Improved Prediction of the Higher Heating Value of Biomass Using an Artificial Neural Network Model Based on the Selection of Input Parameters

Justyna Kujawska (), Monika Kulisz, Piotr Oleszczuk and Wojciech Cel
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Justyna Kujawska: Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland
Monika Kulisz: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Piotr Oleszczuk: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Wojciech Cel: Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland

Energies, 2023, vol. 16, issue 10, 1-16

Abstract: Recently, biomass has become an increasingly widely used energy resource. The problem with the use of biomass is its variable composition. The most important property that determines the energy content and thus the performance of fuels such as biomass is the heating value (HHV). This paper focuses on selecting the optimal number of input variables using linear regression (LR) and the multivariate adaptive regression splines approach (MARS) to create an artificial neural network model for predicting the heating value of selected biomass. The MARS model selected the input data better than the LR model. The best modeling results were obtained for a network with three input neurons and nine neurons in the hidden layer. This was confirmed by a high correlation coefficient of 0.98. The obtained results show that artificial neural network (ANN) models are effective in predicting the calorific value of woody and field biomass, and can be considered a worthy simulation model for use in selecting biomass feedstocks and their blends for renewable fuel applications.

Keywords: HHV forecasting; artificial neural networks; multivariate adaptive regression splines approach (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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