Biomass higher heating value prediction machine learning insights into ultimate, proximate, and structural analysis datasets

References

• Ang, K. L. M., and J. K. P. Seng. 2021. Big data and machine learning with hyperspectral information in agriculture. Institute of Electrical and Electronics Engineers Access 9:36699–718. doi:10.1109/ACCESS.2021.3051196.
   Google Scholar
• Arsenović, M., L. Pezo, S. Stanković, and Z. Radojević. 2015. Factor space differentiation of brick clays according to mineral content: Prediction of final brick product quality. Applied Clay Science 115:108–14. doi:10.1016/j.clay.2015.07.030.
   Web of Science ®Google Scholar
• Bakshaev, A., and R. Rudzkis. 2017. Goodness-of-fit tests based on the empirical characteristic function. Lithuanian Mathematical Journal 57 (2):155–70. doi:10.1007/s10986-017-9350-7.
   Web of Science ®Google Scholar
• Benos, L., A. C. Tagarakis, G. Dolias, R. Berruto, D. Kateris, and D. Bochtis. 2021. Machine learning in agriculture: A comprehensive updated review. Sensors 21 (11):1–55. doi:10.3390/s21113758.
   Web of Science ®Google Scholar
• Bilandzija, N., N. Voca, B. Jelcic, V. Jurisic, A. Matin, M. Grubor, and T. Kricka. 2018. Evaluation of Croatian agricultural solid biomass energy potential. Sustainable Energy Reviews 93:225–30. doi:10.1016/j.rser.2018.05.040.
   Web of Science ®Google Scholar
• Brandić, I., L. Pezo, N. Bilandžija, A. Peter, J. Šurić, and N. Voća. 2022. Artificial neural network as a tool for estimation of the higher heating value of miscanthus based on ultimate analysis. Mathematics 10 (20):1–12. doi:10.3390/math10203732.
   Web of Science ®Google Scholar
• Çakman, G., S. Gheni, and S. Ceylan. 2021. Prediction of higher heating value of biochars using proximate analysis by artificial neural network. Biomass Conversion and Biorefinery 1–9. doi:10.1007/s13399-021-01358-4.
   Web of Science ®Google Scholar
• Callejón-Ferre, A. J., J. Carreño-Sánchez, F. J. Suárez-Medina, J. Pérez-Alonso, and B. Velázquez-Martí. 2014. Prediction models for higher heating value based on the structural analysis of the biomass of plant remains from the greenhouses of Almería (Spain). Fuel 116:377–87. doi:10.1016/j.fuel.2013.08.023.
   Web of Science ®Google Scholar
• Cholewa, T., C. A. Balaras, J. Kurnitski, A. Siuta-Olcha, E. Dascalaki, R. Kosonen, C. Lungu, M. Todorovic, I. Nastase, C. Jolas, M. Cakan 2022. Energy efficient renovation of existing buildings for HVAC professionals, (Rehva guidebook; No. 32). Federation of European Heating, Ventilation and Air Conditioning Associations (REHVA).
   Google Scholar
• Collard, F. X., and J. Blin. 2014. A review on pyrolysis of biomass constituents: Mechanisms and composition of the products obtained from the conversion of cellulose, hemicelluloses and lignin. Renewable and Sustainable Energy Reviews 38:594–608. doi:10.1016/j.rser.2014.06.013.
   Web of Science ®Google Scholar
• Cvetkov-Iliev, A., A. Allauzen, and G. Varoquaux. 2022. Analytics on non-normalized data sources: More learning, rather than more cleaning. Institute of Electrical and Electronics Engineers Access 10:42420–31. doi:10.1109/ACCESS.2022.3168013.
   Google Scholar
• 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 13 (5):1–9. doi:10.1007/s13399-021-01273-8.
   Google Scholar
• Darvishan, A., H. Bakhshi, M. Madadkhani, M. Mir, and A. Bemani. 2018. Application of MLP-ANN as a novel predictive method for prediction of the higher heating value of biomass in terms of ultimate analysis. Energy Sources, Part A: Recovery, Uti-Lization and Environmental Effects 40 (24):2960–66. doi:10.1080/15567036.2018.1514437.
   Google Scholar
• Das, N., P. K. Jena, D. Padhi, M. Kumar Mohanty, and G. Sahoo. 2021. A comprehensive review of characterization, pretreatment, and its applications on different lignocellulosic biomass for bioethanol production. Biomass Conversion and Biorefinery 13 (2):1–25. doi:10.1007/s13399-021-01294-3.
   Web of Science ®Google Scholar
• Fernández-Navarro, F., M. Carbonero-Ruz, D. Becerra Alonso, M. Torres-Jiménez 2016. IEEE Transactions on neural networks and learning systems 1 global sensitivity estimates for neural network classifiers, USA, 1–13.
   Google Scholar
• García Nieto, P. J., E. García-Gonzalo, F. Sánchez Lasheras, J. P. Paredes-Sánchez, and P. Riesgo Fernández. 2019. Forecast of the higher heating value in biomass torrefaction by means of machine learning techniques. Journal of Computational and Applied Mathematics 357:284–301. doi:10.1016/j.cam.2019.03.009.
   Web of Science ®Google Scholar
• Ge, Z., Z. Song, S. X. Ding, and B. Huang. 2017. Data mining and analytics in the process industry: The role of machine learning. Institute of Electrical and Electronics Engineers Access 5:20590–616. doi:10.1109/ACCESS.2017.2756872.
   Google Scholar
• Gironès, V. C., S. Moret, E. Peduzzi, M. Nasato, and F. Maréchal. 2017. Optimal use of biomass in large scale energy systems. Insights for Energy Policy Energy 137:789–97. doi:10.1016/j.energy.2017.05.027.
   Google Scholar
• 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 (January):123944. doi:10.1016/j.fuel.2022.123944.
   Google Scholar
• Ighalo, J. O., A. G. Adeniyi, and G. Marques. 2020. Application 349 of artificial neural networks in predicting biomass higher heating value: An early appraisal. Energy Sources Part A: Recovery, Utilization, and Environmental Effects 1–8. https://www.tandfonline.com/loi/ueso20.
   Web of Science ®Google Scholar
• Ismaila, A., I. Abdullahi, and N. N. Garba. 2013. Investigation on biomass briquettes as energy source in relation to their calorific values and measurement of their total carbon and elemental contents for efficient biofuel utilization. Pelagia Research Library Advances in Applied Science Research 4 (2016):303–09.
   Google Scholar
• Khayyaty, Z., I. F. Ilyasz, A. Jindal, S. Madden, M. Ouzzani, P. Papotti, J. A. Quiané-Ruiz, N. Tang, S. Yin 2015. BigDansing: A system for big data cleansing. Proceedings of the ACM SIGMOD International Conference on Management of Data, 1215–30. 10.1145/2723372.2747646
   Google Scholar
• Kollo, T., and D. Von Rosen. 2005. Advanced multivariate statistics with matrices. Advanced Multivariate Statistics with Matrices. doi:10.1007/1-4020-3419-9.
   Google Scholar
• Kołodziej, J., K. Pudełko, and J. Mańkowski. 2023. Energy and biomass yield of industrial hemp (Cannabis sativa L.) as influenced by seeding rate and harvest time in Polish agro-climatic conditions. Journal of Natural Fibers 20 (1). doi:10.1080/15440478.2022.2159609.
   Web of Science ®Google Scholar
• Li, T. Y., H. Xiang, Y. Yang, J. Wang, and G. Yildiz. 2021. Prediction of char production from slow pyrolysis of lignocellulosic biomass using multiple nonlinear regression and artificial neural network. Journal of Analytical and Applied Pyrolysis 159:105286. doi:10.1016/j.jaap.2021.105286.
   Web of Science ®Google Scholar
• Li, Y., C. Zou, M. Berecibar, E. Nanini-Maury, J. C. W. Chan, P. van den Bossche, J. Van Mierlo, and N. Omar. 2018. Random forest regression for online capacity estimation of lithium-ion batteries. Applied Energy 232:197–210. doi:10.1016/j.apenergy.2018.09.182.
   Web of Science ®Google Scholar
• Maksimuk, Y., Z. Antonava, V. Krouk, A. Korsakova, and V. Kursevich. 2021. Prediction of higher heating value (HHV) based on the structural composition for biomass. Fuel 299 (March):120860. doi:10.1016/j.fuel.2021.120860.
   Google Scholar
• Mansor, A. M., J. S. Lim, F. N. Ani, H. Hashim, and W. S. Ho. 2019. Characteristics of cellulose, hemicellulose and lignin of MD2 pineapple biomass. Chemical Engineering Transaction 72:79–84. doi:10.3303/CET1972014.
   Google Scholar
• Martino, L., and J. Read. 2021. A joint introduction to gaussian processes and relevance vector machines with connections to Kalman filtering and other kernel smoothers. Information Fusion 74:17–38. doi:10.1016/j.inffus.2021.03.002.
   Web of Science ®Google Scholar
• Matin, S. S., and S. C. Chelgani. 2016. Estimation of coal gross calorific value based on various analyses by random forest method. Fuel 177:274–78. doi:10.1016/j.fuel.2016.03.031.
   Web of Science ®Google Scholar
• Maydeu-Olivares, A. 2017. Maximum likelihood estimation of structural equation models for continuous data: standard errors and goodness of fit. Structural Equation Modeling 24 (3):383–94. doi:10.1080/10705511.2016.1269606.
   Web of Science ®Google Scholar
• Noushabadi, A. S., A. Dashti, F. Ahmadijokani, J. Hu, and A. H. Mohammadi. 2021. Estimation of higher heating values (HHVs) of biomass fuels based on ultimate analysis using machine learning techniques and improved equation. Renewable Energy 179:550–62. doi:10.1016/j.renene.2021.07.003.
   Web of Science ®Google Scholar
• Ostertagová, E. 2012. Modelling using polynomial regression. Procedia Engineering 48:500–06. doi:10.1016/j.proeng.2012.09.545.
   Google Scholar
• Pezo, L. L., B. L. Ćurčić, V. S. Filipović, M. R. Nićetin, G. B. Koprivica, N. M. Mišljenović, and L. B. Lević. 2013. Artificial neural network model of pork meat cubes osmotic dehydration. Chemical Industry 67 (3):465–75. doi:10.2298/HEMIND120529082P.
   Google Scholar
• Qiu, J., Q. Wu, G. Ding, Y. Xu, and S. Feng. 2016. A survey of machine learning for big data processing. EURASIP Journal on Advances in Signal Processing. 2016(1). doi:10.1186/s13634-1.
   PubMedGoogle Scholar
• Ranganayaki, V., and S. N. Deepa. 2019. Linear and non-linear proximal support vector machine classifiers for wind speed prediction. Cluster Computing 22 (S1):379–90. doi:10.1007/s10586-018-2005-6.
   Google Scholar
• Schonlau, M., and R. Y. Zou. 2020. The random forest algorithm for statistical learning. Stata Journal 20 (1):3–29. doi:10.1177/1536867X20909688.
   Web of Science ®Google Scholar
• Scornet, E., G. Biau, and J. P. Vert. 2015. Consistency of random forests. The Annals of Statistics 43 (4):1716–41. doi:10.1214/15-AOS1321.
   Web of Science ®Google Scholar
• Soponpongpipat, N., D. Sittikul, and U. Sae-Ueng. 2015. Higher heating value prediction of torrefaction char produced from non-woody biomass. Front Energy 9 (4):461–71. doi:10.1007/s11708-015-0377-3.
   Web of Science ®Google Scholar
• Taki, M., and A. Rohani. 2022. Machine learning models for prediction the Higher Heating Value (HHV) of Municipal Solid Waste (MSW) for waste-to-energy evaluation. Case Studies in Thermal Engineering 31:101823. doi:10.1016/j.csite.2022.101823.
   Web of Science ®Google Scholar
• Tipping, M. E. 2001. Sparse bayesian learning and the relevance vector machine. Journal of Machine Learning Research 1 (3):211–44.
   Web of Science ®Google Scholar
• Tursi, A. 2019. A review on biomass: Importance, chemistry, classification, and conversion. Biofuel Research Journal 6 (2):962–79. doi:https://doi.org/10.18331/BRJ2019.6.2.3.
   Web of Science ®Google Scholar
• Veza, I., H. Irianto Panchal, P. A. Paristiawan, M. Idris, I. M. R. Fattah, N. R. Putra, R. Silambarasan, and R. Silambarasan. 2022. Improved prediction accuracy of biomass heating value using proximate analysis with various ANN training algorithms. Results in Engineering 16 (September):100688. doi:10.1016/j.rineng.2022.100688.
   Google Scholar
• Voća, N., J. Leto, T. Karažija, N. Bilandžija, A. Peter, H. Kutnjak, J. Šurić, and M. Poljak. 2021. Energy properties and biomass yield of miscanthus x giganteus fertilized by municipal sewage sludge. Molecules 26 (14):4371. doi:10.3390/molecules26144371.
   PubMed Web of Science ®Google Scholar
• Xiaorui, L., Y. Jiamin, and Y. Longji. 2023. Predicting the high heating value and nitrogen content of torrefied biomass using a support vector machine optimized by a sparrow search algorithm. RSC Advances 13 (2):802–07. doi:10.1039/d2ra06869a.
   PubMed Web of Science ®Google Scholar
• 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
• Yoon, Y., G. Swales, and T. M. Margavio. 2017. A comparison of discriminant analysis versus artificial neural networks. The Journal of the Operational Research Society 44 (1):51–60. doi:10.1057/jors.1993.6.
   Google Scholar