Data Mining and Knowledge Discovery for Process Monitoring and Control

1 Introduction.- 1.1 Current Approaches to Process Monitoring, Diagnosis and Control.- 1.2 Monitoring Charts for Statistical Quality Control.- 1.3 The Operating Window.- 1.4 State Space Based Process Monitoring and Control.- 1.5 Characteristics of Process Operational Data.- 1.6 System Requirement and Architecture.- 1.7 Outline of the Book.- 2 Data Mining and Knowledge Discovery — an Overview.- 2.1 Definition and Development.- 2.2 The KDD Process.- 2.3 Data Mining Techniques.- 2.4 Feature Selection with Data Mining.- 2.5 Final Remarks and Additional Resources.- 3 Data Pre-processing for Feature Extraction, Dimension Reduction and Concept Formation.- 3.1 Data Pre-processing.- 3.2 Use of Principal Component Analysis.- 3.3 Wavelet Analysis.- 3.4 Episode Approach.- 3.5 Summary.- 4 Multivariate Statistical Analysis for Data Analysis and Statistical Control.- 4.1 PCA for State Identification and Monitoring.- 4.2 Partial Least Squares (PLS).- 4.3 Variable Contribution Plots.- 4.4 Multiblock PCA and PLS.- 4.5 Batch Process Monitoring Using Multiway PCA.- 4.6 Nonlinear PCA.- 4.7 Operational Strategy Development and Product Design — an Industrial Case Study.- 4.8 General Observations.- 5 Supervised Learning for Operational Support.- 5.1 Feedforward Neural Networks.- 5.2 Variable Selection and Feature Extraction for FFNN Inputs.- 5.3 Model Validation and Confidence Bounds.- 5.4 Application of FFNN to Process Fault Diagnosis.- 5.5 Fuzzy Neural Networks.- 5.6 Fuzzy Set Covering Method.- 5.7 Fuzzy Signed Digraphs.- 5.8 Case Studies.- 5.9 General Observations.- 6 Unsupervised Learning for Operational State Identification.- 6.1 Supervised vs. Unsupervised Learning.- 6.2 Adaptive Resonance Theory.- 6.3 A Framework for Integrating Wavelet Feature Extraction and ART2.- 6.4 Applicationof ARTnet to the FCC Process.- 6.5 Bayesian Automatic Classification.- 6.6 Application of AutoClass to the FCC Process.- 6.7 General Comments.- 7 Inductive Learning for Conceptual Clustering and Real-time Process Monitoring.- 7.1 Inductive Learning.- 7.2 IL for Knowledge Discovery from Averaged Data.- 7.3 IL for Conceptual Clustering and Real-time Monitoring.- 7.4 Application to the Refinery MTBE Process.- 7.5 General Review.- 8 Automatic Extraction of Knowledge Rules from Process Operational Data.- 8.1 Rules Generation Using Fuzzy Set Operation.- 8.2 Rules Generation from Neural Networks.- 8.3 Rules Generation Using Rough Set Method.- 8.4 A Fuzzy Neural Network Method for Rules Extraction.- 8.5 Discussion.- 9 Inferential Models and Software Sensors.- 9.1 Feedforward Neural Networks as Software Sensors.- 9.2 A Method for Selection of Training / Test Data and Model Retraining.- 9.3 An Industrial Case Study.- 9.4 Dimension Reduction of Input Variables.- 9.5 Dynamic Neural Networks as Inferential Models.- 9.6 Summary.- 10 Concluding Remarks.- Appendix A The Continuous Stirred Tank Reactor (CSTR).- Appendix B The Residue Fluid Catalytic Cracking (R-FCC) Process.- Appendix C The Methyl Tertiary Butyl Ether (MTBE) Process.- References.