Model demonstrates high accuracy in predicting energy potential of municipal waste and biomass, suggesting improved efficiency in waste management.
In this paper, an artificial neural network (ANN) model of the MLP 5-17-1 type was developed to predict the gross calorific value (HHV) of various waste types based on ultimate analysis. The dataset comprised heterogeneous samples, including biomass, municipal and industrial waste, sludges, and derived fuels, ensuring the model’s diversity and universality. The model achieved high accuracy (R2 = 0.92; RMSE = 2.36; MAE = 1.68; MAPE = 10.99%), comparable to previous research results. The heterogeneity of the samples confirmed wide variations in composition and energy properties, which are crucial for developing a universal predictive model. The results confirm that ANN is a reliable tool for assessing the energy potential of waste and highlight the importance of expanding databases and optimizing parameters in future research.
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Brandić et al. (2025) studied this question.
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