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基于博弈论组合赋权法的煤炭地下气化选址评价建模方法

Modeling Methodology for Site Selection Evaluation of Underground Coal Gasification Based on Combination Weighting Method with Game Theory.

作者信息

Huang Wen-Gang, Zhang Shao-Wei, Wang Guo-Zhi, Huang Jun, Lu Xin, Wu Shu-Liang, Wang Zuo-Tang

机构信息

Shanxi Key Laboratory of Coal and Coal-measure Gas Geology, Taiyuan, Shanxi 030001, China.

School of Earth Sciences, East China University of Technology, Nanchang, Jiangxi 330013, China.

出版信息

ACS Omega. 2023 Mar 14;8(12):11544-11555. doi: 10.1021/acsomega.3c00626. eCollection 2023 Mar 28.

DOI:10.1021/acsomega.3c00626
PMID:37008084
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10061594/
Abstract

The lack of systematic geological work is an essential reason why underground coal gasification (UCG) has not been industrialized for a long time. Building a scientific index system and favorable area evaluation technology for the UCG site selection is the key to breaking through the geological bottleneck. Aiming at the problems of the single index weight determination method, intense subjectivity, and poor reliability of current evaluation models, we put forward an evaluation modeling methodology for the UCG site selection using the combination weighting method with the game theory. The factors of coal resource conditions associated with the potential risk of UCG are systematically analyzed. From the six dimensions of the geological structure, hydrogeology, seam occurrence, coal properties, reserves, and roof lithology, 23 key factors were selected as evaluation indexes to construct a hierarchical model composed of the target layer, category index layer, and index layer. The influence of each index on UCG and its reasonable value range were systematically analyzed. The evaluation index system for UCG site selection was formed. The improved analytic hierarchy process (AHP) was adopted to sequence indices and determine their subjective weight. And the variability, conflict, and information amount of the index data were analyzed by the CRiteria Importance Through Intercriteria Correlation (CRITIC) method to calculate the objective weight. Then, the subjective and objective weights were combined through game theory. On this basis, fuzzy theory was employed to calculate the membership of indices and construct the fuzzy comprehensive judgment matrix. The evaluation model of the UCG site selection was applied to the suitability evaluation of resource conditions of UCG pilot projects at Zhongliangshan (ZLS), Huating (HT), and Shanjiaoshu (SJS) mines in China. The result shows that the resource conditions of HT are the best, followed by ZLS and, finally, SJS, which are consistent with the actual running effects of the three UCG pilot projects. It indicates that the evaluation model can provide a scientific theoretical basis and reliable technical support for the UCG site selection.

摘要

缺乏系统的地质工作是地下气化长期未能实现工业化的一个重要原因。构建科学的地下气化选址指标体系和有利区评价技术是突破地质瓶颈的关键。针对当前评价模型指标权重确定方法单一、主观性强、可靠性差等问题,提出一种基于博弈论组合赋权法的地下气化选址评价建模方法。系统分析了与地下气化潜在风险相关的煤炭资源条件因素。从地质构造、水文地质、煤层赋存、煤质、储量、顶板岩性六个维度选取23个关键因素作为评价指标,构建了由目标层、类别指标层和指标层组成的层次模型。系统分析了各指标对地下气化的影响及其合理取值范围,形成了地下气化选址评价指标体系。采用改进的层次分析法对指标进行排序并确定其主观权重,通过指标间相关性重要性(CRITIC)法分析指标数据的变异性、冲突性和信息量来计算客观权重,然后通过博弈论将主观权重和客观权重相结合。在此基础上,运用模糊理论计算指标的隶属度,构建模糊综合评判矩阵。将地下气化选址评价模型应用于我国中梁山(ZLS)、华亭(HT)和山脚树(SJS)煤矿地下气化试验项目资源条件适宜性评价。结果表明,HT的资源条件最优,其次是ZLS,最后是SJS,这与三个地下气化试验项目的实际运行效果一致。表明该评价模型可为地下气化选址提供科学的理论依据和可靠的技术支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ec6/10061594/4d1693f2ffdb/ao3c00626_0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ec6/10061594/8516af802efc/ao3c00626_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ec6/10061594/865c563834af/ao3c00626_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ec6/10061594/120785dc3fe1/ao3c00626_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ec6/10061594/936154247d8e/ao3c00626_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ec6/10061594/4d1693f2ffdb/ao3c00626_0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ec6/10061594/8516af802efc/ao3c00626_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ec6/10061594/865c563834af/ao3c00626_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ec6/10061594/120785dc3fe1/ao3c00626_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ec6/10061594/936154247d8e/ao3c00626_0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ec6/10061594/4d1693f2ffdb/ao3c00626_0006.jpg

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