Integrated Scheduling and Dynamic Optimization of Batch ...egon.cheme.cmu.edu/ewo/docs/Dow_EWO_Spring_2012_public.pdf · Problem Description Integrated scheduling and dynamic optimization

Integrated Scheduling and Dynamic Optimization ofBatch Processes

Yisu Nie1 Lorenz T. Biegler1 John M. Wassick2 Carlos M. Villa2

1Center for Advanced Process Decision-makingDepartment of Chemical Engineering

Carnegie Mellon University

2The Dow Chemical Company

March 14, 2012

Yisu Nie (Carnegie Mellon University) EWO meeting March 14, 2012 1 / 11

IntroductionBackground and motivation

Current industrial practice for batch production

Development of a batch process

Product recipescale-up−−−−→ Process recipe

scheduling−−−−−→ Production process

− Lost productivity and lowered performance in unit operations− Schedules sensitive to uncertainties in manufacturingI Opportunities for process optimization

Optimizing the performance of a batch process in real-time

Approach An optimization-based framework with integrated

Scheduling: assignment of batch operationsOptimal control: execution of batch operations

Benefit + Adding value to existing assets+ Improving plant profitability and reliability

Steps Off-line ⇒ On-line

Integrated optimizer

Batch plant

Schedule

Control strategy

⇓Integrated optimizer

Batch plant

Schedule

Control strategy

Feedback


IntroductionBackground and motivation

Current industrial practice for batch production

Development of a batch process

Product recipescale-up−−−−→ Process recipe

scheduling−−−−−→ Production process

− Lost productivity and lowered performance in unit operations− Schedules sensitive to uncertainties in manufacturingI Opportunities for process optimization

Optimizing the performance of a batch process in real-time

Approach An optimization-based framework with integrated

Scheduling: assignment of batch operationsOptimal control: execution of batch operations

Benefit + Adding value to existing assets+ Improving plant profitability and reliability

Steps Off-line ⇒ On-line

Integrated optimizer

Batch plant

Schedule

Control strategy

⇓Integrated optimizer

Batch plant

Schedule

Control strategy

Feedback


Problem DescriptionIntegrated scheduling and dynamic optimization of batch plants

GivenI A batch plant with existing equipmentI A time horizon to make productsI Knowledge of process dynamics

DetermineI The optimal production scheduleI The optimal equipment control strategy

Via1 Process representation using the state

equipment network (SEN)2 Mathematical optimization formulation














Problem Solving MethodologyIntegrated optimization based on the SEN

The SEN represents the process system as a directed graphconnecting two kinds of nodes

Material Feed, intermediate and final products

Equipment Process units carrying out operations

The integrated formulation

Objective function Max Profit = Product sales - Material cost - Operating cost

Constraints I Scheduling considerationsAssignment constraints, material balance, capacity constraints, timing

constraintsI Unit operation

Dynamic first-principle models, limits on controls and statesI Material quality measurement

Material blending, quality requirementsI Auxiliary tightening constraints

Tightening timing constraints, mass balance of process units


Problem Solving MethodologyIntegrated optimization based on the SEN

The SEN represents the process system as a directed graphconnecting two kinds of nodes

Material Feed, intermediate and final products

Equipment Process units carrying out operations

The integrated formulation

Objective function Max Profit = Product sales - Material cost - Operating cost

Constraints I Scheduling considerationsAssignment constraints, material balance, capacity constraints, timing

constraintsI Unit operation

Dynamic first-principle models, limits on controls and statesI Material quality measurement

Material blending, quality requirementsI Auxiliary tightening constraints

Tightening timing constraints, mass balance of process units


Case StudyA jobshop batch plant

Equipment units and products manufactured in the plant

Heater×1 Reactor×2 Filter×2 Distillation Column×1

Packaging Line×2

FeedA

Prod1

Prod3

Prod2

30(MU/ton)

50(MU/ton)

20(MU/ton)

5(MU/ton)

Maximizing the net profit within a 10-hour time horizon

MILP solved by CPLEX, MINLP solved by SBB(CONOPT)



Equipment units and products manufactured in the plantHeater×1 Reactor×2 Filter×2 Distillation

Column×1Packaging

Line×2

FeedA

Prod1

Prod3

Prod2

30(MU/ton)

50(MU/ton)

20(MU/ton)

5(MU/ton)


ModelStatistics

Type Var.(Discrete) # Nonlinear Var. # Cons. #

Recipe-based MILP 676(90) 0 1079Integrated MINLP 4978(90) 2292 12507

ModelSolution

Profit(MU) CPU time (s) Node(Best) # Gap(%)

Recipe-based 1374 0.366 288(199) 0.0Integrated 1935 9564 5000(1602) 67.9




Equipment units and products manufactured in the plantHeater×1 Reactor×2 Filter×2 Distillation

Column×1Packaging

Line×2

FeedA

Prod1

Prod3

Prod2

30(MU/ton)

50(MU/ton)

20(MU/ton)

5(MU/ton)


ModelStatistics

Type Var.(Discrete) # Nonlinear Var. # Cons. #

Recipe-based MILP 676(90) 0 1079Integrated MINLP 4978(90) 2292 12507

ModelSolution

Profit(MU) CPU time (s) Node(Best) # Gap(%)

Recipe-based 1374 0.366 288(199) 0.0Integrated 1935 9564 5000(1602) 67.9



Case StudyOptimal production schedules in Gantt charts

Recipe-based approach

Line2

Line1

Column

Filter2

Filter1

Reactor2

Reactor1

Heater

0 2 4 6 8 10

Time(hr)

<1> 80.0 <2> 80.0

<1> 35.0 <2> 35.0 <3> 35.0

<1> 34.4 <2> 45.0 <3> 45.0

<2> 69.4

<3> 80.0 <4> 80.0

<3> 60.0 <4> 60.0

<5> 37.0 <6> 25.0

<4> 39.5 <6> 50.8

HeatingReaction1Reaction2Filtration1Filtration2

Distillation1Distillation2Packaging1Packaging2Packaging3

Integrated approach

Line2

Line1

Column

Filter2

Filter1

Reactor2

Reactor1

Heater

0 2 4 6 8 10

Time(hr)

<1> 73.6 <2> 62.8 <3> 40.0

<1> 34.9 <2> 28.7 <3> 17.8 <4> 35.0

<1> 34.0 <2> 44.9 <3> 45.0

<2> 68.9

<3> 73.6 <4> 57.8 <5> 40.0

<3> 60.0 <4> 58.5 <5> 45.4

<5> 40.3 <6> 33.2

<4> 38.0 <6> 30.0

HeatingReaction1Reaction2Filtration1Filtration2

Distillation1Distillation2Packaging1Packaging2Packaging3

Numbers in a slot: <event pt.> batch size


Case StudyOptimal operating profiles for batch units

Optimal temperature profiles of Reactor 1

4.0 5.0 6.0 7.0 8.0 9.0

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8Sca

led

tem

p.

Time(hr)

Event slot 1: Reaction 1Recipe

Integrated

0.4 0.6 0.8 1.0 1.2

2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6Sca

led

tem

p.

Time(hr)

Event slot 2: Reaction 2

0.4 0.6 0.8 1.0 1.2

3.8 4 4.2 4.4 4.6 4.8 5 5.2 5.4Sca

led

tem

p.

Time(hr)


0.6 0.9 1.2 1.5 1.8

5.6 5.8 6 6.2 6.4 6.6 6.8 7 7.2Sca

led

tem

p.

Time(hr)


Dynamic profiles also obtained for Reactor 2 and Distillation Column


Industrial Application at DowOptimizing a real-world process with the integrated method

Methodology development

Main resultsI An integrated optimization formulation for batch scheduling and dynamic

optimization based on the state equipment networkI A proof-of-concept case study: verified benefits of the integration

Application at Dow: an alkoxylation process

Process characteristicsI Polymerization reactions in semi-batch stirred tank reactorsI Finishing train as a continuous processI Multiple products differ in composite monomers, molecular weights,

functionality, etc.


Industrial Application at DowOptimizing a real-world process with the integrated method

Methodology development

Main resultsI An integrated optimization formulation for batch scheduling and dynamic

optimization based on the state equipment networkI A proof-of-concept case study: verified benefits of the integration

Application at Dow: an alkoxylation process

Process characteristicsI Polymerization reactions in semi-batch stirred tank reactorsI Finishing train as a continuous processI Multiple products differ in composite monomers, molecular weights,

functionality, etc.


Industrial Application at DowAlkoxylation process

Process chemistry*

Key ingredientsI Epoxides (ethylene oxide (EO), propylene oxide (PO))

OO OOI Molecules containing active hydrogen atoms (alcohols, amines)

OH N

H

HI A basic catalyst (potassium hydroxide (KOH))

Basic proceduresI Starters are first mixed with catalyst in the liquid phaseI Alkylene oxides are fed in controlled rates


* Ionescu, M. Chemistry and Technology of Polyols for Polyurethanes; Rapra Technology: Shawbury, U.K., 2005.

Industrial Application at DowDevelopment of process dynamic models

Modeling reaction kinetics

Open literature example: Ethoxylation reaction*I Initiation

HOCH2CH2O−K+ + EO → HOCH2CH2OEO−K+

I Propagation

HOCH2CH2O(EO)−i K+ + EO → HOCH2CH2O(EO)−i+1K+

I Exchange

PiO−K+

growing chain+ PjOH

dormant chain

↔ PiOHdormant chain

+ PjO−K+

growing chain

P = HOCH2CH2(EO)


* Di Serio, M. Tesser, R. Dimiccoli, A. Santacesaria, E. Kinetics of Ethoxylation and Propoxylation of Ethylene Glycol Catalyzed

by KOH Ind. Eng. Chem. Res. 2002

Industrial Application at DowProposed future developments

Planned work for the project

Dynamic model developmentI Alternative monomers, copolymersI Vapor-liquid equilibriumI Molecular weight distributions

Scheduling method developmentI Discrete-time resource task network

Integration and on-line implementation

Conclusions and acknowledgments

Integration of scheduling and dynamic optimization using SENrevisited

Application at Dow

Thank you and any questions?


Industrial Application at DowProposed future developments

Planned work for the project

Dynamic model developmentI Alternative monomers, copolymersI Vapor-liquid equilibriumI Molecular weight distributions

Scheduling method developmentI Discrete-time resource task network

Integration and on-line implementation

Conclusions and acknowledgments

Integration of scheduling and dynamic optimization using SENrevisited

Application at Dow

Thank you and any questions?


Documents

Integrated Scheduling and Dynamic Optimization of Batch ...egon.cheme.cmu.edu/ewo/docs/Dow_EWO_Spring_2012_public.pdf · Problem Description Integrated scheduling and dynamic optimization