An Explainable Artificial Intelligence Framework For Sustainable Biotechnology Predictive Modelling, Process Optimization, And Resource Efficiency
DOI:
https://doi.org/10.69980/z4bjek78Keywords:
Artificial Intelligence, Explainable Artificial Intelligence, Sustainable BiotechnologyAbstract
Artificial intelligence (AI) has emerged as a transformative technology for enhancing efficiency, sustainability, and decision-making across biotechnology applications. However, the increasing complexity of machine learning models has created challenges related to transparency and interpretability, limiting their practical adoption in biotechnology environments. This study proposed an Explainable Artificial Intelligence (XAI) framework for sustainable biotechnology that integrates predictive modeling, process optimization, and resource efficiency assessment within a unified analytical approach. The framework employed advanced machine learning techniques to evaluate relationships among biotechnology process variables and predict performance outcomes while maintaining model transparency through explainability mechanisms. Data preprocessing, feature engineering, predictive model development, explainability analysis, and sustainability assessment were incorporated to support comprehensive evaluation and optimization. The findings demonstrated that AI-based models effectively captured complex interactions among operational variables and achieved strong predictive performance. Feature importance and explainability analyses successfully identified key factors influencing biotechnology outcomes, thereby enhancing model interpretability and supporting informed decision-making. Furthermore, the sustainability assessment indicated opportunities for improving resource utilization and operational efficiency through AI-driven optimization. The proposed framework contributes to the growing integration of artificial intelligence and sustainable biotechnology by providing a transparent, data-driven approach for process improvement and resource management
References
1. Ali, M., Shabbir, K., Ali, S., Mohsin, M., Kumar, A., Aziz, M., ... & Sultan, H. M. (2024). A new era of discovery: how artificial intelligence has revolutionized the biotechnology. Nepal Journal of Biotechnology, 12(1), 1-11.
2. Alizamir, M., Wang, M., Adnan Ikram, R. M., Kim, S., Ahmed, K. O., & Heddam, S. (2024). Developing an efficient explainable artificial intelligence approach for accurate reverse osmosis desalination plant performance prediction: Application of SHAP analysis. Engineering Applications of Computational Fluid Mechanics, 18(1), 2422060.
3. Alruqi, M., Sharma, P., Algburi, S., Khan, M. A., Alsubih, M., & Islam, S. (2024). Biomass energy transformation: harnessing the power of explainable ai to unlock the potential of ultimate analysis data. Environmental Technology & Innovation, 35, 103652.
4. Bhuiyan, M. M. R., Rahaman, M. M., Aziz, M. M., Islam, M. R., & Das, K. (2023). Predictive analytics in plant biotechnology: Using data science to drive crop resilience and productivity. Journal of Environmental and Agricultural Studies, 4(3), 77-83.
5. Fathy, A., Rezk, H., Yousri, D., Alharbi, A. G., Alshammari, S., & Hassan, Y. B. (2023). Maximizing bio-hydrogen production from an innovative microbial electrolysis cell using artificial intelligence. Sustainability, 15(4), 3730.
6. Gupta, R., Ouderji, Z. H., Uzma, Yu, Z., Sloan, W. T., & You, S. (2024). Machine learning for sustainable organic waste treatment: a critical review. npj materials sustainability, 2(1), 5.
7. Gupta, R., Zhang, L., Hou, J., Zhang, Z., Liu, H., You, S., ... & Li, W. (2023). Review of explainable machine learning for anaerobic digestion. Bioresource technology, 369, 128468.
8. Hoang, A. T., Chen, W. H., Guerrero-Pérez, M. O., Castellón, E. R., Escalante, M. C. L., Nguyen, V. N., ... & Truong, T. H. (2026). Turning waste into energy: Application of machine learning and explainable artificial intelligence for determining key factors in wastewater-to-hydrogen conversion. Energy, 139934.
9. Holzinger, A., Keiblinger, K., Holub, P., Zatloukal, K., & Müller, H. (2023). AI for life: Trends in artificial intelligence for biotechnology. New biotechnology, 74, 16-24.
10. Imamoglu, E. (2024). Artificial intelligence and/or machine learning algorithms in microalgae bioprocesses. Bioengineering, 11(11), 1143.
11. Mafat, I. H., Palla, S., & Surya, D. V. (2024). Machine learning and artificial intelligence for algal cultivation, harvesting techniques, wastewater treatment, nutrient recovery, and biofuel production and optimization. In Value added products from bioalgae based biorefineries: opportunities and challenges (pp. 463-487). Singapore: Springer Nature Singapore.
12. Malashin, I., Tynchenko, V., Gantimurov, A., Nelyub, V., Borodulin, A., & Tynchenko, Y. (2024). Predicting sustainable crop yields: Deep learning and explainable AI tools. Sustainability, 16(21), 9437.
13. Martin, R. J., Mittal, R., Malik, V., Jeribi, F., Siddiqui, S. T., Hossain, M. A., & Swapna, S. L. (2024). XAI-powered smart agriculture framework for enhancing food productivity and sustainability. IEEE access, 12, 168412-168427.
14. Mehta, A., Niaz, M., Adetoro, A., & Nwagwu, U. (2024). Advancements in manufacturing technology for the biotechnology industry: The role of artificial intelligence and emerging trends. International Journal of Chemistry, Mathematics and Physics, 8(2), 12-18.
15. Morabito, F., Adornetto, C., Monti, P., Amaro, A., Reggiani, F., Colombo, M., ... & Greco, G. (2023). Genes selection using deep learning and explainable artificial intelligence for chronic lymphocytic leukemia predicting the need and time to therapy. Frontiers in oncology, 13, 1198992.
16. Nguyen, D. D., Tanveer, M., Mai, H. N., Pham, T. Q. D., Khan, H., Park, C. W., & Kim, G. M. (2023). Guiding the optimization of membraneless microfluidic fuel cells via explainable artificial intelligence: Comparative analyses of multiple machine learning models and investigation of key operating parameters. Fuel, 349, 128742.
17. Nguyen, V. G., Sharma, P., Ağbulut, Ü., Le, H. S., Cao, D. N., Dzida, M., ... & Tran, V. D. (2024). Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches. International Journal of Green Energy, 21(12), 2771-2798.
18. Pan, Z. (2023). Machine Learning for Real-time Optimization of Bioprocessing Parameters: Applications and Improvements. Artificial Intelligence and Machine Learning Review, 4(3), 30-42.
19. Saen, R. F., Yousefi, F., & Azadi, M. (2024). Artificial intelligence powered predictions: enhancing supply chain sustainability. Annals of Operations Research, 1-44.
20. Shah, M., Wever, M., & Espig, M. (2025). A framework for assessing the potential of artificial intelligence in the circular bioeconomy. Sustainability, 17(8), 3535.
21. Zhao, H., Hillson, N., Kleese van Dam, K., & Tanjore, D. (2022). Artificial intelligence and machine learning for bioenergy research: opportunities and challenges
