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Zhen-Ming Sun
Center for Sustainable Development and Energy Policy Research (SDEP), School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, China

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Journal article
Published: 15 February 2021 in Journal of Cleaner Production
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Carbon emissions reduction is not only physical, but also rooted in powerful economic actions. Coal-related emissions reduction strategies mainly focus on the physical quantity of carbon emissions, rather than the economic efficiency. So it’s necessary to strengthen the analysis of carbon emission reduction strategies from the perspective of economic value. Based on the theory of coal supply chain management, this study builds a carbon dioxide accounting model of China’s coal supply chain. The indicators for the process-based carbon emissions and carbon emissions per unit of economic value of various coal products are evaluated from the perspective of the whole coal life cycle. The results show that the processing and conversion of coal are the most important sources of carbon emissions in the coal life cycle. Coal-fueled electricity is the largest contributor to carbon emissions of all coal products. The coal-to-heat process has the highest carbon emissions per unit of economic value. Emission reduction strategies for coal supply chain are given in this research from both the physical and economic perspective. To realize emission reduction based on the physical perspective, it’s helpful to decrease coal consumption by reducing the proportion of coal-fired power and the gas capture technology should be promoted to facilitate gas utilization. Implementing the coal transition to natural gas policy and developing heat recovery technology have an effect on carbon emissions reduction from the economic viewpoint of coal supply chain.

ACS Style

Bing Wang; Liting He; Xiao-Chen Yuan; Zhen-Ming Sun; Pengshuai Liu. Carbon emissions of coal supply chain: An innovative perspective from physical to economic. Journal of Cleaner Production 2021, 295, 126377 .

AMA Style

Bing Wang, Liting He, Xiao-Chen Yuan, Zhen-Ming Sun, Pengshuai Liu. Carbon emissions of coal supply chain: An innovative perspective from physical to economic. Journal of Cleaner Production. 2021; 295 ():126377.

Chicago/Turabian Style

Bing Wang; Liting He; Xiao-Chen Yuan; Zhen-Ming Sun; Pengshuai Liu. 2021. "Carbon emissions of coal supply chain: An innovative perspective from physical to economic." Journal of Cleaner Production 295, no. : 126377.

Research article
Published: 20 October 2020 in Discrete Dynamics in Nature and Society
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Gas safety evaluation has always been vital for coal mine safety management. To enhance the accuracy of coal mine gas safety evaluation results, a new gas safety evaluation model is proposed based on the adaptive weighted least squares support vector machine (AWLS-SVM) and improved Dempster–Shafer (D-S) evidence theory. The AWLS-SVM is used to calculate the sensor value at the evaluation time, and the D-S evidence theory is used to evaluate the safety status. First, the sensor data of gas concentration, wind speed, dust, and temperature were obtained from the coal mine safety monitoring system, and the prediction results of sensor data are obtained using the AWLS-SVM; hence, the prediction results would be the input of the evaluation model. Second, because the basic probability assignment (BPA) function is the basis of D-S evidence theory calculation, the BPA function of each sensor is determined using the posterior probability modeling method, and the similarity is introduced for optimization. Then, regarding the problem of fusion failure in D-S evidence theory when fusing high-conflict evidence, using the idea of assigning weights, the importance of each evidence is allocated to weaken the effect of conflicting evidence on the evaluation results. To prevent the loss of the effective information of the original evidence followed by modifying the evidence source, a conflict allocation coefficient is introduced based on fusion rules. Ultimately, taking Qing Gang Ping coal mine located in Shaanxi province as the study area, a gas safety evaluation example analysis is performed for the assessment model developed in this paper. The results indicate that the similarity measures can effectively eliminate high-conflict evidence sources. Moreover, the accuracy of D-S evidence theory based on enhanced fusion rules is improved compared to the D-S evidence theory in terms of the modified evidence sources and the original D-S evidence theory. Since more sensors are fused, the evaluation results have higher accuracy. Furthermore, the multisensor data evaluation results are enhanced compared to the single sensor evaluation outcomes.

ACS Style

Zhenming Sun; Dong Li. Coal Mine Gas Safety Evaluation Based on Adaptive Weighted Least Squares Support Vector Machine and Improved Dempster–Shafer Evidence Theory. Discrete Dynamics in Nature and Society 2020, 2020, 1 -12.

AMA Style

Zhenming Sun, Dong Li. Coal Mine Gas Safety Evaluation Based on Adaptive Weighted Least Squares Support Vector Machine and Improved Dempster–Shafer Evidence Theory. Discrete Dynamics in Nature and Society. 2020; 2020 ():1-12.

Chicago/Turabian Style

Zhenming Sun; Dong Li. 2020. "Coal Mine Gas Safety Evaluation Based on Adaptive Weighted Least Squares Support Vector Machine and Improved Dempster–Shafer Evidence Theory." Discrete Dynamics in Nature and Society 2020, no. : 1-12.

Review article
Published: 16 October 2020 in Discrete Dynamics in Nature and Society
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Coal mining, regarded as a high-risk industry, has a strong demand for virtual reality (VR) to fulfill safety and emergency rescue training. In the past ten years, VR technology has significantly improved miner training on both the hardware and software side. However, it still has some drawbacks, such as expensive and unsuitable hardware, lack of satisfactory user experience, without direct browser access, and lack of humanized and intelligent design. To solve these problems, a cloud-based VR system is designed for the training of coal miners in this paper. The system, with browser/client architecture, includes eight modules demonstrating the full procedure of an underground coal mine. The online cloud-rendered video streaming is adopted to provide enough computing and rendering power and hence a better browser-based user experience. Furthermore, game artificial intelligence (AI) is also introduced into the system to increase the emotional exchange between the system and users. Unlike traditional VR training software, this system designs two virtual miners to enhance the experience of trainees. The first virtual miner is a task-oriented non-player-character (NPC) which conveys general knowledge about the mine and guides the users in visiting the underground work sites. The second virtual miner is a disaster-oriented character which prepares the users for typical disasters. The system has been successfully implemented in a laboratory environment, and its performance has been validated. Yet, further practices are needed to stimulate more innovative applications of VR-based miner training and disaster drilling.

ACS Style

Mei Li; Zhenming Sun; Zhan Jiang; Zheng Tan; Jinchuan Chen. A Virtual Reality Platform for Safety Training in Coal Mines with AI and Cloud Computing. Discrete Dynamics in Nature and Society 2020, 2020, 1 -7.

AMA Style

Mei Li, Zhenming Sun, Zhan Jiang, Zheng Tan, Jinchuan Chen. A Virtual Reality Platform for Safety Training in Coal Mines with AI and Cloud Computing. Discrete Dynamics in Nature and Society. 2020; 2020 ():1-7.

Chicago/Turabian Style

Mei Li; Zhenming Sun; Zhan Jiang; Zheng Tan; Jinchuan Chen. 2020. "A Virtual Reality Platform for Safety Training in Coal Mines with AI and Cloud Computing." Discrete Dynamics in Nature and Society 2020, no. : 1-7.

Journal article
Published: 29 September 2020 in Sustainability
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Demand-side management provides important opportunities to integrate renewable sources and enhance the flexibility of urban power systems. With the continuous advancement of the smart grid and electricity market reform, the potential for residential consumers to participate in energy demand response is significantly enhanced. However, not enough is known about the public perception of energy demand response, and how sociopsychological and external factors could affect public willingness to participate. This study investigates the public perception of and willingness to participate in urban energy demand response through a questionnaire survey and employs multiple linear regression models to explore the determinants of public willingness to participate. The results suggest that income level, energy-saving attitudes, behaviors, external motivation factors, and energy-saving technologies are the key factors that determine public willingness to participate. Although most respondents are willing to participate, the effects of monetary incentives are more significant than the effect of spiritual inducements, and respondents are more sensitive to compensation than to dynamic electricity prices. The further improvement of residential responsiveness requires continuous infrastructure building by technical support, public energy-saving awareness, and public perception of energy demand response. Policy implications are proposed to achieve a sufficient residential response from an aggressive policy framework and energy-saving behavioral guidance.demand-side management; willingness to participate; determinants; spiritual incentives

ACS Style

Bing Wang; Qiran Cai; Zhenming Sun. Determinants of Willingness to Participate in Urban Incentive-Based Energy Demand-Side Response: An Empirical Micro-Data Analysis. Sustainability 2020, 12, 8052 .

AMA Style

Bing Wang, Qiran Cai, Zhenming Sun. Determinants of Willingness to Participate in Urban Incentive-Based Energy Demand-Side Response: An Empirical Micro-Data Analysis. Sustainability. 2020; 12 (19):8052.

Chicago/Turabian Style

Bing Wang; Qiran Cai; Zhenming Sun. 2020. "Determinants of Willingness to Participate in Urban Incentive-Based Energy Demand-Side Response: An Empirical Micro-Data Analysis." Sustainability 12, no. 19: 8052.

Conference paper
Published: 01 January 2020 in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Coal mining, regarded as a high-risk industry, has strongly demand on Virtual Reality (VR) training environment for safety mining and emergency rescue. In order to solve the problems of the current VR platform of lack of immersive experience and interest, a Browser/Client VR simulation system are designed and realized. Three key techniques are studies, including cloud rendering, AI behavior tree and mining disaster animation. Unlike WebGL and HTML5, cloud rendering provides photo-realistic quality. 2 AI characters are designed to guide users to have an overall understanding of the mine and experience the effect mine disasters. The system has been successfully applied in the Virtual Reality Teaching and Experiment Laboratory for undergraduate in China University of Mining & Technology-Beijing. The research, as a new tool for miner training and disaster drilling, has a signification meaning of the work safety IT construction.

ACS Style

Mei Li; Zhenming Sun; Zhan Jian; Zheng Tan; Jinchuan Chen. A Virtual Reality Simulation System for Coal Safety Based on Cloud Rendering and AI Technology. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020, 497 -508.

AMA Style

Mei Li, Zhenming Sun, Zhan Jian, Zheng Tan, Jinchuan Chen. A Virtual Reality Simulation System for Coal Safety Based on Cloud Rendering and AI Technology. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2020; ():497-508.

Chicago/Turabian Style

Mei Li; Zhenming Sun; Zhan Jian; Zheng Tan; Jinchuan Chen. 2020. "A Virtual Reality Simulation System for Coal Safety Based on Cloud Rendering and AI Technology." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 497-508.

Conference paper
Published: 01 January 2020 in Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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In order to improve the accuracy of coal mine gas safety evaluation results, a gas safety evaluation model based on D-S evidence theory data fusion is proposed, and multi-sensor fusion of gas safety evaluation is realized. First, the prediction results of the weighted least squares support vector machine are used as the input of D-S evidence theory, and the basic probability assignment function of each sensor is calculated by using the posterior probability modeling method, and the similarity measure is introduced for optimization. Secondly, aiming at the problem of fusion failure in D-S evidence theory when fusing high-conflict evidence, the idea of assigning weights is used to allocate the importance of each evidence to weaken the impact of conflicting evidence on the evaluation results. In order to prevent the loss of the effective information of the original evidence after modifying the evidence source, a conflict allocation coefficient is introduced on the basis of fusion rules. Finally, a gas safety evaluation example analysis is carried out on the evaluation model established in this paper. The results show that the introduction of similarity measures can effectively eliminate high-conflict evidence sources; the accuracy of D-S evidence theory based on improved fusion rules is improved by 2.8% and 15.7% respectively compared to D-S evidence theory based on modified evidence sources and D-S evidence theory; as more sensors are fused, the accuracy of the evaluation results is higher; the multi-sensor data evaluation results are improved by 63.5% compared with the single sensor evaluation results.

ACS Style

Zhenming Sun; Dong Li; Yunbing Hou. Research on Coal Mine Gas Safety Evaluation Based on D-S Evidence Theory Data Fusion. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020, 509 -522.

AMA Style

Zhenming Sun, Dong Li, Yunbing Hou. Research on Coal Mine Gas Safety Evaluation Based on D-S Evidence Theory Data Fusion. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 2020; ():509-522.

Chicago/Turabian Style

Zhenming Sun; Dong Li; Yunbing Hou. 2020. "Research on Coal Mine Gas Safety Evaluation Based on D-S Evidence Theory Data Fusion." Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering , no. : 509-522.

Conference paper
Published: 08 November 2019 in IOP Conference Series: Earth and Environmental Science
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Coal-to-clean energy transition is a significant strategy for China to protect the environment and achieve sustainable development. This research reviews the present policy framework, targeted sector for this energy consumption reform and discusses the supply status of natural gas industry. The conditions for efficiently implementing the coal-to-clean energy projects are explored by literature review and data mining. The decision to choose a proper implementation schedule depends on the factors of economic, environment, infrastructure, technology and resources endowment. The possible impacts of coal-to-clean energy transition actions on the society of China are analysed from the changes of energy mix, challenges from stable energy supply and measures for safeguarding energy security. The results of this study could be helpful for the successful implementation of this transition in the other areas.

ACS Style

Bing Wang; Tong Qin; Ge Hong; Wen-Rong Fan; Zhen-Ming Sun. Challenges and opportunities of coal-to-clean energy transition in China: a hard but long work. IOP Conference Series: Earth and Environmental Science 2019, 330, 032081 .

AMA Style

Bing Wang, Tong Qin, Ge Hong, Wen-Rong Fan, Zhen-Ming Sun. Challenges and opportunities of coal-to-clean energy transition in China: a hard but long work. IOP Conference Series: Earth and Environmental Science. 2019; 330 (3):032081.

Chicago/Turabian Style

Bing Wang; Tong Qin; Ge Hong; Wen-Rong Fan; Zhen-Ming Sun. 2019. "Challenges and opportunities of coal-to-clean energy transition in China: a hard but long work." IOP Conference Series: Earth and Environmental Science 330, no. 3: 032081.

Journal article
Published: 01 November 2018 in Journal of Cleaner Production
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Mineral resources are still the rigid demand of China’s development and their sustainability assessment is central to regional development. This paper introduces an improved Analytic Hierarchy Process-based Normal cloud model which incorporated randomness and fuzziness and presents an index system applicable to empirical analysis on 31 provinces of China. The weight system of evaluation indicators is determined by an improved analytic hierarchy process (IAHP) approach with good accessibility and attainability. Regional disparities of resources sustainability are demonstrated by spatial distributions of mineral resources sustainability with the help of GIS technology. The sustainability level is determined by the combination of resources, environment and society dimensions. The results indicate that the areas with high sustainability do not necessarily happen in resource-rich regions. The most important implications are the relatively lower sustainability of the developed areas and the higher sustainability in western provinces. The scores for indicators reveal that compared with western and central areas, the coastal provinces are less sustainable in environmental carrying capacity, but with higher sustainable social supporting capacity. Robustness test notes that the results are sensitive to the adjustment of weight system due to the preference of decision makers. Finally, differentiated regional strategies for improving overall sustainability are proposed.

ACS Style

Chao-Qun Cui; Bing Wang; Yi-Xin Zhao; Qian Wang; Zhen-Ming Sun. China's regional sustainability assessment on mineral resources: Results from an improved analytic hierarchy process-based normal cloud model. Journal of Cleaner Production 2018, 210, 105 -120.

AMA Style

Chao-Qun Cui, Bing Wang, Yi-Xin Zhao, Qian Wang, Zhen-Ming Sun. China's regional sustainability assessment on mineral resources: Results from an improved analytic hierarchy process-based normal cloud model. Journal of Cleaner Production. 2018; 210 ():105-120.

Chicago/Turabian Style

Chao-Qun Cui; Bing Wang; Yi-Xin Zhao; Qian Wang; Zhen-Ming Sun. 2018. "China's regional sustainability assessment on mineral resources: Results from an improved analytic hierarchy process-based normal cloud model." Journal of Cleaner Production 210, no. : 105-120.

Journal article
Published: 23 November 2017 in Energies
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Renewable energy can help to tackle energy poverty issues of the availability of modern energy services and the sustainability of energy supply. Based on the concept of the Energy Development Index, published by International Energy Agency, this paper builds the clean energy development index and applies the Grey incidence decision method to analyze regional energy poverty issues in China. A model using panel data investigates the influencing factors that are governing energy poverty alleviation and the relationship between energy poverty and social development. The improved index system not only considers the access to modern energy services, but also addresses the cleanliness of energy supply and the transition to clean energy consumption for cooking. The results indicate that due to insufficient clean energy development, China’s Northeast and West regions have experienced increasing energy poverty problems, while energy poverty in the Southwest region has improved considerably because of its renewable energy development. Urbanization, affordability, and renewable energy development can increase access to modern energy services, contributing to energy poverty alleviation. However, the role of rural household consumption levels in energy poverty alleviation should be considered in rural energy policy.

ACS Style

Bing Wang; Hua-Nan Li; Xiao-Chen Yuan; Zhen-Ming Sun. Energy Poverty in China: A Dynamic Analysis Based on a Hybrid Panel Data Decision Model. Energies 2017, 10, 1942 .

AMA Style

Bing Wang, Hua-Nan Li, Xiao-Chen Yuan, Zhen-Ming Sun. Energy Poverty in China: A Dynamic Analysis Based on a Hybrid Panel Data Decision Model. Energies. 2017; 10 (12):1942.

Chicago/Turabian Style

Bing Wang; Hua-Nan Li; Xiao-Chen Yuan; Zhen-Ming Sun. 2017. "Energy Poverty in China: A Dynamic Analysis Based on a Hybrid Panel Data Decision Model." Energies 10, no. 12: 1942.