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Refrigeration system synthesis based on de-redundant model by particle swarm optimization algorithm

Chinese Journal of Chemical Engineering 2022年第10期50卷 412-422页

作者： Danlei Chen Yiqing Luo Xigang Yuan School of Chemical Engineering and Technology Tianjin UniversityTianjin 300072China State Key Laboratory of Chemical Engineering Tianjin UniversityTianjin 300072China

Simultaneous optimization of refrigeration system(RS)and its heat exchanger network(HEN)leads to a large-scale non-convex mixed-integer non-linear programming(MINLP)***,researchers usually adopted simplifications to confine problem scale from being too large at the cost of reducing solution *** study established an optimization framework for the simultaneous optimization of RS and ***,A more comprehensive and compact model was developed to guarantee a relatively complete solution space while reducing model scale as well as its solving *** this model,a tandem arrangement of connecting sub-coolers and expansion valves was considered in the superstructure;and the pressure/temperature levels were optimized as continuous *** this basis,we proposed a"two-step transformation method"to equivalently transform the cross-level structure into a no n-cross-level structu re,and the de-redundant superstructu re was established with ensuring comprehensiveness and ***,the MINLP model was developed and solved by particle Swarm Optimization ***,our methodology was validated to get better optimal results with less CPU time in two case studies,an ethylene RS in an existing plant and a reported propylene RS.

关键词： Refrigeration system Optimal design Process systems particle swarmoptimization Mathematical modeling

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PV Power Short-Term Forecasting Model Based on the Data Gathered from Monitoring Network

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China Communications 2014年第A02期11卷 61-69页

作者： ZHONG Zhifeng TAN Jianjun ZHANG Tianjin ZHU Linlin School of Computer and Information Engineering Hubei University Wuhan 430062 Hubei Province P. R. China School of Information Engineering Hubei Minzu University Enshi 45000 Hubei Province P. R. China

The degree of accuracy in predicting the photovoltaic power generation plays an important role in appropriate allocations and economic operations of the power plants based on the generating capacity data gathered from the geographically separated photovoltaic plants through network. In this paper, a forecasting model is designed with an optimization algorithm which is developed with the combination of PSO （particle Swarm Optimization） and BP （Back Propagation） neural network. The proposed model is further validated and the experiment results show that the predication model assures the prediction accuracy regardless the day type transitions and other relevant factors, in the proposed model, the prediction error rate is worth less than 20% in all different climatic conditions and most of the prediction error accuracy is less than 10% in sunny day, and whose precision satisfies the management requirements of the power grid companies, reflecting the significance of the proposed model in engineering applications.

关键词： grid-connected PV plant short-termpower generation prediction particle swarmoptimization BP neural network

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