Robust Control of Heat Exchangers
Vasickaninová, A.
Bakošová, M.
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How to Cite

Vasickaninová A., Bakošová M., 2012, Robust Control of Heat Exchangers, Chemical Engineering Transactions, 29, 1363-1368.
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Abstract

This work deals with the design and application of a neuro-fuzzy controller to a heat exchanger and with possibilities to use the coefficient diagram method for heat exchanger control. The heat exchanger is a tubular one and it is used for pre-heating of kerosene by hot water. The heat exchanger can be represented as a system with interval parametric uncertainty.
Fuzzy logic control has emerged as one of the most fruitful areas in fuzzy set theory, and many practical applications in both industry and household appliances, as well as studies on the theory itself, have been reported in many works.
Coefficient Diagram Method gives control systems that are very stable and robust, system responses without overshoot and very small settling time. The controller design by coefficient diagram method is based on the choice of the coefficients of the characteristic polynomial of the closed loop system according to the convenient performance criteria such as equivalent time constant, stability indices, and stability limits.
Most processes are nonlinear, and their control is a difficult yet important problem. The heat exchanger is an example one such nonlinear process. In the presented paper, the performance of set point tracking and disturbance rejection in two controller methods is investigated. Initially, the third order plus dead time model of the process was obtained. Then, the neuro-fuzzy controller and controller using the coefficient diagram method were designed. Finally, the performances of the two controllers are compared.
The simulations of control were done in Matlab/Simulink environment. The presented experimental results show applicability of mentioned approaches to safer control of nonlinear process. The control response obtained by CDM controller has smaller overshoots. On the other side, the use of the neuro-fuzzy controller led to smaller consumption of the heating medium.
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