Abstract
Abstract To solve the problem of multi-objective performance optimization based on ant colony algorithm, a multi-objective performance optimization method of ORC cycle based on an improved ant colony algorithm is proposed. Through the analysis of the ORC cycle system, the thermodynamic model of the ORC system is constructed. Based on the first law of thermodynamics and the second law of thermodynamics, the ORC system evaluation model is established in a MATLAB environment. The sensitivity analysis of the system is carried out by using the system performance evaluation index, and the optimal working parameter combination is obtained. The ant colony algorithm is used to optimize the performance of the ORC system and obtain the optimal solution. Experimental results show that the proposed multi-objective performance optimization method based on the ant colony algorithm for the ORC cycle needs a shorter optimization time and has a higher optimization efficiency.
Highlights
IntroductionIn the literature [5], a multi-objective optimization method for a traction power supply system based on improved particle swarm optimization algorithm is proposed [5]
An Organic Rankine Cycle (ORC) uses organic matter with a low boiling point and high evaporation pressure as theIn the literature [5], a multi-objective optimization method for a traction power supply system based on improved particle swarm optimization algorithm is proposed [5]
The multi-objective performance optimization method of ORC cycle based on the improved ant colony algorithm is compared with the multi-objective optimization method of the traction power supply system based on improved particle swarm optimization and the thermal performance optimization method based on low-temperature geothermal cascade split-flow organic Rankine cycle system
Summary
In the literature [5], a multi-objective optimization method for a traction power supply system based on improved particle swarm optimization algorithm is proposed [5]. A simulation method of vehicle network coupling system model and load flow distribution is used. A multi-objective optimization model for the traction power supply system is constructed with the main design principle as the constraint and the total capacity of the whole line and the minimum average power loss as the objectives. In order to improve the global search ability and convergence speed in the process of solving the model, a chaotic multi-objective particle swarm optimization algorithm based on Pareto entropy is designed. A fuzzy membership function is used to calculate the satisfaction degree of each objective function in the Pareto solution set to determine the final system optimization scheme. In the literature [6], a
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