Predictions of elemental composition of coal and biomass from their proximate analyses using ANFIS, ANN and MLR

International Journal of Coal Science & Technology

Samson Bada¹  Abiodun Ismail Lawal²  Musa Adebayo Idris³  Moshood Onifade⁴  Adeyemi Emman Aladejare⁵ 

[1] Clean Coal and Sustainable Energy Research Group, Faculty of Engineering and the Built Environment, University of the Witwatersrand;Department of Mining Engineering, Federal University of Technology;Division of Mining and Geotechnical Engineering, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology;Informetrics Research Group, Ton Duc Thang University;Oulu Mining School, University of Oulu;

关键词: Biomass;    Coal;    Elemental composition;    Proximate analysis;    Soft computing;    Regression analysis;   

DOI  :  10.1007/s40789-020-00346-9

来源: DOAJ

【 摘 要 】

Abstract The elemental composition of coal and biomass provides significant parameters used in the design of almost all energy conversion systems and projects. The laboratory tests to determine the elemental composition of coal and biomass is time-consuming and costly. However, limited research has suggested that there is a correlation between parameters obtained from elemental and proximate analyses of these materials. In this study, some predictive models of the elemental composition of coal and biomass using soft computing and regression analyses have been developed. Thirty-one samples including parameters of elemental and proximate analyses were used during the analyses to develop multiple prediction models. Dependent variables for multiple prediction models were selected as carbon, hydrogen, and oxygen. Using volatile matter, fixed carbon, moisture and ash contents as independent variables, three different prediction models were developed for each dependent parameter using ANFIS, ANN, and MLR. In addition, a routine for selecting the best predictive model was suggested in the study. The reliability of the established models was tested by using various prediction performance indices and the models were found to be satisfactory. Therefore, the developed models can be used to determine the elemental composition of coal and biomass for practical purposes.

【 授权许可】

Unknown