Machine Learning Improves Performance Prediction and Interpretation of Efficiency Influencing Factors of a Novel Basalt-Fiber-Bundle Thermal Flow-Reversal Reactor for Methane Recovery

Rao Kuang, Bin Du (), Peter D. Lund, Jun Wang and Yanying Liu
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Rao Kuang: State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou 570228, China
Bin Du: Key Laboratory of Solar Energy Science and Technology in Jiangsu Province, Institute of Energy and Environment, Southeast University, Nanjing 210096, China
Peter D. Lund: School of Mechanical Engineering, Southeast University, Nanjing 211189, China
Jun Wang: Key Laboratory of Solar Energy Science and Technology in Jiangsu Province, Institute of Energy and Environment, Southeast University, Nanjing 210096, China
Yanying Liu: Key Laboratory of Solar Energy Science and Technology in Jiangsu Province, Institute of Energy and Environment, Southeast University, Nanjing 210096, China

Energies, 2025, vol. 18, issue 21, 1-21

Abstract: Low-concentration methane emissions from mines can be recovered using different reactor designs. Here, different artificial intelligence network techniques were employed to predict thermal performance of a basalt-fiber-bundle thermal flow-reversal reactor and investigate the influence of input parameters. The Back Propagation (BP) model gave the best accuracy (R 2 = 0.974 for outlet temperature, 0.967 for thermal efficiency), exceeding that of traditional Computational Fluid Dynamics (CFD) simulations. For the present design, when flow velocity exceeded 1.5 m/s, the outlet gas temperature shifted from rising to falling, explained by the heat transfer between the gas and the solid inside the flow channel. Increasing the length of the flow-reversal period in the high-temperature phase reduced the outlet temperature, e.g., an increase from 60 s to 200 s decreased the outlet temperature by 34.1 K. Increasing inlet methane concentration (e.g., from 0.3% to 0.8%) first showed a slight improvement in thermal efficiency but further increase accelerated the oxidation reaction rate inside the reactor, reducing the temperature difference between the solid and gas in the channel, which slowed the heat exchange process and resulted in a downward trend in efficiency. The results indicate that the reactor can handle a wide range of exhaust gas concentrations, being suitable to treat low-methane-concentration exhaust gas. The BP model helped to establish the theoretical basis for setting optimal parameters values for the operation of the proposed reactor.

Keywords: machine learning; thermal flow-reversal reactor; basalt fiber bundle; multiple linear regression; CFD; thermal performance prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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