Risk management for mine closure： A cloud model and hybrid semi-quantitative decision method.pdf

Risk management for mine closure A cloud model and hybrid semi-quantitative decision Chao-qun Cui 1,2,*, Bing Wang 1,2,3,*, Yi-xin Zhao 1,2, Yong-Jin Zhang 4, and Li-ming Xue 2 1 Beijing Key Laboratory for Precise Mining of Intergrown Energy and Resources, China University of Mining and Technology Beijing, Beijing 100083, China 2 School of Energy and Mining Engineering, China University of Mining and Technology Beijing, Beijing 100083, China 3 Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China 4 School of Law, Hebei University of Economics and Business, Shijiazhuang 050061, China Received 30 October 2019; revised 5 February 2020; accepted 7 February 2020 Abstract Mine closure is associated with many negative impacts on society and the environment. If these effects are not rationally addressed, they would pose risks of mine closure. Thus, a risk management is needed to mitigate these adverse impacts and address mine-closure issues. An integral framework for mine-closure risk management that includes risk assessment and risk treatment was proposed. Given the fuzziness and randomness of the transation between qualitative and quantitative knowledge in the risk assessment process, a novel risk as- sessment based on the cloud model was presented, which fully considers the uncertainty in risks themselves and in the reasoning pro- cess. Closed mine reutilization is an effective risk treatment option in response to the identified high risks, but it requires selecting optimal reut- ilization strategies for the successful implementation of the reuse plan. To this end, a hybrid semi-quantitative decision is proposed to optimize decision-making. The results of a case study showed that this risk management ology can help budget planning for risk treat- ment and provide an instructional framework to effectively reduce the negative effects of closed mines. Keywords mine closure; risk assessment; risk treatment; cloud model; risk matrix 1. Introduction A common misconception is that mine closure means the end of mining operations and abandonment without any risk [1]. However, long-term maintenance and monitoring of derelict mines are far from over. Mining enterprises still need to address various social and environmental impacts to mitig- ate the unwanted outcomes that occur in closed mines, such as ensuring that the staff and mining area residents receive compensation and settlement [2–3], restoring a viable eco- system that is healthy and safe [4], and handling the assets and liabilities reasonably [5]. Immature mine-closure man- agement greatly increases the possibility and consequence of adverse effects associated with such closures, thereby caus- ing numerous risks of potential mine closure [6]. Mine closure results from various reasons. Mine closure caused by resource depletion is defined as mature closure, whereas that precipitated by economic, social, or policy reas- ons is called premature closure [7]. Compared with the er type, the latter would result in more serious negative effects due to uncontrollability. Particularly in resource-rich countries with weak governmental management and imma- ture mine-closure plans, premature closure leads to multiple crises, such as breakdowns of local and regional public health and livelihoods, destruction of the natural environment, and a sharp decline in local finance [5,8]. These crises could worsen and become much more common with a prolonged period of mine closure, and some of the potential and emer- ging dangers are long-term and even fatal. Therefore, risk management for mine closure should be examined seriously [9]. With rigorous initiatives of “mine closure and production reduction campaign” implemented in China, a large number of coal mines prematurely closed in a short time from 87000 in 1995 to approximately 5800 in 2018 [10–11]. As the coal mining in China is still dominated by underground mining accounting for 90 of production, the closed coal mines mainly comprise underground ones. Unfortunately, at *These authors contributed equally to this work. Corresponding authors Bing Wang E-mail bingwang_bit; Yong-Jin Zhang E-mail zhangyongjin0310 University of Science and Technology Beijing and Springer-Verlag GmbH Germany, part of Springer Nature 2020 International Journal of Minerals , Metallurgy and Materials Volume 27, Number 8, August 2020, Page 1021 https//doi.org/10.1007/s12613-020-2002-7 the present stage, China still has neither effective regulatory policies for mine-closure management nor operational risk management ologies for reference [12–13]. A large number of closed mines without supervision have been aban- doned, posing an excessive economic burden to the country and society [14]. Many coal mines were closed without ad- equate preparation, resulting in unknown disasters and limit- ations for future use [15]. With this problem, setting up an in- tegrated mine-closure risk MCR management system to guide the reduction of potential negative effects of closing mines has become an urgent problem to solve. This study uses closed underground coal mines hereafter referred to as “closed mines” or “mine closure” as the research object to investigate risk management. The MCR management system comprises risk assessment and risk treatment [16–17]. The purpose of this system is to mitigate the impacts of closed mines and offset the increased environmental and social costs. MCR assessment aims to identify the critical risks associated with mine closure by uation outcomes. Considering that effective risk assess- ment is the precondition for targeted reutilization pattern im- plementation, we need to accurately determine the signific- ant risks. Thus, selecting a scientific and practical assess- ment is a very important research topic in risk man- agement. As MCR assessment inevitably involves pervasive uncertainty and imprecision, such as fuzziness and random- ness, which appear in the probability or consequence u- ation of a hazard event, accurately determining the risk levels is difficult [18]. A number of assessment s have been proposed for risk-level determination, such as fuzzy exten- ded analytic hierarchy process, which Koulinas et al. [19] ad- opted to assess safety risk in worksites; the uation model based on set pair analysis that Chong et al. [20] implemented for assessing occupational hazards in coal mines; the fuzzy synthetic uation that Akter et al. [21] applied to uate the vulnerability of climate-related hazards; and the risk assessment model combining neural networks and a ge- netic algorithm that Kaeeni et al. [22] provided for assessing derailment accident risk. Although these s have made progress in the reasoning of risk-level determination, they still have the unresolved problem of how to incorporate the randomness and fuzziness of indicators when assessing risk levels. Due to the shortage of data and uncertainty of risks, these models only considered fuzziness or randomness in as- sessment, causing deviation between uation results and practical situation. Moreover, using a single value in these s to represent the level of risk does not adequately re- flect the fuzziness and randomness of risk factors. Consider- ing that risk has two dimensions often referred to as the probability and consequence of hazard events, the risk mat- rix approach RMA, which combines the probability and consequence of a hazard event, is an essential tool for risk as- sessment [23–24]. Nevertheless, this faces two prominent problems that are yet unresolved and seriously af- fect the credibility of the results. First is that this clas- sifies risk grades by quantitative calculation, which belongs to the hard division without considering the fuzziness of the boundary. The other one is that this determines the risk level according to the risk matrix table, failing to render a scientific and reasonable uncertain reasoning mechanism. Moreover, as uators are accustomed to using natural lan- guage rather than numerical s when expressing the probability of occurrence and severity of consequences of risk events, the RMA cannot effectively solve the problem of the fuzziness of the natural language description and randomness of the occurrence of events [25]. Con- sequently, an essential task is searching for a novel that can solve the uncertain knowledge representation and present a clear reasoning mechanism. The cloud model proposed by Li et al. [26] is an uncer- tainty analysis model based on fuzzy set theory and probabil- ity theory. It adopts membership degree to describe fuzziness and uses event occurrence probability to express its random- ness, which provides an effective tool in transing between natural language and quantitative expressions [18]. Currently, the cloud model has been applied in many aspects. Guo et al. [25] proposed a risk assessment based on cloud model theory and applied it to the risk assessment of natural gas pipelines, which verifies the feasibility and ra- tionality of the cloud model for risk assessment. Wu et al. [27] employed the cloud model in the risk assessment of drought hazards, and Zhang et al. [28] assessed the risk of adjacent buildings in tunneling environments based on the cloud model, greatly enhancing the robustness of the cloud model in risk assessment. Therefore, this paper proposes an improved risk matrix based on the cloud model IRMCM to assess MCR. Risk treatment is the second step in risk management. It aims to provide effective treatment strategies for the identi- fied risks [29]. Compared with the reactionary monitoring and control of closed mine risks, closed mine reutilization CMR has been recognized as the most proactive measure for risk action because it not only modifies mine-closure problems but also brings economic benefits for companies and communities [30]. CMR is an important aspect of clean- er coal production practices because it can fully tap the po- tential and vitality of idle resources, save social costs, minim- ize closure and post-closure costs, and provide employment opportunities for the unemployed [31]. As the recovery budget of abandoned mines is limited, it needs to determine which reutilization strategy is best for treating the critical risks [32]. In general, deciding on optimal reutilization op- tions involves multiple complex environmental and socio- economic factors and mine conditions [33–34]. In this con- 1022Int. J. Miner. Metall. Mater. , Vol. 27 , No. 8 , Aug. 2020 text, multi-criteria decision-making MCDM is the most common approach to solve decision problems [35–36]. Various MCDM s are known, such as analytic hierarchy process AHP [19,32], technique for order prefer- ence by similarity to ideal solution TOPSIS [36], prefer- ence ranking organization for enrichment uation [37], grey correlation [38], and rank sum ratio [39]. Compared with other s, the TOPSIS can make the most use of the original data ination and accur- ately depict the gaps among all the uation schemes. In addition, this has other advantages, such as no strict limit on the number of indicators and a simple calculation process. In decision-making problems, the indicators’ weights are commonly determined by the AHP [40]. However, for frameworks with a large number of indicators, the weight determination by traditional AHP involves a relat- ively large amount of computation in judgment matrix con- sistency testing. Therefore, the AHP should be im- proved to enhance its perance in weight determination. This study used the scale expansion instead of pairwise com- parison to improve the judgment matrix construction called the IAHP [38], which can avoid coincidence exam- ination and define the weighing scientifically. Combining the IAHP and TOPSIS s, this study presented a hybrid semi-quantitative decision to provide a quantitative and transparent process for optimal ordering of the reutiliza- tion patterns. To illustrate the functionality of the proposed risk man- agement ology in terms of closed mine impact reduc- tion, this study took a typical suburban closed mine named Muchengjian coal mine as the research object. The main is- sues addressed in this study can be summarized as follows 1 how to establish an organized MCR framework and a CMR suitability analysis indicators system, 2 how to achieve robust and visual assessment results on the MCR, and 3 how to select the optimal reutilization options for re- sponding to the critical risks. The rest of this paper is organ- ized as follows. Section 2 introduces the ologies of the risk assessment and risk treatment. Section 3 provides a case study for verifying the proposed s. Section 4 presents the results and discussion. Section 5 provides the conclusions and policy implications. 2. ology 2.1. General framework for this study A complete MCR management approach includes three steps risk identification, risk assessment, and risk treatment. Risk identification involves three tasks. The first is to de- termine the mine-closure hazard events and assess the prob- ability and consequences of such events through an expert group. The composition of an expert team directly affects the quality of risk assessment, so the most relevant decision- makers with mine memory and different interests have to be selected. The second step is to apply the IRMCM to determine the magnitude of risks and identify the critical risks. In the third step, the two-level IAHP is employed to calculate the weights of the attributes of the CMR suitability analysis framework and then these weights into the TOPSIS to select the targeted reutilization patterns. These reutilization patterns are regarded as the best ways to deal with the high risks assessed in the second step. The fun- damental framework of this study is summarized in Fig. 1. 2.2. Risk assessment 2.2.1. Mine closure risk classification Identifying the major adverse events related to mine clos- ure and judging the likelihood and consequences of each event are the prerequisites for risk assessment. The legacy is- sues associated with closed mines are usually complex be- cause each mine has its own unique natural conditions. Therefore, the risk list of a closed mine should be obtained by a questionnaire survey on the negative effects of mine clos- ure. The questionnaires should be completed by an expert group involving mining companies, governments, stakehold- ers, and communiti