APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR SOLID WASTE PROJECTION AND ENERGY CONTENT EVALUATION
DOI:
https://doi.org/10.17501/26510251.2022.3102Keywords:
artificial neural network, hyperparameter optimization, lower calorific value, uncertainty analysis, waste segregationAbstract
Forecasts of the heterogeneous municipal solid waste (MSW) generation are vital for sustainable MSW management. Artificial neural network (ANN) models have been successfully demonstrated to predict complex MSW trends, but the negligence of the MSW’s heterogeneous characteristics hinders the further application of the predictions. This study aims to adopt robust ANN models coupling Bayesian hyperparameter optimization and uncertainty analysis to forecast the heterogeneous MSW generation in a country with further application in energy content evaluation. The impact of the hyperparameter optimization is illustrated by comparing the forecast uncertainties of ANN models using the Bayesian-optimized hyperparameters and default hyperparameters using ensemble forecasts. The relative standard deviations of the ensemble forecasts show that overfitting is more susceptible in the default models (11.1% – 44,400%) than in the Bayesian-optimized models (3.64% – 27.7%). By using the Bayesian-optimized models, Malaysia’s heterogeneous MSW generation is projected individually based on its physical composition. The total MSW generation in Malaysia is expected to grow by 12% from 2020 to 2030, with food waste as the dominant composition (44%). The energy content of the overall MSW is evaluated based on the lower calorific value of the forecasted MSW. The forecasted increase in food waste generation reduces the lower caloric value of Malaysia’s MSW below the lower limit to burn without supporting fuel (i.e., 6.5 MJ/kg). An alternative scenario where food waste is segregated from the overall MSW shows an increase in the lower calorific value from 5.874 MJ/kg to 10.31 MJ/kg. Without food waste segregation, the development of organic waste treatment facilities in Malaysia should be emphasized instead of incinerators. This study not only offers useful information for capacity planning of MSW treatment facilities using a data-driven approach, but it also broadens the scope for future related research by examining the application of the forecasted outcomes using ANN models.
Downloads
References
Abbasi, M., & El Hanandeh, A. (2016). Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Management, 56, 13-22. doi: 10.1016/j.wasman.2016.05.018
Abdallah, M., Abu Talib, M., Feroz, S., Nasir, Q., Abdalla, H., & Mahfood, B. (2020). Artificial intelligence applications in solid waste management: A systematic research review. Waste Management, 109, 231-246. doi: 10.1016/j.wasman.2020.04.057
Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938. doi: 10.1016/j.heliyon.2018.e00938
Ayeleru, O. O., Fajimi, L. I., Oboirien, B. O., & Olubambi, P. A. (2021). Forecasting municipal solid waste quantity using artificial neural network and supported vector machine techniques: A case study of Johannesburg, South Africa. Journal of Cleaner Production, 289, 125671. doi: 10.1016/j.jclepro.2020.125671
Baptista, R., & Poloczek, M. (2018). Bayesian optimization of combinatorial structures International Conference on Machine Learning, Stockholm, Sweden.
Bui, T. D., Tsai, F. M., Tseng, M.-L., & Ali, M. H. (2020). Identifying sustainable solid waste management barriers in practice using the fuzzy Delphi method. Resources, Conservation and Recycling, 154, 104625. doi: 10.1016/j.resconrec.2019.104625
Cheng, H., Ding, X., Zhou, W., & Ding, R. (2019). A hybrid electricity price forecasting model with Bayesian optimization for German energy exchange. International Journal of Electrical Power & Energy Systems, 110, 653-666. doi: 10.1016/j.ijepes.2019.03.056
Gómez-Sanabria, A., Kiesewetter, G., Klimont, Z., Schoepp, W., & Haberl, H. (2022). Potential for future reductions of global GHG and air pollutants from circular waste management systems. Nature Communications, 13(1), 106. doi: 10.1038/s41467-021-27624-7
Hoque, M. M., & Rahman, M. T. U. (2020). Landfill area estimation based on solid waste collection prediction using ANN model and final waste disposal options. Journal of Cleaner Production, 256, 120387. doi: 10.1016/j.jclepro.2020.120387
Jaadi, Z. (2019). Everything you need to know about interpreting correlations. Towards Data Science. Retrieved 25 October 2021 from https://towardsdatascience.com/eveything-you-need-to-know-about-interpreting-correlations-2c485841c0b8
Jin, X.-B., Zheng, W.-Z., Kong, J.-L., Wang, X.-Y., Bai, Y.-T., Su, T.-L., & Lin, S. (2021). Deep-learning forecasting method for electric power load via attention-based encoder-decoder with Bayesian optimization. Energies, 14(6). doi: 10.3390/en14061596
JPSPN. (2016). Dasar Pengurusan Sisa Pepejal Negara 2016. Putrajaya, SGR: National Solid Waste Management Department.
Kaza, S., Yao, L., Bhada-Tata, P., & Van Woerden, F. (2018). What a waste 2.0: a global snapshot of solid waste management to 2050. Washington, DC: World Bank Publications.
MathWorks. (2021a). Function fitting neural network. MathWorks. Retrieved 29 November 2021 from https://www.mathworks.com/help/deeplearning/ref/fitnet.html?s_tid=doc_ta
MathWorks. (2021b). Gradient descent with adaptive learning rate backpropagation. MathWorks. Retrieved 29 November 2021 from https://www.mathworks.com/help/deeplearning/ref/traingda.html
National Research Council. (2000). Waste incineration and public health. Washington, DC: National Academies Press.
Sergeyev, Y. D., Kvasov, D. E., & Mukhametzhanov, M. S. (2018). On the efficiency of nature-inspired metaheuristics in expensive global optimization with limited budget. Scientific Reports, 8(1), 453. doi: 10.1038/s41598-017-18940-4
Singh, A. (2019). Solid waste management through the applications of mathematical models. Resources, Conservation and Recycling, 151, 104503. doi: 10.1016/j.resconrec.2019.104503
Sultana, N., Hossain, S. M. Z., Abusaad, M., Alanbar, N., Senan, Y., & Razzak, S. A. (2022). Prediction of biodiesel production from microalgal oil using Bayesian optimization algorithm-based machine learning approaches. Fuel, 309, 122184. doi: 10.1016/j.fuel.2021.122184
Vitorino de Souza Melaré, A., Montenegro González, S., Faceli, K., & Casadei, V. (2017). Technologies and decision support systems to aid solid-waste management: a systematic review. Waste Management, 59, 567-584. doi: 10.1016/j.wasman.2016.10.045
World Bank. (1999). Municipal solid waste incineration: World Bank technical guidance report. Washington, DC: The World Bank.
Wu, F., Niu, D., Dai, S., & Wu, B. (2020). New insights into regional differences of the predictions of municipal solid waste generation rates using artificial neural networks. Waste Management, 107, 182-190. doi: 10.1016/j.wasman.2020.04.015
Xu, A., Chang, H., Xu, Y., Li, R., Li, X., & Zhao, Y. (2021). Applying artificial neural networks (ANNs) to solve solid waste-related issues: A critical review. Waste Management, 124, 385-402. doi: 10.1016/j.wasman.2021.02.029
Yao, C., Cai, D., Bu, J., & Chen, G. (2017). Pre-training the deep generative models with adaptive hyperparameter optimization. Neurocomputing, 247, 144-155. doi: 10.1016/j.neucom.2017.03.058
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Proceedings of the World Conference on Waste Management
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License.
