%0 Journal Article
%T 基于改进多目标差分进化的蒸汽动力系统碳排放和生产成本优化<br>Optimization of Carbon Emission and Production Cost of Steam Power System Based on Improved Multi-Objective Differential Evolution
%A 吴长华
%A 李泽秋
%J Modeling and Simulation
%P 829-841
%@ 2324-870X
%D 2022
%I Hans Publishing
%R 10.12677/MOS.2022.113077
%X 化工企业生产过程中向空气排放大量温室气体，在双碳背景下，其需兼顾节能降本和减少碳排放两个矛盾的优化目标。本文先通过排放因子法对蒸汽动力系统的碳排放加以评价作为环境指标，再通过产能和耗能设备的运行参数、能源管网内部不同公用工程的产耗平衡，构建多目标优化的MINLP模型，提出一种动态参数多目标差分进化算法(D-MOEA)对MINLP模型进行求解，算法引进参数自适应调整策略，旨在提高算法的全局寻优能力，增强收敛能力。以某乙烯工厂实例为背景，用排放因子法作为碳排放的评价指标，使用动态参数多目标差分进化算法进行运算，结果表明排放因子法建立模型的有效性，以及算法的收敛性和精度更高。<br />
Chemical enterprises emit a large number of greenhouse gases into the air in the production process. Under the background of double carbon, they need to take into account the two contradictory optimization objectives of energy conservation and cost reduction and carbon emission reduction. In this paper, the emission factor method is used to evaluate the carbon emission of steam power system as an environmental index. Then, according to the operation parameters of production capacity and energy-consuming equipment and the production and consumption balance of different utilities in the energy pipe network, a multi-objective optimization MINLP model is established. A dynamic parameter multi-objective differential evolution algorithm (D-MOEA) is proposed to solve the MINLP model. The algorithm introduces a parameter adaptive adjustment strategy to improve the global optimization ability and convergence ability of the algorithm. Taking an example of an ethylene plant as the background, the emission factor method is used as the evaluation index of carbon emission, and the dynamic parameter multi-objective differential evolution algorithm is used for calculation. The results show that the emission factor method is effective in establishing the model, and the convergence and accuracy of the algorithm are higher.
%K 蒸汽动力系统，碳排放，生产成本，MINLP，多目标差分进化算法<br>Steam Power System
%K Carbon Emissions
%K Production Costs
%K MINLP
%K Multi-Objective Differential Evolution Algorithm
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=51687