Machine learning models for prediction the Higher Heating Value (HHV) of Municipal Solid Waste (MSW) for waste-to-energy evaluation
Time-resolved knowledge of physico-chemical properties of Municipal Solid Waste (MSW) materials and their thermal energy content is one of the important subjects needed to build waste incineration power plants around the world. For this purpose, machine learning models were used to predict the Highe...
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| Published in | Case studies in thermal engineering Vol. 31; p. 101823 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.03.2022
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2214-157X 2214-157X |
| DOI | 10.1016/j.csite.2022.101823 |
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| Abstract | Time-resolved knowledge of physico-chemical properties of Municipal Solid Waste (MSW) materials and their thermal energy content is one of the important subjects needed to build waste incineration power plants around the world. For this purpose, machine learning models were used to predict the Higher Heating Value (HHV) of MSW based on the initial materials. Four types of machine learning methods: Radial Bias Function Artificial Neural Network (RBF-ANN), Multilayer Perceptron Artificial Neural Network (MLP-ANN), Support Vector Machine (SVM) and Adaptive Nero-Fuzzy Inference System (ANFIS) were used for modeling the HHV with six different inputs (carbon, water, hydrogen, oxygen, nitrogen, sulfur, and ash). The results showed that RBF-ANN can predict the HHV of MSW with higher accuracy than other models. The overall Mean Absolute Percentage Error (MAPE) for MLP-ANN, SVM and ANFIS models were 7.3, 11.77 and 23.76%, respectively. The MAPE of the best topology for RBF model (6-17-1) with spread factor of 0.8 reach to 0.45%. Finally, the results of this study proved that ANN's can be used as a practical tool with high accuracy and reliability for design and management of waste incineration plants. |
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| AbstractList | Time-resolved knowledge of physico-chemical properties of Municipal Solid Waste (MSW) materials and their thermal energy content is one of the important subjects needed to build waste incineration power plants around the world. For this purpose, machine learning models were used to predict the Higher Heating Value (HHV) of MSW based on the initial materials. Four types of machine learning methods: Radial Bias Function Artificial Neural Network (RBF-ANN), Multilayer Perceptron Artificial Neural Network (MLP-ANN), Support Vector Machine (SVM) and Adaptive Nero-Fuzzy Inference System (ANFIS) were used for modeling the HHV with six different inputs (carbon, water, hydrogen, oxygen, nitrogen, sulfur, and ash). The results showed that RBF-ANN can predict the HHV of MSW with higher accuracy than other models. The overall Mean Absolute Percentage Error (MAPE) for MLP-ANN, SVM and ANFIS models were 7.3, 11.77 and 23.76%, respectively. The MAPE of the best topology for RBF model (6-17-1) with spread factor of 0.8 reach to 0.45%. Finally, the results of this study proved that ANN's can be used as a practical tool with high accuracy and reliability for design and management of waste incineration plants. |
| ArticleNumber | 101823 |
| Author | Taki, Morteza Rohani, Abbas |
| Author_xml | – sequence: 1 givenname: Morteza orcidid: 0000-0002-3059-4984 surname: Taki fullname: Taki, Morteza email: mtaki@asnrukh.ac.ir, mortezataaki@gmail.com organization: Department of Agricultural Machinery and Mechanization Engineering, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, P.O. Box: 6341773637, Mollasani, Iran – sequence: 2 givenname: Abbas surname: Rohani fullname: Rohani, Abbas email: arohani@um.ac.ir organization: Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran |
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| Keywords | Radial bias function Incineration power plant Modeling Clean energy |
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| Title | Machine learning models for prediction the Higher Heating Value (HHV) of Municipal Solid Waste (MSW) for waste-to-energy evaluation |
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