Optimization of Chinese coal-fired power plants for cleaner production using Bayesian network
Optimization of Chinese coal-fired power plants for cleaner production using Bayesian network
复制标题
DOI：
10.1016/j.jclepro.2020.122837
复制
发表时间：
2020-11
影响因子：
11.1
通讯作者：
Xuan Zhao;Benhong Peng;E. Elahi;Chaoyu Zheng;Anxia Wan
中科院分区：
文献类型：
--
作者：
Xuan Zhao;Benhong Peng;E. Elahi;Chaoyu Zheng;Anxia Wan
近年来，人们越来越关注清洁生产转型对工业企业的影响。本研究评估了每一过程的效率，使企业家能够选择以前在行业中执行CP转换的整个过程的可选过程。利用贝叶斯网络对CP改造各步骤的投资风险(时间和成本)进行排序。利用一种过程模拟的方法--设计结构矩阵(DSM)对燃煤电厂案例研究的数据集进行了模拟，为贝叶斯网络学习做准备。流程模拟是基于专家和经验得出的每个CP转换步骤之间的关系。本研究为行业企业家安排CP转型步骤提供了理论指导和可操作的方法，并允许他们在与预算不一致的情况下将整个过程分成几个阶段。研究结果表明，在燃煤电厂CP改造案例中，高效率优先选择科技类。
Concern over the influence of cleaner production (CP) transformation on the industrial enterprises has increased in recent years. This research evaluated each process efficiency that enabled entrepreneurs to choose optional ones that previously executed the whole procedure in CP transformation in industry. The risk of invest (time and cost) for each step of CP transformation was ranked using Bayesian network. Datasets from case study of coal fired power plants were simulated using a method of procedure simulation, Design Structure Matrix (DSM), which sought to be prepared for Bayesian network learning. The procedure simulation was based on relationships that each CP transformation step has with each other achieved from experts and experiences. The research gave theoretical guidance and an operable method to industrial entrepreneurs for arranging CP transformation steps and allow them to separate the whole procedure into several stages if inconsistent with budget. Findings indicate that the science and technology categories are preferred for high efficiency in the coal fired power plant CP transformation case.