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• W2890734615 abstract "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." @default.
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• W2890734615 date "2018-01-01" @default.
• W2890734615 modified "2023-09-26" @default.
• W2890734615 title "Application of Intelligent Virtual Reference Feedback Tuning to Temperature Control in a Heat Exchanger" @default.
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• W2890734615 doi "https://doi.org/10.1007/978-981-13-2384-3_29" @default.
• W2890734615 hasPublicationYear "2018" @default.
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