01b - Jan Van Impe - BioTec Research Themes

8/3/2019 01b - Jan Van Impe - BioTec Research Themes

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Model based

Analysis, Design, Optimization and Control of

Complex (Bio)Chemical Conversion Processes

Bioprocess Technology and Control - KULeuven


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Prelude

Design, optimization and control

of (bio)chemical conversion processes

based on

Historical

experience

time consuming capital intensive

operation/operator

specific

on-line measurementsin silico design,

optimization,

and control studies

Mathematical

model


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practical implementation

optimization and control

manageabilityaccuracy

complex enough to

cover main dynamics

Prelude: complexity trade-off

MODEL


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accuracy

manageability

Primary model

Prelude: methodology


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accuracy manageability

Model

complexityreduction

Prelude: methodology


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reaction transportaccumulation

Balance type equations


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Complexity

related to

# of states

time & space

dependency

reactionkinetics


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Complexity

related to

# of states

Carbon and nitrogenremoving activatedsludge systems- biodegradation

- sedimentation

Theme #1:

Fast & reliablesimulationsOptimization &

control

Objectives:


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Complexity

related to

# of states


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Theme #1: Unit operations

ASM1 model


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Complexity: ASM1 model

()


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input output

Complexity reduction


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Aerated tank

Ss

Xbh

Xp

Sno

Snd

Xs

Xba

So

Snh

Xnd

time[day] time[day]


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Theme #2: Filamentous bulking

Influent Effluent

Aeration tank Sedimentation tank

Activated sludge


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Process

Control

Influent

Wastewater

Aeration Tank

Environment

Microbial

Community

Selection

Effluent Water

Quality

Improvement

Long term objectives

Image Analysis

Procedure


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Experimental set-up @ BioTeC

Influent

Effluent

EFFLUENT

Turbidity

Quality

SLUDGE

Concentration

Loading

Settleability

Characteristics


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Robustness test

Influence of microscope, camera and sludge type ?


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ARX model


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Theme #3: sWWTPS

Rotating Biological

Contactor

Submerged Aerated

Filter


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Milestones

Model complexity reduction for unit operations

Linear Multi (or Fuzzy) Model approach withhigh predictive quality (input or state driven)

Significant reduction in computation time due toanalytic solution of LTI state space model(within 1 class)

Simple linear model for

risk assessmentand feedback (MPC) control Microbial dynamics:

exploiting image analysis information

Application to (s)WWTPS


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Complexity

related to

reaction kinetics

* Metabolism of bacteriumAzospirillum brasilense

* Quorum sensing of bacteriumSalmonella typhimurium*Lag/growth/inactivation/survival

Case studies:

Macroscopic/microscopiccell metabolism modeling

Objective:


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High added value of specialty chemicals(food additives, vaccins, enzymes, )

Quantification of the influence of external signals on cell metabolism (A. brasilense), and

quorum sensing (S. typhimurium).

Optimal experimental design of

bioreactor experiments

Complexity


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Primary modeling: identification of 14 parameters

EFT [h] EFT [h]

Co[%]

Malate

[g/L]

OD578D

[1/h]


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Primary modeling: validation

EFT [h] EFT [h]

Co[%]

Malate

[g/L]

OD578D

[1/h]


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Sensitivity function based model reduction

Sensitivity functionsreflect the sensitivity of model predictions

to (small) variations in model parameterswith given inputs

time

0

5

-5

j

i

p

y

time

0

0.001

-0.001

j

i

p

y


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Reduced model: identification experiment

EFT [h] EFT [h]

Co[%]

Malate

[g/L]

OD578D

[1/h]


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Reduced model: validation experiment

EFT [h] EFT [h]

Co[%]

Malate

[g/L]

OD578D

[1/h]


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max

Nmax

Escherichia coli K12 (MG1655), Brain Heart infusion, 36.3C

Microbial growth @ constant

temperature

Stationary phase

Exponential phase

Lag phase


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Estimation of microbial growth kinetics as

function of temperature

Tmin Topt Tmaxsub-optimal temperature range

)()( minmax TTbT

SQUARE ROOT MODEL [Ratkowsky et al., 1982]

b

b minT

Tmin


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Constrained input optimisation


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1st experiment: based on po



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2nd experiment: based on p1



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Global identification of experiment 1 & 2



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Milestones

Macroscopic modeling: Sensitivity functionanalysis as a powerful tool to reduce the complexityof a physiology based, first principles model

Microscopic modeling:IBM (Individual based Modeling) linking

bio-informatics, with

macroscopic mass balance type models

Optimal experimental design of computercontrolled bioreactor experiments


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Complexity

related to

reactionkinetics

Fed-batch growth

process with non-monotonic kinetics

Case study:

Feedback stabilization:keep Cs constant

Objective:


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Case study

u

time

Two valued function!


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Case study

u

time



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Case study

u

time



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Controller (on-line Cx measurements)

Feedforward (OC) Stabilizing feedback

observer

I-action

P-action

= +1 = -1or


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Stabilizing feedback controller for fed-batchnon-monotonic growth processes

Only based on on-line biomassconcentration measurements

Adaptive: no detailed kinetics informationneeded ( observer)

Conclusions

C l i


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Complexity

related to

time & spacedependency

Tubular chemical reactors

Case study:

Optimal jacket fluidtemperature control of- classicalreactors, and

- novel typereactors

Objective:


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Tubular chemical reactor


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C = reactant concentration[mole/L]

T = reactor concentration[oK]

Tw= jacket fluid temperature[oK]

Model for tubular reactor: PDE/DPS


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Combined terminal/integral objective

Conversion

Hot spots

Temperaturerun-away

Determine optimal jacket fluid temperature profile

( )2


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Comparison with suboptimal profiles

maximum-singular-minimum profile

optimal, but

singular part difficult to implement

maximum-minimum profile

not optimal, but

practically realizable

how much optimality is lost?


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0.3

Comparison with suboptimal profiles (I):

Conversion


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0.7

Comparison with suboptimal profiles (II):

Hot Spots


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Milestones: optimal control theory for

optimal analytical jacket fluid temperature

profiles for classical chemical reactors

steady state

transient

optimization ofnovel type reactors

cyclically operated reverse flow reactors

circulation loop reactors

optimal reactordesign

l di


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Postludium

Dealing with complexity during modeling foroptimization and control of

(bio)chemical processes:

a multimodal problem at the interface ofvarious disciplines

We will pass several cases in review over theyears to come

i i l


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emerging generic results

Development of widely applicable andtransferable quantitative tools for complex

(bio)chemical processes

WP3

WP1 WP2

WP4

Documents

01b - Jan Van Impe - BioTec Research Themes