Abstract
Recovery of energy from municipal solid waste (MSW) will not only add to the national electrical energy generation capacity, but it will also minimize the quantity of waste that ends up in landfill, consequently mitigating its environmental impact. This study has developed ANFIS model to forecast the energy content of waste generated in Johannesburg, South Africa, based on the physical component of the waste: plastic, paper, organics, metals, and textile as input against the energy content. The fuzzy c-means (FCM) clustering technique was explored for data clustering in the ANFIS model. The model was trained with 70% of the data and 30% for validation. The performance of the network was evaluated using root mean square error (RMSE), mean absolute deviation (MAD), and mean absolute percentage error (MAPE). The RMSE, MAD, and MAPE of the model were 0.3418, 0.2692, and 7.7991, respectively. The forecast accuracy of ANFIS was compared with ANN, giving a MAPE of 7.7991 and 13.7870, respectively. ANFIS gave a better forecast accuracy and recommended for energy content prediction of municipal solid waste.
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References
Department of Environmental Affairs (DEA) (2012) National Waste Information Baseline Report
Bhailall S (2015) Landfills gas emissions and the associated air quality, energy and climate change implications in South Africa.  PhD Dissertation, University of the witwatersrand.
Estiati I, Freire FB, Freire JT, Aguado R, Olazar M (2016) Fitting performance of artificial neural networks and empirical correlations to estimate higher heating values of biomass. Fuel 180:377–383
ÖzyuǧUran A, Yaman S (2017) Prediction of calorific value of biomass from proximate analysis. Energy Procedia 107:130–136
Sheng C and Azevedo JLTÃ (2005) Estimating the higher heating value of biomass fuels from basic analysis data. Biomass Bioenergy 28:499–507
Anshar M, Negeri P, Pandang U, Nasir F, Universiti A, Anshar M (2015) The energy potential of municipal solid waste for power generation in Indonesia. Jurnal Mekanikal 37:42–54
Gong S, Sasanipour J, Shayesteh MR, Eslami M, Baghban A (2017) Radial basis function artificial neural network model to estimate higher heating value of solid wastes. Energy Source Part A Recover Util Environ Eff 39(16):1778–1784
Akkaya E, Demir A (2010) Predicting the heating value of municipal solid waste-based materials: an artificial neural network model. Energy Source Part A Recover Util Environ Eff 32(19):1777–1783
Shu HY, Lu HC, Fan HJ, Chang MC, Chen JC (2006) Prediction for energy content of taiwan municipal solid waste using multilayer perceptron neural networks. J Air Waste Manag Assoc 56(6):852–858
Ch Ogwueleka T, Ogwueleka FN (2010) Modelling Energy content of municipal solid waste using artificial neural network. J Environ Heal Sci Eng 7(3):259–266
Baghban A, Ebadi T (2019) GA-ANFIS modeling of higher heating value of wastes: application to fuel upgrading. Energy Source Part A Recover Util Environ Eff 41(1):7–13
Suleymani M, Bemani A (2018) Application of ANFIS-PSO algorithm as a novel method for estimation of higher heating value of biomass. Energy Source Part A Recover Util Environ Eff 40(3):288–293
Drudi KCR, Drudi R, Martins G, Antonio GC, Leite JTC (2019) Statistical model for heating value of municipal solid waste in Brazil based on gravimetric composition. Waste Manag 87:782–790
Eboh FC, Ahlström P, Richards T (2016) Estimating the specific chemical exergy of municipal solid waste. Energy Sci Eng 4(3):217–231
Akkaya E and Ahmet D (2009). Energy Content of Municipal Solid Waste by Multiple regression Analysis. In: 5th International Advanced Technology Symposium. Karabuk, Turkey, May, pp 13–15
Shi H, Mahinpey N, Aqsha A, Silbermann R (2016) Characterization, thermochemical conversion studies, and heating value modeling of municipal solid waste. Waste Manag 48:34–47
Chang YF, Lin CJ, Chyan JM, Chen IM, Chang JE (2007) Multiple regression models for the lower heating value of municipal solid waste in Taiwan. J Environ Manage 85(4):891–899
Lin X, Wang F, Chi Y, Huang Q, Yan J (2015) A simple method for predicting the lower heating value of municipal solid waste in China based on wet physical composition. Waste Manag 36:24–32
Karaboga D & Kaya E (2018) Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. In: Artificial Intelligence Review. pp 1–31
Akbarzadeh S, Arof AK, Ramesh S, Khanmirzaei MH, Nor RM Â (2014) Prediction of conductivity by adaptive neuro-fuzzy model. PLoS ONE 9(3):e92241Â
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Adeleke, O., Akinlabi, S.A., Adedeji, P.A., Jen, TC. (2020). Energy Content Modelling for Municipal Solid Waste Using Adaptive Neuro-Fuzzy Inference System (ANFIS). In: Emamian, S.S., Awang, M., Yusof, F. (eds) Advances in Manufacturing Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-5753-8_17
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