14.5 Review/Preview

The architecture for achieving adaptive control set forth in Chapter 13 was applied to a different chemical system. It was used to maintain the temperature in a system in which an exothermic chemical reaction was taking place. The adaptive controller is based on three specific elements: (1) a control element, (2) an analysis element, and (3) a learning element.

The control element consists of an FC and is responsible for manipulating the hexamine system. The analysis element recognizes when and to what extent the hexamine system’s process dynamics change. The analysis element employs a computer model of the hexamine system and a GA. The learning element uses the results of the analysis element to update the control strategy of the control element. The learning element employs a fuzzy controller and a genetic algorithm.

The results provided in this Chapter suggest that the approach to adaptive control introduced in Chapter 13 is robust. Further, the results presented here demonstrate the capability of the approach to incorporate additional factors into the adaptation of the control system. The multi-purpose objective considered here is similar to those generally considered in industry. Furthermore, few traditional control systems are able to simultaneously consider numerous control objectives.

This chapter and the one immediately before it were used to introduce the idea of adaptive process control. In the next chapter we will present an application of the adaptive control system to a process from the minerals industry. Then, in the remainder of the book we will consider facets of the GA-FC combination that we have not yet addressed in this book.

References

Fogler, H. S. Elements of Chemical Reaction Engineering. Prentice-Hall, Englewood Cliffs, NJ (1986).

Goldberg, D. E. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA (1989).

Holland, J. H. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, MI (1975).

Karr, C. L. Analysis and Optimization of an Air-Injected Hydrocyclone, PhD Thesis, The University of Alabama, Tuscaloosa, AL (1989).

Karr, C. L. Genetic algorithms for fuzzy logic controllers. AI Expert, 6, (2), 26–33 (1991).

Karr, C. L. and Gentry, E. J. Real-time pH control using fuzzy logic and genetic algorithms. Proceedings of the Annual Meeting of the SME (preprint number 92-49), Phoenix, AZ.

Kelly, E. G., and Spottiswood, D. J. Introduction to Mineral Processing. John Wiley & Sons, New York (1982).

Kermode, R. I., and Stevens, W. F. Experimental verification of the mathematical model for a continuous stirred-tank reactor. The Canadian Journal of Chemical Engineering, April, 68–72 (1965).