Physico-chemical characterization using a statistical method is a crucial first step in the process of recovering energy from solid waste. The objective of this study is the creation of the Optimization-Prediction of Higher Heating Value (HHV) Model (OPHhvM) to be classified as an innovation in the field. In addition to a more complex model optimization problem solving applied in Food and Combustible Waste (FCW) in Ouagadougou, the capital of Burkina Faso in West Africa, using a structured methodology. This study focuses on FCW generated by households in order to optimize the energy source through the calorific value in electricity produced by different technological processes such as combustion, incineration, gasification and cogeneration. Several methods have been used to obtain a reliable result, one being linear regression through different models, the other being artificial neutral network through Support Vector Machine (SVM) with Support Vector Classifier (SVC) and Support Vector Regression (SVR). The Optimization and Prediction of HHV Models (OPHhvM)) are qualified using six performance matrices, such as coefficient of determination (R2), Mean Square Error (MSE), Root Mean Square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and standard error of estimate (SEE). The results showed that the linear optimization and prediction model with the combination of volatility material, ash content and cabon fix is the best model, as indicated by the following equation: HHV = 10.982 + 0.1136VM-0.2848AC + 4.91694FC. The results of the OPHhvM model show a difference between the maximum and minimum value of R2[0.91; 0.87], MSE_SVM [0.17; 0.12], MAE_SVM [0.40; 0.27], RMSE_SVM [0.41; 0.35], MAPE_SVM (%) [10.15; 6 .06], SEE_SVM [0.39; 0.34]. These values indicate the significant relationship between the endogenous and exogenous variables, and the OPHhvM can use the best tools to quantify the future value of the energy released through HHV.

Higher Heating Value Analysis for Waste to Energy Intelligence Model Optimization-Prediction

Di Maria, Francesco
Supervision
;
2025

Abstract

Physico-chemical characterization using a statistical method is a crucial first step in the process of recovering energy from solid waste. The objective of this study is the creation of the Optimization-Prediction of Higher Heating Value (HHV) Model (OPHhvM) to be classified as an innovation in the field. In addition to a more complex model optimization problem solving applied in Food and Combustible Waste (FCW) in Ouagadougou, the capital of Burkina Faso in West Africa, using a structured methodology. This study focuses on FCW generated by households in order to optimize the energy source through the calorific value in electricity produced by different technological processes such as combustion, incineration, gasification and cogeneration. Several methods have been used to obtain a reliable result, one being linear regression through different models, the other being artificial neutral network through Support Vector Machine (SVM) with Support Vector Classifier (SVC) and Support Vector Regression (SVR). The Optimization and Prediction of HHV Models (OPHhvM)) are qualified using six performance matrices, such as coefficient of determination (R2), Mean Square Error (MSE), Root Mean Square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and standard error of estimate (SEE). The results showed that the linear optimization and prediction model with the combination of volatility material, ash content and cabon fix is the best model, as indicated by the following equation: HHV = 10.982 + 0.1136VM-0.2848AC + 4.91694FC. The results of the OPHhvM model show a difference between the maximum and minimum value of R2[0.91; 0.87], MSE_SVM [0.17; 0.12], MAE_SVM [0.40; 0.27], RMSE_SVM [0.41; 0.35], MAPE_SVM (%) [10.15; 6 .06], SEE_SVM [0.39; 0.34]. These values indicate the significant relationship between the endogenous and exogenous variables, and the OPHhvM can use the best tools to quantify the future value of the energy released through HHV.
2025
9783031870422
9783031870439
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1601456
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