Intelligent Control of Heat Exchangers

摘要

This work deals with the design and application of a neuro-fuzzy controller for a heatrnexchanger. To deal with the problem of parameter adjustment, efficient neuro-fuzzyrnscheme known as the ANFIS (Adaptive Network-based Fuzzy Inference System) can bernused. The ANFIS is a cross between an artificial neural network and a fuzzy inferencernsystem (FIS) and represents Takagi-Sugeno fuzzy model as generalized feedforwardrnneural network, and trains it with plant I/O data, thereby adjusting the parameters of thernantecedent membership functions as well as those of the functional consequents. Thernneuro-fuzzy control of the heat exchanger is compared with classical PID control. Thernsimulation results confirm that fuzzy is one of the possibilities for successful control ofrnheat exchangers. The advantage of this approach is that it is not a linear-model-basedrnstrategy. Comparison of the simulation results obtained using fuzzy and those obtainedrnusing classical PID control demonstrates the effectiveness and superiority of thernproposed approach because of the smaller consumption of the heating medium.