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首页> 外文期刊>Journal of information and computational science >Comprehensive Evaluation of Cleaner Production in Thermal Power Plants Using Particle Swarm Optimization Based Least Squares Support Vector Machines
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Comprehensive Evaluation of Cleaner Production in Thermal Power Plants Using Particle Swarm Optimization Based Least Squares Support Vector Machines

机译:基于最小二乘支持向量机的粒子群算法对火电厂清洁生产的综合评价

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摘要

Thermal power plant cleaner production means to consistently apply the overall prevention environmental strategy to electricity production process, which will play a significant role in enhancing the competitiveness of the thermal power plants and achieving sustainable development. On the basis of investigation and analysis of domestic and foreign research profile on cleaner production, according to the characteristics of the thermal power plants cleaner production we construct power plants clean production evaluation index system. In this paper, we put forward the least squares support vector machine algorithm with particle swarm optimization algorithm to determine the optimal parameter combination to achieve the comprehensive evaluation models of cleaner production. Through the comprehensive evaluation of five power plants cleaner production and its comparison with the traditional method of least squares support vector machine, we found that the average relative error is less than 0.285%, which verified the validity and effect of this model when evaluating cleaner production.
机译:火力发电厂的清洁生产意味着在电力生产过程中始终采用整体预防环境战略,这将在提高火力发电厂的竞争力和实现可持续发展方面发挥重要作用。在对国内外清洁生产研究概况进行调查分析的基础上,根据火电厂清洁生产的特点,构建了电厂清洁生产评价指标体系。本文提出了最小二乘支持向量机算法和粒子群算法确定最优参数组合,以实现清洁生产的综合评价模型。通过对五家电厂清洁生产的综合评价,并将其与传统的最小二乘支持向量机方法进行比较,发现平均相对误差小于0.285%,验证了该模型在清洁生产评价中的有效性和有效性。 。

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