A Detailed Review on Advances in Sustainable Biofuel Production

Author

Dr. B. V. Suresh, M. Dheeraj Kumar, M. Jyothi Prakash, M. Devarushi, M. Kartheek, S. Jahnavi


Abstract

 With the increasing need for clean, renewable, and sustainable energy, biofuels have become a promising alternative to fossil fuels that could replace them to a significant extent. This is because they can play a critical role in reducing greenhouse gas emissions and energy insecurity while promoting the transition to a low-carbon economy. Biofuels are produced from renewable biomass feedstocks including agricultural and forestry residues, municipal waste, algae, and energy crops. This review discusses biofuel production and highlights their potential as sustainable alternatives to fossil fuels. It further reveals the state of the art in biomass feedstocks, biofuel production processes, and technologies, including physicochemical, thermochemical, biochemical, and chemical processes. In addition, the review covers the production routes and recent advances in biodiesel, renewable diesel, bioethanol, biobutanol, biomethane, biohydrogen, and sustainable aviation fuel (SAF). Moreover, the review addresses the role of techno-economic analysis and life-cycle assessment in sustainable biofuel production, as well as the impact of artificial intelligence (AI), smart biorefineries, Industry 4.0, machine learning, the Internet of Things (IoT), and big data analytics on biofuel production. The review also identifies research gaps and future directions for sustainable biofuel production. As a result, this review provides a comprehensive understanding of biofuels and their sustainable production for researchers, industrialists, and policymakers with specific emphasis on enhancing production efficiency, techno-economic feasibility, and environmental sustainability.



Keywords

Biofuels, Biomass, Pretreatment, Fermentation, Pyrolysis, Gasification, Biodiesel, Sustainable aviation fuel, Techno-economic analysis, Life-cycle assessment, Artificial intelligence, Biorefinery



Full Text:

Download Paper PDF


References


[1] Neupane, D. (2023). Biofuels from Renewable Sources, a Potential Option for Biodiesel Production. Bioengineering, 10, 29. https://doi.org/10.3390/bioengineering10010029.

[2] Knothe, G. (2010). Biodiesel and renewable diesel: A comparison. Progress in Energy and Combustion Science, 36, 364–373. https://doi.org/10.1016/j.pecs.2009.11.004.

[3] Xu, H., Ou, L., Li, Y., Hawkins, T. R., & Wang, M. (2022). Life Cycle Greenhouse Gas Emissions of Biodiesel and Renewable Diesel Production in the United States. Environmental Science & Technology, 56, 7512–7521. https://doi.org/10.1021/acs.est.2c00289.

[4] Mirnezami, S. A., Strezov, V., & Huda, N. (2026). Decarbonising biofuel pathways: A life cycle assessment of hybrid renewable energy integration in microalgae-based biodiesel production. Biomass and Bioenergy, 207, 108759. https://doi.org/10.1016/j.biombioe.2025.108759.

[5] Yeassin, R., Adnan, M. M. R., Chowdhury, M. M. A., Mia, A., Chowdhury Enan, M. A. J., & Fuad, M. H. (2025). A comprehensive review of biomass and biofuels and their progress using digital technologies. Energy Conversion and Management: X, 28, 101254.

[6] Song, M.-K., Kim, D.-W., Mohanty, S., Son, M., Lee, S. S., Salama, E.-S., Li, X., Kumar, R., & Jeon, B.-H. (2026). Energy-efficient machine learning approaches for enhanced biofuel (biodiesel) production: A review. Energy and AI, 24, 100696.

[7] Ahmad, I., Sana, A., Kano, M., Cheema, I. I., Menezes, B. C., Shahzad, J., Ullah, Z., Khan, M., & Habib, A. (2021). Machine learning applications in biofuels’ life cycle: Soil, feedstock, production, consumption, and emissions. Energies, 14(16), 5072.

[8] Albarracin-Arias, J. A., Romans-Casas, M., Dessì, P., Balaguer, M. D., & Puig, S. (2026). Digitalization of microbial electrosynthesis: Understanding operational variables for ethanol production optimization. Chemical Engineering Journal, 534, 174960.

[9] Farheen, V. H., Abdullah, M. A., & Moorthy, I. G. (2026). Lignocellulosic bioethanol production: A review on pretreatment strategies, biofuel separation, and artificial intelligence/machine learning-based sustainable optimization. Current Research in Biotechnology, 11, 100355.

[10] Sewsynker-Sukai, Y., Faloye, F., & Gueguim Kana, E. B. (2017). Artificial neural networks: An efficient tool for modelling and optimization of biofuel production (a mini review). Biotechnology & Biotechnological Equipment, 31(2), 221–235.

[11] Arias, A., Feijoo, G., & Moreira, M. T. (2023). How could artificial intelligence be used to increase the potential of biorefineries in the near future? A review. Environmental Technology & Innovation, 32, 103277.

[12] Kanwal, F., Aslam, A., & Torriero, A. A. J. (2025). Microalgae-based biodiesel: Integrating AI, CRISPR and nanotechnology for sustainable biofuel development. Emerging Topics in Life Sciences, 8(3), 131–143.

[13] Naveed, M. H., Khan, M. N. A., Mukarram, M., Naqvi, S. R., Abdullah, A., Ul Haq, Z., Ullah, H., & Al Mohamadi, H. (2024). Cellulosic biomass fermentation for biofuel production: Review of artificial intelligence approaches. Renewable and Sustainable Energy Reviews, 189, 113906.

[14] Ishola, N. B., Epelle, E. I., & Betiku, E. (2024). Machine learning approaches to modeling and optimization of biodiesel production systems: State of art and future outlook. Energy Conversion and Management: X, 23, 100669.

[15] Ahorsu, R., Medina, F., & Constantí, M. (2018). Significance and Challenges of Biomass as a Suitable Feedstock for Bioenergy and Biochemical Production: A Review. Energies, 11, 3366. https://www.mdpi.com/1996-1073/11/12/3366

[16] Galbe, M., & Wallberg, O. (2019). Pretreatment for biorefineries: a review of common methods for efficient utilisation of lignocellulosic materials. Biotechnology for Biofuels, 12, 294. https://link.springer.com/article/10.1186/s13068-019-1634-1

[17] Kumar, A. K., & Sharma, S. (2017). Recent updates on different methods of pretreatment of lignocellulosic feedstocks: a review. Bioresources and Bioprocessing, 4, 7. https://link.springer.com/article/10.1186/s40643-017-0137-9

[18] Sun, F., Aguayo, M. M., Ramachandran, R., & Sarin, S. C. (2018). Biomass feedstock supply chain design – a taxonomic review and a decomposition-based methodology. International Journal of Production Research. https://doi.org/10.1080/00207543.2018.1475766

[19] Rodias, E., Berruto, R., Bochtis, D., Sopegno, A., & Busato, P. (2019). Green, Yellow, and Woody Biomass Supply-Chain Management: A Review. Energies, 12, 3020. https://www.mdpi.com/1996-1073/12/15/3020

 

 

Share your valuable work from Social Media Buttons