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NORDIC PROCESS CONTROL WORKSHOP, TURKU, 18.1.2018

State of the art of integration of scheduling and control – remaining challengesIiro Harjunkoski, Aalto University / ABB Corporate Research Germany

I want to acknowledge Professors Michael Baldea (University of Texas, Austin) and Marianthi Ierapetritou (Rutgers University) for providing parts of the presented material (FOCAPO 2017 plenary talk).

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Acknowledgements

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Several Optimization Layers

Today’s Production System Workflow

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Scheduling

Production targets Produced amounts

Recipe execution

Batch sizes, assignments, start times Progress, equipment availability

Continuousoptimization

Set-points, constraints End times, yields, quality parameters

Advanced control

Targets Measured and estimated variables

Low-level control

References Controls variables, measured data

Manipulated variables Measurements, binary feedback

PlanningDemands, costs

ProcessRaw materials

UtilitiesProducts

Waste

Optimization!

Optimization!

Optimization!

Optimization!

Optimization!

Focus here: We should in facttalk about integration of scheduling and control system

Slide 3

Hierarchy of Process Operational Decisions

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Production management• Assume steady-state operation• Typically carried out off-line• Business function

Control • Account for dynamics• Online, in real-time• Operational function

Historically: different time scales afforded separationProduction management and control carried out independently: different objectives, personnel

Seborg et al., Wiley, 2010, Baldea and Harjunkoski, Comput. Chem. Eng., 71, 377-390, 2014, Shobrys and White, Comput. Chem. Eng, 26, 149—160, 2002. Zhuge and Ierapetritou, AIChE J. 3304-3319, 2015.

PROCESS

Regulatory control(seconds – minutes)

Multivariable and constraint control (minutes – hours)

Scheduling(hours – days)

Planning (weeks – months)

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Often a schedule is ”aged” already by the time it is rolled out to the plant floor

• More realistic scheduling decisions by utilizing information from the control layer

• Avoid infeasibilities by understanding the basics of process dynamics

Control typically focus on stability and efficiency and not on the future

• Better control actions knowing longer-term plan e.g. during changeovers

• How to control optimally e.g. during changing energy pricing

Frequent re-schedules e.g. due to market changes may result in very poor control

• Making the time scales to overlap

Digitalization can be expected to lower the integration effort – at least raises the expectations

Mismatch between the two layers

Why Integration of Scheduling and Control?

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Improved productivity (+200%), reduced energy (-30%), & longer product life (+30%)

Industry

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SELECTION

Connected robotsManufacturing execution systems Energy assessment Cybersecurity assessment

Digital simulation for robot deployment

Power quality monitoring & demand-response Distributed control systems

Remote monitoring & optimization

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Mastering the control room

What does it take to win in digital?

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From physical to digital differentiation

Maintenance

Operation

Control

Service action

Set points

Control signals

Plant / equip. health

Operational data

Measurements

PROCESS

Regulatory control(seconds – minutes)

Multivariable and constraint control (minutes – hours)

Scheduling(hours – days)

Planning (weeks – months)

Vertical Integration of Operation Decisions

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Mezoscale interactions

- Overlap in the time scales of production management and process controlmotivates considering the integrated problem

Goal: Mechanisms for synchronizing production scheduling with the control system, accounting for dynamics

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Main Challenge

BENEFITS• Scheduling: become aware of process state/dynamics• Supervisory Control: become aware of future changes in production;

improved response• Rescheduling

-

ProcessSupervisory controller

Scheduling

outputs

y

inputs

u

setpoints/targets

ysp

+

process state for rescheduling

schedule for predicting

Identify computationally tractable, scheduling-relevant representations of the process dynamics: - Capture closed-loop behavior and the presence of a controller

Zhuge and Ierapetritou, Ind. Eng. Chem. Res. 51, 8550−8565, 2012. Baldea and Harjunkoski, Comput. Chem. Eng., 71, 377-390, 2014

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Baldea, Harjunkoski, Park, Du., AIChE J., 2015; Du, Park, Harjunkoski, Baldea. Comput. Chem. Eng., 79, 59-69, 2015

Concept 1: Scale-Bridging Model

Scale-Bridging Model: • Capture closed-loop input-output dynamics• Embed in scheduling calculation

Baldea and Harjunkoski, Comput. Chem. Eng., 71, 377-390, 2014

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-

ProcessSupervisory controller

Scheduling

outputs

y

inputs

u

setpoints/targets

ysp

+

process state for rescheduling

schedule for predicting

Scale-Bridging Model

Scheduling

outputs

y

setpoints/targets

ysp

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Concept 2: Explicit MPC

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Conventional MPC• Expensive online computation

Advantages of mp-MPC• Online optimization for fast dynamic • Reduce the computational complexity when integrated with scheduling level

Bemporad, A.; Bozinis, N. A.; Dua, V.; Morari, M.; Pistikopoulos, E. N. Comput. Chem Eng. 8, 301-306, 2000.

On-line Optimization via off-line Parametric Optimization

Concept 3: Fast MPC

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Integration of scheduling and fast MPC

• PWA approximations of nonlinear dynamic, simplify control computation

• Integrated problem incorporating PWA system

• Inner and outer loops for the integration of scheduling and control.

Zhuge, J., Ierapetritou, M. Aiche Journal. 61(10), 3304-3319, 2015. Dias, L. S., Zhuge, J., Ierapetritou, M. Aiche Journal. 62(10), 3822-3823, 2016

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Q

• Dynamic model

• Reaction

• State variable x: concentration of R

• Control input u: feed flow Q

• Three products with steady state information and market information

Product u [L/h] x [mol/L]

Demand [kg/h]

Inventory cost [$/kg]

Product price [$/kg]

A 400 0.3032 20 1.8 130

B 1000 0.393 25 2 125

C 2500 0.5 10 1.7 120

Case study: cyclic production SISO CSTR

Flores-Tlacuahuac, A., Grossmann, I. Ind Eng Chem Res, 45, 15, 2006.

Case study: Results

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mp-MPC Fast MPC SBM-based

CPU Time (s) 83 1 5

Optimal sequence A-B-C A-B-C A-B-C

Cycle time 20.29 18.04 18.37

Revenue ($) 79646.44 88886.62 94743.61

Raw material cost ($) 15547.48 16405.73 18772.19

Inventory cost ($) 6214.34 5468.120 8241.69

Profit ($) 57884.61 67012.77 67729.72

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Case study: dynamic profiles

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Make the right decisions in a competitive environment

Plant Operations must be adapted

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Closing control loops for industrial processes

Industrial Automation high-level value proposition

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Operations loop Asset loop

Asset analytics(model-based/

data-based)

ServiceSensing (asset status)

Real-time control

(DCS/PLC)

Actuation(e.g. motors, drives, valves,

azipods)

Sensing (e.g. instru-

ments, analyzers)

Dependencies(e.g. production

scheduling)

Example case: Boliden Garpenberg mine

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Case: Boliden Garpenberg Value drivers

Customer situation:Sweden’s oldest mine needed to expand and modernizeNeeded to reduce operating costs and increase effective use of geological resourcesUnderground mine operates 24/7

Result

Volume production

Uptime

Time to repair

Energy

Health

People productivity

Revenue

Opex

Volume production

Uptime

Time to repair

Energy

Health

People productivity

Revenue

Opex

Increased throughput --milled ore tonnage rose about 60 percent to 2.22 million tons

Instant access to information for equipment troubleshooting and maintenance

Production costs per ton decreased with lower energy consumption, water use

ABB solution: Integrated automation platform to control powerful mill drives, hoists, electrical systems, power management, motors, and ventilation systemRemote services and monitoring

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Closed-loop short term scheduling

The scheduling process at ABB

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Not trivial to estimate and calculate the true value…

Value Driver Breakdown

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Free cash flow

Produced units

Reduce COGS & OpEx

Availability

Productivity

Quality

Material

Productivity

Energy

Uptime

Time to repair

Parts produced

Idle time

Operating time

Defective units

Lifecycle cost

Oper. risk

Sales Price

Working cap

CapEx

Inventory

Flexibility

Sales

Real estate

Time to market

Accounts R/P

Product attractiveness

Equipment

PersonnelHealth

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• Finding the best methodological approaches and understanding their pros and cons

• Development of systematic & general approach for deriving scheduling-relevant low order process models

• Breaking the formal silos between scheduling and control to enable full data exchange

• Close the scheduling loop

• Implement feedback mechanisms for rescheduling in the presence of process faults/disturbances

• Proving the value of integration in practice – a challenge even on the theoretical level

• Basically a problem in any optimization within operations

• Define meaningful “Tennessee Eastman”-like benchmark problems

• Ensuring that the right people are working together towards a common and well understood goal

• …

• Can big data analytics and machines learning provide a glue between the layers?

True industrial success stories still missing

Perspectives and challenges

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NORDIC PROCESS CONTROL WORKSHOP, TURKU, 18.1.2018

State of the art of integration of scheduling and control – remaining challengesIiro Harjunkoski, Aalto University / ABB Corporate Research Germany