Adaptive process control using biological paradigms

• Published in: Journal of network and computer applications Vol. 19; no. 1; pp. 21 - 44
• Main Author: Karr, Charles L.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 1996
• ISSN: 1084-8045; 1095-8592
• DOI: 10.1006/jnca.1996.0004

Researchers at the US Bureau of Mines have combined several biologically oriented techniques into a comprehensive approach to adaptive process control. The three specific techniques from the field of artificial intelligence used to produce the adaptive process control systems are: (1) fuzzy logic, (2) genetic algorithms, and (3) neural networks. Fuzzy logic is a technique in which the human «rule-of-thumb» approach to decision making is modelled. Genetic algorithms are search algorithms based on the mechanics of natural genetics that are able to rapidly locate near-optimum solutions to difficult problems. Neural networks are crude paradigms of the mammalian brain that have been used to model industrial systems. The paper provides an overview of the architecture used to achieve adaptive process control, and demonstrates its effectiveness in the control of three industrial systems: (1) a titration system, (2) an exothermic chemical reaction, and (3) a column flotation unit.