A data-driven approach for the prediction of coal seam gas content using machine learning techniques

Akdas, Satuk Bugra; Fişne, Abdullah

doi:10.1016/j.apenergy.2023.121499

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…However, both papers follow a predomoinantly heuristic trial-and-errorbased approach for ANN design. The most effective and efficient design of ANNs is still an open research question across a variety of disciplines [59][60][61][62][63][64][65].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Hyperparameter Optimization of Artificial Neural Networks for Optimal Feedforward Torque Control of Synchronous Machines

Monzen,

Stroebl,

Palm

et al. 2024

IEEE Open J. Ind. Electron. Soc.

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Multi-objective hyperparameter optimization (MO-HPO) is applied to find optimal artificial neural network (ANN) architectures used for optimal feedforward torque control (OFTC) of synchronous machines. The proposed framework allows to systematically identify Pareto optimal ANNs with respect to multiple (partly) contradictory objectives such as approximation accuracy and computational burden of the considered ANNs. The obtained Pareto optimal ANNs are trained and implemented on a realtime system and tested experimentally for a nonlinear reluctance synchronous machine (RSM) against non-Pareto optimal ANN designs and a state-of-the-art OFTC approach. Finally, based on the most recent results from ANN approximation theory, guidelines for Pareto optimal ANN-based OFTC design and implementation are provided.

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Section: Introductionmentioning

confidence: 99%

Multiobjective Hyperparameter Optimization of Artificial Neural Networks for Optimal Feedforward Torque Control of Synchronous Machines

Monzen,

Stroebl,

Palm

et al. 2024

IEEE Open J. Ind. Electron. Soc.

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“…Zhang et al 20 established a calculation model of gas content in deep coal seam with correction coefficient based on the relationship between measured gas saturation and buried depth of coal seam by nonlinear analysis method. Meng et al 21 , Akdas et al 22 , Wei et al 23 used artificial intelligence algorithms, such as support vector regression, machine learning, neural network of PCA-AHPSO-SVR and other methods to predict coal seam gas content, and achieved good application results in some areas at home and abroad. The second is the isothermal adsorption method.…”

Section: Introductionmentioning

confidence: 99%

The geological factors affecting gas content and permeability of coal seam and reservoir characteristics in Wenjiaba block, Guizhou province

Feng,

Li,

Yang

et al. 2023

Sci Rep

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The gas content and permeability of coal reservoirs are the main factors affecting the productivity of coalbed methane. To explore the law of gas content and permeability of coal reservoirs in the Zhijin area of Guizhou, taking No.16, No.27 and No.30 coal seams in Wenjiaba mining area of Guizhou as the engineering background, based on the relevant data of coalbed methane exploration in Wenjiaba block, the geological structure, coal seam thickness, coal quality characteristics,coal seam gas content and permeability of the area were studied utilizing geological exploration, analysis of coal components and methane adsorption test. The results show that the average thickness of coal seams in this area is between 1.32 and 1.85 m; the average buried depth of the coal seam is in the range of 301.3–384.2 m; the gas content of No.16 and No.27 coal seams is higher in the syncline core. The gas content of the No.30 coal seam forms a gas-rich center in the south of the mining area. The buried depth and gas content of coal seams in the study area show a strong positive correlation. Under the same pressure conditions, the adsorption capacity of dry ash-free basis is significantly higher than that of air-dried coal. The permeability decreases exponentially with the horizontal maximum principal stress and the horizontal minimum principal stress. The horizontal maximum primary stress and the flat minimum prominent stress increase with the increase of the buried depth of the coal seam. The permeability and coal seam burial depth decrease exponentially. This work can provide engineering reference and theoretical support for selecting high-yield target areas for CBM enrichment in the block.

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A novel dual-way inference modeling method for coal coking: Predicting H2 and CH4 concentrations in coke oven gas and inferring optimal reaction conditions

Zhang,

Ren,

et al. 2025

Fuel

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A data-driven approach for the prediction of coal seam gas content using machine learning techniques

Cited by 5 publications

References 17 publications

Multiobjective Hyperparameter Optimization of Artificial Neural Networks for Optimal Feedforward Torque Control of Synchronous Machines

Multiobjective Hyperparameter Optimization of Artificial Neural Networks for Optimal Feedforward Torque Control of Synchronous Machines

The geological factors affecting gas content and permeability of coal seam and reservoir characteristics in Wenjiaba block, Guizhou province

A novel dual-way inference modeling method for coal coking: Predicting H2 and CH4 concentrations in coke oven gas and inferring optimal reaction conditions

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