Performance Analysis of Intelligent Computational Algorithms for Biomass Higher Heating Value Prediction
Keywords:
Artificial intelligence, biomass, heating value, neural network, proximate analysisAbstract
Higher heating value (HHV) is an essential parameter to consider when evaluating and choosing biomass substrates for combustion and power generation. Traditionally, HHV is determined in the laboratory using an adiabatic oxygen bomb calorimeter. Meanwhile, this approach is laborious and cost-intensive. Hence, it is essential to explore other viable options. In this study, two distinct artificial intelligence-based techniques, namely, a support vector machine (SVM) and an artificial neural network (ANN) were employed to develop proximate analysis-based biomass HHV prediction models. The input variables comprising ash, volatile matter, and fixed carbon were paired to form four separate inputs to the prediction models. The overall findings showed that both the ANN and the SVM tools can guarantee accurate prediction in all the input combinations. The optimal prediction performances were observed when fixed carbon and volatile matter were paired as the input combination. This combination showed that the ANN outperformed the SVM, having presented the least root mean squared error of 0.0008 and the highest correlation coefficient of 0.9274. This study, therefore, concluded that the ANN is more preferred compared to SVM for biomass HHV prediction based on the proximate analysis.
References
Abba, S. I.; A. Saleh; N. Hamza; A. I. Tukur and N. A. Wahab. (2019). Modelling of Uncertain System: A comparison study of Linear and Non-Linear Approaches, IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), 1–6
Akkaya, E. (2016). ANFIS based prediction model for biomass heating value using proximate analysis components, Fuel, 180, pp. 687–693. doi: 10.1016/j.fuel.2016.04.112.
Dai, Z.; Z. Chen; A. Selmi; K. Jermsittiparsert; N. M. Denić and Z. Nеšić. (2021). Machine learning prediction of higher heating value of biomass, Biomass Conversion and Biorefinery. doi: 10.1007/s13399-021-01273-8.
Dodo, U. A.; E. C. Ashigwuike; J. N. Emechebe and S. I. Abba. (2022). Prediction of energy content of biomass based on hybrid machine learning ensemble algorithm, Energy Nexus, 8, pp. 1–15. doi: 10.1016/j.nexus.2022.100157.
Dodo, U. A. and Ashigwuike, E. C. (2023). In-depth physico-chemical characterisation and estimation of the grid power potential of municipal solid wastes in Abuja city, Energy Nexus, 10, pp. 1–9. doi: 10.1016/j.nexus.2023.100192.
Dodo, U. A.; E. C. Ashigwuike; and E. M. Eronu. (2021). Renewable Energy Readiness in Nigeria: A Review Focusing on Power Generation, Uniabuja Journal of Engineering and Technology, 1(1), pp. 115–144
Elmaz, F.; Ö. Yücel and A. Y. Mutlu. (2019). Predictive modeling of biomass gasification with machine learning-based regression methods, Energy, p. 116541. doi: 10.1016/j.energy.2019.116541.
Estiati, I.; F. B. Freire; J. T. Freire; R. Aguado and M. Olazar. (2016). Fitting performance of artificial neural networks and empirical correlations to estimate higher heating values of biomass, FUEL, 180, pp. 377–383. doi: 10.1016/j.fuel.2016.04.051.
Ezzahra Yatim, F.; I. Boumanchar; B. Srhir; Y. Chhiti; C. Jama and F. Ezzahrae M’hamdi Alaoui. (2022). Waste-to-energy as a tool of circular economy: Prediction of higher heating value of biomass by artificial neural network (ANN) and multivariate linear regression (MLR), Waste Management, 153, pp. 293–303. doi: 10.1016/j.wasman.2022.09.013.
García Nieto, P. J.; E. García–Gonzalo; B. M. Paredes–Sánchez and J. P. Paredes–Sánchez. (2022). Forecast of the higher heating value based on proximate analysis by using support vector machines and multilayer perceptron in bioenergy resources, Fuel, 317(122824), pp. 1–10. doi: 10.1016/j.fuel.2021.122824.
Güleç, F.; D. Pekaslan; O. Williams and E. Lester. (2022). Predictability of higher heating value of biomass feedstocks via proximate and ultimate analyses – A comprehensive study of artificial neural network applications, Fuel, 320(123944), pp. 1–16. doi: 10.1016/j.fuel.2022.123944.
Gunamantha, M. (2016). Prediction of Higher Heating Value Bioorganic Fraction of Municipal Solid Waste from Proximate Analysis Data’, International Journal of Engineering Research & Technology, 5(2), pp. 442–447.
Nhuchhen, D. R. and Salam, P. A. (2012). Estimation of higher heating value of biomass from proximate analysis : A new approach, Fuel, 99, pp. 55–63. doi: 10.1016/j.fuel.2012.04.015.
Olatomiwa, L.; A. A. Sadiq; O. M. Longe; J. G. Ambafi; K. E. Jack; T. Adekunle and S. Adeniyi. (2022). An Overview of Energy Access Solutions for Rural Healthcare Facilities, Energies, 15(9554), pp. 1–23.
Olatunji, O. O.; S. Akinlabi; N. Madushele; P. A. Adedeji and I. Felix (2019). Multilayer perceptron artificial neural network for the prediction of heating value of municipal solid waste, AIMS Energy, 7(December), pp. 944–956. doi: 10.3934/energy.2019.6.944.
Phichai, K.; P. Pragrobpondee; T. Khumpart and S. Hirunpraditkoon. (2013). Prediction Heating Values of Lignocellulosics from Biomass Characteristics, International Journal of Chemical and Molecular Engineering, 7(7), pp. 532–535.
Qian, X.; S. Lee; A. Soto and G. Chen. (2018). Regression Model to Predict the Higher Heating Value of Poultry Waste from Proximate Analysis, Resources, 7(39), pp. 1–14. doi: 10.3390/resources7030039.
Rostami, A. and Baghban, A. (2018). Application of a supervised learning machine for accurate prognostication of higher heating values of solid wastes, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, pp. 1–7. doi: 10.1080/15567036.2017.1360967.
Uzun, H.; Z. Yıldız; J. L. Goldfarb and S. Ceylan. (2017). Improved prediction of higher heating value of biomass using an artificial neural network model based on proximate analysis, Bioresource Technology, 234, pp. 122–130. doi: 10.1016/j.biortech.2017.03.015.
Wong, Y. J.; S. K. Arumugasamy; C. H. Chung; A. Selvarajoo and V. Sethu. (2020). Comparative study of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) for modeling of Cu (II) adsorption from aqueous solution using biochar derived from rambutan (Nephelium lappaceum) pee, Environmental Monitoring and Assessment, 192(7), pp. 1–20. doi: 10.1007/s10661-020-08268-4.
Yaka, H.; M. Akin; O. Yucel and H. Sadikoglu. (2022). A comparison of machine learning algorithms for estimation of higher heating values of biomass and fossil fuels from ultimate analysis, Fuel, 320, pp. 1–10. doi: 10.1016/j.fuel.2022.123971.
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