Prediction of coal self-ignition tendency using machine learning. (1st October 2022)

• Record Type: Journal Article
• Authors: Zhang, Lidong; Song, Zeyang; Wu, Dejian; Luo, Zhenmin; Zhao, Shanshan; Wang, Yaohan; Deng, Jun
• Is Part Of: Fuel. Volume 325(2022)
• Publication Date: 2022-10-01
• Subjects: Self-ignition tendency -- Regression prediction -- Neural network -- Random forest -- Machine learning; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2022.124832
• Languages: English
• ISSNs: 0016-2361

Abstract

Highlights: The physicochemical processes and effects of external factors are represented by MLP and RF models. The error of prediction using MLP and RF with 204 CPT experimental data is less than 10%. RF performs better in generalization than MLP. Machine learning models are greatly dependent on both data size and CPT distribution. Machine Learning is demonstrated as a novel approach for effective prediction of coal self-ignition tendency. Abstract: Self-ignition of coal emits hazardous particles and toxic gases, polluting environment and threatening people's health. Prediction of self-ignition tendency of coal is of great significance to prevent hazards of coal self-ignition. However, it is very challenging to forecast the self-ignition tendacy of coal, because of complex physicochemical processes and highly nonlinear correlation between factors and self-ignition tendency. In this work, machine learning methods (Multilayer Perceptron (MLP) and Random Forest (RF)) are used to represent the complex physicochemical processes and effects of external factors. The regression prediction models with regarding to crossing point temperature (CPT) and 13 input features are established. The dependence of input features is examined using the feature engineering. Two hundreds and four CPT samples are collected, in which 142 (70%) samples and 62 (30%) samples are divided as training data and testing data, respectively. Results show that the accuracy of both MLP and RF predicted CPTs in the testing data reaches 90%, which proves good predictability of machine-learning based models with several hundreds of samples. This work improves prediction of the self-ignition tendency of coal impacted by complex physicochemical properties and a variety of external factors. It may help to predict other fuels susceptible to self-ignition e.g., oil shale and biomass fuels.