Jiaxing Chen

Multipopulation differential evolution algorithm based on the opposition-based learning for heat exchanger network synthesis

ABSTRACT Multipopulation differential evolution combined with opposition-based learning is developed to improve the convergence efficiency and optimization accuracy for heat exchanger network synthesis. The algorithm is based on a stagewise superstructure simultaneous optimization model without considering stream splitting. The candidate population and its opposite population are searched in parallel. Mutation operations are implemented on both populations to provide a full information exchange among populations at each generation. A regrouping schedule is introduced to avoid premature convergence. The algorithm is applied to five heat exchanger network cases of different sizes. More economic networks are found using this method with less computational time.

Improved model control strategy with dynamic adaptation for heat exchangers in energy system

ABSTRACT We propose a novel model control with dynamic adaptation to improve the control accuracy and speed of heat exchangers. The proposed method first applies an accurate mathematical model of heat exchanger as a motion model to create the inverse problem algorithm of manipulated variable. Subsequently, it improves the transient performance using a dynamic adaption of manipulated variable. Compared the control performance to feedback control method, the dynamic adaptation of manipulated variable fed in an exponential function resulted in both approximately a 100% reduction in overshoot and settling time elimination in response to inlet temperature and flow rate disturbances of objective fluid.