Review of machine learning applications for predicting the quality of biomass briquettes for sustainable and low-carbon energy solutions

Abstract

This review discusses how Machine Learning has been applied to predict the quality of biomass briquettes produced from agricultural and municipal solid organic waste, which are crucial for advancing green and low-carbon energy solutions. Traditional methods of assessment of briquette quality involve destructive laboratory experiments, do not favor sample reuse, are time-consuming, and labor-intensive, posing barriers to efficient production. This paper reviews literature on various Machine Learning models applied for predicting and optimizing briquette quality parameters, including combustion, physical, and emission properties. Several Machine Learning models have shown promising results in predicting and optimizing these key parameters for example, a Random Forest model with R² of 0.9936 in deformation energy prediction and Artificial Neural Networks with R² of 0.8936 in the prediction of impact resistance. By enhancing the accuracy and efficiency of briquette quality predictions, Machine Learning algorithms contribute to the development of high-quality biomass briquettes, thereby creating sustainable and low-carbon energy systems. This review points to critical literature gaps regarding model generalizability across diverse biomass feedstocks and integration of broader quality parameters. Addressing these gaps will advance AI-based solutions, promote greener energy practices, and support sustainable development. The findings are intended to aid researchers, industry professionals, and policymakers in advancing the production of high-quality biomass briquettes for cleaner energy and sustainable development.

Keywords

Machine learning / Briquettes / Sustainable energy / Low-carbon solutions / Agricultural waste / Municipal waste

CRediT authorship contribution statement

Constance Nakato Nakimuli: Writing - review & editing, Writing - original draft, Visualization, Methodology, Investigation. Fred Kaggwa: Writing - review & editing. Johan De Greef: Writing - review & editing. David Kilama Okot: Writing - review & editing, Writing - original draft, Supervision. Julien Blondeau: Writing - review & editing, Supervision, Resources, Funding acquisition. Simon Kawuma: Writing - review & editing, Supervision.

Declaration of generative AI in the writing process

During the preparation of this work, the authors used ChatGPT to improve readability and language. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Funding information

This research has been funded by the University as a Facilitator Community Based Sustainable Solutions to Demographic Challenges in South Western Uganda (UCoBS) project which is a collaboration between Mbarara University of Science and Technology (MUST) Uganda, Vrije Universiteit Brussel (VUB)Belgium, supported by the VLIR-UOS Institutional University Cooperation (IUC) program.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Constance Nakato Nakimuli reports financial support was provided by MUST UCOBS. Constance Nakato Nakimuli reports financial support was provided by Flemish Inter-university Council. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

[1] Abdulkareem, S., Hakeem, B.A., Ahmed, I.I., Ajiboye, T.K., Adebisi, J.A., Yahaya, T., 2018. Combustion characteristics of bio-degradable biomass briquettes. J. Eng. Sci. Technol. 13 (9), 2779-2791.

[2] Achebe, C., Umeji, A., Chukwuneke, J., 2018. Energy evaluation of various compositions of biomass waste briquettes. Adv. Res. 13 (6), 1-11.

[3] Afolabi, I.C., Epelle, E.I., Gunes, B., Güleç F., Okolie, J.A., 2022. Data-driven machine learning approach for predicting the higher heating value of different biomass classes. Cleanroom Technol. 4 (4), 1227-1241.

[4] Alan, H., Köker, A.R., 2023. Analyzing and mapping agricultural waste recycling research: an integrative review for conceptual framework and future directions. Resour. Policy 85, 103987.

[5] Ali, A., Chhabra, D., Kumari, M., Manisha, Pinkey, Tiwari, S., Sahdev, R.K., 2024. Optimization and characterization of hybrid bio-briquettes produced from the mixture of sawdust, sugarcane bagasse, and paddy straw. Environ. Sci. Pollut. Control Ser. 31 (10), 15467-15490.

[6] Ali, A., Kumari, M., Laura, J.S., Rizwanullah, M., Manisha, Chhabra, D., Sahdev, R.K., 2023. Optimal composition of biomass pellet for enhancing calorific value using MOGA-ANN: a mixture of paddy straw, sawdust, cow dung, and paper pulp. Biomass Convers. Bioref. 1-17.

[7] Aliyu, M., Mohammed, I.S., Lawal, H., Dauda, S.M., Balami, A., Usman, M., Ndagi, B., 2021. Effect of compaction pressure and biomass type (Rice husk and sawdust) on some physical and combustion properties of briquettes. ARID Zone J. Eng. Technol. Environ. 17 (1), 61-70.

[8] Amin, N.A.S., Istadi, I., Roeva, O., 2012. Different tools on multi-objective optimization of a hybrid artificial neural network-genetic algorithm for plasma chemical reactor modelling. In: Real-World Applications of Genetic Algorithms. IntechOpen.

[9] Asamoah, B., Nikiema, J., Gebrezgabher, S., Odonkor, E., Njenga, M., 2016. A Review on Production, Marketing and Use of Fuel Briquettes. World Agro Forestry Centre.

[10] Ascher, S., Watson, I., You, S., 2022. Machine learning methods for modelling the gasification and pyrolysis of biomass and waste. Renew. Sustain. Energy Rev. 155, 111902.

[11] ASTM, International, 2008. ASTM D2166-85. Standard Test Method for Unconfined Compressive Strength. ASTM International, West Conshohocken, PA.

[12] Badillo, S., Banfai, B., Birzele, F., Davydov, I.I., Hutchinson, L., Kam-Thong, T., Zhang, J.D., 2020. An introduction to machine learning. Clin. Pharmacol. Ther. 107 (4), 871-885.

[13] Bamisaye, A., Ige, A.R., Adegoke, I.A., Ogunbiyi, E.O., Bamidele, M.O., Adeleke, O., Adegoke, K.A., 2023. Eco-friendly de-lignified and raw Celosia argentea waste solid biofuel: comparative studies and machine learning modelling. Fuel 340, 127412.

[14] Bamisaye, A., Ige, A.R., Adegoke, K.A., Adegoke, I.A., Bamidele, M.O., Adeleke, O., Maxakato, N.W., 2024a. H2SO4-treated and raw watermelon waste bio-briquettes: comparative, eco-friendly and machine learning studies. Fuel 358, 129936.

[15] Bamisaye, A., Ige, A.R., Adegoke, K.A., Adegoke, I.A., Bamidele, M.O., Alli, Y.A., Idowu, M.A., 2024b. Amaranthus hybridus waste solid biofuel: comparative and machine learning studies. RSC Adv. 14 (16), 11541-11556.

[16] Böhler, L., Görtler, G., Krail, J., Kozek, M., 2019. Carbon monoxide emission models for small-scale biomass combustion of wooden pellets. Appl. Energy 254, 113668.

[17] Boldini, D., Grisoni, F., Kuhn, D., Friedrich, L., Sieber, S.A., 2023. Practical guidelines for the use of gradient boosting for molecular property prediction. J. Cheminf. 15 (1), 73.

[18] Deshannavar, U.B., Hegde, P.G., Dhalayat, Z., Patil, V., Gavas, S., 2018. Production and characterization of agro-based briquettes and estimation of calorific value by regression analysis: an energy application. Mater. Sci. Energy Technol. 1 (2), 175-181.

[19] Dorogush, A.V., Ershov, V., Gulin, A., 2018. CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363.

[20] Eling, J., Okot, D.K., Menya, E., Atim, M.R., 2024. Densification of raw and torrefied biomass: a review. Biomass Bioenergy 184, 107210.

[21] EPA, 2012. Revised air quality standards for particle pollution and updates to the Air Quality Index (AQI). United States Environmental Protection Agency. https://www.epa.gov/sites/default/files/2016-04/documents/2012áqi_factsheet.pdf. (Accessed 20 August 2024). n.d.

[22] Elsisi, S.F., Omar, M.N., Samak, A.A., Gomaa, E.M., Elsaeidy, E.A., 2023. Production the briquettes from mixture of agricultural residues and evaluation its physical, mechanical and combustion properties. Misr J. Agric. Eng. 40 (4), 393-418.

[23] Florica, N., Alin, B.M., Cristian, C., Herman, L., Galceava, S., Ciobanu, T., 2023. Artificial neural networks in industrial engineering forecasting. In: 9th Conference with International Participation on Knowledge Management and Informatics. Kopaonik.

[24] Ferguson, H., 2012. Briquette businesses in Uganda. GVEP Int. https://energy4impact.org/sites/default/files/briquette_businesses_in_uganda.pdf.

[25] Güleç F., Pekaslan, D., Williams, O., Lester, E., 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.

[26] Guo, Z., Wu, J., Zhang, Y., Wang, F., Guo, Y., Chen, K., Liu, H., 2020. Characteristics of biomass charcoal briquettes and pollutant emission reduction for sulfur and nitrogen during combustion. Fuel 272, 117632.

[27] Habib, M.A., Abdulrahman, G.A., Alquaity, A.B., Qasem, N.A., 2024. Hydrogen combustion, production, and applications: a review. Alex. Eng. J. 100, 182-207.

[28] Ighalo, J.O., Igwegbe, C.A., Adeniyi, A.G., 2022. Multi-layer perceptron artificial neural network (MLP-ANN) prediction of biomass higher heating value (HHV) using combined biomass proximate and ultimate analysis data. Model. Earth Syst. Environ. 8 (3), 3177-3191.

[29] ISO, 2014. In: Part. 3: Graded Wood Briquettes,in: Solid Biofuels—Fuel Specifications and Classes. ISO, Geneva, Switzerland, 17225-3.

[30] Kabas, O., Ercan, U., Dinca, M.N., 2024. Prediction of briquette deformation energy via ensemble learning algorithms using physico-mechanical parameters. Appl. Sci. 14 (2), 652. https://doi.org/10.3390/app14020652.

[31] Kakooza, H., 2014. The benefits of using briquettes as energy sources over the use of wood fuel at household level. BSc Thesis, Kampala International University, Department of Environmental Management, Uganda.

[32] Kaleli, A., Sungur, B., Basar, C., 2023. Comparative study of machine learning methods integrated with different optimisation algorithms for prediction of thermal performance and emissions in a pellet stove. Energy Sources, Part A Recovery, Util. Environ. Eff. 45 (3), 7673-7693.

[33] Kebede, T., Berhe, D.T., Zergaw, Y., 2022. Combustion characteristics of briquette fuel produced from biomass residues and binding materials. J. Energy 2022 (1), 4222205.

[34] Kimutai, S.K., Kimutai, I.K., 2019. Investigation of physical and combustion properties of briquettes from cashew nut shell and cassava binder. Int. J. Educ. Res. 7 (11), 15-26.

[35] Kinsley, H., Kukieła, D., 2020. Neural Networks from Scratch in Python. Harrison Kinsley.

[36] Kocer, A., Kabas, O., Zabava, B.S., 2023. Estimation of compressive resistance of briquettes obtained from groundnut shells with different machine learning algorithms. Appl. Sci. 13 (17), 9826.

[37] Kpalo, S.Y., Zainuddin, M.F., Manaf, L.A., Roslan, A.M., 2020. A review of technical and economic aspects of biomass briquetting. Sustainability 12 (11), 4609.

[38] Kujawska, J., Kulisz, M., Oleszczuk, P., Cel, W., 2023. Improved prediction of the higher heating value of biomass using an artificial neural network model based on the selection of input parameters. Energies 16 (10), 4162.

[39] Kumar, G., Thampi, G., Mondal, P.K., 2021. Predicting performance of briquette made from millet bran: a neural network approach. Adv. J. Graduate Res. 9 (1), 1-13.

[40] Li, H., Liu, X., Legros, R., Bi, X.T., Lim, C.J., Sokhansanj, S., 2012. Pelletization of torrefied sawdust and properties of torrefied pellets. Appl. Energy 93, 680-685.

[41] Mani, S., Tabil, L.G., Sokhansanj, S., 2004. Grinding performance and physical properties of wheat and barley straws, corn stover and switchgrass. Biomass Bioenergy 27 (4), 339-352.

[42] Mansora, A.M., Lima, J.S., Anib, F.N., Hashima, H., Hoa, W.S., 2019. Characteristics of cellulose, hemicellulose and lignin of MD2 pineapple biomass. Chem. Eng. 72 (1), 79-84.

[43] Marreiro, H.M., Peruchi, R.S., Lopes, R.M., Andersen, S.L., Eliziário, S.A., Rotella Junior, P., 2021. Empirical studies on biomass briquette production: a literature review. Energies 14 (24), 8320.

[44] Mastelini, S.M., Nakano, F.K., Vens, C., de Leon Ferreira, A.C.P., 2022. Online extra trees regressor. IEEE Transact. Neural Networks Learn. Syst. 34 (10), 6755-6767.

[45] Muntean, A., 2020. Assessment of Factors Affecting Productivity of Briquetting Presses and Quality of Briquettes. Dissertation Thesis. Czech University of Life Sciences Prague, Faculty of Tropical AgriSciences, Department of Sustainable Technologies. Available at: https://theses.cz/id/b7pg96/zaverecna_prace.pdf. (Accessed 2 September 2024).

[46] Montavon, G., Samek, W., Müller, K.-R., 2018. Methods for interpreting and understanding deep neural networks. Digit. Signal Process. 73, 1-15.

[47] Monteiro, T.O., Barradas Filho, A.O., Villa-Vélez, H.A., Cruz, G., 2024. Estimation of the main air pollutants from different biomasses under combustion atmospheres by artificial neural networks. Chemosphere 352, 141484.

[48] Musabbikhah, M., Putro, S., Bakhri, S., 2022. The effect of particle size, pressure, holding time and drying temperature to the physical and mechanical properties of briquettes. Mater. Sci. Forum.

[49] Ngusale, G.K., Oloko, M., Awuor, F.O., 2021. Briquetting as a means of recovering energy from organic market waste. Energy Sources, Part A Recovery, Util. Environ. Eff. 1-16.

[50] Obi, O., Pecenka, R., Clufford, M., 2022. A review of biomass briquette binders and quality parameters. Energies, vol. 15. s Note: MDPI stays neu-tral with regard to jurisdictional claims in … p. 2426, 2022.

[51] Obi, O.F., Ezema, J.C., Okonkwo, W.I., 2017. Energy performance of biomass cookstoves using fuel briquettes. Biofuels.

[52] Okot, D.K., Bilsborrow, P.E., Phan, A.N., 2018. Effects of operating parameters on maize COB briquette quality. Biomass Bioenergy 112, 61-72.

[53] Okot, D.K., Bilsborrow, P.E., Phan, A.N., 2019. Briquetting characteristics of bean strawmaize cob blend. Biomass Bioenergy 126, 150-158.

[54] Oladeji, J., Enweremadu, C., 2013. A predictive model for the determination of some densification characteristics of corncob briquettes. Mater. Process. Energy: Commun. Curr. Res. Technol. Dev. 16, 23-30.

[55] Oladosu, K., Babalola, S., Kareem, M., Ajimotokan, H., Kolawole, M., Issa, W., Ponle, E., 2023. Optimization of fuel briquette made from bi-composite biomass for domestic heating applications. Sci. Afr. 21, e01824.

[56] Oladosu, K.O., Asafa, T.B., Alade, A.O., Erinosho, M.F., 2021. Artificial neural network prediction of CO emission and ash yield from co-combustion of empty fruit bunch, palm kernel shell and kaolin. Environ. Sci. Pollut. Control Ser. 28, 42596-42608.

[57] Olugbade, T., Ojo, O., Mohammed, T., 2019. Influence of binders on combustion properties of biomass briquettes: a recent review. BioEnergy Res. 12, 241-259.

[58] OSHA, 2024. Permissible exposure limits - Annotated tables. United States Department of Labor. https://www.osha.gov/laws-regs/regulations/standardnumber/1926. (Accessed 20 August 2024).

[59] Osintsev, K., Prikhodko, I.S., Nikitina, D., Bolkov, Y.S., Shishkov, A., 2021. The use of organic waste of the agro-industrial complex for the production of fuel using neural network algorithms. IOP Conf. Ser. Earth Environ. Sci.

[60] Pilusa, T.J., Huberts, R., Muzenda, E., 2013. Emissions analysis from combustion of ecofuel briquettes for domestic applications. J. Energy South Afr. 24 (4), 30-36.

[61] Plevris, V., Solorzano, G., Bakas, N.P., Ben Seghier, M.E.A., 2022. Investigation of performance metrics in regression analysis and machine learning-based prediction models. 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2022).

[62] Resende, D.R., da Silva Araujo, E., Lorenço, M.S., Lira Zidanes, U., Akira Mori, F., Fernando Trugilho, P., Lúcia Bianchi, M., 2022. Use of neural network and multivariate statistics in the assessment of pellets produced from the exploitation of agro-industrial residues. Environ. Sci. Pollut. Control Ser. 29 (47), 71882-71893.

[63] Rocha, M., Neves, J., 1999. Preventing premature convergence to local optima in genetic algorithms via random offspring generation. Multiple Approaches to Intelligent Systems:12Th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE-99, Cairo, Egypt, May 31-June 3, 1999. Proceedings 12.

[64] Saeed, A.A.H., Yub Harun, N., Bilad, M.R., Afzal, M.T., Parvez, A.M., Roslan, F.A.S., Afolabi, H.K., 2021. Moisture content impact on properties of briquette produced from rice husk waste. Sustainability 13 (6), 3069.

[65] Saptadi, N., Suyuti, A., Ilham, A.A., Nurtanio, I., 2023a. Literature study on the role of artificial intelligence waste management into biomass briquettes toward smart city governance. AIP Conf. Proc.

[66] Saptadi, N., Suyuti, A., Ilham, A.A., Nurtanio, I., 2023b. Modeling of organic waste classification as raw materials for briquettes using machine learning approach. Int. J. Adv. Comput. Sci. Appl. 14 (3).

[67] Sodhi, P., Awasthi, N., Sharma, V., 2019. Introduction to machine learning and its basic application in python. Proceedings of 10th International Conference on Digital Strategies for Organizational Success.

[68] Uzun, H., Yıldız, Z., Goldfarb, J.L., Ceylan, S., 2017. Improved prediction of higher heating value of biomass using an artificial neural network model based on proximate analysis. Bioresour. Technol. 234, 122-130.

[69] Vermeulen, A.F., 2019. Industrial Machine Learning: Using Artificial Intelligence as a Transformational Disruptor. Apress.

[70] Wimmer, P., 2018. Methods for Interpreting and Understanding Deep Neural Networks. Information to All Users, 28417042. ProQuest Number.

[71] World Bioenergy Association, 2022. Global bioenergy statistics. https://www.worldbioenergy.org/uploads/221223%20WBA%20GBS%202022.pdf.

[72] World, Bank, 2023. What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050. World Bank Publications, Washington, DC. https://datatopics.worldbank.org/what-a-waste/trends_in_solid_waste_management.html. (Accessed 25 July 2024).

[73] Yank, A., Ngadi, M., Kok, R., 2016. Physical properties of rice huusk and bran briquettes under low presure densiification for rural applications. Biomass and Bioenergy 84, 22-30.