Enhanced prediction of heating value of municipal solid waste using hybrid neuro-fuzzy model and decision tree-based feature importance assessment

Abstract

This study proposes a hybrid network of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA) to predict the higher heating value (HHV) of municipal solid waste (MSW). To enhance the robustness and accuracy of the model and optimize its ability to capture the complex non-linear relationship in the MSW dataset, eight membership functions (MF)-type of the grid partitioning (GP) clustering approach were tested. Moreover, understanding the relative importance and contribution of different waste properties to HHV prediction is critical for improving the model's predictive capability and optimizing the waste-to-energy (WTE) process. To this end, the feature importance analysis of MSW input variables was carried out using the decision tree regressor with the Gini importance (GI) metrics to identify the most influential variable. Key waste properties, including ultimate analysis data, ash and moisture content were used as input variables for the model. The result shows that the GP-clustered GA-ANFIS model based on triangular-shaped MF-type (tri-MF) has the most accurate HHV predictions with Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Deviation (MAD) values of 0.7642, 13.677, 1.5913 and 0.9821 at the training and 0.6364, 16.216, 1.2437 and 0.7821 at the testing stage. Feature importance assessment revealed ash content as the most important predictor of HHV based on GI-value of 0.519668 (about 50% cumulative importance). Additionally, sulphur and nitrogen, along with ash content, dominated the HHV prediction and exhibited the highest predictive power on HHV with about 80% cumulative importance. The robust integrated approach of hybrid neuro-fuzzy model, with decision tree-based feature importance assessment, offers an effective approach for enhancing the prediction of HHV of MSW. The outcome of the study enhances WTE systems, facilitating more efficient and sustainable energy recovery from MSW.

Keywords

Higher heating value / Municipal solid waste / Feature importance / Genetic algorithm / ANFIS / Decision tree

CRediT authorship contribution statement

Oluwatobi Adeleke: Writing - review & editing, Writing - original draft, Visualization, Validation, Project administration, Methodology, Formal analysis, Data curation, Conceptualization. Obafemi O. Olatunji: Visualization, Validation, Investigation, Formal analysis, Data curation. Tien-Chien Jen: Writing - review & editing, Supervision, Resources, Project administration, Investigation, Funding acquisition, Conceptualization. Iretioluwa Olawuyi: Writing - original draft, Investigation, Formal analysis, Data curation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.gerr.2025.100119.

References

[1] Adedeji, P.A., Akinlabi, S., Madushele, N., Olatunji, O.O., 2020. Wind turbine power output very short-term forecast: a comparative study of data clustering techniques in a PSO-ANFIS model. J. Clean. Prod. 254, 120135. https://doi.org/10.1016/j.jclepro.2020.120135.

[2] Adeleke, O., Akinlabi, S.A., Jen, T.C., Dunmade, I., 2022a. Environmental impact assessment of the current, emerging, and alternative waste management systems using life cycle assessment tools: a case study of Johannesburg, South Africa. Environ. Sci. Pollut. Control Ser. 29 (5), 7366-7381. https://doi.org/10.1007/s11356-021-16198-y.

[3] Adeleke, O., Akinlabi, S., Jen, T.C., Adedeji, P.A., Dunmade, I., 2022b. Evolutionarybased neuro-fuzzy modelling of combustion enthalpy of municipal solid waste. Neural Comput. Appl. 2. https://doi.org/10.1007/s00521-021-06870-2.

[4] Adeleke, O., Akinlabi, S., Jen, T., Dunmade, I., Adeleke, O., Akinlabi, S., Jen, T., Dunmade, I., Adeleke, O., Akinlabi, S., Jen, T., Dunmade, I., 2021. Transactions of the Royal Society of South Africa towards sustainability in municipal solid waste management in South Africa : a survey of challenges and prospects towards sustainability in municipal solid waste management in South Africa : a survey of ch. https://doi.org/10.1080/0035919X.2020.1858366.

[5] Adeleke, O., Bamisaye, A., Adesina Adegoke, K., Abimbola Adegoke, I., Jen, T.-C., 2025. Optimizing the energy values of solid biofuel through acidic pre-treatment: an evolutionary-based neuro-fuzzy modelling and feature importance analysis. Fuel 380, 133182. https://doi.org/10.1016/j.fuel.2024.133182.

[6] Afaq, S., Rao, S., 2020. Significance of epochs on training A neural network. Int. J. Sci. Tech. Res. 19 (6), 485-488. www.ijstr.org.

[7] Amen, R., Hameed, J., Albashar, G., Kamran, H.W., Hassan Shah, M.U., Zaman, M.K.U., Mukhtar, A., Saqib, S., Ch, S.I., Ibrahim, M., Ullah, S., Al-Sehemi, A.G., Ahmad, S.R., Klemeš J.J., Bokhari, A., Asif, S., 2021. Modelling the higher heating value of municipal solid waste for assessment of waste-to-energy potential: a sustainable case study. J. Clean. Prod. 287. https://doi.org/10.1016/j.jclepro.2020.125575.

[8] Apsemidis, A., Psarakis, S., Moguerza, J.M., 2020. A review of machine learning kernel methods in statistical process monitoring. Comput. Ind. Eng. 142, 106376. https://doi.org/10.1016/j.cie.2020.106376.

[9] Awasthi, M.K., Sarsaiya, S., Chen, H., Wang, Q., Wang, M., Awasthi, S.K., Li, J., Liu, T., Pandey, A., Zhang, Z., 2019. Global status of waste-to-energy technology. In: Current Developments in Biotechnology and Bioengineering. Elsevier B.V. https://doi.org/10.1016/b978-0-444-64083-3.00003-8.

[10] Bagheri, M., Esfilar, R., Sina, M., Kennedy, C.A., 2019. A comparative data mining approach for the prediction of energy recovery potential from various municipal solid waste, 116 (September).

[11] Boie, W., 1953. Fuel technology calculations. Energ. Tech. 3, 309-316.

[12] Chen, Z., Zhao, M., Lv, Y., Wang, I., Tariq, G., Zhao, S., Ahmed, S., Dong, W., Ji, G., 2024. Higher heating value prediction of high ash gasification-residues: comparison of white, grey, and black box models. Energy 288. https://doi.org/10.1016/j.energy.2023.129863.

[13] Clark, W.A.V., Deurloo, M.C., 2005. Categorical modeling/automatic interaction detection. In: Kempf-LeonardK. (Ed.), Encyclopedia of Social Measurement. Elsevier, pp. 251-258. https://doi.org/10.1016/B0-12-369398-5/00359-5.

[14] Deshwal, S., Kumar, A., Chhabra, D., 2020. Exercising hybrid statistical tools GA-RSM, GA-ANN and GA-ANFIS to optimize FDM process parameters for tensile strength improvement. CIRP J. Manuf. Sci. Tech. 31, 189-199. https://doi.org/10.1016/j.cirpj.2020.05.009.

[15] Dong, W., Chen, Z., Chen, J., Ting, Z.J., Zhang, R., Ji, G., Zhao, M., 2022. A novel method for the estimation of higher heating value of municipal solid wastes. Energies 15 (7). https://doi.org/10.3390/en15072593.

[16] Drissi El-Bouzaidi, M., El Bardouni, T., Chham, E., El Hajjaji, O., Nouayti, A., Idrissi, A., El-Bakkari, K., 2024. Unfolding of the neutron spectrum in a nuclear reactor using genetic algorithms method. Nucl. Eng. Des. 422, 113156. https://doi.org/10.1016/j.nucengdes.2024.113156.

[17] ECN, 2018. Phyllis2, database for biomass and waste. https://phyllis.nl/Browse/Standard/ECN-Phyllis. (Accessed 10 August 2022), 2018.

[18] Gutierrez-Gomez, A.C., Gallego, A.G., Palacios-Bereche, R., Tofano de Campos Leite, J., Pereira Neto, A.M., 2021. Energy recovery potential from Brazilian municipal solid waste via combustion process based on its thermochemical characterization. J. Clean. Prod. 293, 126145. https://doi.org/10.1016/j.jclepro.2021.126145.

[19] Jang, J.S.R., 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Sys. Man Cybern 23 (3), 665-685. https://doi.org/10.1109/21.256541.

[20] Khanmohammadi, S., Kizilkan, O., Musharavati, F., 2021. Chapter 17 - multiobjective optimization of a geothermal power plant. In: ColpanC.O., EzanM.A., KizilkanO. (Eds.), Thermodynamic Analysis and Optimization of Geothermal Power Plants. Elsevier, pp. 279-291. https://doi.org/10.1016/B978-0-12-821037-6.00011-1.

[21] Kokash, N., Makhnist, L., 2024. Using decision trees for interpretable supervised clustering. SN Comp. Sci. 5 (2), 268. https://doi.org/10.1007/s42979-023-02590-7.

[22] Kumar, R., Hynes, N.R.J., 2019. Prediction and optimization of surface roughness in thermal drilling using integrated ANFIS and GA approach. Eng. Sci. Tech. Int. J. https://doi.org/10.1016/j.jestch.2019.04.011xxxx.

[23] Liu, Y., Zhang, Z., Zhu, D., Bo, L., Yang, S., Yue, Y., Wang, Y., 2024. Mine water cooperative optimal scheduling based on improved genetic algorithm. Heliyon 10 (6), e27289. https://doi.org/10.1016/j.heliyon.2024.e27289.

[24] Lloyd, W.G., Davenport, D.A., 1980. Applying thermodynamics to fossil fuels: heats of combustion from elemental compositions. J. Chem. Educ. 57 (1), 56. https://doi.org/10.1021/ed057p56.

[25] Mateus, M.M., Bordado, J.M., Galhano dos Santos, R., 2021. Simplified multiple linear regression models for the estimation of heating values of refuse derived fuels. Fuel 294 (February). https://doi.org/10.1016/j.fuel.2021.120541.

[26] Meraz, L., Domínguez, A., Kornhauser, I., Rojas, F., 2003. A thermochemical conceptbased equation to estimate waste combustion enthalpy from elemental composition. Fuel 82 (12), 1499-1507. https://doi.org/10.1016/S0016-2361(03)00075-9.

[27] Mola, M., Amiri-Ahouee, R., 2021. ANFIS model based on fuzzy C-mean, grid partitioning and subtractive clustering to detection of stator winding inter-turn fault for PM synchronous motor. Int. Trans. Electr. Energy Sys. 31 (3), e12770. https://doi.org/10.1002/2050-7038.12770.

[28] Nhuchhen, D.R., Abdul Salam, P., 2012. Estimation of higher heating value of biomass from proximate analysis: a new approach. Fuel 99, 55-63. https://doi.org/10.1016/j.fuel.2012.04.015.

[29] Noushabadi, A.S., Dashti, A., Ahmadijokani, F., Hu, J., Mohammadi, A.H., 2021. Estimation of higher heating values (HHVs) of biomass fuels based on ultimate analysis using machine learning techniques and improved equation. Renew. Energy 179, 550-562. https://doi.org/10.1016/j.renene.2021.07.003.

[30] Rahul, Gupta, A., Bansal, A., Roy, K., 2021. Solar energy prediction using decision tree regressor. In: 2021 5th International Conference on Intelligent Computing and Control Systems. ICICCS, pp. 489-495. https://doi.org/10.1109/ICICCS51141.2021.9432322.

[31] Sagastume Gutiérrez, A., Cabello Eras, J.J., Hens, L., Vandecasteele, C., 2020. The energy potential of agriculture, agroindustrial, livestock, and slaughterhouse biomass wastes through direct combustion and anaerobic digestion. The case of Colombia. J. Clean. Prod. 269. https://doi.org/10.1016/j.jclepro.2020.122317.

[32] Sarkheyli, A., Zain, A.M., Sharif, S., 2015. Robust optimization of ANFIS based on a new modified GA. Neurocomputing 166, 357-366. https://doi.org/10.1016/j.neucom.2015.03.060.

[33] Vasileva-Stojanovska, T., Vasileva, M., Malinovski, T., Trajkovik, V., 2015. An ANFIS model of quality of experience prediction in education. Appl. Soft Comput. 34, 129-138. https://doi.org/10.1016/j.asoc.2015.04.047.

[34] Wilson, D.L., 1972. Prediction of heat of combustion of solid wastes from ultimate analysis. Environ. Sci. Technol. 6 (13), 1119-1121. https://doi.org/10.1021/es60072a011.

[35] Wu, X., Liu, J., 2009. A new early stopping algorithm for improving neural network generalization. In: 2009 Second International Conference on Intelligent Computation Technology and Automation, vol. 1, pp. 15-18. https://doi.org/10.1109/ICICTA.2009.11.

[36] Zhang, Z., Al-Bahrani, M., Ruhani, B., Heybatian Ghalehsalimi, H., Zandy Ilghani, N., Maleki, H., Ahmad, N., Nasajpour-Esfahani, N., Toghraie, D., 2023. Optimized ANFIS models based on grid partitioning, subtractive clustering, and fuzzy C-means to precise prediction of thermophysical properties of hybrid nanofluids. Chem. Eng. J. 471, 144362. https://doi.org/10.1016/j.cej.2023.144362.