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云模型-集对分析在生物质气化站危险性评价中的应用

Application of a Cloud Model-Set Pair Analysis in Hazard Assessment for Biomass Gasification Stations.

作者信息

Yan Fang, Xu Kaili

机构信息

School of Resources and Civil engineering, Northeastern University, Shenyang, Liaoning, P. R. China.

出版信息

PLoS One. 2017 Jan 11;12(1):e0170012. doi: 10.1371/journal.pone.0170012. eCollection 2017.

Abstract

Because a biomass gasification station includes various hazard factors, hazard assessment is needed and significant. In this article, the cloud model (CM) is employed to improve set pair analysis (SPA), and a novel hazard assessment method for a biomass gasification station is proposed based on the cloud model-set pair analysis (CM-SPA). In this method, cloud weight is proposed to be the weight of index. In contrast to the index weight of other methods, cloud weight is shown by cloud descriptors; hence, the randomness and fuzziness of cloud weight will make it effective to reflect the linguistic variables of experts. Then, the cloud connection degree (CCD) is proposed to replace the connection degree (CD); the calculation algorithm of CCD is also worked out. By utilizing the CCD, the hazard assessment results are shown by some normal clouds, and the normal clouds are reflected by cloud descriptors; meanwhile, the hazard grade is confirmed by analyzing the cloud descriptors. After that, two biomass gasification stations undergo hazard assessment via CM-SPA and AHP based SPA, respectively. The comparison of assessment results illustrates that the CM-SPA is suitable and effective for the hazard assessment of a biomass gasification station and that CM-SPA will make the assessment results more reasonable and scientific.

摘要

由于生物质气化站包含多种危险因素,因此需要进行危害评估且意义重大。本文采用云模型(CM)改进集对分析(SPA),并提出了一种基于云模型 - 集对分析(CM - SPA)的生物质气化站危害评估新方法。该方法提出用云权重作为指标权重,与其他方法的指标权重不同,云权重由云数字特征表示;因此,云权重的随机性和模糊性使其能有效反映专家的语言变量。然后,提出用云联系度(CCD)代替联系度(CD),并给出了CCD的计算算法。利用CCD,危害评估结果用一些正态云表示,正态云由云数字特征反映;同时,通过分析云数字特征确定危害等级。之后,分别采用CM - SPA和基于层次分析法(AHP)的SPA对两个生物质气化站进行危害评估。评估结果对比表明,CM - SPA适用于生物质气化站的危害评估且有效,能使评估结果更合理、科学。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ea0/5226786/46bac5697a45/pone.0170012.g001.jpg

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