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Article Title

Modelling the Relationship Between Self-Ignition Temperature and Physicochemical Parameters of Coal Mine Waste with the Application of the Partial Least Squares Method

Abstract

Purpose The aim of the work presented in this paper was the construction of a regression model describing the relationship between the experimentally determined value of the maximum temperature observed during the coal mine waste fire and physicochemical parameters characterizing the coal mine waste. Methods The model was constructed with the application of the Partial Least Squares method. The experimental data analysed was acquired through the use of a laboratory test stand with a fixed bed reactor and analytical method of gas chromatography. Results The constructed model was characterized by a good fit of the data used in its construction and the strong predictive ability for the new data. It illustrated the significant impact of the content of H and Fe2O3 and trace elements: Co, Cu, Pb, Sr, V and Zn in a sample on the value of the maximum temperature reached during the fire of coal mine waste. Practical implications The practical importance of the work presented is clear in the light of the role of coal in the Polish economy and environmental aspects related to coal mining and the coal-based energy sector, in particularly to coal waste disposal and utilization. The model constructed contributes to the development of methods of self-ignition and fire risk assessment on coal waste dumps by determining the relationship between waste dump fire occurrence, the temperature observed during the fire and the physicochemical parameters characterizing the coal mine waste. Originality/value The novelty of the study presented in the paper consists in both finding the relationships modelled and the data extraction methods applied in the research field concerned.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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