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Manage Crew Change Through Coupling of First Principles Simulation, Digital Platforms, AI and ML

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Manage Crew Change Through Coupling of First Principles Simulation, Digital Platforms, AI and ML

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Simulation programs have historically been run on PCs as fully self-contained programs. The UI, calculation codes and calculations themselves have all been part of the same program on the PC itself, perhaps reading and writing against databases, file stores on the network or reading process data from plant information management systems. But the computing world is changing and many programs are now hosted in the Cloud. Building and running simulation cases is done through a web browser and calculations run without UIs in wrappers called Containers, accessed through web interfaces. Data lives in cloud data stores. The immense computer power of the Cloud and ability to multi-thread simulation technology for parallel processing has opened up the potential to run tens of thousands of simulations, with hundreds at a time, over short time periods in an automated manner – in doing so creating unprecedented levels of codified asset knowledge able to be applied at scale. This presentation will showcase the huge potential for AI/ML algorithms to be utilized and scaled more widely when coupled/trained by first principles simulation, with major benefits in how simulation is used in: Automated model building, maintenance and continuous deviation analysis; Integrated reliability and predictive equipment availability with planning and optimization; and Equipment fouling propensity predictions.

Simulation programs have historically been run on PCs as fully self-contained programs. The UI, calculation codes and calculations themselves have all been part of the same program on the PC itself, perhaps reading and writing against databases, file stores on the network or reading process data from plant information management systems. But the computing world is changing and many programs are now hosted in the Cloud. Building and running simulation cases is done through a web browser and calculations run without UIs in wrappers called Containers, accessed through web interfaces. Data lives in cloud data stores. The immense computer power of the Cloud and ability to multi-thread simulation technology for parallel processing has opened up the potential to run tens of thousands of simulations, with hundreds at a time, over short time periods in an automated manner – in doing so creating unprecedented levels of codified asset knowledge able to be applied at scale. This presentation will showcase the huge potential for AI/ML algorithms to be utilized and scaled more widely when coupled/trained by first principles simulation, with major benefits in how simulation is used in: Automated model building, maintenance and continuous deviation analysis; Integrated reliability and predictive equipment availability with planning and optimization; and Equipment fouling propensity predictions.

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Manage Crew Change Through Coupling of First Principles Simulation, Digital Platforms, AI and ML

  1. 1. Mike Aylott Chief Technology Officer KBC, A Yokogawa Company November 11, 2020 Manage Crew Change through coupling of First Principles Simulation, Digital Platforms, AI and ML
  2. 2. TOPICS How First Principles Simulation powers Operations Planning Challenges Digitalization Opportunity Summary 1 2 3 4
  3. 3. 1 month 1 day Current Time 1 to 15 days 1 to 3 months 1 year 2 to 10 years Strategy Supply Chain Optimization Asset Optimization Process Simulation Supply Chain Management Production Accounting Variance Analysis Supply Chain Scheduling Production Scheduling Production Planning Corporate Business Plans Strategic Plan Fiscal Targets Feed and Inventory Plan Model Update Operating Plan Actual Production Operating Targets Supply Chain Planning Investment Planning Annual Planning Process & Utility Optimization Profit Improvement Process Unit Management AP C Debottlenecking Enterprise Data Integrated Cloud Solution with AI Integration Model Update Feasibility Study Process and Off-sites Automation Procedural Automation Digital Value Chain and Asset Optimization Actual Operation Schedule Process Designs
  4. 4. Today’s Challenges ▪ Limited integration between tools ▪ Linear models have limited validity ▪ Plan does not reflect logistic constraints ▪ Sub optimal schedule ▪ Updating models is time-consuming and SME dependent ▪ Largely heuristic data reconciliation ▪ Data siloed, poor quality ▪ Slow recognition of opportunities to open constraints ▪ Control strategies not updated with schedule changes
  5. 5. Collect & Validate Data Deploy Validate & tune responses Update linked and surrogate models Build or update model Calibrate against data Specific challenges of first principle models ▪ All models have strengths and weaknesses ◆ Time-consuming to build ◆ Data quality dependant ▪ All models need maintenance ◆ Situations change: models have to keep track ◆ Feedstocks, operating scenarios, unit & catalyst performance ▪ Very dependent on subject matter expertise which risks being lost ◆ Updates consequently infrequent
  6. 6. Improving through Digitalization
  7. 7. Why is DX Now Different From The Past? ▪ New platform technologies create new opportunities ◆ Cloud-based with more flexible storage, elastic/scalable compute power, flexible workflow tools leads to more streamlined automation ◆ Greater insight from big data analysis atop data lakes ◆ AI Engine maturity allows ML to supplement first principle models ▪ Bigger toolbox supports agility to respond to change and enables stronger value creation ◆ Helps meet Energy Transition challenges ◆ Helps responsiveness to market demand changes ◆ Helps drive toward leaner organizations
  8. 8. ▪ Technology ◆ Migrating all applications to plug into cloud platforms ◆ Expanding capability using AI/ML ◆ Cloud and application agnostic approach ▪ Consulting ◆ Re-tooling/imagining consulting solutions to enable agile value creation ▪ Part of longer-term drive toward Industrial Autonomy Our Response to Digitalization Drivers
  9. 9. ▪ Data driven, automated identification of optimization using AI ▪ Intelligent, automated work processes ▪ Automated data management and integration using semantics ▪ Cloud enabled to facilitate: • Scalability • Collaboration • Rapid enhancements • Knowledge management • Integration • Visualization • IIoT Digital Future
  10. 10. Using Simulation with AI Use first principles and production accounting models to reconcile data Use synthetic data from calibrated simulation models to drive Machine Learning (ML) models for planning, scheduling and optimization Use refinery-wide simulation models to rank improvement ideas generated from knowledge bases Use simulation models to support broader-scale AI operations tools Automate reporting actuals vs. simulated vs. plan vs. schedule vs. optimum Use AI to analyze KPIs and deviations over time for improvement opportunities and when to regenerate models
  11. 11. Summary
  12. 12. ▪ COVID-19 & Energy Transition coupled with crew change are wake-up calls for our industry Taking The Plunge ▪ Our simulation technologies are up for the challenge ◆ Requires we become more responsive which impacts processes ◆ Requires we capture knowledge and retool as staffs change ▪ Digitalization methods & technologies are enablers ◆ Allows margin improvements when market permits ◆ Provides adjustable information flows when change is needed ◆ Supports automation for leaner organizations ◆ Initial cloud versions becoming available now
  13. 13. The names of corporations, organizations, products and logos herein are either registered trademarks or trademarks of Yokogawa Electric Corporation and their respective holders. The Time is Now Today’s Digitalization technologies provide enablers to modernize and streamline value chain optimization and first principles simulation.

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