Data Driven Methods For Fault Detection And Diagnosis In Chemical Processes A Real World No Fluff Guide To Early Warning Safer Operations Less Downtime And Cost Savings Through Smart Process Monitoring And Data Analytics
UPC:
✔️ handbook on data-driven process monitoring for safer operations
Data Driven Methods For Fault Detection And Diagnosis In Chemical Processes is a practical guide for engineers and data scientists focused on early warning and safer operations in chemical plants. It demonstrates how data-driven process monitoring can reduce downtime and lower costs by catching faults before they escalate.
✅ Softcover reprint of the original 1st ed. 2000
✅ Covers data-driven process monitoring techniques: PCA, Fisher discriminant analysis, PLS, canonical variate analysis
✅ Demonstrates techniques on Tennessee Eastman plant simulator with detailed strengths and weaknesses
✅ Includes classroom homework problems and case studies for hands-on practice
✅ Helps readers select the right technique for a given process application
💡 What is data driven fault detection and diagnosis in chemical processing and why does it matter for plant safety? This handy guide explains the concept and benefits.
- data-driven fault detection and diagnosis basics for chemical processes
- hands-on data analytics for process monitoring and early warning
- guidance on selecting PCA, Fisher discriminant analysis, PLS, canonical variate analysis
- real-world examples using a plant simulator to show outcomes