Next Generation MPC – Where is Technology Headed?focapo-cpc.org/pdf/Kalafatis.pdf• Many...

Next Generation MPC – Where is Technology Headed?Alex Kalafatis Michael HarmseJohn Campbell

AspenTech Product Management

8th CPC ConferenceJanuary 10th, 2017

© 2017 Aspen Technology, Inc. All rights reserved.2

AspenTech Disclaimer

Aspen Technology may provide information regarding possible future product developments including new products, product features, product interfaces, integration, design, architecture, etc. that may be represented as “product roadmaps or product visions”.

Any such information is for discussion purposes only and does not constitute a commitment by Aspen Technology to do or deliver anything in these product roadmaps or otherwise.

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Aspen MPC Innovation Timeline

Increase Process Profitability with MPC – DMCplus

Maintain the Benefits of MPC – Aspen Watch etc.

Implement More Efficiently –Aspen SmartStep etc.

Early ‘90s Late ‘90s Early 2000’s Now

Integrated Workflow

Automation of knowledge DMC3


Calibrate Mode Model Quality

Analysis – MQA Auto Slicing Auto ID

Aspen MPC Innovation Timeline

[DMC]

Performance MonitoringInferential Modeling

DMCplus

NonlinearInferentialModeling

80s

90s

1998

2000

2002 AutomatedTesting

2003

NonlinearControl

Colinearity Detectionand Repair

ModernIntegratedPlatform

Adaptive Modeling

DMCplus EngineEnhancements

MultivariableStep Testing

State Space Controller

Structured Model ID Robust Control Smart Tune DMC3 BuilderComposite in

the Platform

Nonlinear MPCin the Builder

DMCplus inthe Platform

20052006

20082009 2010

20132016DMC3

Increase Process Profitability with Aspen MPC

Maintain Benefits and implement more efficiently

Automation of knowledge


Automation of Knowledge Work

• The McKinsey Global Institute (MGI) identified “Automation of Knowledge Work” as the 2nd most impactful disruptive technology area in the 2013 report “Disruptive technologies: Advances that will transform life, business, and the global economy”

• MGI defines automation of knowledge work as “the use of computers to perform tasks that rely on complex analyses, subtle judgments, and creative problem solving.”

• Enabling technologies– computing technology– machine learning– natural user interfaces (such as speech recognition

technology)


Automation of Manufacturing Workflows in Aspen MPC DMC3: 3rd Generation DMCplus

Role Control/Process Engineer

Knowledge Work Activity

Automated model identification/ adaptation and controller configuration

Automated maintenance and update of APC systems

Enabling Technologies

• Automated plant perturbation• Model generation• Controller configuration• Performance monitoring and

assessment• Robust control

Automate complex analysis or workflow

Generate significant customer value

Reduce barrier to adoption or deployment

Assistants (wizards) based on best practices or heuristics


Model Identification: Evolution of Subspace ID Technology

• Subspace ID algorithms appeared in 1990s A set of new identification algorithms emerging since 1990’s Canonical Variate Analysis(CVA), Larimore,1990; Multivariable Output Error State sPace (MOESP), Verhaegen & Dewilde, 1992; Numerical Subspace State Space System Identification (N4SID),Van Overschee and De Moor, 1994;

• Subspace ID developments in closed-loop– Pre-estimate Hf (Shi and MacGregor, 2001; Larimore, 2004)– SIM via PCA (SIMPCA, Wang and Qin, 2002; Huang et al., 2005)– SSARX (Jansson, 2003)– Innovations estimation (Qin and Ljung, 2003)– Whitening filter approach (Chiuso and Picci, 2004, 2005)– Observer-Kalman filter ID (OKID, Juang and Phan, 1994)

• With its many advantages, Subspace ID Technology has matured and is the key for high fidelity DMC models


Genealogy of Aspen Identification Technology

1998DMI (FIR ID)DMCplus 2.0

2001DMCplus 4.1Subspace IDSmart-Step*

2004DMCplus 6.0Subspace IDModel UncertaintyMulti-Step*BDN Nonlinear*

2012Adaptive MPC*Closed-loop Subspace IDAuto-data slicing*Calibrate**

2014Constrained Subspace ID**Model GradeModel Structure carry-over**

* US Patent grated ** US Patent Pending


Identification WorkflowData Management• Collection - Automatic• Preparation: Auto-Slicing

Case Management• Linearization Transforms

- Visual with Optimization capabilities• Trial Creation - wizards• Deadtime Identification• Structure Specification - Constrained ID

Model Creation• Model Assembly – Online/Remote• Conditioning (Colinearity repair)• Uncertainty / Frequency Response Analysis• Model Quality Analysis Dashboard

- Online/Remote


Traditional MPC Maintenance ChallengesRequires costly and disruptive step tests

Difficult to schedule plant tests

Non-intuitive MPC tools and workflows

Significant efforts and skills required to revamp controllers

LP/QP Tuning becomes challenging as number of variables increase

LP/QP Tuning must be updated after model, process or economics change

Iterative and time consuming process - Millions in benefits lost per site

- Low MPC utilization

- Manual reactive MPC maintenance

- Increased technical and operational staff workload

- Revamps are done every 4-5 years

ONLINE

Model Excel Simulations

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&docid=OxZ93kXShm2EdM&tbnid=s1FiOMKxKLc6bM:&ved=0CAUQjRw&url=http://www.engineerlive.com/Process-Engineer/Automation_Control/Solve_the_skills_shortage_and_protect_asset_safety/23491/&ei=vH0tUbL7G6iU2gWco4CYCA&bvm=bv.42965579,d.b2I&psig=AFQjCNEN-apDmO-jfUitFRo1Pql9X0nUpg&ust=1362022157059136

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&frm=1&source=images&cd=&cad=rja&docid=OxZ93kXShm2EdM&tbnid=s1FiOMKxKLc6bM:&ved=0CAUQjRw&url=http://www.engineerlive.com/Process-Engineer/Automation_Control/Solve_the_skills_shortage_and_protect_asset_safety/23491/&ei=vH0tUbL7G6iU2gWco4CYCA&bvm=bv.42965579,d.b2I&psig=AFQjCNEN-apDmO-jfUitFRo1Pql9X0nUpg&ust=1362022157059136


Reactive vs Preventive Maintenance Benefits Typical Refinery Process Unit e.g. Crude Unit

≈ $10m


The engineer decides the trade-off between optimal control and step testing

Non-Invasive Approach

Test Control

Calibrate: 3rd Generation of Automated Step TestingOptimizing Control while testing

No operator intervention


Optimizing Control during Calibrate Testing• Cyan line represents the upper CV limit for LCNYLD

• Operators increased limit to try and get more LCN

• Calibrate followed changes without excessive constraint violations


Calibrate – Preventative Maintenance Continuous improvement: Maintain controllers for maximum benefits

• Maintenance becomes a built-in process not a project– Automated testing in closed loop– Closed-loop Model ID– Auto-slicing– Economic Relaxation– Model Quality Analysis

– Root Cause, Measures progress continuously Online Analysis

Automated Testing

Automated Slicing


DMC3 Automation of Knowledge: Smart Tune

Enhanced optimization algorithm

Eliminate complicated tuning by directly specifying objectives

Tuning wizard to effortlessly configure the optimizer

Provides visual view of the controller strategy online


Traditional vs Calibrate Workflow

•PID loop tuning• Instrumentation review

•Design

•Supervised step testing

•Data collecting

•Data slicing• Identification

•Open & closed loop testing

•Tuning

•Plant/model mismatch

•Additionalconstrains

•Correct mismatch

•Add constrains•Fine tuning

•PID loop tuning• Instrumentation review•Design•Historical data gathering

• Initial strategy and ‘seed’ model

•Automated step testing•Automated data collecting•Automated data slicing•Automated modeling•Process optimization with a robust controller

•Fine identification

•Open & closed loop testing

•Fine Tuning

Pretest Test

Modeling

Commissioning Monitoring Troubleshooting

Test (Calibrate mode) and monitoring Comm.Pretest

Modeling

Start generating MPC benefits after 3-4 months

Start generating MPC benefits EARLY!

Project Start

3-4 months

3-4 weeks


3rd Generation of APC: Enables MPC Best PracticesOptimizing Control during non-disruptive background testing

Self-sustaining solutions and model adaptation to new operating conditions

Preventive maintenance is part of a continuous improvement process

Ability to revamp multiple process units simultaneously

Robust control action in the presence of model mismatch and disturbances

Low Cost of Ownership and maintenance

Performance management across the whole enterprise

Ease of use, intuitive modeling workflows including LP/QP tuning

MPC applications across all processes including secondary units


Empowering UsersSelf-sufficiency Across the MPC Lifecycle

RapidDevelopmentPreventiveMaintenance and

Automation of knowledge

Simplify

usability

Embeddedworkflows andautomated tools & wizards

Optimizingwhile testing

High Fidelity Models

© 2017 Aspen Technology, Inc. All rights reserved.19 © 2017 Aspen Technology, Inc. All rights reserved.19

MPC Remote Monitoring


MPC Challenges: Chevron Upstream Presentation


MPC Remote Monitoring: Chevron Presentation


Open Standards for Embedding Automation in Distributed Control Systems


Impact of IIoT on Distributed Control Systems • IIoT are already having a huge impact on both asset

management and operations management applications. – These include cloud hosting of operations management applications

• Many automation end users are already embracing virtualization technology for the past few years

• Decoupling of applications from process automation systems has started to happen. OPC UA provides the integration between control systems and other applications.

• In parallel, efforts to decouple the DCS process I/O from process controllers and develop more open system architectures will enable independent hardware and software components to interoperate with a minimum of customization.

ARC Report on DCS Global Market Analysis - 2016


Open Process Automation™ Forum

• Consortium’s mandate is to develop a standard architecture for these new open process automation platforms – Similar open architectures have been developed in the

aerospace industry

• To develop a new Operations Platform, i.e. a new type of Level3 OT platform that will be implemented using highly standardized IT-like software and hardware, potentially using an on-premise cloud platform which will make extensive use of virtualization and of open source

• To develop a new electronic (DCN) equipment dedicated to managing a single control loop in Level1 ARC Insights – April 2016


Advanced Inferentials - Advanced Analytics


Analytics Value Matrix


Column AnalyticsRuntime View: Early Detection of Probable Flooding Event

Probable incipient Flood in stripping section in 2 hours. Demethanizer bottoms too rich in methane. Increase set point control of Demethanizer by 2 deg C.

Probable Flood event in C2Splitter in 1 hr time: methane leakage in Demethanizer bottom stream detected

Increase set point control of Demethanizer bottom stream

Incipient Flood risk increased

Time to Event in minutes

Prob

abilit

y of

Eve

nt O

ccur

ring


Prescriptive AnalyticsEnsemble model for Column Analytics

1st Principle Model

Empirical Model

Column Hydraulics

Pattern Model

Historian

Rules

Root Cause Model

Feed Estimator

Alerts

Data Conditioning


Prescriptive Maintenance

Symptom Analysis

Diagnosis

Consideration of Treatments

Prescription for Action


Prescriptive Maintenance by

Autonomous Agents™

Each one interprets every failure signal, every minute of the day …forever – and tells you what will happen, when, and how to avoid it

Agents embed learning, adaptive, big data, predictive, and prescriptive action


Many Agents per Asset

Failure Agent 001Knows precise signature of patterns

leading to bearing failure

Anomaly AgentKnows all learned patterns

matching all normal operating states

Failure Agent 002Knows precise signature of patterns leading

to drive coupling failure Failure Agent 003+Many other agents assigned to detect

exact failure patterns


Process Sensorse.g. Press, Temp, Level,

Flow…

Equipment Boundary

Machine Sensorse.g., Vibrations

Process Sensors Machine Sensorse.g., Vibrations

Upstream Process Sensors

Sensors “on and around machines”ensure agents alert root cause of issues

Sensors On & Around Equipment


Advanced Analytics – Synergy with MPCPattern Matching – Data mining – Data autoslicing

Collaboration work with Prof. Eamonn Keogh, University of California


Batch APC


Partial Least-Squares for Batch-wise Unfolded Data

• Models the time varying covariance structure among all the process variables over the entire time history of the batch– Different model weights on each variable for every time period– Average non-linear trajectory for each time-varying variable is subtracted off before modeling– Effectively provides a non-linear model of the transient, time-varying batch behavior

• Relates initial conditions (Z) and time-varying trajectory information (X) to final batch outcomes (Y)

• Entire time history summarized by few latent variables in well-conditioned model space

Vision


Batch Alignment

• Need to handle batch-to-batch variation in execution speed for different phases or entire batch

• Various methods are available

• Perform alignment at key decision points for batch control

• Solve the QP problem

Vision


Fortune 100 Specialty Foods Manufacturer

• Over 1 million batches processed

• Close to 100% uptime when sensors maintained

• Standard deviation in key quality attributes reduced by 50%

• Productivity boost of 20%

• Operator involvement reduced by 80%


Active Areas of Research- Synergies with Academia

Bringing Research to Practice and Practice to Research


Bringing Research to Practice and Practice to Research Areas for Potential Collaboration

• Integration of MPC with Advanced Analytics – Machine Learning in developing “seed” models from historical data– Variable & constraint selection for controller design through data mining

• Nonlinear MPC

• Templating and embedding MPC solutions

• Coordination of Scheduling with MPC

• Plant-wide Dynamic Optimization

• Integration of Rigorous Dynamic Models with MPC

• Cloud hosting of Operations Management applications

Vision

Next Generation MPC – Where is Technology Headed?focapo-cpc.org/pdf/Kalafatis.pdf• Many...

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