Online Plant Data Validation and Reconciliation (DVR)

Day 1: Introduction to Data Validation and Reconciliation (DVR)

• Importance of Accurate Plant Data

• Overview of DVR Concepts and Applications

• Types of Measurement Errors: Random, Gross, and Systematic

• Overview of Process Modeling and Constraints

• Standards and Practices (ISO, ISA, and industry frameworks)

Day 2: Fundamentals of Process Modeling for DVR

• Building Process Models for DVR Applications

• Mass and Energy Balance Principles

• Sensor and Measurement Network Design

• Introduction to Constraint Equations

• Redundancy and Observability Analysis

• Hands-On Lab: Creating a Simple DVR Model

Day 3: Reconciliation Algorithms and Error Detection

• Mathematical Methods Used in DVR (Least Squares Estimation)

• Error Detection and Data Filtering Techniques

• Hypothesis Testing for Gross Error Detection

• Confidence Intervals and Data Quality Indicators

• Validation of Reconciled Data

• Case Study: Data Reconciliation in a Refinery Heat Exchanger Network

Day 4: Software Tools, Integration, and Real-Time DVR

• DVR Software Platforms (e.g., Aspen Plus, Sigmafine, AVEVA)

• Configuration of DVR Systems in Online Mode

• Integration with DCS, SCADA, and Data Historians

• Using DVR Outputs for KPI Monitoring and Reporting

• Interfacing DVR with Digital Twins and AI Analytics

Day 5: Implementation Strategy and Performance Monitoring

• Best Practices for Deploying DVR in Operational Plants

• Project Planning: From Pilot to Full Rollout

• Change Management and Operator Engagement

• Monitoring the Performance of DVR Systems

• Final Assessment: DVR Model Development and Presentation

• Certification, Course Wrap-Up, and Action Planning