Next Generation MPC Where is Technology Headed?

Transcription

1 Next Generation MPC Where is Technology Headed? Alex Kalafatis Michael Harmse John Campbell AspenTech Product Management 8 th CPC Conference January 10 th, 2017

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. 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. 2

3 Aspen MPC Innovation Timeline Early 90s Late 90s Early 2000 s Now Increase Process Profitability with MPC DMCplus Maintain the Benefits of MPC Aspen Watch etc. Integrated Workflow Implement More Efficiently Aspen SmartStep etc. Automation of knowledge DMC3 3

4 Aspen MPC Innovation Timeline Performance Monitoring Inferential Modeling Colinearity Detection and Repair Nonlinear Control Nonlinear Inferential Modeling Automated Testing State Space Controller Modern Integrated Platform Multivariable Step Testing 2008 Nonlinear MPC in the Builder DMCplus Engine Enhancements Adaptive Modeling 2009 DMCplus in the Platform Composite in the Platform 2010 Calibrate Mode Model Quality Analysis MQA Auto Slicing Auto ID Maintain Benefits and implement more efficiently 2013 Structured Model ID Robust Control Smart Tune DMC3 Builder 2016 DMC3 Automation of knowledge 90s DMCplus 4 80s [DMC] Increase Process Profitability with Aspen MPC

5 Automation of Knowledge Work The McKinsey Global Institute (MGI) identified Automation of Knowledge Work as the 2 nd 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) 5

6 Automation of Manufacturing Workflows in Aspen MPC DMC3: 3 rd Generation DMCplus Role Knowledge Work Activity Control/Process Engineer 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 6

7 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 H f (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 7

8 Genealogy of Aspen Identification Technology 2014 Constrained Subspace ID** 1998 DMI (FIR ID) DMCplus DMCplus 4.1 Subspace ID Smart-Step* 2004 DMCplus 6.0 Subspace ID Model Uncertainty Multi-Step* BDN Nonlinear* 2012 Adaptive MPC* Closed-loop Subspace ID Auto-data slicing* Calibrate** Model Grade Model Structure carry-over** * US Patent grated ** US Patent Pending 8

9 Identification Workflow Data 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 9

10 Traditional MPC Maintenance Challenges Requires 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 Model Excel Simulations ONLINE - Manual reactive MPC maintenance - Increased technical and operational staff workload - Revamps are done every 4-5 years 10

11 Reactive vs Preventive Maintenance Benefits Typical Refinery Process Unit e.g. Crude Unit $10m 11

12 Calibrate: 3 rd Generation of Automated Step Testing Optimizing Control while testing Non-Invasive Approach The engineer decides the trade-off between optimal control and step testing No operator intervention Test Control 12

13 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 13

14 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 14

15 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 15

16 Traditional vs Calibrate Workflow Project Start Pretest Test Modeling Commissioning Monitoring Troubleshooting PID loop tuning Instrumentation review Design Supervised step testing Data collecting Data slicing Identification Open & closed loop testing Tuning Plant/model mismatch Additional constrains Correct mismatch Add constrains Fine tuning 3-4 months Start generating MPC benefits after 3-4 months Pretest Test (Calibrate mode) and monitoring Modeling Comm. 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 Open & identificat closed ion loop testing Fine Tuning 3-4 weeks Start generating MPC benefits EARLY! 16

17 3 rd Generation of APC: Enables MPC Best Practices Optimizing 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 Ease of use, intuitive modeling workflows including LP/QP tuning Performance management across the whole enterprise MPC applications across all processes including secondary units Low Cost of Ownership and maintenance 17

18 Empowering Users Self-sufficiency Across the MPC Lifecycle Simplify usability Rapid Development Preventive Maintenance and Embedded workflows and automated tools & wizards Optimizing while testing High Fidelity Models Automation of knowledge 18

19 MPC Remote Monitoring Aspen Technology, Inc. All All rights reserved.

20 MPC Challenges: Chevron Upstream Presentation 20

21 MPC Remote Monitoring: Chevron Presentation 21

22 Open Standards for Embedding Automation in Distributed Control Systems Aspen Technology, Inc. All All rights reserved.

23 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

24 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 onpremise 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

25 Advanced Inferentials - Advanced Analytics Aspen Technology, Inc. All All rights reserved.

26 Analytics Value Matrix 26

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28 Column Analytics Runtime View: Early Detection of Probable Flooding Event Increase set point control of Demethanizer bottom stream Incipient Flood risk increased 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 Probability of Event Occurring Time to Event in minutes 28

29 Prescriptive Analytics Ensemble model for Column Analytics Feed Estimator 1 st Principle Model Rules Alerts Column Hydraulics Data Conditioning Empirical Model Historian Root Cause Model Pattern Model 29

30 Prescriptive Maintenance Symptom Analysis Diagnosis Consideration of Treatments Prescription for Action 30

31 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 31

32 Many Agents per Asset Anomaly Agent Knows all learned patterns matching all normal operating states Failure Agent 001 Knows precise signature of patterns leading to bearing failure Failure Agent 002 Knows precise signature of patterns leading to drive coupling failure Failure Agent 003+ Many other agents assigned to detect exact failure patterns 32

33 Sensors On & Around Equipment Equipment Boundary Upstream Process Sensors 33 Sensors on and around machines ensure agents alert root cause of issues Process Sensors e.g. Press, Temp, Level, Flow Machine Sensors e.g., Vibrations

34 Advanced Analytics Synergy with MPC Pattern Matching Data mining Data autoslicing Collaboration work with Prof. Eamonn Keogh, University of California 34

35 Batch APC Aspen Technology, Inc. All All rights reserved.

36 Partial Least-Squares for Batch-wise Unfolded Data Vision 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 36

37 Batch Alignment Vision 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 37

38 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% 38

39 Active Areas of Research - Synergies with Academia Bringing Research to Practice and Practice to Research Aspen Technology, Inc. All All rights reserved.

40 Bringing Research to Practice and Practice to Research Areas for Potential Collaboration Vision 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 40