Application of metaheuristic based artificial neural network and multilinear regression for the prediction of higher heating values of fuels

This paper describes the development of metaheuristic based artificial neural network (i.e., ANN optimized with PSO, ANN-PSO) and multilinear regression models to predict the higher heating values (HHV) of solid fuels based on the parameters of proximate and ultimate analyzes of the solid fuels. Three hundred data points of HHVs, proximate and ultimate analyzes obtained from published papers on solid fuels are used in this study. The results of the proximate and ultimate analyzes are used in the training and development of ANN-PSO models as well as the development of multilinear regression models. The models were tested for performance validation. The performances of the proposed models were evaluated using mean absolute error (MAE), average absolute error (AAE) and average biased error (ABE). Based on good agreement between results and other statistical performance parameters, ANN-PSO models perform better than multilinear regression models. The ANN-PSO models can predict the higher heating values of solid fuels for practical applications. The ANN-PSO models demonstrated excellent predictive ability showing predicted experimental HHV ratio that is close to 1.00.

Automated summary

This paper describes the development of metaheuristic based artificial neural network (ANN-PSO) and multilinear regression models for predicting the higher heating values (HHV) of solid fuels. The ANN-PSO models outperformed the multilinear regression models in terms of predictive performance. They showed excellent ability in predicting the HHV of solid fuels for practical applications.