Evaluation of the Influencing Factors of Using Underground Space of Abandoned Coal Mines to Store Hydrogen Based on the Improved ANP Method

Storage is currently a major obstacle to the promotion of hydrogen energy. Hydrogen storage in abandoned coal mines can achieve the effective use of underground space while meeting the growing demand for energy storage facilities, which can bring economic and environmental benefits. However, research in this area has been limited to the conceptual discussion stage, without establishing a scientific evaluation method for the potential of modifying and utilizing abandoned coal mine space. In this study, based on the analytic network process (ANP), the Apriori algorithm is introduced to mine the association rules for various influencing factors. First, the Apriori algorithm is applied to mine association rules between indicators, eliminate unnecessary influence relationships, simplify the network structure model, and optimize the ANP weight calculation results; second, the solution method of judgment matrix is improved with triangular fuzzy numbers, and the index weight is solved by fuzzy nine marks instead of the method of nine scale, which is convenient for experts to give the fuzzy scale while better reflecting the opinions of experts. Finally, the ANP algorithm is applied to rank the weights of the obtained influencing factors, discuss the main factors with higher weights, and analyze the feasibility of converting candidate coal mines into hydrogen storage facilities using the derived evaluation method in the case study. The evaluation methods and conclusions presented in this study provide analytical tools and a decision basis for analyzing the feasibility of converting underground space of abandoned coal mines into hydrogen storage facilities and assessing the economic indicators.

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Assessment of the influencing factors on subsidence at abandoned coal mines in South Korea

Environmental Earth Sciences ◽

10.1007/s12665-012-1768-y ◽

2012 ◽

Vol 68 (3) ◽

pp. 647-654 ◽

Cited By ~ 12

Author(s):

Dong-Kil Lee ◽

Navid Mojtabai ◽

Hyun-Bock Lee ◽

Won-Kyung Song

Keyword(s):

South Korea ◽

Influencing Factors ◽

Coal Mines ◽

Abandoned Coal Mines

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Enhanced K_way Method In APRIORI Algorithm for Mining the Association Rules Through Embedding SQL Commands

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i10.5256 ◽

2019 ◽

Vol 7 (10) ◽

pp. 52-56

Author(s):

Basel A. Dabwan ◽

Mukti E. Jadhav

Keyword(s):

Association Rules ◽

Apriori Algorithm

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Enabling Storage and Utilization of Low-Carbon Electricity: Power to Formic Acid

Energy & Environmental Science ◽

10.1039/d0ee03011b ◽

2021 ◽

Author(s):

Kuo-Wei Huang ◽

Sudipta Chatterjee ◽

Indranil Dutta ◽

Yanwei Lum ◽

Zhiping Lai

Keyword(s):

Hydrogen Storage ◽

Formic Acid ◽

Storage Capacity ◽

Energy Carrier ◽

Hydrogen Energy ◽

Low Carbon ◽

Hydrogen Storage Capacity ◽

Electricity Power ◽

Low Toxicity

Formic acid has been proposed as a hydrogen energy carrier because of its many desirable properties, such as low toxicity and flammability, and a high volumetric hydrogen storage capacity of...

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Recent Advances in Hydrogen Storage Materials

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.512-515.1438 ◽

2012 ◽

Vol 512-515 ◽

pp. 1438-1441 ◽

Cited By ~ 2

Author(s):

Hong Min Kan ◽

Ning Zhang ◽

Xiao Yang Wang ◽

Hong Sun

Keyword(s):

Liquid Phase ◽

Hydrogen Storage ◽

Earth Metal ◽

Solid Material ◽

Hydrogen Energy ◽

Hydrogen Storage Material ◽

Ambient Conditions ◽

Storage Material ◽

Lithium Borohydride ◽

Recent Advances

An overview of recent advances in hydrogen storage is presented in this review. The main focus is on metal hydrides, liquid-phase hydrogen storage material, alkaline earth metal NC/polymer composites and lithium borohydride ammoniate. Boron-nitrogen-based liquid-phase hydrogen storage material is a liquid under ambient conditions, air- and moisture-stable, recyclable and releases H2controllably and cleanly. It is not a solid material. It is easy storage and transport. The development of a liquid-phase hydrogen storage material has the potential to take advantage of the existing liquid-based distribution infrastructure. An air-stable composite material that consists of metallic Mg nanocrystals (NCs) in a gas-barrier polymer matrix that enables both the storage of a high density of hydrogen and rapid kinetics (loading in <30 min at 200°C). Moreover, nanostructuring of Mg provides rapid storage kinetics without using expensive heavy-metal catalysts. The Co-catalyzed lithium borohydride ammoniate, Li(NH3)4/3BH4 releases 17.8 wt% of hydrogen in the temperature range of 135 to 250 °C in a closed vessel. This is the maximum amount of dehydrogenation in all reports. These will reduce economy cost of the global transition from fossil fuels to hydrogen energy.

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Improved Apriori Algorithm in the Power System Fault

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.1510 ◽

2014 ◽

Vol 556-562 ◽

pp. 1510-1514

Author(s):

Li Qiang Lin ◽

Hong Wen Yan

Keyword(s):

Power System ◽

Association Rules ◽

Fault Analysis ◽

The Other ◽

Apriori Algorithm ◽

Extreme Climate ◽

Time Space ◽

Low Efficiency ◽

Candidate Item ◽

Improved Algorithm

For the low efficiency in generating candidate item sets of apriori algorithm, this paper presents a method based on property division to improve generating candidate item sets. Comparing the improved apriori algorithm with the other algorithm and the improved algorithm is applied to the power system accident cases in extreme climate. The experiment results show that the improved algorithm significantly improves the time efficiency of generating candidate item sets. And it can find the association rules among time, space, disasters and fault facilities in the power system accident cases in extreme climate. That is very useful in power system fault analysis.

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Outburst prediction and influencing factors analysis based on Boruta-Apriori and BO-SVM algorithms

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210466 ◽

2021 ◽

pp. 1-18

Author(s):

Zhang Zixian ◽

Liu Xuning ◽

Li Zhixiang ◽

Hu Hongqiang

Keyword(s):

Feature Selection ◽

Prediction Model ◽

Influencing Factors ◽

Association Rules ◽

Coal And Gas Outburst ◽

Feature Subset ◽

Gas Outburst ◽

Proposed Model ◽

Optimal Feature Subset ◽

Feature Dimension

The influencing factors of coal and gas outburst are complex, now the accuracy and efficiency of outburst prediction and are not high, in order to obtain the effective features from influencing factors and realize the accurate and fast dynamic prediction of coal and gas outburst, this article proposes an outburst prediction model based on the coupling of feature selection and intelligent optimization classifier. Firstly, in view of the redundancy and irrelevance of the influencing factors of coal and gas outburst, we use Boruta feature selection method obtain the optimal feature subset from influencing factors of coal and gas outburst. Secondly, based on Apriori association rules mining method, the internal association relationship between coal and gas outburst influencing factors is mined, and the strong association rules existing in the influencing factors and samples that affect the classification of coal and gas outburst are extracted. Finally, svm is used to classify coal and gas outbursts based on the above obtained optimal feature subset and sample data, and Bayesian optimization algorithm is used to optimize the kernel parameters of svm, and the coal and gas outburst pattern recognition prediction model is established, which is compared with the existing coal and gas outbursts prediction model in literatures. Compared with the method of feature selection and association rules mining alone, the proposed model achieves the highest prediction accuracy of 93% when the feature dimension is 3, which is higher than that of Apriori association rules and Boruta feature selection, and the classification accuracy is significantly improved, However, the feature dimension decreased significantly; The results show that the proposed model is better than other prediction models, which further verifies the accuracy and applicability of the coupling prediction model, and has high stability and robustness.

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