Abstract
This paper has been carried out to investigate the electrical production from biogas of food waste in Batu Pahat area, Johor, Malaysia. The objectives of this research are to identify the potential of food waste in Batu Pahat, Johor for electricity production using Artificial Neural Network (ANN) technique. The data of food waste was collected in 2016 and 2017. The prediction using ANN are divided into two model that are Model 1 for prediction of biogas production (m3) while Model 2 for prediction of electricity production (MWh). The input for Model 1 were the average food waste, daily feedstock volume, feedstock retention time, temperature, yield factor, and digester volume and for input in Model 2 are gas leaking, energy for plant operation, efficiency in electricity production and biogas production. As the results, the highest electricity was produced in 2016 that was produced maximum that is 613.87 MWh per month. It shows that, the higher the volume of average food waste, the highest the electricity in MWh.
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References
US Energy Information Administration (2013) Annual energy outlook
IRENA (2016) Measuring small-scale biogas capacity and production. International Renewable Energy Agency (IRENA), Abu Dhabi
Nagapan S, Ismail AR, Ade A (2012) Construction waste management: Malaysian perspective. Paper presented at the International Conference on Civil and Environmental Engineering Sustainability (IConCEES), Malaysia, pp 229–309
Ogwueleka TCh, Ogwueleka FN (2010) Modeling energy content of municipal solid waste using artificial neural network. Artif Neural Netw 7(3):259–266
Yetilmezsoy K, Turkdogan FI, Temizel I, Gunay A (2013) Development of ANN-based models to predict biogas and methane productions in anaerobic treatment of molasses wastewater. Int J Green Energy 10(9):885–907. https://doi.org/10.1080/15435075.2012.727116
Palaniswamy D, Ramesh G, Sivasankaran S, Kathiravan N (2016) Optimising biogas from food waste using a neural network model. Proc Inst Civ Eng—Munic Eng 170(4):221–229. https://doi.org/10.1680/jmuen.16.00008
Orhorhoro EK, Ebunilo PO (2017) Development of a predictive model for biogas yield using artificial neural networks (ANNs) approach. Am J Energy Power Eng 4(6):71–77
Nadzri (2013) Development of a National Strategic Plan for Food Waste Management in Malaysia. Retrieved from: https://www.uncrd.or.jp/content/documents/Hanoi%203R %20Forum%20PS5_Malaysia.pdf
Department of Statistic Malaysia (2011) Taburan penduduk dan ciri-ciri asas demografi.pdf, pp 1–134
Malaysia Kini (2016) Battling food waste. Malaysia Kini. Retrieved from: http://www.malaysiakini.com/letters/359487
Moh YC, Abd Manaf L (2014) Overview of household solid waste recycling policy status and challenges in Malaysia. Resour Conserv Recycl
Lo HM et al (2010) Modeling biogas production from organic fraction of MSW co-digested with MSWI ashes in anaerobic bioreactors. Bioresour Technol 101(16):6329–6335. https://doi.org/10.1016/j.biortech.2010.03.048
Jamal SSB (2009) An application of artificial neural network for DC servo motor. Universiti Teknologi Malaysia
Sumathi S, Sivanandam SN, Deepa SN (2006) Neural network using Matlab 6.0, 2nd edn. Tata McGraw-Hill Education, New Dehli
Tarassenko L (1998) A guide neural computing application. New York
Sumanthi DS, Sivanandam SN (2006) Introduction to neural networks using MATLAB 6.0
Heidari E, Sobati MA, Movahedirad S (2016) Accurate prediction of nanofluid viscosity using a multilayer perceptron artificial neural network (MLP-ANN). Chemom Intell Lab Syst 155:73–85. https://doi.org/10.1016/j.chemolab.2016.03.031
Fazeli A, Bakhtvar F, Jahanshaloo L, Che Sidik NA, Bayat AE (2016) Malaysia’s stand on municipal solid waste conversion to energy: a review. Renew Sustain Energy Rev 58:1007–1016. https://doi.org/10.1016/j.rser.2015.12.270
Ameen AM, Pasupuleti J (2014) Sensitivity of artificial neural network based model for photovoltaic system actual performance, pp 241–244
Crocker F (2017) Prediction of photovoltaic (PV) output via artificial neural network (ANN) based on real climate condition. Master’s dissertation, Universiti Tun Hussein Onn Malaysia
SWCorp (2017) Hargai Makanan Elakkan Pembaziran
Jawa C (2019) Modeling of biogas system using MATLAB Simulink. Degree’s dissertation, Universiti Tun Hussein Onn Malaysia
Acknowledgements
The authors would like to acknowledge the Research Management Center (RMC), Universiti Tun Hussein Onn Malaysia (UTHM), Batu Pahat, Johor, Malaysia for the financial support of this search. This research is partly by RMC under the GPPS Vot H410 Grant.
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Jawa, C.R., Jumaat, S.A., Yahya, M. (2022). Development of Artificial Neural Network (ANN)-Based Model to Predict Electricity Production from Food Waste Biogas System. In: Isa, K., et al. Proceedings of the 12th National Technical Seminar on Unmanned System Technology 2020. Lecture Notes in Electrical Engineering, vol 770. Springer, Singapore. https://doi.org/10.1007/978-981-16-2406-3_79
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DOI: https://doi.org/10.1007/978-981-16-2406-3_79
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