% M

gS

t

Axial position

39 RPM Inside the blender

160 RPM Inside the Blender

39 RPM At the exit

160 RPM At the exit

(a) (b)

(a) (b)

91

4.5 Tables for Chapter 4

Table 4-1: Analysis of variance (ANOVA) for the NV (Normalized Variance).

df SS MS F P

RPM 3 0.001 0.000 23.388 0.0000014

Flow rate 1 0.000 0.000 0.038 0.8471845

Blade configs 1 0.000 0.000 15.868 0.0007958

RPM*Flow rate 3 0.000 0.000 0.764 0.5282048

Flow rate*Blade

config 1 0.000 0.000 0.192 0.6661593

Blade config*RPM 3 0.000 0.000 11.318 0.0001752

Error 19 0.000 0.000

Total 31 0.002

92

Chapter 5 Continuous monitoring of powder mixing process by

NIR Spectroscopy

Continuous monitoring of blend uniformity is the first step towards implementing process

control for continuous blending operations, or to facilitate rejection of non-uniform

powder from the blending operation. In this chapter two case studies are presented where

on-line NIR analyzers (CDI and VTT) were used to monitor the continuous powder

blending process. As described in Chapter 4, it is required to specify the sample size

being analyzed by the NIR analyzer while reporting any blend uniformity measurement.

In the continuous blending process, typically powder is in a state of motion and

inherently there is always a certain degree of spectral averaging involved in the

measurement. Blend uniformity, quantified as RSD (Relative Standard Deviation)

between the in-line measurements, is dependent on the degree of averaging. It is

necessary to select the averaging window depending on the sample size of interest (one

unit dose), which is often not the case in the current industrial practice. Once the correct

averaging window for the in-line measurements is determined, the blend uniformity

measured from the in-line measurements can be directly compared with the off-line

measurements. In order to be able to do such a comparison, quantification of error

associated with in-line and off-line measurement is necessary.

This chapter is broadly divided in two parts. In this first, a methodology for building

chemometric calibration models using the on-line spectral data is presented. Here

calibration model building exercise was based on the data acquired using a CDI on-line

NIR analyzer. In the second part, a methodology is presented that allows quantification of

the error associated with the in-line measurements, as well as the relationship between in-

93

line and off-line blend uniformity measurements. This methodology was developed using

a multi-point fiber optic probe NIR monitoring system provided by VTT.

5.1 Chemometric calibration model development using on-line NIR spectral

data

5.1.1 Equipment and experimental set-up

The experimental set-up designed for monitoring blend uniformity at the discharge of the

continuous blender is shown in Figure 5-1 (a). A chute was installed at the outlet of the

blender. The connecting piece used to mount the CDI Spectrometer on the powder

conveying chute is shown in Figure 5-1 (b). The level of powder on the chute was

dependent on the flow rate and the cohesivity of the powder. The angle of inclination was

adjusted such that the distance of the flowing powder from the spectrometer remained

approximately constant.

5.1.2 Materials and pre-blend preparation

The materials chosen for this study were micro-crystalline cellulose (Avicel-PH 102,

FMC BioPolymer), acetaminophen (micronized, Mallinckrodt Inc.), colloidal silicon

dioxide (Carbosil), and magnesium stearate (Mallinckrodt Inc.). Pure acetaminophen was

pre-blended with silicon dioxide (3%) in a V-blender with intensifier bar rotating at

1000 rpm to improve the flow properties since it posed difficulties in feeding through

loss-in-weight feeders due to its highly cohesive nature. Avicel was also pre-blended with

magnesium stearate (1% (w/w)). All experiments were performed at a constant total flow

rate with a constant rotation rate of the agitator in the continuous blender. Different

94

experimental conditions consisted of different levels of APAP. The feed rates used in the

experimental runs are presented in Table 5-1.

5.1.3 NIR Spectroscopy

5.1.3.1 Preparation of calibration samples

Calibration samples used in this study were prepared in a V-blender (4 quarts, Patterson

Kelley). The calibration samples consisted of 600 g blends where acetaminophen (APAP)

ranged in concentration from 0 to 15% (w/w), with samples prepared at approximately

1.5% intervals. APAP was used diluted with Carbosil and Avicel lubricated with

magnesium stearate in the same manner as in the continuous mixing experiments. The

following sections describe spectral acquisition and development of the calibration

model.

5.1.3.2 Instrumentation

A CDI (Control Development Inc., South Bend, IN) Blend Uniformity Analyzer NIR

spectrometer was used to acquire the spectra of the flowing powders. This instrument

includes a 5.4 W dual tungsten halogen light source, and an indium gallium arsenide

(InGaAs) diode array that is thermoelectrically cooled and has 256 elements to cover the

908–1687 nm spectral area. Reference spectra were obtained with an AutoCal feature that

includes an internal Hg/Ar lamp used for wavelength calibration and a spectralon

reference plate that is automatically placed by the spectrometer in the optical path for

both wavelength and baseline reflectance calibrations. Each sample was analyzed by the

NIR spectrometer in dynamic mode with an integration time of 5 ms and co-adding 32

95

scans of the powder flowing through a chute specially designed for the continuous mixer.

The spectrometer transferred the spectra via wireless communication to a laptop

computer.

5.1.3.3 Estimation of sample size for NIR measurement

The sample size analyzed during the online monitoring was estimated as follows. The

NIR illumination spot was approximately 25 mm in diameter. The penetration depth for

the NIR radiation was assumed to be 1 mm [110,111]. The velocity of the powder was

determined to estimate the length of time over which the NIR radiation interacted with

the sample. The velocity was determined by measuring the average residence time with a

small amount of colored tracer material introduced at the beginning of the chute, and

measuring the time it took to travel across the chute using a chronometer. The

measurement with tracer was repeated 10 times, and the average velocity for a 30°

inclined chute was found to be 18 cm/s. Each spectrum was an average of 32 scans, with

a scan time of 5 ms for an estimated sampling time of 160 ms. Hence, the scanning length

(velocity*time) was estimated to be 2.88 cm. Having the length, spot diameter and

thickness of penetration measured, the volume of the sample was calculated to be

0.73 cm3. The bulk density of APAP–Avicel mixture was measured to be 0.36 g/cm

3.

Thus, sample size (mass) was calculated to be 0.26 g for each co-added spectrum.

For the calibration samples, a total of 10–20 co-added spectra per sample were acquired,

depending on the flow rate. The blends were emptied three times down the chute to

collect three sets of spectra with the calibration blends. In each experiment, three

consecutive spectra were averaged.

96

The calibration blends were also used to develop an off-line calibration model to predict

the drug concentration and further evaluate the method's precision. Samples of the 3%,

6% and 8% APAP blends were collected for offline analysis. Samples weighing

approximately 10 g were retrieved from the chute after 30, 60, 90 and 120 s. A total of

five NIR spectra were obtained for each sample collected. After acquiring each spectrum,

the powder in the area illuminated was removed and this procedure was repeated four

times. The drug content was then predicted by the off-line calibration model.

5.1.3.4 Software and NIR data processing

Spectra were collected with the Spec 32 (version 1.6.0.6) software provided by Control

Development (South Bend, IN). The Pirouette 4.02 software developed by Infometrix

(Bothell, WA) was used to evaluate and extract information from the spectra obtained.

All calibration models were developed using the partial least squares (PLS) regression

method. First and second-derivative (1st der and 2nd der) spectra were obtained by using

the Savitzky–Golay algorithm with a 15-point moving window and a second-order

polynomial. Standard normal variate (SNV) was also used as spectral pretreatment. PLS

calibration models were constructed by cross-validation, using the leave-class-out

method. Calibration and external prediction sample sets were chosen via scores plot of a

principal component analysis (PCA) of the first two PCs. Both sample sets encompassed

the complete concentration range. The quality of the models was assessed in terms of root

mean square errors of cross validation and prediction (RMSECV/RMSEP).

97

5.1.4 Results

5.1.4.1 Exploratory data analysis

Figure 5-2 shows the NIR spectra of pure APAP and Avicel plus a blend of 15% (w/w)

APAP. The changes in APAP concentration are reflected on certain bands (1130, 1220

and 1490, 1650 nm). Certainly, the last two mentioned bands presented the larger spectral

changes; however, these ranges also showed the most intense Avicel bands. The first

band (1130 nm) is due to APAP and is not affected by overlapping with the excipient

spectral bands. The second band (1220 nm) is due to Avicel. Figure 5-3 shows the scores

plot (PC-1 and PC-2) of a principal component analysis calculated with the entire

calibration data set (0–15%APAP), after SNV and first derivative spectral pretreatment

for the 1100–1390 nm range. The main source of variability (PC-1, 65.6% spectral

variance) is the APAP concentration. The second PC explained 28.6% of X-spectral

variance. Each cluster of spectra is clearly separated according to the concentration. The

ordering of all clusters depended on the concentration confirming that the 1100–1390 nm

spectral range is adequate to further develop a calibration model for APAP determination.

5.1.4.2 Development of NIR calibration model

The calibration model was developed with spectra of the blends flowing through the

chute with a set of 11 calibration blends ranging in drug concentration from 0 to 14.46%

(w/w) and varying in steps of about 1.5% (w/w). This experiment was repeated three

times. The calibration model was first developed using the set of spectra from the first

experiment with the calibration blends. These spectra were then used to predict the APAP

concentration in the same samples from the second and third run. During the method

98

development stage, several wavelength ranges were evaluated; however, the best

calibration models were obtained over the 1100–1390 nm range, and Table 5-2 describes

the figures of merit for calibration models using this range. The different spectral

pretreatments were carefully evaluated due to the changes in the baseline of the spectra

acquired. This initial evaluation led to the selection of the 1100–1390 nm range, and to

narrowing down the pretreatment to only two possibilities: the standard normal variate

(SNV) and SNV followed by first derivative transformation. The number of PLS factors

was also evaluated and selected based on the minimum error of prediction for cross-

validation and external prediction samples. The spectra from the three runs with the

calibration blends described in Table 5-2 were then combined into one calibration model

with a total of 156 calibration spectra, with the objective of obtaining a more robust

calibration model.

The new model provided a root mean square error of cross validation (RMSECV) of

0.41% (w/w) for the samples predicted in a leave-class-out cross validation. Each

concentration was defined as a class, so that when a sample of a specific concentration

was predicted all samples of that concentration were left out of the calibration model. The

calibration model was also challenged using a Pirouette routine that randomly selected

60% of the samples, which were then used to develop the calibration model. The

remaining 40% of the samples were not included in the calibration model, and were

predicted with a RMSEP of 0.34% (w/w). The lower RMSEP obtained with random

sample selection reflects that leave-class-out cross validation is a very challenging

method for evaluation of the calibration model, since the concentration of the predicted

samples is not included in the calibration set. The calibration model was then established

99

with the 156 samples from the spectra of the three experiments described in Table 5-2,

and this model was used for all predictions of the drug content of spectra obtained in the

continuous mixing experiments. Leave-class-out cross validation and random sample

selection did not generate different calibration models, these were methods to evaluate

the model and one model based on the 156 samples was developed. Figure

5-4 demonstrates the linearity of the obtained method. The scatter plot shows the

predicted APAP concentration using the NIR method (leave-class-out cross-validation

and external prediction values) versus the reference method (analytical balance). The

external prediction results shown in Figure 5-4 are those obtained for the randomly

selected validation samples that provided an RMSEP of 0.34% (w/w). Statistical t-testing

(95% significance level) was evaluated and there is no significant difference between the

slope and intercept with 1 and 0, respectively.

The RMSEP value of 0.41% (w/w) indicates the method's accuracy over all the

concentration ranges evaluated, providing a summary of the method's performance

throughout the different concentration levels evaluated. Because the calibration model

was developed to evaluate the performance of the continuous mixer for blends where

APAP varied from 2 to 10% (w/w), the model's accuracy and precision should be

estimated at each of the concentration levels presented in Table 5-3. The use of leave-

class-out cross validation facilitated the calculation of the RMSEP (accuracy) and

precision (standard deviation) at each of the concentrations. The model's accuracy was

considered adequate at all concentrations except for the 1.46% (w/w) blend. A separate

calibration model was developed using the spectra from 0 to 6% (w/w) blends, based on

previous studies where the determination of low concentration samples improved when a

100

narrower range of concentrations was used [112]. The 0–6% (w/w) was considered

adequate in terms of accuracy with a RMSEP of 0.34% for the 1.46% (w/w) blend, and

precision (standard deviation of 0.26% (w/w)). This model included two PLS factors for

the 1100–1390 nm spectral range and was used only for the 2% (w/w) continuous mixing

run.

The method's precision was also evaluated. In this application, blend homogeneity was

assessed in terms of the precision of the drug concentrations determined for the blend

flowing through the chute. The variation depends on the distribution of the drug in the

blend and also on the unavoidable random error of the analytical method. Thus, the

precision of the calibration model was also evaluated at the different concentration

levels. Table 5-3 presents the standard deviation which ranged from 0.2 to 0.4% (w/w) at

the different concentration levels, with a pooled standard deviation of 0.26. These

calculated standard deviations encompass the variation of the analytical method as well

as variations related to the chemical composition of the calibration blends.

The precision of the NIR method was further evaluated through the development of a

calibration model for spectra obtained off-line in a laboratory setting, where the powder

blend was not flowing as in the spectra collected in the chute following the continuous

mixer. The RMSEP for this model was now lower and the standard deviation ranged

from 0.07 to 0.19% (w/w), with a pooled standard deviation of 0.11% (w/w). These

results indicate that the off-line analysis of the calibration blends provides a variation

(pooled standard deviation) of about 0.11%. When the same blends are analyzed as they

flow through the chute, the method's variation is about 0.26% (pooled standard

deviation). These results also indicate that the minimum variation that could be expected

101

from continuous mixing is about 0.26%, based on the random error of the analytical

method.

5.1.4.3 Evaluation of continuous mixing runs

Figure 5-5 shows the predicted concentrations for the continuous mixing experiments

providing APAP concentrations every 0.5 s. A typical start-up process for the continuous

mixer can be described as follows. Once the mixing process is started, the hold-up (mass)

of the powder in the continuous blender increases with time and reaches a constant value.

When the mixer is operating under constant hold-up, the incoming and outgoing feed

rates are equal and the process is considered under steady state operation. Close

evaluation of these experiments showed that 60–80 s were required for the steady state to

occur, with the materials and conditions used. Spectral data were also collected during

the start-up (before steady state), and the concentration profiles showed some deviations

from the theoretical mean value. These deviations occur due to the different start-up

times for APAP and Avicel feeders and also due to the transient hold-up in the mixer.

The steady state is shown in Figure 5-5 by the vertical line at 80 s across all the

concentration profiles.

The standard deviation was used as the homogeneity index to determine the uniformity of

the blend. The standard deviation was calculated for each run using the predictions under

steady state (for all concentrations predicted after 80 s) as presented in Table 5-4. The

highest standard deviation obtained was 0.64% for the 6% (w/w) run, and the second

highest was 0.50% for the 8% (w/w) blend. A one-sided F-test indicated that these

standard deviations were greater than the pooled standard deviation of 0.26 obtained for

the analytical method. The standard deviation of the 2, 3, and 10% (w/w) blends was

102

much lower and considered similar to the variation expected from the analytical method.

These results do not guarantee that the blend uniformity will be reduced to standard

deviation of 0.3–0.6% (w/w) as segregation can occur after blending. Additional studies

are planned where the drug concentration will be evaluated after tableting.

5.1.5 Conclusions

The NIR spectroscopy and multivariate data analysis was demonstrated as an effective

tool for the real-time determination of active ingredient in the output blend. The CDI

spectrometer was found to be feasible for measuring blend uniformity in continuous flow

systems. The sample size analyzed in the on-line NIR measurements was approximately

0.26 g which is close to the typical unit dose used in the pharmaceutical industry. In three

of the five blending runs, the on-line blend uniformity measurements performed were

close the analytical method error. This indicates that continuous mixer is capable of

producing homogeneous blends as close as to the calibration standards. However, a

further investigation is suggested in estimating the error in the measurement and relating

that to the sample size. In the next session, a methodology is presented to estimate error

in the in-line measurements.

5.2 Continuous monitoring using VTT Spectrometer

5.2.1 Equipment and experimental set-up

Single-point spectrometers are used in most of the present-day PAT applications.

However, the use of multipoint NIR systems has some advantages in continuous

pharmaceutical manufacturing. One advantage is instrument or process failure

diagnostics: one can compare the results of multiple measurement points at the same

103

measurement position and diagnose the process or instrument failure. Another advantage

is that one instrument can serve the whole continuous manufacturing line, because one

just needs to add probes at the desired measurement positions. In this work, a multipoint

NIR measurement system was developed for the continuous mixing process application.

It consisted of the following main parts:

1. Fiber-optic light source

2. 5-point fiber-optic probes

3. Spectral camera (Specim Spectral camera NIR) with fiber-optic inputs

4. Measurement software for acquisition of spectra, predicting the concentrations in

real time and sending data to the process control system (custom program written

with Labview, National Instruments, Austin, TX)

Of these, the 5-point fiber-optic probes were specifically tailored for the process by VTT,

Finland. Figure 5-6 shows a schematic of the multipoint NIR system and how it was used

in the continuous blending process. The fiber-optic light source (VTT, Finland, see

Figure 5-7 (a)) provides the illumination for the probes, and the spectral camera (Spectral

camera NIR, Specim, Finland, see Figure 5-7 (b)) is used to collect the spectra from all of

the probes simultaneously. The spectral camera is able to collect up to 50 spectra per

second from each of the fiber-optic channels simultaneously. The maximum number of

probes that can be attached to the spectral camera is 106. The useable wavelength range

of the spectral camera is 980 – 1680 nm.

104

The main objective in using the multipoint NIR system to monitor the continuous mixing

process is to use the NIR predictions of mean API concentration and homogeneity (RSD)

to control the process. The best possible measurement position was found to be just at the

discharge of the continuous blender (see Figure 5-8 (a) and (b)). Two configurations for

the probe were tested: the ‗above-the-chute‘ configuration (Figure 5-8 (a)) and the

―below-the-chute‖ configuration (Figure 5-8 (b)). The benefit of the ―above-the-chute‖

configuration is that no window is needed in the chute, and therefore there are no

problems of powder accumulation on the window. The benefit of the ‗below-the-chute‘

configuration is that the probe is shielded against dust, which is formed when the powder

falls from the blender outlet onto the chute. The results presented in this paper correspond

to the ‗above-the-chute‘ configuration, which works well with powders with a relatively

large particle size which do not tend to produce dust.

5.2.2 Methods

5.2.2.1 UV Assay

The formulation used in this study consisted of 3% Granulated Acetaminophen and 96%

Avicel PH-101 and 1% Magnesium Stearate. Micronized acetaminophen (d50~20µm)

was used to build a calibration curve of UV absorption. The following procedure was

followed to prepare calibration standards. Samples were weighed as 100, 90, 80, 70, 60,

and 50 mg of pure acetaminophen. They were added in a 500 mL volumetric flask. 20

mL of methanol (MeOH) was added and samples were stirred until the acetaminophen

was completely dissolved. Distilled water was added in the volumetric flask to make the

total volume of 500 mL. A 5 mL aliquot was taken out from the solution into a 100 mL

105

flask and diluted further with distilled water. An aliquot from the 100 mL vol. flask was

taken further into a quartz cuvette for UV absorption measurement. UV absorption at 244

nm was recorded as triplicate. This procedure was repeated for all the calibration

standards. A calibration curve was fit using linear regression with R2>0.99. The UV

reader used was Ocean Optics USB4000 Miniature UV/VIS Fiber Optic Spectrometer.

Since the formulation used in the mixing experiments contain granulated acetaminophen

(which also contained PVP), it was necessary to check for the interference of PVP at 244

nm. The following procedure was followed to check for PVP interference. Since the

formulation contained 3% COMPAP, the total PVP in a 500 mg tablet will be 1.5 mg.

Twice of the theoretical amount (3 mg PVP) was added in a 500 mL vol. flask. Distilled

water and MeOH was added in the flask following the same dilution procedure as that of

other tablets. The solution was centrifuged and an aliquot from that solution was

subjected to UV analysis. No significant interference was observed, as the UV absorption

reading was very close to a case with just distilled water.

The UV assay was validated by performing recovery experiments. 1940 mg of MCC and

60 mg of micronized APAP was weighed and added in a 2 L flask. Subsequently, 60 mL

of MeOH was added, and the flask was stirred to give the methanol ample opportunity to

dissolve the APAP. Then, 100 ml of water was added and the flask was stirred further for

another 20 minutes before completing to volume in a 2.00 L volumetric flask. The

solution was centrifuged at 3000 rpm for 5 minutes before testing it in the UV

spectrometer at 244 nm. The drug concentration in the solution was calculated using the

calibration curve. This procedure was repeated three times to calculate the average drug

recovery. This study was repeated using 80% and 120% of the drug content. Recovery

106

was found to be between 98 – 102% of the drug, and an RSD of less than 2.5% (w/w)

was obtained.

5.2.2.2 PLS (Partial Least Square) model development for NIR Spectroscopy

Calibration samples with APAP concentrations ranging from 0 % to 6.52 % (by mass)

were prepared manually and mixed in a vortex shaker. The magnesium stearate

concentration in the calibration set was randomized around a mean value of 1%. The list

of off-line calibration samples is presented in Table 5-5.

Two 5-point probes were used in measuring the calibration set. To allow a simultaneous

measurement with both 5-point probes, each calibration sample was divided in two

rectangular aluminum containers. The procedure for measurement was as follows:

5. Divide the sample into the two containers.

6. Mix the powders manually in the container to avoid segregation effects.

7. Place the containers in the measurement position of the two 5-point probes and

take one measurement.

8. Mix the samples manually again to get a different representation of the sample

surface.

9. Go back to item 3 and repeat ten times.

10. In the middle of the ten repeats, exchange the containers so that both probes will

see each of the containers.

Measuring in this fashion gave 14 samples x 10 repetitions x 10 probes = 1400

measurements altogether.

107

Figure 5-9 shows the unprocessed spectra measured from the calibration set together with

the baseline corrected spectra. The APAP absorption bands are located at approximately

1130and 1650 nm. They are hardly visible in the spectra by the naked eye because of the

low APAP concentration range. Still, it is possible to use all the 1400 spectra in the

calibration model. However, this leads to high levels of sampling noise since the

measurement spot size was only about 3 mm in diameter. A better way is to average over

the subsamples (i.e. over the ten repetitions / sample), leaving behind only 140 spectra

(14 samples x 10 probes). In this way, the probe-to-probe variation is preserved in the

calibration model, but the effect of sampling noise is minimized. In the following

description of the calibration model and analysis of in-line data, the spectra used were

always averaged in this manner unless otherwise stated.

The standard PLS approach was selected as the calibration model. The first step is to

choose a suitable preprocessing method. The methods tested included baseline correction,

multiple scattering correction (MSC), standard normal variate correction (SNV), first and

second derivative, and combinations therewith.

The most notable interference in the raw spectra is the shift of the baseline, which is

nearly completely removed by the baseline correction (cf. Figure 5-9). Moreover, there

does not seem to be any substantial scaling variation in the spectra. Therefore, methods

that try to cope with the scaling variations, such as MSC and SNV, tend to remove

meaningful spectral information, thus resulting in suboptimal calibration performance.

Hence it does not come as a surprise that the baseline correction alone worked the best

out of the methods tested. The specific baseline correction method used in this work was

the projection of a straight line and linear tilt variations out of the spectra, i.e. a

108

detrending-type approach. If we arrange a set of spectra row by row in the matrix , the

baseline correction can be expressed in matrix notation [113]:

Here, denotes the baseline corrected spectra, is the projection operator, and is the

unit matrix. The denotes a column vector of ones, and denotes a column vector of the

wavelength scale.

Figure 5-10 shows the scatter plot resulting from using the PLS model for the calibration

set (left side) and the scatter plot after cross-validation (right side). Leave-one-out cross-

validation was used, where one sample at a time was removed from the calibration set.

The PLS model was used to predict the API concentration of in-line measurements. In the

analysis of this work, the average predicted API concentration over the five points of a 5-

point probe was used. To give some perspective on the calibration performance for the

probe-averaged results, the scatter plots averaged over the five points of each probe are

shown in Figure 5-11. As expected, the calibration performance is considerably better

after averaging, especially for probe number two.

5.2.3 Results

5.2.3.1 Methodology for estimating error in the NIR measurement

2

0

22 )()( ss MixingTotal (5-1)

s represents the sample size being analyzed. Equation (5-1) shows that the total

variability ))(( 2 sTotal in concentration measured at the blender discharge can be

109

expressed as the sum of the variability due to mixing ))(( 2 sMixing and analytical method

error )( 2

0. In equation (5-1), total variance and variance due to mixing are both functions

of the sample size, whereas method error is independent of the sample size. The RSD is

defined in equation (5-2). In equation (5-3), all the terms are normalized by the square of

the mean concentration.

C

N

CC

CRSD

N

n

i

1

)(1

2

(5-2)

2

0

22 )()( RSDsRSDsRSD MixingTotal (5-3)

)()( 22

0

2 sRSDRSDsRSD MixingTotal (5-4)

ssRSDMixing .)(2

(5-5)

The normalized variance (or )(2 sRSDMixing ) can be expressed by a power law relationship

as shown in equation (5-5). After substituting this relationship in equation (5-4), it can be

re-written as equation (5-6).

sRSDsRSDTotal .)( 2

0

2

(5-6)

For a random blend, the exponent in equation (5-5) is -1. In this case study, our

objective is to determine this relationship for a set of data from UV absorption

spectroscopy and NIR spectroscopy. In order to obtain the values of the coefficients in

the powder law and the method error, the following procedure was followed:

Equation (5-6) was logarithmically linearized as equation (5-7).

110

)()(.))(( 2

0

2 LnsLnRSDsRSDLn Total (5-7)

In equation (5-7), ))(( 2

0

2 RSDsRSDLny Total is expressed as a linear function of

)(sLnx , where and )(Ln are the slope and the intercept respectively. The optimal

values of ,2

0RSD and were obtained by maximizing the regression coefficient

coefficient 2R for this linear equation. The optimization problem is shown in the following

equations.

Objective function: 2

11

2

2

11

2

1112

)()(

)(

N

i

i

N

i

i

N

i

i

N

i

i

N

i

i

N

i

i

N

i

ii

yyNxxN

yxyxN

R

(5-8)

Constraints: 0])([

0

2

0

2

),..,2,1(

2

0

RSDsRSD

RSD

NiTotal

Values of 2

0RSD were selected iteratively by using an optimization program in MATLB.

5.2.3.2 Data collection of RSD as a function of sample size

RSD data as a function of sample size was acquired using in-line NIR spectroscopy and

off-line UV absorption assays.

5.2.3.2.1 In-line measurements and data fitting

In-line measurements were performed by mounting the fiber optic probe above the chute,

and projecting the NIR radiation onto the flowing powder. Measurement was performed

at five spots arranged in the transverse direction of the flow. The experimental set-up is

shown in Figure 5-7. In order to capture the effect of sample size on RSD, sample size

was varied by averaging different numbers of scans. Sample size could also be varied by

111

increasing the cross sectional area of the NIR beam. However, this imposes limits on the

lowest and the highest sample size that can possibly be analyzed, and it also affects the

intensity of NIR radiation, which is higher if the spot size is smaller. In our measurement

set-up, a constant spot diameter )3( mmd was used. With an increasing number of scans,

the area exposed to the NIR radiation increases. The scanning process is discrete; it

requires 25 milliseconds to acquire each spectrum sec)025.0( , and 20 milliseconds to

post-process it. The area being analyzed per scan can be approximated as

4/.. 2ddvA , where v is the powder velocity. The velocity of the powder on the

chute was measured by performing impulse tests. Two probes were installed on the chute,

the first one at the beginning of the chute and the other at the end of the chute. A tracer

was inserted in the flowing stream of powder on the chute, and the time required to travel

the distance between two probes was measured. This test was repeated for about 10-15

times to obtain a good estimate of the velocity of the powder on the chute. The total area

analyzed for one unit sample size (g) can be calculated just by multiplying the area for

one scan )4/..( 2ddvA and the number scans )(n being averaged. Penetration depth

of the NIR radiation )(l for similar pharmaceutical powders was obtained from literature

[110,111] and found to be 1 mm. It is expected that the penetration depth of the NIR

radiation will vary as a function of particle size and the intensity of light being used, but

for this particular case study 1mm penetration depth was assumed in order to develop the

basic methodology. Thus the sample size (g) being analyzed in the NIR measurement can

be expressed as ... lAns , where ( 36.0 g/cm3) is the bulk density of the powder.

Calculation of RSD for the in-line measurements was performed using the following

procedure. For the length of the steady state run, N consecutive measurements were

112

predicted using the previously developed PLS model. By increasing the averaging

window )(m , the number of averaged measurements )/( mNn decreased. When the

highest number of scans were averaged, n was as low as 10. In the interest of capturing

the effect of sample size (and not confounding it with different number of samples) on

RSD, n was kept constant to be 10. Thus RSD was calculated for only 10 samples

(selected randomly from a pool data at each sample size) under varying sample sizes.

The relationship between RSD and sample size is shown in Figure 5-12. RSD decreases

with increase in sample size, and eventually becomes a steady after a sample size of 0.3 g

at approximately 0.02.

5.2.3.2.2 Off-line measurements (UV Absorption Assay)

Off-line analysis of blend uniformity was performed by sampling tablets instead of

sampling powder blends due to the fact that it was found difficult to extract samples of

considerably smaller size from the blending process. Tablet samples provide an easy way

to reproduce the sample size. Once the powder is compacted in the tablet form, samples

of smaller size can be made simply by cutting pieces from the tablet. The cutting

procedure leads to some variability in the sample size, but this contribution was found to

be negligible. In addition, using this method samples do not segregate, which they would

happen if a large powder sample was sub-divided. A total of 20 samples were analyzed at

each sample size. In order to measure blend uniformity at a sample size greater than one

tablet, two tablets were dissolved together, which gives a sample size twice that of a

single tablet. Raw data consisting of sample size, mean concentration, RSD and the

confidence intervals for RSD is presented in Table 5-6. The relationship between RSD

and sample size for the off-line data is shown in Figure 5-13. RSD decreases with

113

increasing sample size and eventually shows a plateau around 0.02, which is similar to

the case of in-line measurements. This result indicates that 0.02 is the minimum possible

RSD that can be reached for this particular powder mixture. Since the UV absorption

calibration model was found to be very accurate (R2>0.99), the similarity between the

two cases also indicates that the base-line RSD of 0.02 for the in-line measurements is

primarily due to the inherent non-uniformity in the powder blend, and not analytical

method error.

5.2.3.3 Fitting of the experimental data in the mathematical model

In order to quantify the method error, the fitting procedure described in the section 4.1

was applied for the in-line as well as off-line RSD data. Table 5-7 summarizes the model

fitting results. The model fitting procedure was exercised for different ranges of the RSD

vs. sample size data. This step is necessary to determine the optimal range of RSD vs.

sample size data, which minimizes the number of experiments required to arrive at the

same conclusion. A p-value of <0.0001 was obtained for the in-line data for all possible

selections of sample size ranges, which indicates good fitting of the experimental data.

The fitting parameter )( 2

0RSD was found to be zero in all the ranges except for the case of

0-0.1g. Off-line data fitting was relatively poor because fewer data points were available.

Model fitting up to a range of 0-0.2 g was good (p-value <0.1); model fitting was not

good for the case of 0-0.1g range (p-value =0.31). In this case also, method error )( 2

0RSD

was found to be zero.

Model fitting results concluded that 02.0RSD is purely because of the inherent non-

uniformity in the powder mixture, and that the variance contribution of the analytical

114

method error is negligible. Another observation from these results is that a range of 0-

0.43 is enough to determine the method error, and more information in the asymptotic

region of the curve (RSD vs. sample size) is not very useful. These results also provide an

important guideline to select an optimal unit dose size based on blend uniformity criteria.

The number of scans to be averaged to measure one unit dose (0.43g) was found to be 62.

This number changes depending on the NIR penetration depth, powder bulk density and

the scanning area used in the measurement set-up. The experimental data and model

predictions are shown in Figure 5-14 (In-line data) and Figure 5-15 (Off-line data)

respectively. The values of and )(Ln were very close for both in-line and off-line

data (Table 5-7) which shows that the same mixing model applies to both cases.

5.2.4 Conclusions

A case study of an application PAT based on NIR spectroscopy to continuous blending

was demonstrated. In-line measurement at the blender discharge was made possible by

utilizing a chute on which powder was allowed to flow by gravity. Sample size of the

powder being analyzed in the in-line measurements was determined by measuring the

velocity of the powder on the chute. Measurement of velocity made it possible to

determine the number scans required to be averaged to measure RSD relevant to one unit

dose sample size.

A methodology was presented which provides a way to quantify the analytical method

error in the in-line blend uniformity measurements, and compare that with the off-line

measurements. For the case study presented in this paper, the method error in the in-line

as well as off-line measurements was found to be negligible. The base-line RSD of 0.02

was attributed to the inherent non-uniformity of the powder blend. This methodology

115

provides a direct relationship between PAT and laboratory data, which is very important

for reducing the analytical testing time in the pharmaceutical industry. This study is very

useful for allow real time release (RTR) of drug products based on blend uniformity

criteria, and to determine the optimal size of a unit dose.

116

5.3 Figures for Chapter 5

Figure 5-1: (a) CDI Spectrometer installed on a powder conveying chute at the

mixer discharge (b) Chute

Spectrometer mounting

connection Chute

(a) (b)

117

Figure 5-2: NIR spectra for acetaminophen and for 0% and 15% (w/w) powder

blends

Figure 5-3: Scores plot from principal component of analysis of calibration set

spectra in 1100–1390 nm spectral range.

118

Figure 5-4: API content predicted by NIR for cross validation and external

validation samples

Figure 5-5: NIR predictions from monitoring the continuous mixing process for

three representative blends

119

Figure 5-6: Schematic of the multipoint NIR measurement equipment, it consists of

a fiber-optic light source, fiber-optic probes and a fiber-optic spectral camera

Figure 5-7: (a) Schematic picture (left) and photograph (right) of the multipoint

fiber-optic light source. It has 24 output fibers with a ST connector (b) The fiber-

optic spectral camera (Spectral camera NIR, Specim Ltd., Oulu, Finland)

Chute

Powder

Mixer

Collection fiber

bundles Real-time calculation

module

Illumination fiber

bundles

5 measurementspots

Light source

Spectral camera

To process control

Probe

Output fibres

Lamp

Collection mirror

Fibre

bundle

Chopper

blade

Imaging

mirror

(b)

(a)

120

Figure 5-8: Experimental set-up – (a) Above-the-chute configuration (b) Below-the-

chute configuration

Figure 5-9: The unprocessed calibration set spectra (left) and the spectra after the

baseline correction (right)

Probe Mixer outlet

Tablet press inlet Chute

Continuous mixer Mixer outlet

Measurement window Probe

1000 1100 1200 1300 1400 1500 1600-0.2

-0.1

0

0.1

0.2

0.3Absorbance spectra

Wavelength [nm]

Ab

so

rba

nce

1000 1100 1200 1300 1400 1500 1600-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08Baseline corrected spectra

Wavelength [nm]

BL

-co

rr. sp

ectr

a

(a) (a) (b)

121

Figure 5-10: The scatter plot of using the PLS model with the calibration set (left)

and after cross-validation (right)

Figure 5-11: The scatter plot of cross-validation after averaging the results of each

sample over the 5 measurement points of probe number 1 (left) and probe number 2

(right)

0 2 4 6-1

0

1

2

3

4

5

6

7RMSEC : 0.31257 cc : 0.9879 SEC : 0.31369 slope : 0.97595 offset : 0.078244 bias : -7.3402e-015 CV : 9.6401 R2 : 0.97595 #of smpl : 140

Reference

Pre

dic

tio

n

Calibration set, 5 factors used

0 2 4 6-1

0

1

2

3

4

5

6

7RMSECV : 0.37711 cc : 0.98235 SECV : 0.37841 slope : 0.96203 offset : 0.12955 bias : 0.0059958 CV : 11.6292 R2 : 0.96501 #of smpl : 140

Reference

Pre

dic

tio

n

Cross-validation, 5 factors used

0 2 4 6

0

1

2

3

4

5

6

7 RMSEC : 0.2688 cc : 0.9941 SEC : 0.27685 slope : 0.91282 offset : 0.25073 bias : -0.032943 CV : 8.5079 R2 : 0.98248 #of smpl : 14

Reference

Pre

dic

tio

n

Cross-validation, 5 factors used

0 2 4 6

0

1

2

3

4

5

6

7 RMSEC : 0.18819 cc : 0.99607 SEC : 0.18964 slope : 1.0112 offset : 0.0083758 bias : 0.044934 CV : 5.828 R2 : 0.99178 #of smpl : 14

Reference

Pre

dic

tio

n

Cross-validation, 5 factors used

122

Figure 5-12: Blend uniformity (RSD) as a function of sample size (NIR

Spectroscopy)

Figure 5-13: Blend uniformity (RSD) as a function of sample size (UV Absorption)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 0.2 0.4 0.6 0.8 1 1.2

RS

D

Sample size(g)

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0 0.2 0.4 0.6 0.8 1

RS

D

Sample size(g)

1/2 Tablet 1 Tablet

2 Tablets 1/2 Tablet

1/4 Tablet

123

Figure 5-14: (a) RSD2 as a function of sample size (Comparison between NIR data

and mathematical model) (b) Linear regression for the best case ( 02

0RSD )

Figure 5-15: (a) RSD2 as a function of sample size (Comparison between UV

absorption data and mathematical model) (b) Linear regression for the best case (

02

0RSD )

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0 0.5 1 1.5

RS

D2

Sample size (g)

Experimental (NIR) Model

(a)

y = -0.7615x - 7.8212

R² = 0.7221

-10

-8

-6

-4

-2

0

-6 -4 -2 0

Ln

(R

SD

2)

Ln (Sample size)

Experimental (NIR)

Linear (Experimental (NIR))

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0 0.5 1

RS

D2

Sample size(g)

Experimental (UV Absorption)

Model

y = -0.66x - 7.9135

R² = 0.8201

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

-4 -3 -2 -1 0

Ln

(R

SD

2)

Ln (Sample size)

Experimental (UV Absorption)

Linear (Experimental (UV Absorption))

(b)

(a) (b)

124

5.4 Tables for Chapter 5

Table 5-1: Experimental conditions for continuous mixing experiments

% APAP (w/w) APAP feed rate (kg/h) Avicel feed rate (kg/h)

2 0.6 29.4

3 0.9 29.1

6 1.8 28.2

8 2.4 27.6

10 3 27

Table 5-2: Development of calibration models and its initial evaluation

Spectral pre-treatment Sample set RMSEP (%)

1st Derivative Cross 0.6

Run 2 0.58

Run 3 0.65

2nd

Derivative Cross 0.68

Run 2 0.49

Run 3 0.71

SNV Cross 0.37

Run 2 0.33

Run 3 0.35

SNV – 1st Derivative Cross 0.33

Run 2 0.27

Run 3 0.35

SNV – 2nd

Derivative Cross 0.42

Run 2 0.48

Run 3 0.5

Table 5-3: Evaluation of model precision (standard deviation) and model accuracy

(RMSEP) at each calibration concentration.

% APAP (w/w) Predicted

Average(w/w)

Standard

Deviation

RMSEP % (w/w)

0 0.24 0.33 0.40

1.46 2.08 0.37 0.71

2.9 2.89 0.23 0.05

4.47 4.28 0.40 0.42

5.42 5.40 0.20 0.46

7.29 6.86 0.30 0.52

8.72 8.39 0.10 0.34

10.11 10.52 0.13 0.42

11.78 11.79 0.24 0.24

12.99 13.26 0.23 0.35

14.46 14.65 0.34 0.38

125

Table 5-4: Evaluation of continuous mixer experiments at various APAP

concentrations.

% APAP (w/w) Mean Concentration (%) Standard deviation

2 1.42 0.15

3 3 0.20

6 6.85 0.64

8 8.36 0.50

10 10.11 0.29

Table 5-5: Off-line calibration samples

Sample # APAP [%] MgSt [%] Avicel 101

[%]

1 0.00 0.42 99.58

2 0.50 0.80 98.70

3 1.02 1.20 97.78

4 1.50 0.90 97.60

5 2.00 1.60 96.40

6 2.52 1.42 96.06

7 3.00 1.10 95.90

8 3.50 1.70 94.80

9 4.00 1.50 94.50

10 4.50 0.70 94.80

11 5.00 0.60 94.40

12 5.50 1.30 93.20

13 6.00 0.50 93.50

14 6.52 1.00 92.48

126

Table 5-6: Off-line (UV absorption) data of sample size, mean concentration,

variance, RSD and confidence intervals

Sample

size (g)

Mean

concentration

)(C

Variance

)( 2 RSD

C

Confidence Interval (C.I.)

for RSD

2

1,2/

2)1(

N

N

2

1,2/1

2)1(

N

N

0.025 2.795 0.031 0.063 0.048 0.092

0.050 2.914 0.032 0.061 0.046 0.091

0.100 2.880 0.015 0.042 0.032 0.063

0.220 2.804 0.004 0.021 0.016 0.031

0.435 2.764 0.006 0.028 0.021 0.042

0.860 2.773 0.004 0.022 0.017 0.033

Table 5-7: Model fitting results for in-line and off-line data

Range

(g)

)(Ln 2R valuep )( 2

0RSD )( 0RSD

In-line data (NIR Spectroscopy)

0-0.96 -0.76 (-0.84, -

0.68)

-7.82 (-7.92,-

7.72)

0.72 0.0000 0 0

0-0.43 -0.85(-1.00,-0.70) -8.03(-8.31,-

7.74)

0.68 0.0000 0 0

0-0.22 -1.05(-1.30,-0.79) -8.54(-9.14,-

7.94)

0.71 0.0000 0 0

0-0.1 -1.62(-2.02,-1.21) -10.32(-11.49,-

9.15)

0.85 0.0000 1.74E-04

0.013

Off-line data (UV Spectroscopy)

0-0.86 -0.66(-1.10,-0.23) -7.91 (-8.89,-

6.94)

0.82 0.013 0 0

0-0.43 -0.74(-1.45,-0.03) -8.14(-9.90,-

6.34)

0.79 0.04 0 0

0-0.2 -1.00(-2.20,0.19) -8.92(-12.20,-

5.65)

0.87 0.07 0 0

0-0.1 -0.55(-4.33,3.22) -7.94(-

18.99,4.03)

0.78 0.31 0 0

127

Chapter 6 Development of integrated continuous mixing and de-

lumping process

In previous chapters, the feasibility of a continuous powder mixing process was shown

for cases of APAP (silicated) blending and MgSt blending. However for cases involving

blending of highly cohesive powders, continuous mixing process loses its flexibility. If

the material to be blended is very cohesive, in a batch blender the necessary shear can be

applied by using an intensifier bar. In the continuous blender, increase in the rotation rate

leads to decrease in residence time making it difficult to apply high shear on the powder.

Experiments with pure acetaminophen and Avicel were conducted in the continuous

blender which showed presence of agglomerates. Feeding, which is an essential part of

the continuous mixing process, also adds more constraints on the process. Feeding highly

cohesive material is difficult. Although feeding can be improved using proper tooling,

sometimes pre-blending with glidants is the easiest and most efficient step. However, pre-

blending introduces additional steps, which sometimes are intrinsically batch mode.

One possibility to address these issues would be to use an in-line mill in the process to

break the agglomerates early, perhaps right after the feeding step. The product from the

mill could be fed in the continuous mixer, providing additional mixing. Having two units

as opposed to one can provide better control on the shear environment in the process.

Alternatively, the mill can be used at the discharge of the mixer to improve micro-

homogeneity of the blend. In this chapter, an integrated process consisting of de-lumping

using a Comil and mixing using the Gericke mixer is developed.

128

6.1 Mixing effects in low shear (Gericke mixer) and high shear mixing (Quadro -

Comil) continuous mixing equipment

A case study of mixing of cohesive and free flowing powders in a continuous system was

examined. Micronized APAP and Micro-Crystalline Cellulose (MCC) were used as the

model materials to represent cohesive and free flowing powders respectively. The mixing

problem in this case can be decomposed as a combination of macro-mixing in the blender

and micro-mixing in the mill. Macro-mixing which is governed by the bulk powder flow

behavior in this mixer is required to compensate for the incoming feed rate variability

(axial mixing), and to mix the initially unmixed powders (radial mixing). Micro-mixing is

also required de-lump the agglomerates of acetaminophen and reduce the scale of mixing

from agglomerates to primary particles. In this case study, the combined performance of

Comil (Quadro) and a continuous mixer (Gericke) for micro and macro mixing was

examined. Mixing experiments in individual units as well as integrated experiments were

performed to optimize the overall mixing performance.

6.1.1 Equipment

A conical mill (Comil) manufactured by Quadro (Model # 197) (Figure 6-1) was utilized

in this study. Since the mill was used for de-lumping purposes and not size reduction, it

was operated using a round impeller (Model # 1601), and using screens with round holes.

The screen diameter was selected such that stagnation of the material in the mill was

minimal, and hence primary particle breakage was avoided. However screens with large

diameter cannot be used since their efficiency for de-lumping is insufficient. Screens with

hole diameter of 600 and 800 µm were used in this study which provided good de-

lumping efficiency.

129

6.1.2 Results

6.1.2.1 Low-shear mixing

The design of the impeller used in the Gericke continuous mixer is shown in Figure

2-1(b). The paddle type impeller imposes axial and radial flow on the powder. The shear

environment in this blender can be qualified as low-shear since the powder in the high

shear zone (the region between the impeller tip and the mixer shell) is a small fraction of

the overall hold-up in the mixer, and the net movement of the powder is in the axial

direction. The operational range of impeller speeds for this mixer lies between 0-300

RPM which correspond to tip speeds in the range of 0-150 cm/s. Under lower impeller

speeds (40-100 RPM), powder is relatively less fluidized in the mixer and its flow

behavior can be described as a powder bed stirred by the impeller. Under these

conditions, the axial velocities lie in the range of 0.6-1 cm/sec, which indicates low shear

environment. Under higher impeller speeds (160-250 RPM), powder bed is completely

fluidized in the mixer, which leads to fewer particle-particle contacts, which again

imposes low shear on the powder.

The effectiveness of the Gericke mixer for de-lumping and mixing was examined by

mixing the two representative materials, micronized APAP and Avicel-200 under 40,160

and 250 RPM impeller speeds. While the process is operating under steady state, samples

were extracted at the mixer discharge for assessing the blend uniformity. Samples were

subsequently analyzed by NIR spectroscopy to determine the content of APAP. Mixing

performance, measured as the RSD between concentrations of the extracted samples, as a

function of impeller speed is shown in Figure 6-2 (b). At the lowest speed (40 RPM),

130

worst mixing performance (RSD=0.18) was obtained. With an increase in speed, RSD

decreased to as low as ~0.1. As described in the previous section, the analytical method

error in the NIR measurement is ~ 0.44. For the case of 10% APAP formulation, the

inherent RSD for a well-mixed powder sample can be assumed to be ~0.04. Powder is

considered to be well mixed if the RSD between the sample concentrations is close to the

analytical method error. In this case since the minimum RSD obtained was almost twice

the minimum possible RSD, the performance of the mixer was considered to be sub-

optimal. Agglomerates of acetaminophen, visible to naked eyes were observed in the

powder samples. Incomplete de-lumping or micro-mixing was thus witnessed in this

case. In order to identify the contribution of incomplete axial mixing, a simulation of

axial mixing was performed using equation 5. A convolution algorithm was run in

MATLAB using RTD datasets at 40,160 and 250 RPM, and the incoming feed rate

datasets. Simulation results showed that the contribution of incomplete axial mixing to

the variability in concentration at the mixer discharge was minimal. As described in the

previous section, radial mixing capability of the mixer is maximal under the intermediate

rotation rates. Given that the mixer was operated under optimal macro-mixing conditions

(axial mixing and radial mixing), poor micro-mixing was identified as the primary source

of mixing variability.

6.1.2.2 High shear mixing

The conical milling section and position of the impeller in the Comil is shown in Figure

6-1(b). The impeller imposes a radial flow on the material which makes it pass through

the screen. The Comil was operated at 1420 and 2280 RPM, which correspond to a tip

speed of 802 and 1290cm/s. The extent of shear in co-mill is significantly greater than the

131

Gericke mixer since the material has to pass through the high shear zone before it exits

the mill. The schematic of the experimental set-up for conducting mixing in the Comil is

illustrated in Figure 6-3 (a). A full factorial DoE with two levels of impeller speed (1420

RPM, 2280 RPM) and two screen sizes (600, 800 µm) was conducted. Since the main

function of co-mill in this case study is de-lumping and not size reduction, the screen size

was chosen such that the hole diameter is significantly greater than the d90 of the particle

size distribution of the powder blend (d90= ~ 400 µm).

Continuous mixing in co-mill was facilitated by feeding individual powders using the

same feeding setting as used for the Gericke mixer. As shown in Figure 6-3 (b), lower

impeller speeds and smaller screens lead to better mixing performance. Quantitatively,

the effect of screen size was relatively less than the effect of speed. Presence of screen

significantly reduced agglomerates in the powder blend. Smaller error bars in the RSD

measurement is also an indication of reduced number of agglomerates in the powder

blend. However RSD values obtained over the operating range of the mill (0.08-0.15)

being greater than the analytical method error (RSD=0.04), mixing performance again

was considered to be sub-optimal. In this case since the de-lumping/micro-mixing

behavior was better than the Gericke mixer, poor macro-mixing was identified as the

potential source of mixing variability.

In order to qualify the macro-mixing capability of the Comil, residence time of the

powder in the mill was measured. Residence time was measured by monitoring the

steady-state powder hold-up in the continuous mixer. As shown in Figure 6-4, residence

time in the mill increase from 1.4 s to 4.2 s as the impeller speed increases. This trend

indicates that with an increase in speed, stagnation of the material in the mill increases,

132

possibly near the closed area of the screen. The fact that an increase in residence time in

the mill does not necessarily decrease RSD clearly indicates that under high speeds there

is a possibility of short-circuiting of the material. Very low mill speeds (< 1400 RPM)

were not used since de-lumping efficiency of the mill is less under lower speeds, and

there is a possibility of preferential stagnation in the mill.

6.1.2.3 Integrated low shear mixing – de-lumping process:

Mixing experiments performed in individual units led to the conclusion that it was

difficult to achieve good blend uniformity using either of the individual equipment.

Mixing experiments were then performed in an integrated fashion which included two

scenarios, namely ‗low-shear mixing first‘ and ‗high-shear mixing first‘. In this set of

experiments, process parameters of only the first unit were varied; the second unit was

operated only under the optimal (best) operating condition.

6.1.2.3.1 Low-shear mixing first

The schematic of the experimental set-up for the first scenario is shown in Figure 6-5 (a).

Impeller speed of the Gericke mixer was varied from 40 to 250 RPM; the Comil was

operated at a constant speed of 1420 RPM. As shown in Figure 6-5 (b), RSD values

slightly decreased after the milling stage. However RSD values were still higher than the

minimum possible RSD. These results indicate that further mixing is necessary. Comil

essentially de-lumps the remaining agglomerates from the incoming powder stream

which creates high concentrations of APAP locally. There needs to be another mixing

stage to mix the de-lumped APAP uniformly with rest of the powder. In this scenario,

133

since a post-low-shear mixing stage is missing, overall mixing performance remains sub-

optimal.

6.1.2.3.2 High-shear mixing first

The schematic of the experimental set-up for the second scenario is shown in Figure 6-6

(a). In this case, speed of the Comil was varied from 1420-2280 RPM, and the Gericke

mixer was operated only at 160 RPM. Mixing performance showed significant

improvement after the second mixing stage; RSD values being less than the analytical

method error indicates that the mixing performance cannot be optimized any further with

the existing analytical method. A separate mixing experiment was performed by mixing

the initially de-lumped material in a batch blender. De-lumped powder was blended for

30 min in a ‗8 Quart V-blender‘ operating at 12.5 shell RPM. Similar degree of mixing

was achieved; RSDs in the range of 0.02-0.04 were obtained. Since RSD values obtained

after the batch mixing step were close to the analytical method error, further mixing is not

required.

6.1.3 Conclusions

Mixing behavior in a low shear mixer (Gericke) and a high shear Comil (Quadro) was

examined. Gericke mixer was found to be a poor micro-mixer/de-lumper and a good

macro-mixer, whereas the Comil was found to be a good micro-mixer and poor macro-

mixer. Short-circuiting of the material was identified as the main source of poor macro-

mixing in the Comil. Macro-mixing capability of the Gericke mixer and micro-mixing

capability of the Comil was utilized by integrating them together. Integrated system with

high shear mixing first provided the best possible mixing performance.

134

6.2 Figures for Chapter 6

Figure 6-1: (a) Conical mill (Comil - Quadro Model # 197) (b) Milling chamber with

conical round impeller

(a) (b)

135

Figure 6-2: (a) Schematic of the experimental set-up for mixing in Gericke

Continuous mixer (b) Effect of impeller speed on blend uniformity (RSD)

Figure 6-3: (a) Schematic of the experimental set-up for mixing in Comil (b) Effect

of impeller speed and screen size

Figure 6-4: Effect of operational parameters of mill on residence time

0

0.1

0.2

0.3

0.4

0.5

0 100 200 300

RS

D

Impeller Speed (RPM)

0

0.05

0.1

0.15

0.2

0 20 40 60 80

RS

D

% Mill Speed

800 µm Screen 600 µm Screen

0

1

2

3

4

5

0 500 1000 1500 2000 2500

Res

iden

ce T

ime

(s)

Mill Speed (SPM)

600 µm Screen

σ2

i

RSD

APAP Avicel

σ2

i

RSD

APAP Avicel

(a) (b)

(a) (b)

136

Figure 6-5: (a) Schematic of the experimental set-up for integrated low and high

shear mixing (Low-shear mixing first) (b) Mixing performance after low and high

shear mixing

Figure 6-6: (a) Schematic of the experimental set-up for integrated low and high

shear mixing (High-shear mixing first) (b) Mixing performance after high and low

shear mixing

0

0.05

0.1

0.15

0.2

0 100 200 300

RS

D

Impeller rotation rate (RPM)

RSD - post de-lumping

RSD - post mixing(b)

0

0.05

0.1

0.15

0.2

0 50 100

RS

D

% Mill Speed

Mixing in continuous blender after

millingMixing in Co-mill

(b)

σ2

i

RSD1

APAP Avicel

RSD

2

σ2

i

RSD1

APAP Avicel

RSD

2

(a)

(a)

137

Chapter 7 Conclusions and recommendations

7.1 Conclusions

The dissertation work is divided into four research aims. The first research aim is focused

on the characterization of bulk powder flow behavior in the mixer. An approach based on

RTDs was used, and important parameters of the RTD including mean residence time,

and mean centered variance were compared. RTDs were measured for a set of process

parameters (impeller speed, flow rate), design parameters (impeller blade configuration,

weir position) and material properties (cohesion, bulk density). In Chapter 2, it was

shown that the mean residence time decreases with increase in impeller rotation rate, but

the degree of dispersion (proportional to the mean centered variance) increases with

increase in impeller speed. The total shear applied on the powder during its residence

time, proportional to the number of blade passes, was found to be the maximum at the

intermediate rotation rates. Increase in flow rate decreases residence time, but this effect

was found to interact with the impeller speed. The ‗Alternate‘ blade configuration

showed higher mean residence time than the ‗All Forward‘ blade configuration; however

the degree of dispersion between the two blade configurations was not significantly

different. In Chapter 3, DEM modeling was applied to simulate the granular flow

behavior in the continuous mixer. A comparison between the simulations and

experiments showed that the qualitative trends between different variables are captured

reasonably well in the simulations. However, some differences were found when a

quantitative comparison was made. The onset of fluidization in DEM simulations was at

a lower impeller speed than in the experiments. This behavior was attributed to the fact

138

that the number of particles used in the DEM simulations is significantly less than the

real case scenarios.

In order to utilize the RTD data for dynamic modeling of the continuous mixer, the data

was fitted using FPEs. The analytical solution of the FPE, for the case of Danckwerts‘

open-open vessel boundary conditions was applied. The RTD model showed excellent

fitting results. In Chapter 2, A PLS analysis of the important model parameters including

the mean residence time and the axial dispersion coefficient was performed. The relative

trends between these parameters as a function of process parameters were identified, and

design rules were proposed to select optimal combinations of process parameters based

on the material properties of the powder. Furthermore, mathematical models suitable for

process control purposes, for the estimated parameters as a function of impeller speed are

presented. These models along with the dynamic RTD model can be used to model the

dynamic behavior of the continuous mixer.

In the second research aim, mixing performance was characterized for typical

pharmaceutical formulations which consisted of APAP-Avicel and APAP-Avicel-MgSt

mixtures. In the first case, as presented in Chapter 4, the effect of impeller speed on blend

uniformity was found to be statistically significant. Followed by the impeller speed, the

effect of blade configuration was also statistically significant. The mixing performance

was found to be the best under intermediate rotation rates, and for ‗Aleternate‘ blade

configuration. The flow rate however did not show any significant effect. Since the axial

mixing capability of the continuous mixer is the highest at the lowest impeller speed,

these results indicate that axial mixing is not the limiting mechanism. However the radial

mixing capability, gauged from the number of blade passes, is the maximum at the

139

intermediate rotation rates. In the case of APAP-Avicel mixture, the highest number of

blade passes, applied under intermediate rotation rates lead to maximum homogenization.

In Chapter 4, for the case of MgSt blending, the effects of total shear applied in blending

were further clarified. The blends prepared at the intermediate rotation rate, when

compacted into tablets, exhibited lower hardness, and the uniformity of MgSt distribution

in tablets was the maximum at the intermediate rotation rate. For the particular

formulation examined here, the hydrophobicity of the powder was found to be relatively

less sensitive to the process parameters. In conclusion, the number of blade passes

measurement was found to be a useful variable that can predict blend uniformity and

physicochemical properties of intermediate blends and tablets.

In Chapter 4, it was shown that the minimum RSD obtained in the continuous mixing

runs, for the APAP-Avicel mixture was ~ 0.08. According to the FDA guidance, the

acceptable RSD value for blend uniformity should be less than 0.06, for a sample size

upto three unit doses. Since the RSD values obtained in our experimental investigation

were higher than the FDA limit, a further analysis into the measurement methodology

was necessary. The main reason for the higher RSD values was attributed to the smaller

sample size (~ 10 mg) analyzed in the off-line NIR measurements.

In order to implement continuous mixing process in the pharmaceutical product

manufacturing scenarios (Direct compression, Wet granulation etc.), an on-line method

for monitoring blend uniformity is necessary. In Chapter 5, two NIR systems, namely

CDI and VTT were examined. With the CDI system, a chemo-metric calibration model

was built, using the on-line spectral data. It was demonstrated that the CDI system is fast

enough to accurately capture the blender dynamics. It was also shown that the sample

140

size was ~ 0.26 g which is close to the unit dose commonly practiced in the

pharmaceutical industry. In a continuous pilot plant, blend uniformity measurement is

required at several locations. In order to meet this goal, a multi-channel NIR

spectrometer, capable of analyzing NIR data from multiple channels, supplied by VTT

was used in our continuous direct compression pilot unit. The feasibility of VTT

spectrometer was demonstrated by measuring blend uniformity at the blender discharge

at five points placed cross-sectionally on a powder conveying chute. The sample size

being analyzed in the NIR measurement was estimated by measuring the powder velocity

on the chute. Furthermore, the characteristic relationship between the RSD measured

using on-line data and sample size was acquired by averaging measurements for varying

scanning windows. A similar relationship was also acquired using wet-chemistry RSD

measurements, acquired using samples of varying sizes. These two relationships were

compared by fitting the RSD data in a mathematical model. The unknown parameter in

the model, the measurement method error, was found to be negligible in both the cases.

This result concludes that the base-line RSD of 0.02 observed in the on-line

measurements or 0.08 in the off-line measurements is a result of the inherent blend non-

uniformity at that particular sample size.

In Chapter 6, a blending strategy suitable for mixing cohesive materials was developed

by integrating a co-mill with the continuous powder mixer. The Comil was found to be a

good micro-mixer but a poor macro mixer, whereas the bladed continuous mixer was

found to be a good macro-mixer but a poor micro mixer. The optimal combination was

found to be high shear mixing first, followed by low shear mixing.

141

In this dissertation, continuous mixing strategies for free-flowing powders, lubricants and

cohesive materials were designed and optimized. The understanding of the observed

mixing phenomena was made possible using the residence time distribution

measurements. Furthermore, a predictive mathematical model, suitable for process

control was developed. Real-time blend uniformity monitoring technologies for

continuous blending applications were developed, which are still in their infancy in both

academic as well as industrial practice. A methodology of directly relating in-line blend

uniformity measurements with the wet-chemistry measurements, developed in this thesis

directly supports the RTR concept. Real Time Release (RTR), meaning releasing the

product without analytical testing, has a significant importance in pharmaceutical

industry. Thus applying this methodology in pharmaceutical manufacturing could lead to

significant cost benefit. The integration of continuous blending with de-lumping provides

alternative blending methodology in continuous systems.

7.2 Recommendations for future work

The future work in continuous mixing can be directed in two ways. One direction could

be more scientific, which could involve characterizing the continuous mixing process

from the micro-mixing point of view. In this dissertation, the major focus was on the

characterization of continuous powder mixing from macro-mixing standpoint. Using

RTDs the degree of convection and dispersion was characterized, and related to the

mixing performance at macro level. In the future work, characterization of micro-mixing

can be performed using NIR chemical imaging [114,115]. The micro-structures created in

the blending processes have significant impact on the properties on blends [116], physical

properties of tablets [101,117] and also the drug release rate from tablets [101]. The

142

evolution of micro-structures in continuous blenders if measured using the correct

analytical tools can lead to better process understanding, and also facilitate scale-up of

blending processes. The second direction for future work could be developing new

blending technologies suitable for wide variety of powders. A couple of examples, which

demonstrate new technology, and an integration of existing process in the continuous

manufacturing scenario are presented in the following sections.

7.2.1 New Blender designs:

In Chapter 6 it was shown that the existing continuous blender design cannot provide the

necessary shear environment for mixing cohesive powders. It was also demonstrated that

cohesive powders in a continuous processing scenario can be mixed efficiently utilizing a

combination of high-shear mixer followed by a low-shear mixer. A new blender, capable

of providing both high shear as well as low shear environment, at the required level needs

to be designed.

7.2.2 Development of an integrated feeder-mixer system with a

recirculation tank

A case study was performed to assess the feasibility of powder recirculation system for a

continuous powder mixing system. This system is advantageous in any continuous

powder processing line. The specific advantages include the recirculation of the excess or

out of specification material produced by the continuous mixing process. Out of

specification material could be produced during the start-up of the process while feeders

and the blender are not operating under steady state. If the duration of start-up is

significantly smaller than the overall length of the run, start-up costs are negligible.

143

However in pharmaceutical manufacturing while dealing with expensive APIs, even the

start-up costs are considerable. During the normal operation of the process (under steady

state), there could be situations depending on the operating flow rates of the subsequent

units, excess material might be produced. E.g. Consider a direct compression processing

scenario which involves feeders feeding into a continuous mixer followed by a

compression unit. A situation may occur where the speed of the continuous mixer needs

to be increased. Increase in the speed momentarily increases the output flow rate. This

event generates some extra material which may overfill the hopper of the tablet press. In

such a case, one possible solution is to increase the tableting speed. However it may not

be desirable to change the tableting speed from the optimal point, as the tablet properties

may change undesirably with an increase in the speed. In that case one may need to

recycle the excess material, or hold into an extra capacity. Introducing a powder

recirculation tank may provide a practical solution to this problem.

The schematic of the powder recirculation system is shown in Figure 7-1. Operation of

the powder recirculation system can be explained as follows. A level sensor can be

mounted on the hopper of the subsequent processing unit (Tablet press, capsule filler or

roller compactor). A signal will be sent to the control system indicating overfilling of the

hopper. This will allow the stream splitter to send the excess powder to the surge

capacity. Blender such as ribbon blender could be used as the surge capacity. Powder will

then be mixed in the ribbon blender for a certain amount of time. Once the blend

becomes uniform, this blend will then be conveyed into a feeder. Feeder will then feed

this material into the continuous mixer. An in-line NIR sensor will be necessary to

measure the composition of the recycle stream. Depending on the composition of the

144

recycle powder stream, feed rates of other components can be adjusted. This entire

system can be operated intermittently as required.

7.2.2.1 Dynamics of an integrated feeder-mixer and recycle system

The dynamics of the recirculation tank system was demonstrated for an integrated

feeders-mixer and recirculation tank. Continuous processes which are operated for longer

times, feeders often need to be refilled. When a feeder undergoes a refill, an overshoot in

the feed rate is created which subsequently creates an overshoot in the feed rate at the

discharge of the mixer. The magnitude of this overshoot needs to be minimized in order

to avoid overfilling of the hopper of the next unit operation. A recirculation tank can be

introduced in such a system to minimize these overshoots.

In order to understand the dynamics of this system, a simulation was performed using

feeder refill data and RTDs of the blenders. A block diagram illustrating this problem is

shown in Figure 7-2.

In order to obtain the dynamic response of the integrated system, equations (3-6) were

solved simultaneously using MATLAB. RTD model )(E used in equations (1) and (1)

was assumed to be a 1-D axial dispersion model. RTD model parameters (residence time

( ), dead time ( 0 ) and Peclet number ( Pe )) were obtained by fitting the experimental

impulse response data. A similar RTD model was assumed for the recirculation tank.

Dynamic response of the integrated system was computed for a particular input feed rate

dataset (feeder undergoing refills).

As shown in Figure 7-3, overshoot in the feed rate at the outlet of the mixer decreases

with increase in the recirculation flow rate. For excessive recycle flow rates (10 times or

145

50 time in the input flow rate, the system response became sluggish. A shift in the

baseline can be observed in Figure 7-3(b). In conclusion, recycle only to a certain extent

was found to improve the overall performance.

)1

.1

(

)01

(4

2

1

)01

(1

exp)

1.

1/()

01(2

1)(

1

Pe

PeE

(7-1)

)2

.2

(

)02

(4

2

2

)02

(1

exp)

2.

2/()

02(2

1)(

2

Pe

PeE

(7-2)

)()()( 21 tMtFtF in (7-3)

1

0

212 )()()(

T

dEtMtM

(7-4)

2

0

212 )()()(

T

dEtFtF

(7-5)

)()()( 12 tMtFtFout (7-6)

146

7.3 Figures for Chapter 7

Figure 7-1: Schematic of the continuous processing line with a recirculation tank

Figure 7-2: Schematic of an integrated feeder, mixer and recirculation tank system

NIR Sensor

Feeder 2 Feeder 1

Recycle

Tablet Press

Recirculation tank/mixer

Mixer

Recirculation

tank

Fin

,

Cin

F2,

Cout

M1,

Cout

M2,

C1

F1,

C2

Feeder -1

Feeder -2

Fout

,

Cout

147

Figure 7-3: (a) Dynamic response of the continuous mixer for feeder refills (b)

Mixer response for recycle flow rates 10 and 50 times of input flow rate

M: Recycle flow rate F: Input flow rate

M: Recycle flow rate F: Input flow rate

(b)

(a)

148

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[115] W. Li, A. Woldu, R. Kelly, J. McCool, R. Bruce, H. Rasmussen, et al.,

Measurement of drug agglomerates in powder blending simulation samples by near

infrared chemical imaging, Int.J.Pharm. 350 (2008) 369-373.

[116] K. Pingali, R. Mendez, D. Lewis, B. Michniak-Kohn, A. Cuitino, F. Muzzio,

Mixing order of glidant and lubricant – Influence on powder and tablet properties,

Int.J.Pharm. 409 (2011) 269-277.

[117] I.C. Sinka, F. Motazedian, A.C.F. Cocks, K.G. Pitt, The effect of processing

parameters on pharmaceutical tablet properties, Powder Technol 189 (2009) 276-284.

158

CURRICULUM VITA

Aditya U. Vanarase

2002-2006 Attended Institute of Chemical Technology (ICT, formerly UDCT),

Department of Chemical Engineering, Mumbai, India

2006 B.Chem. in Chemical Engineering. Institute of Chemical Technology,

Mumbai, India.

2006-2011 Attended Rutgers, The State University of New Jersey, Department of

Chemical & Biochemical Engineering, New Brunswick, NJ.

2009 Summer Internship, Abbott Laboratories, North Chicago, IL.

2009 M.S. in Chemical & Biochemical Engineering. Rutgers, The State University

of New Jersey, New Brunswick, NJ.

2011 Ph.D. in Chemical & Biochemical Engineering. Rutgers, The State University

of New Jersey, New Brunswick, NJ.

PUBLICATIONS

A. U. Vanarase, F. J. Muzzio, ―Effect of operating conditions and design

parameters in a continuous powder mixer‖, Powder Technol., 2011, 208(1), 26-36

A. U. Vanarase, M. Alcalà, J. I. Jerez Rozo, F. J. Muzzio, R. J. Romañach, ―Real-

time monitoring of drug concentration in a continuous powder mixing process

using NIR spectroscopy‖, Chem. Eng. Sci., 2010, 65(21), 5728–5733

Y. Gao, A. U. Vanarase (Shared 1st authorship), F. J. Muzzio, M. G. Ierapetritou

―Characterizing continuous powder mixing using residence time distribution‖,

Chem. Eng. Sci., 66(3), 417-425

P. M. Portillo, A. U. Vanarase, A. Ingram, J. K. Seville, M. G. Ierapetritou, F. J.

Muzzio, ―Investigation of the effect of impeller rotation rate, powder flow rate,

and cohesion on powder flow behavior in a continuous blender using PEPT‖,

Chem. Eng. Sci., 2010, 65(21), 5658-5668

F. Boukouvala, R. Ramachandran, A. U. Vanarase, F. Muzzio, M. Ierapetritou

―Computer aided design and analysis of continuous pharmaceutical

159

manufacturing processes‖, Computer Aided chemical engineering, 2011, 29, 216-

220

BOOK CHAPTER

A. U. Vanarase, Y. Gao, A. Dubey, M. Ierapetritou, F. J. Muzzio,

―Pharmaceutical Process Scale-Up, 3rd

Ed.‖, (M. Levin, Editor) Marcel Dekker

Publishing Company