Generalized Laguerre Polynomial Optimization-Based Novel Machine Learning Regression Model for Energy Recovery from Waste

The waste-to-energy process relies on the recovery and use of energy produced by the combustion of waste, which allows for waste treatment while ensuring energy production. To improve the prediction accuracy of energy recovered from waste combustion, a new prediction model based on orthogonal Laguerre polynomials is proposed in this work. Exploiting the orthogonality of Laguerre polynomials reduces approximation errors and facilitates parameter estimation through the calculation of orthogonal moments. To validate the proposed method, the propose prediction model was trained on a dataset published online and then used to predict the higher heating value (HHV) of municipal solid waste (MSW), in order to recover energy from this waste. A comparative study was conducted with predictive methods used in the field of energy recovery from waste. Evaluation metrics such as the coefficient of determination (R2), the root mean square error (RMSE), and the mean absolute percentage error MAPE were used in this comparative study. The results obtained show that the proposed model achieves its best performance for a polynomial of degree n=5, with an optimal value of α=0.9 determined by the Artificial Bee Colony (ABC) algorithm. In this configuration, the model obtains a minimum RMSE of 1.09, compared to 5.12 for the LSTM-MLP model, which is the highest-performing reference method among those compared. These results demonstrate the ability of the proposed model to predict the higher calorific value HHV of municipal solid waste with significantly greater accuracy.