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 Application of 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.