Next Generation MPC – Where is Technology Headed?Alex Kalafatis Michael HarmseJohn Campbell
AspenTech Product Management
8th CPC ConferenceJanuary 10th, 2017
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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.
Any such commitment must be explicitly set forth in a written contract between the customer and Aspen Technology, executed by an authorized officer of each company.
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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
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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
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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)
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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
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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
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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
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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
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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
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Reactive vs Preventive Maintenance Benefits Typical Refinery Process Unit e.g. Crude Unit
≈ $10m
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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
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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
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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
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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
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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
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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
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Empowering UsersSelf-sufficiency Across the MPC Lifecycle
RapidDevelopmentPreventiveMaintenance and
Automation of knowledge
Simplify
usability
Embeddedworkflows andautomated tools & wizards
Optimizingwhile testing
High Fidelity Models
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MPC Remote Monitoring
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MPC Challenges: Chevron Upstream Presentation
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MPC Remote Monitoring: Chevron Presentation
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Open Standards for Embedding Automation in Distributed Control Systems
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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
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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
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Advanced Inferentials - Advanced Analytics
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Analytics Value Matrix
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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
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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
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Prescriptive Maintenance
Symptom Analysis
Diagnosis
Consideration of Treatments
Prescription for Action
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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
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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
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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
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Advanced Analytics – Synergy with MPCPattern Matching – Data mining – Data autoslicing
Collaboration work with Prof. Eamonn Keogh, University of California
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Batch APC
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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
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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
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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%
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Active Areas of Research- Synergies with Academia
Bringing Research to Practice and Practice to Research
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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