Application of Intelligent Virtual Reference Feedback Tuning to Temperature Control in a Heat Exchanger
Heat exchangers are frequently incorporated in industrial processes. Temperature control in heat exchangers is very important for the safety and economic benefits of the industrial system. However, it is still challenging to control the temperature of heat exchangers because of the complicated thermal dynamic phenomena. In this paper, an improved data-driven control method, i.e. intelligent virtual reference feedback tuning based on multi-population cooperative human learning optimization (IVFRT-MCHLO), is developed to design the optimal controller for a water heat exchanger. The controller is designed based on IVRFT method and a novel multi-population cooperative human learning optimization (MCHLO) algorithm is proposed to find out the optimal controller. The experimental results demonstrate that the proposed IVFRT-MCHLO has better control performance as the multi-population cooperation strategy of MCHLO improves the global search ability greatly.
KeywordsHeat exchanger Temperature control Human learning optimization Intelligent virtual reference feedback tuning Data-driven
This work is supported by National Natural Science Foundation of China (Grant No. 61633016 & 61703262), Key Project of Science and Technology Commission of Shanghai Municipality under Grant No. 16010500300 and 15220710400, Shanghai Sailing Program under Grant No. 16YF1403700, and Natural Science Foundation of Shanghai (No. 18ZR1415100).
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