Modern computer-based control systems are able to collect a large amount of information, display it to operators and store it in databases but the interpretation of the data and the subsequent decision making relies mainly on operators with little computer support. This book introduces developments in automatic analysis and interpretation of process-operational data both in real-time and over the operational history, and describes new concepts and methodologies for developing intelligent, state space-based systems for process monitoring, control and diagnosis. The book brings together new methods and algorithms from process monitoring and control, data mining and knowledge discovery, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process monitoring and control systems.

Table of Contents:
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.