Research on prediction model of coal spontaneous combustion temperature based on SSA-CNN

Kai Wang, Kangnan Li, Feng Du, Xiang Zhang, Yanhai Wang and Jiazhi Sun

Energy, 2024, vol. 290, issue C

Abstract: To predict the coal spontaneous combustion temperature accurately and efficiently, this study proposes a model based on the Sparrow Search Algorithm (SSA) and Convolutional Neural Network (CNN). Firstly, the study analyzes the main gas reactions during the coal oxidation to pyrolysis process. Six gas indicators, namely O2, CO, C2H4, CO/ΔO2, C2H4/C2H6, and C2H6, are closely related to coal temperature. Subsequently, a prediction indicator system is established. Then, the excellent data mining capabilities of CNN are leveraged through deep learning, along with their unique advantages in local perception and weight sharing, and a CNN prediction model framework is constructed. Moreover, the comparison between the algorithm performances is executed and SSA is selected for optimization. Utilizing its exceptional global search capability and adaptability, SSA optimizes the seven hyper-parameters of the model, significantly enhancing prediction accuracy. In the final step, SSA-CNN is compared with five reference models on test samples. The SSA-CNN model showcases a maximum relative error of 0.155, outperforming other models. Moreover, the RMSE of this model yields 8.4500, which is also lower than other models. The results suggest that the combination of the selected gas indicators with the SSA-CNN model can accurately predict the spontaneous combustion temperature of coal.

Keywords: Coal spontaneous combustion; Index gases; Temperature prediction; Sparrow search algorithm; Convolutional neural network (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544223035521
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:290:y:2024:i:c:s0360544223035521

DOI: 10.1016/j.energy.2023.130158

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().