Industrial Flowsheet

Optimization and Estimation

(IFOE) using IMPL

Jeffrey D. Kelly

Industrial Algorithms LLC. “Better Data + Better Decisions = Better Business”

2/4/2014 Copyright, Industrial Algorithms LLC.

Agenda

• IAL Introduction

• What is IMPL?

• What is IFOE?

• Advanced Planning & Scheduling (APS)

• Advanced Performance Management (APM)

2

Our Mission

• To provide advanced modeling and solving

tools for developing and deploying industrial

applications in important decision-making

and data-mining areas.

• Our market is:

– Operating companies in the process industries.

– Consulting service providers.

– Application software providers.

3

Our Focus

• IAL develops and markets IMPL, the world’s

leading software for flowsheet optimization and

estimation in both off and on-line environments.

• IAL provides Industrial Modeling Frameworks

(IMF’s) for many problem types in various

process industries which provides a pre-project

or pre-solution advantage (head-start).

• IMPL is an “enabling technology” intended to

drive and accelerate your time-to-benefits.

4

Our Differentiator • Advanced Project Development/Deployment

(APD).

“To install and implement small projects with

significant payback v. installing and implementing

large projects/products with little or limited

payback.”

• This requires knowledge of your “bottlenecks”

and “backoff’s” and to target or converge on

these problem areas for Advanced Industrial

Applications (AIA).

5

Backoff’s = reducing actual capability for complexity, uncertainty, operational, organizational, etc. reasons.

Our Industrial Modeling and

Solving Platform (IMPL)

• IMPL stands for “Industrial Modeling &

Programming Language” and is our proprietary

platform.

– You can “interface”, “interact”, “model” and

“solve” any process-chain, supply-chain, demand-

chain and/or value-chain optimization problem.

• IMPL is suitable for:

– Advanced Planning & Scheduling (APS),

– Advanced Production Optimization (APO) and

– Advanced Performance Management (APM). 6

• Problems are configured/coded using “sheets”

for each “shape” either by:

– Interfacing with our flat-file Industrial Modeling

Language (IML), or

– Interactively with our Industrial Programming

Language (IPL) embedded in any computer

programming language such as C, C++, Fortran,

C#, Java, Python, Excel/VBA, etc.

– Data are keyed by our UOPSS shapes where the

attributes are specified using our Quantity, Logic &

Quality Phenomena (QLQP) including Time.

How do we configure/code

problems in IMPL?

7

How do we configure/code

problems in IMPL? (cont’d)

8

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

! Constriction Data (Practices/Policies)

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

&sUnit,&sOperation,@rUpTiming_Lower,@rUpTiming_Upper

Blender,Crudeoil,3.0,

&sUnit,&sOperation,@rUpTiming_Lower,@rUpTiming_Upper

&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState,@rUpTiming_Lower,@rUpTiming_Upper

TK3,Crudeoil,o,,Pipestill,Fuels,i,,19.0,

TK4,Crudeoil,o,,Pipestill,Fuels,i,,19.0,

&sUnit,&sOperation,&sPort,&sState,&sUnit,&sOperation,&sPort,&sState,@rUpTiming_Lower,@rUpTiming_Upper

&sUnit,&sOperation,@rFillDrawDelaying_Lower,@rFillDrawDelaying_Upper

TK1,Light,9.0,

TK2,Heavy,9.0,

TK3,Crudeoil,3.0,

TK4,Crudeoil,3.0,

&sUnit,&sOperation,@rFillDrawDelaying_Lower,@rFillDrawDelaying_Upper

&sUnit,&sOperation,&sPort,&sState,@iMultiUse_Lower,@iMultiUse_Upper

Blender,Crudeoil,o,,1,1

Pipestill,Fuels,i,,1,1

&sUnit,&sOperation,&sPort,&sState,@iMultiUse_Lower,@iMultiUse_Upper

&sUnit,@sZeroDownTiming

Pipestill,on

&sUnit,@sZeroDownTiming

IML Frames (Sheets) in CSV Format

How do we configure/code

problems in IMPL? (cont’d)

9

IPL Functions (Subroutines) in Python (CTYPES) !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

! Construction Data (Pointers)

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

rtnstat = interacter.IMPLreceiveUO(uname,oname,utype,usubtype,uuse,IMPLkeep)

rtnstat = interacter.IMPLreceiveUOPS(uname,oname,pname,sname,ptype,psubtype,IMPLkeep)

rtnstat = interacter.IMPLreceiveUOPSUOPS(uname,oname,pname,sname,uname2,oname2,pname2,sname2,IMPLkeep)

rtnstat = interacter.IMPLreceiveUOOG(uname,oname,ogname,IMPLkeep)

rtnstat = interacter.IMPLreceiveUOGOGO(uname,ogname,ogname2,oname,IMPLkeep)

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

! Capacity Data (Parameters)

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

rtnstat = interacter.IMPLreceiveUOrate(uname,oname,lower,upper,IMPLkeep)

rtnstat = interacter.IMPLreceiveUOPSteerate(uname,oname,pname,sname,lower,upper,IMPLkeep)

rtnstat = interacter.IMPLreceiveUOPStotalrate(uname,oname,pname,sname,lower,upper,IMPLkeep)

rtnstat = interacter.IMPLreceiveUOPSyield(uname,oname,pname,sname,c_double(1.0),c_double(1.0),c_double(0.0),IMPLkeep)

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

! Command Data (Provisos)

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!

rtnstat = interacter.IMPLreceiveUOsetupopen(uname,oname,c_double(1.),c_double(-2.),IMPLkeep)

rtnstat = interacter.IMPLreceiveUOsetuporder(uname,oname,lower,upper,begin,end,IMPLkeep)

rtnstat = interacter.IMPLreceiveUOPSUOPSsetuporder(uname,oname,pname,sname,uname2,oname2,pname2,sname2,lower,upper,begin,end,IMPLkeep)

rtnstat = interacter.IMPLreceiveUOPSholduporder(uname,oname,pname,sname,lower,upper,target,begin,end,IMPLkeep)

• IMPL has six (6) system architecture parts we

call SIIMPLE:

– Server, Interfacer (IML), Interacter (IPL), Modeler,

Presolver DLL’s and an Executable (the

executable can be coded in any computer

programming language that can call DLL’s/SO’s).

– Interfacer, Interacter and Modeler are domain-

specific whereas the Server, Presolver and

Executable are not i.e., they are domain-inspecific

or generic for any type of optimization and

estimation problem.

What is the system architecture

of IMPL?

10

• “Primal Heuristic” used intuitively and naturally

in industry for decades to find “globally feasible”

solutions to MINLP problems.

• “Conjunction Values” are time-varying

parameters which “guide” each sub-problem

solution where “cuts” (extra constraints) can

also be added to avoid known infeasible and/or

inferior areas of the search-space.

How do we converge/convert to

solutions in IMPL?

Quality (NLP) Logistics (MILP)

Lower, Upper & Target Bounds on Yields

Lower & Upper Bounds on Setups & Startups

Conjunction Values

11

12

How do we converge/convert to

solutions in IMPL? (cont’d)

• For NLP (quality) we have bindings to:

– CONOPT, IPOPT, KNITRO, XPRESS-SLP as well

as our “home-grown” SLPQPE.

– SLPQPE out-sources the matrix to 3rd-party LP/QP

solvers. If the objective function has quadratic

terms then a QP is called at each major iteration

(appropriate for data reconciliation & parameter

estimation, nonlinear control, etc.).

• For MILP (logistics) we have bindings to:

– COINMP, GLPK, LPSOLVE, SCIP, CPLEX,

GUROBI, LINDO and XPRESS.

Industrial Flowsheet Optimization

and Estimation (IFOE) • IFOE requires the following five (5) features:

(1) Unambiguous Representation of the Flowsheet,

Network or Superstructure (UOPSS).

(2) Semantic-Variables and Meta-Constraints (QLQP).

(3) Time Model with Automatic Digitization (Discretized

& Distributed-Time).

(4) Optimization and Estimation (Simulation)

Interoperability (Under/Over/Exactly-Determined).

(5) Data Quality Checking: Range, Rate-of-Change,

Stationarity, Clustering, Scoring, Balancing, etc.

13

How do we represent the

flowsheet? Perimeters – Supply/Demand Points or Sources/Sinks

Pools – Inventory or Holdup

Batch-Processes – Variable-Size & Fixed-Time (VSFT) or Fixed-Size & Variable-Time (FSVT)

Continuous-Processes – Blenders, Splitters, Separators, Reactors , Fractionators & Black-Boxes

Parcels – Moveable/Transportable Inventory or Holdup with Round-Trip Travel-Time

Pipelines – Moveable/Transportable Inventory as FIFO (First-In-First-Out)

Port-In – Flows into a Unit (similar to a nozzle).

Port-Out – Flows out of a Unit

Dimensional-Processes – Geometry Transforms (Reels, Rolls, Sheets, Ingots, Logs, etc.)

Pilelines – Stackable Inventory as LIFO (Last-In-First-Out)

14

• IMPL models the “logistics” (quantity & logic)

and “quality” (quantity & quality) sub-problems

independently but integrated.

– Quantity details:

– Flows, holdups, yields, etc.

– Logic details:

– Setups, startups, switchovers, shutdowns and sequence-

dependent switchovers, etc.

– Quality details:

– Densities, components, properties, conditions and

coefficients i.e., catalyst activity, enthalpy, etc.

How do we formulate the

variables and constraints?

15

* Similar to: Cires, Hooker & Yunes (July 2013)’s

Meta-Constraints & Semantic Typing *

• IMPL expects all data to be dynamic (time-

varying) and entered in continuous-time

(begin/end-times) as “orders”/“transactions”:

– Discrete-time:

– Common time-grid with equally-spaced time-points.

– Suitable for logistics optimization (MILP).

– Distributed-time:

– Common time-grid with unequally-spaced time-points.

– Suitable for quality optimization (NLP).

How do we digitize the problem

data?

16

• Same Model with Different Data:

– Optimization using Future Data.

– Estimation using Past/Present Data.

– Adapt, Tune, Fit, Update, Calibrate, etc. Parameters

similar to Moving Horizon Estimation (MHE).

• Quality of Data prior to Optimization/Estimation:

– Similar to pre-solving, “pre-screen” data for gross

inconsistencies such as limit violations, unsteady-

state behavior, unexpected operating regions,

imbalances in simple conservation laws, etc.

How do we manage

interoperability and data quality?

17 Kelly, J.D., Hedengren, J.D., “A Steady-State Detection (SSD) Algorithm to Detect Non-Stationary Drifts in

Processes”, Journal of Process Control, 23, 2013.

Advanced Planning and

Scheduling (APS) • IMPL supports both planning and scheduling

optimization problems handling both logistics

and quality details in a coordinated manner.

• IMPL uses “discrete-time” with “big” (planning)

and “small” (scheduling) time-buckets or time-

periods for logistics optimization.

• IMPL uses “discrete-time” and “distributed-time”

i.e., continuous-time/event-based using a global

or common time-grid for quality optimization.

18

Kelly, J.D., "Formulating Production Planning Models", Chemical Engineering Progress, January, 43, 2004.

Zyngier, D., Kelly, J.D., "Multi-Product Inventory Logistics Modeling in the Process Industries", In: W. Chaovalitwonse,

K.C. Furman and P.M. Pardalos, Eds., Optimization and Logistics Challenges in the Enterprise", Springer, 61-95, 2009.

Crude-oil Blend Scheduling

Optimization (CBSO-IMF)

19

Kelly, J.D., Mann, J.L., “Crude-Oil Blend Scheduling Optimization: An Application with Multi-Million Dollar Benefits”,

Hydrocarbon Processing, June/July, 2003.

Menezes, B.C., Kelly, J.D., Grossmann, I.E., “Improved Swing-Cut Modeling for Planning and Scheduling of Oil-Refinery

Distillation Units”, Ind. & Eng. Chem. Res., 52, 2013.

Fractionation of Crude-Oil Mix

into Several Cuts or Streams

Crude-Oil

Segregation

• Key logistics details …

– 3-h uptime (run-length) for blend header.

– 19-h uptime for tank-to-pipestill flows.

– 9-h fill-draw-delay (mixing-time) for storage tanks.

– 3-h fill-draw-delay for charge tanks.

– 1 flow-out at-a-time for the blend header.

– 1 flow-in at-a-time for the pipestill.

– 0-h downtime (continuous) for the pipestill.

20

Crude-oil Blend Scheduling

Optimization (CBSO-IMF)

Crude-oil Blend Scheduling

Optimization (CBSO-IMF)

21

Product Blend Scheduling

Optimization (PBSO-IMF)

22 Kelly, J.D., “Logistics: The Missing Link in Blend Scheduling Optimization”, Hydrocarbon Processing, June, 2006.

Castillo, P.A.C., Kelly, J.D., Mahalec, V., “Inventory Pinch Algorithm for Gasoline Blend Planning”, AIChE J., 59, 2013.

One Blend Header with

Two Modes or Grades

• Key logistics details …

– 1-norm (abs) flow-smoothing on blend header flow.

– 6 to 8-h uptime for blend header.

– 6 to 8-h uptime for blender-to-tank flows.

– 4-h fill-draw-delay (mixing-time) for product tanks.

– 1 flow-out at-a-time for the blend header.

– 0 holdup on switching-when-empty for multi-product

tanks.

23

Product Blend Scheduling

Optimization (PBSO-IMF)

Product Blend Scheduling

Optimization (PBSO-IMF)

24

25

Key Quality Details …

Conditions-&sMacro,@sValue

E200,SIP(200.0:200.0;0.0;NT01;NY01;OT10;NY10;OT30;NY30;OT50;NY50;OT70;NY70;OT90;NY90;NT99;NY99;775.0;1.0)

…

E750,SIP(750.0:200.0;0.0;NT01;NY01;OT10;NY10;OT30;NY30;OT50;NY50;OT70;NY70;OT90;NY90;NT99;NY99;775.0;1.0)

Conditions-&sMacro,@sValue

&sCoefficient,@sType,@sPath_Name,@sLibrary_Name,@sFunction_Name,@iNumber_Conditions,@rPerturb_Size,@sCondition_Names

HOTH,dynamic,c:\IMPL\PhysProp\,steam67_H,steam67_H,1,1e-6,HOTT

COLDH,dynamic,c:\IMPL\PhysProp\,steam67_H,steam67_H,1,1e-6,COLDT

WARMH,dynamic,c:\IMPL\PhysProp\,steam67_H,steam67_H,1,1e-6,WARMT

&sCoefficient,@sType,@sPath_Name,@sLibrary_Name,@sFunction_Name,@iNumber_Conditions,@rPerturb_Size,@sCondition_Names

Compute Steam/Water Enthalpies using a Foreign STEAM67.DLL

Compute Evaporations from Distillation Curve using Monotonic Interpolation

Properties-&sMacro,@sValue

DT1001,7.40120*(D10-D01)^0.60244

DT3010,4.90040*(D30-D10)^0.71644

DT5030,3.03050*(D50-D30)^0.80076

DT7050,2.52820*(D70-D50)^0.82002

DT9070,3.04190*(D90-D70)^0.75497

DT9990,0.11798*(D99-D90)^1.66060

T01,T50 - DT5030 - DT3010 - DT1001

T10,T50 - DT5030 - DT3010

T30,T50 - DT5030

T50,0.87180*D50^1.02580

T70,T50 + DT7050

T90,T50 + DT7050 + DT9070

T99,T50 + DT7050 + DT9070 + DT9990

Properties-&sMacro,@sValue

Convert from ASTM D86 to True Boiling Point (TBP) Temperatures

XFCN-@sPath_Name,@sLibrary_Name,@sFunction_Name

C:\IMPL\Procedures\,xfunc.dll,xfunc

XFCN-@sPath_Name,@sLibrary_Name,@sFunction_Name

Compute Z using a Foreign XFUNC.DLL

Properties-&sMacro,@sValue

Z,3.0 * XFCN(LOG(X),Y/10.0)) ̂ 2.0

Properties-&sMacro,@sValue

26

Advanced Performance

Management (APM) • Employs Nonlinear and Dynamic Data

Reconciliation and Regression (NDDRR)

technology also known as “Error-in-Variables

Method” (EVM) & “Moving Horizon Estimation”

(MHE) with sensitivity diagnostic information.

• APM includes:

– Advanced Production Accounting (APA),

– Advanced Property Tracking/Tracing (APT) and

– Advanced Process Monitoring (APM). Kelly, J.D., "A Regularization Approach to the Reconciliation of Constrained Data Sets", Computers & Chemical

Engineering, 1771, 1998.

Kelly, J.D., "Techniques for Solving Industrial Nonlinear Data Reconciliation Problems", Computers & Chemical

Engineering, 2837, 2004.

27

Advanced Performance

Management (APM) (cont’d) • Solution data accessed via a flowsheet pointer

i.e., pointer = (UOPSS,QLQP,time-period).

• Statistical diagnostics such as:

– Redundancy and observability for measured and

unmeasured variables respectively.

– Hotelling T2 statistic for overall objective function.

– Maximum-power (Student-t) statistics for the

individual measurements and constraints.

– 95% confidence intervals for unmeasured variables

i.e., parameters or coefficients. Kelly, J.D., Zyngier, D., "A New and Improved MILP Formulation to Optimize Observability, Redundancy and

Precision for Sensor Network Problems", AIChE Journal, 54, 2008.

28

Advanced Performance

Management (APM) (cont’d) • Supports both Unconstrained (Y12M, MA28,

PARDISO, etc.) and Constrained (IPOPT,

CONOPT, KNITRO, etc.) NDDRR solutions.

• Solves for Observability, Redundancy and

Variances using a unique Sparse Matrix

Algorithm (CHOL, LU and LDL) compared to

Dense Matrix Algebra i.e., Reduced Row

Echelon Form (RREF), Gauss-Jordan (GJ), QR

Factorization and SVD.

Kelly, J.D., “On Finding the Matrix Projection in the Data Reconciliation Solution", Computers & Chemical

Engineering, 1553, 1998.

Kelly, J.D., “Reconciliation of Process Data using other Projection Matrices", Computers & Chemical

Engineering, 785, 1999.

Advanced Production Accounting

(APA-IMF)

29 Kelly, J.D., Mann, J.L., "Improve Yield Accounting by Including Density Measurements Explicitly", Hydrocarbon

Processing, January, 2005.

Single Defect in Tank Density

Inflates Objective Function

Over Hotelling T2

Advanced Property Tracking

(APT-IMF)

30

Kelly, J.D., Mann, J.L., Schulz, F.G., "Improve Accuracy of Tracing Production Qualities using Successive

Reconciliation", Hydrocarbon Processing, April, 2005.

Recycle

Track or Trace Qualities in Tanks Over

Time with Recycles – Useful for

Operating Downstream Processes

Advanced Process Monitoring

(APM-IMF)

31

Henson, M.A., Seborg, D.E., Nonlinear Process Control, Prentice Hall, New Jersey, 1997.

Conditions-&sMacro,@sValue

eqCa,V * Ca - (V * Ca[-1] + q * dt * (Caf - Ca) -`

k0 * dt * V * Ca * EXP(-EoverR / T))

eqT,rho * Cp * V * T - (rho * Cp * V * T[-1] +`

q * dt * rho * Cp * (Tf - T) +`

k0 * dt * V * mdelH * Ca * EXP(-EoverR / T) +`

UA * dt * (Tc - T))

Conditions-&sMacro,@sValue

Dynamic Model w/ Lagged or

Time-Shifted Variables [] in Bold

Material Flow Analysis (STAN2,

http://www.stan2web.net/)

32 Cencic, O.; Rechberger, H.,”Material Flow Analysis with Software STAN”, Journal of Environmental Engineering and

Management, 18, 2008.

* IMPL is Embedded in STAN2 *

33

• More accurate and timely diagnosis of “bad”

instrument readings i.e., less Type I and II

errors (false negatives and positives).

• More precise estimates of parameters or

unmeasured variables i.e., better vetting of

“unobservable” variables with loose confidence-

intervals spanning zero (0).

• Easier modeling, data integration and system

implementation allowing for more parts of the

plant to be monitored more frequently.

Benefits of APM

Kelly, J.D., ”The Necessity of Data Reconciliation: Some Practical Issues”, NPRA Computer Conference,

November, 2000.

34

• Without continuous improvement i.e.,

“parameter-feedback” (adapting Ymodel), “Plan

v. Actual” offsets are significant and persistent.

Benefits of APS with APM

Kelly, J.D., Zyngier, D., "Continuously Improve Planning and Scheduling Models with Parameter Feedback",

FOCAPO, July, 2008.

Supply Reactor Tank Demand

True Plant

Tank Holdup

(Variable)

Supply

(Solution)

Reactor Yield

(Parameter)

Demand

(Parameter)

1

2

3

4

5

6

7

0 1 2 3 4 5 6 7 8 9

Supply (true) Inventory (true)

Supply (fixed y) Inventory (fixed y)

Supply (updated y) Inventory (updated y)

St = (Itarget – It + Dt) / Ymodel* It = It-1 + St * Yplant - Dt*

Planned/Target

Actual Offset

Thank You!

35