Machine learning approach for categorical biomass higher heating value prediction based on proximate analysis

References

• Breiman, L. 2001. Random Forests. Machine Learning 45 (1):5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Ceylan, Z., E. Pekel, S. Ceylan, and S. Bulkan. 2018. Biomass higher heating value prediction analysis by ANFIS, PSO-ANFIS and GA-ANFIS. Global NEST Journal 20:589–97. doi:10.30955/gnj.002772.
   Web of Science ®Google Scholar
• Chen, W.-H., S. Nižetić, R. Sirohi, Z. Huang, R. Luque, M. P. Agis, R. Sakthivel, X. Phuong Nguyen, and A. Tuan Hoang. 2022. Liquid hot water as sustainable biomass pretreatment technique for bioenergy production: A review. Bioresource Technology 344:126207. doi:10.1016/j.biortech.2021.126207.
   PubMed Web of Science ®Google Scholar
• Dashti, A., A. Sajadi Noushabadi, M. Raji, A. Razmi, S. Ceylan, and A. H. Mohammadi. 2019. Estimation of biomass higher heating value (HHV) based on the proximate analysis: Smart modeling and correlation. Fuel 257:115931. doi:10.1016/j.fuel.2019.115931.
   Web of Science ®Google Scholar
• Florin, P., R. Mahu, I. V. Ion, and E. Rusu. 2020. A mathematical model of biomass combustion physical and chemical processes. Energies 13 (23):6232. doi:10.3390/en13236232.
   Web of Science ®Google Scholar
• Frank, E., M. A. Hall, and I. H. Witten. 2017. The WEKA workbench. Data Mining 553–71. doi:10.1016/b978-0-12-804291-5.00024-6.
   Google Scholar
• García, R., C. Pizarro, A. G. Lavín, and J. L. Bueno. 2014. Spanish biofuels heating value estimation. Part II: Proximate analysis data. Fuel 117:1139–47. doi:10.1016/j.fuel.2013.08.049.
   Web of Science ®Google Scholar
• Gunn, S. R. 1998. Support vector machines for classification and regression. ISIS Technical Report 14:5–16.
   Google Scholar
• Hosseinpour, S., M. Aghbashlo, M. Tabatabaei, and M. Mehrpooya. 2017. Estimation of biomass higher heating value (HHV) based on the proximate analysis by using iterative neural network-adapted partial least squares (INNPLS). Energy 138:473–79. doi:10.1016/j.energy.2017.07.075.
   Web of Science ®Google Scholar
• Karkevandi-Talkhooncheh, A., A. Rostami, A. Hemmati-Sarapardeh, M. Ahmadi, M. M. Husein, and B. Dabir. 2018. Modeling minimum miscibility pressure during pure and impure CO2 flooding using hybrid of radial basis function neural network and evolutionary techniques. Fuel 220:270–82. doi:10.1016/j.fuel.2018.01.101.
   Web of Science ®Google Scholar
• Kazemi Shariat Panahi, H., M. Dehhaghi, J. E. Kinder, and T. C. Ezeji. 2019. A review on green liquid fuels for the transportation sector: A prospect of microbial solutions to climate change. Biofuel Research Journal 23 (3):995–1024. doi:10.18331/BRJ2019.6.1.2.
   Google Scholar
• Kuan Xing, J., K. Luo, H. Wang, Z. Gao, and J. Fan. 2019. A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning approaches. Energy 188:116077. doi:10.1016/j.energy.2019.116077.
   Web of Science ®Google Scholar
• Lal, S., J. Herbert, P. Arjunan, and A. Suryan. 2022. Advancements in renewable energy transition in India: A review. Energy Sources, Part A: Recovery, Utilization and Environmental Effects. doi:10.1080/15567036.2021.2024921.
   Google Scholar
• Mahdaviara, M., A. Rostami, F. Keivanimehr, and K. Shahbazi. 2021. Accurate determination of permeability in carbonate reservoirs using Gaussian process regression. Journal of Petroleum Science and Engineering 196:920–4105. doi:10.1016/j.petrol.2020.107807.
   Web of Science ®Google Scholar
• Mahdaviara, M., A. Rostami, and K. Shahbazi. 2020. State-of-the-art modeling permeability of the heterogeneous carbonate oil reservoirs using robust computational approaches. Fuel 268:16–2361. doi:10.1016/j.fuel.2020.117389.
   Web of Science ®Google Scholar
• Mutlu, A. Y., and O. Yucel. 2018. An artificial intelligence based approach to predicting syngas composition for downdraft biomass gasification. Energy 165:895–901. doi:10.1016/j.energy.2018.09.131.
   Web of Science ®Google Scholar
• Olatunji, O., S. Akinlabi, and N. Madushele. 2020. Application of artificial intelligence in the prediction of thermal properties of biomass. In Valorization of biomass to value-added commodities. Green energy and technology, edited by M. Daramola and A. Ayeni. Springer International Publishing. doi:10.1007/978-3-030-38032-8_4.
   Google Scholar
• Pattanayak, S., C. Loha, L. Hauchhum and Sailo, L. 2021. Application of MLP-ANN models for estimating the higher heating value of bamboo biomass. Biomass Conversion and Biorefinery 11 (6):2499–508. doi:10.1007/s13399-020-00685-2.
   Web of Science ®Google Scholar
• Perera, S. M. H. D., C. Wickramasinghe, B. K. T. Samarasiri, and M. Narayana. 2021. Modeling of thermochemical conversion of waste biomass – A comprehensive review. Biofuel Research Journal 32 (4):1481–528. doi:10.18331/BRJ2021.8.4.3.
   Web of Science ®Google Scholar
• Rostami, A., and A. Baghban. 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 40 (5):558–64. doi:10.1080/15567036.2017.1360967.
   Web of Science ®Google Scholar
• Rostami, A., A. Hemmati-Sarapardeh, and S. Shamshirband. 2018. Rigorous prognostication of natural gas viscosity: Smart modeling and comparative study. Fuel 222:766–78. doi:10.1016/j.fuel.2018.02.069.
   Web of Science ®Google Scholar
• Shengbo, G., P. Nai Yuh Yek, Y. Wang Cheng, C. Xia, W. Adibah Wan Mahari, R. Keey Liew, W. Peng, T.-Q. Yuan, Tabatabaei, M., Aghbashlo, M. and Sonne, C. 2021. Progress in microwave pyrolysis conversion of agricultural waste to value-added biofuels: A batch to continuous approach. Renewable and Sustainable Energy Reviews 135:1364–0321.
   Web of Science ®Google Scholar
• Sourav Ghosh, G. R. R. and T. Thomas. 2021. Machine learning-based prediction of supercapacitor performance for a novel electrode material: Cerium oxynitride. Energy Storage Materials 40:426–38. doi:10.1016/j.ensm.2021.05.024.
   Web of Science ®Google Scholar
• Tuan Hoang, A., I. M. R. F. Hwai Chyuan Ong, C. Tung Chong, R. S. Chin Kui Cheng, and O. Yong Sik. 2021. Progress on the lignocellulosic biomass pyrolysis for biofuel production toward environmental sustainability. Fuel Processing Technology 223:106997. doi:10.1016/j.fuproc.2021.106997.
   Web of Science ®Google Scholar
• Tursi, A. 2019. A review on biomass: Importance, chemistry, classification, and conversion. Biofuel Research Journal 22 (2):962–97910. doi:10.18331/BRJ2019.6.2.3.
   Web of Science ®Google Scholar
• 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: 0960-8524: 122–30. doi:10.1016/j.biortech.2017.03.015.
   PubMed Web of Science ®Google Scholar
• Vapnik, V. 1998. Statistical learning theory. New York: Wiley.
   Google Scholar
• Xing, J. K., K. Luo, H. Pitsch, H. O. Wang, Y. Bai, C. G. Zhao, and J. R. Fan. 2019. Predicting kinetic parameters for coal devolatilization by means of artificial neural networks. Proceedings of the Combustion Institute, 37 (3):2943–50. doi:10.1016/j.proci.2018.05.148.
   Google Scholar
• Yates, D., and M. Z. Islam. 2021. Fast forest: Increasing random forest processing speed while maintaining accuracy. Information Sciences 557:130–52. doi:10.1016/j.ins.2020.12.067.
   Web of Science ®Google Scholar