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.
The topics covered include:
- a fresh look at current systems for process monitoring, control and diagnosis;
- a framework for developing intelligent, state-space-based systems;
- a review of data mining and knowledge discovery;
- data preprocessing for feature extraction, dimension reduction, noise removal and concept formation;
- multivariate statistical analysis for process monitoring and control;
- supervised and unsupervised methods for operational state identification;
- variable causal relationship discovery in graphical models and production rules;
- software sensor design;
- historical data analysis;
Data Mining and Knowledge Discovery for Process Monitoring and Control is important reading for researchers and graduate students in process control and data and knowledge engineering. Control and process engineers should also find this book of value.
