Improving continuous process operation using data analytics delta v application of data analyti
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The document discusses applying data analytics to improve continuous process operation. It describes developing models using process data to enable online fault detection and quality parameter prediction. A field trial was conducted on a CO2 recovery process that uses a 2-stage flash skid. The data analytics models allow operators to more quickly respond to conditions impacting process operation and quality.
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Improving continuous process operation using data analytics delta v application of data analyti
- 1. Improving Continuous Process Operation Using Data Analytics DeltaV Application of Data Analytics
- 15. P&ID for 2-Stage Flash Skid
- 18. Selection of Training Data – Semi Rich Amine
- 19. Sensitivity Analysis – Semi-Rich Amine
- 20. Training Model Select State Parameter
- 21. Selection of Model – PCA/PLS/NN
- 23. Fault Detection View – Normal Operation
- 24. Fault Detection - Startup
- 29. Introduction of Process Fault Fault may be introduced
- 30. Model Verification Using Simulator Data
- 31. Feed Temperature Fault Introduced
- 33. Examine Impact on CO2 Loading
Editor's Notes
- Key Success Factors Easy Out-of-Box (base offering) Channel can deliver Eliminates extra system integration Applicable across multiple markets Enhances Operations Experience Makes life easier for operators Less work, fewer incidents Provides insight into process operation System Differentiation & Devices Pull-through Delivers PlantWeb promise to reduce variability and increase availability Incorporates intelligent device information Technology Differentiation Batch Analytics
- 1. If a fault is indicated in the analytics overview screen, then selecting the batch number will bring up the Fault Detection view. 2. If either Fault Detection plot exceeds or approaches the upper control limit of 1.0, click on that point in the trend and -> Select the Parameter in the lower corner of the screen that contributed to the fault 3a. Evaluate the parameter trends from process operation standpoint -> take corrective action if necessary 3b. Inspect impact of fault on quality prediction plot to find out how quality may be affected