Predicting Performance of Briquette Made from Millet Bran: A Neural Network Approach
Millet bran possesses good fuel quality and can be successfully used as a professional feedstock for producing solid biofuel. In this paper, a framework for developing an Artificial Neural Network (ANN) to estimate the performance of millet bran briquettes is presented by using experimental data to train, test, and validate the ANN. With the capacity of the developed multi-layer ANN, the effects of moisture content, temperature, and applied pressure on the density, durability, and impact resistance are predicted. Different cases considering three parameters as inputs to the ANN, namely, moisture content, temperature, and applied pressure were analyzed. The outputs of the ANN are the density, durability, and impact resistance for each of the input parameters separately. By comparing with the experimental values, it is shown that the ANN-based method can predict the data well with a Mean Square Error (MSE) value ~ 0.2%. Further, Multiple Linear Regression (MLR) model is used to check the efficiency of ANN prediction from which it is shown that the proposed ANN-based method provides useful guidance for the prediction of the physical parameters efficiently, with the least deviation and high accuracy.
Keywords:Biomass, Millet bran, Briquette, Artificial Neural Network, Multiple Linear Regression
Yank A, Ngadi M, Kok R. Physical properties of rice husk and bran briquettes under low pressure densification for rural applications. Biomass and Bioenergy 2016;84:22–30. https://doi.org/10.1016/j.biombioe.2015.09.015.
Chen L, Xing L, Han L. Renewable energy from agro-residues in China: Solid biofuels and biomass briquetting technology. Renew Sustain Energy Rev 2009;13:2689–95. https://doi.org/10.1016/j.rser.2009.06.025.
Lim JS, Abdul Manan Z, Wan Alwi SR, Hashim H. A review on utilisation of biomass from rice industry as a source of renewable energy. Renew Sustain Energy Rev 2012. https://doi.org/10.1016/j.rser.2012.02.051.
Lubwama M, Yiga VA. Development of groundnut shells and bagasse briquettes as sustainable fuel sources for domestic cooking applications in Uganda. Renew Energy 2017. https://doi.org/10.1016/j.renene.2017.04.041.
Zhang G, Sun Y, Xu Y. Review of briquette binders and briquetting mechanism. Renew Sustain Energy Rev 2018. https://doi.org/10.1016/j.rser.2017.09.072.
Mani S, Tabil LG, Sokhansanj S. Effects of compressive force, particle size and moisture content on mechanical properties of biomass pellets from grasses. Biomass and Bioenergy 2006. https://doi.org/10.1016/j.biombioe.2005.01.004.
Kaliyan N, Vance Morey R. Factors affecting strength and durability of densified biomass products. Biomass and Bioenergy 2009. https://doi.org/10.1016/j.biombioe.2008.08.005.
Zhang J, Zheng D, Wu K, Zhang X. The optimum conditions for preparing briquette made from millet bran using Generalized Distance Function. Renew Energy 2019. https://doi.org/10.1016/j.renene.2019.03.079.
Kumar A, Kumar N, Baredar P, Shukla A. A review on biomass energy resources, potential, conversion and policy in India. Renew Sustain Energy Rev 2015. https://doi.org/10.1016/j.rser.2015.02.007.
Muazu RI, Stegemann JA. Effects of operating variables on durability of fuel briquettes from rice husks and corn cobs. Fuel Process Technol 2015. https://doi.org/10.1016/j.fuproc.2015.01.022.
Lee S min, Ahn BJ, Choi DH, Han GS, Jeong HS, Ahn SH, et al. Effects of densification variables on the durability of wood pellets fabricated with Larix kaem p feri C. and Liriodendron tulipifera L. sawdust. Biomass and Bioenergy 2013. https://doi.org/10.1016/j.biombioe.2012.10.015.
Demirbaş A. Physical properties of briquettes from waste paper and wheat straw mixtures. Energy Convers Manag 1999. https://doi.org/10.1016/S0196-8904(98)00111-3.
Yaman S, Şahan M, Haykiri-Açma H, Şeşen K, Küçükbayrak S. Fuel briquettes from biomass-lignite blends. Fuel Process Technol 2001. https://doi.org/10.1016/S0378-3820(01)00170-9.
Panwar V, Prasad B, Wasewar KL. Biomass residue briquetting and characterization. J Energy Eng 2011. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000040.
Tian Z, Qian C, Gu B, Yang L, Liu F. Electric vehicle air conditioning system performance prediction based on artificial neural network. Appl Therm Eng 2015. https://doi.org/10.1016/j.applthermaleng.2015.06.002.
Benli H. Determination of thermal performance calculation of two different types solar air collectors with the use of artificial neural networks. Int J Heat Mass Transf 2013. https://doi.org/10.1016/j.ijheatmasstransfer.2012.12.042.
Basheer IA, Hajmeer M. Artificial neural networks: Fundamentals, computing, design, and application. J Microbiol Methods 2000. https://doi.org/10.1016/S0167-7012(00)00201-3.
Russell SJ, Norvig P. Artificial Intelligence: A Modern ApproachRussell, S. J., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach. Artificial Intelligence. https://doi.org/10.1017/S0269888900007724. 2010. https://doi.org/10.1017/S0269888900007724.
Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NAE, Arshad H. State-of-the-art in artificial neural network applications: A survey. Heliyon 2018. https://doi.org/10.1016/j.heliyon.2018.e00938.
Mohanraj M, Jayaraj S, Muraleedharan C. Applications of artificial neural networks for thermal analysis of heat exchangers - A review. Int J Therm Sci 2015. https://doi.org/10.1016/j.ijthermalsci.2014.11.030.
Kalogirou SA. Artificial neural networks in renewable energy systems applications: A review. Renew Sustain Energy Rev 2000. https://doi.org/10.1016/S1364-0321(01)00006-5.
Jensen RR, Karki S, Salehfar H. Artificial neural network-based estimation of mercury speciation in combustion flue gases. Fuel Process. Technol., 2004. https://doi.org/10.1016/j.fuproc.2003.11.020.
Haykin S. Neural networks: a comprehensive foundation by Simon Haykin. Knowl Eng Rev 1999. https://doi.org/10.1017/S0269888998214044.
Esen H, Inalli M, Sengur A, Esen M. Forecasting of a ground-coupled heat pump performance using neural networks with statistical data weighting pre-processing. Int J Therm Sci 2008. https://doi.org/10.1016/j.ijthermalsci.2007.03.004.
Caner M, Gedik E, Keĉebaŝ A. Investigation on thermal performance calculation of two type solar air collectors using artificial neural network. Expert Syst Appl 2011. https://doi.org/10.1016/j.eswa.2010.07.090.
Duchesne MA, MacChi A, Lu DY, Hughes RW, McCalden D, Anthony EJ. Artificial neural network model to predict slag viscosity over a broad range of temperatures and slag compositions. Fuel Process. Technol., 2010. https://doi.org/10.1016/j.fuproc.2009.10.013.
Liu Y, Wang H, Zhang H, Liber K. A comprehensive support vector machine-based classification model for soil quality assessment. Soil Tillage Res 2016. https://doi.org/10.1016/j.still.2015.07.006.
Tan P, Zhang C, Xia J, Fang QY, Chen G. Estimation of higher heating value of coal based on proximate analysis using support vector regression. Fuel Process Technol 2015. https://doi.org/10.1016/j.fuproc.2015.06.013.
Gong S, Sasanipour J, Shayesteh MR, Eslami M, Baghban A. Radial basis function artificial neural network model to estimate higher heating value of solid wastes. Energy Sources, Part A Recover Util Environ Eff 2017. https://doi.org/10.1080/15567036.2017.1370513.
Abbasi M, Rastgoo MN, Nakisa B. Monthly and seasonal modeling of municipal waste generation using radial basis function neural network. Environ Prog Sustain Energy 2019. https://doi.org/10.1002/ep.13033.
Panchal F, Panchal M. Optimizing Number of Hidden Nodes for Artificial Neural Network using Competitive Learning Approach. 2015.
Akkaya E. ANFIS based prediction model for biomass heating value using proximate analysis components. Fuel 2016. https://doi.org/10.1016/j.fuel.2016.04.112.
Obafemi O, Stephen A, Ajayi O, Nkosinathi M. A survey of artificial neural network-based prediction models for thermal properties of biomass. Procedia Manuf., 2019. https://doi.org/10.1016/j.promfg.2019.04.103.
Stinchcombe M, White H. Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions, 1989. https://doi.org/10.1109/ijcnn.1989.118640.
Cybenko G. Approximation by superpositions of a sigmoidal function. Math Control Signals, Syst 1989;2:303–14. https://doi.org/10.1007/BF02551274.
Sayin C, Ertunc HM, Hosoz M, Kilicaslan I, Canakci M. Performance and exhaust emissions of a gasoline engine using artificial neural network. Appl Therm Eng 2007. https://doi.org/10.1016/j.applthermaleng.2006.05.016.
Özgören YÖ, Çetinkaya S, Saridemir S, Çiçek A, Kara F. Predictive modeling of performance of a helium charged Stirling engine using an artificial neural network. Energy Convers Manag 2013. https://doi.org/10.1016/j.enconman.2012.12.007.
Zheng X, Jiang Z, Ying Z, Song J, Chen W, Wang B. Role of feedstock properties and hydrothermal carbonization conditions on fuel properties of sewage sludge-derived hydrochar using multiple linear regression technique. Fuel 2020. https://doi.org/10.1016/j.fuel.2020.117609.
Sheela KG, Deepa SN. Review on methods to fix number of hidden neurons in neural networks. Math Probl Eng 2013. https://doi.org/10.1155/2013/425740
How to Cite
Copyright (c) 2020 Gaurav Kumar, Gireeshkumaran Thampi B.S., Pranab Kumar Mondal
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Click here for more information on Copyright policy
Click here for more information on Licensing policy