Microbial Bioprocess Efficiency Improvement Through Artificial Intelligence and Machine Learning (AI-ML) Tools

Abstract

Developing eco-friendly microbial bioprocesses for the sustainable development of industries is a prime need. However, optimizing such bioprocesses is an expensive and time-consuming task. Various factors affecting the output need to be controlled during any bioprocess. Artificial intelligence (AI) and machine learning (ML) tools are efficient alternatives to conventional approaches for optimizing process variables and finding the interaction between different factors. AI-ML tools are used for microbial strain selection and bioprocess optimization, scale-up, monitoring, and control, saving time and increasing the efficiency of the bioprocess. Tools like Artificial Neural Networks (ANN), Adaptive-Network-based Fuzzy Inference System (ANFIS), and Artificial Bee Colony (ABC) have provided superior results for the optimization of microbe-assisted production of various bio-metabolites and enzymes. This review highlights the use of various AI-ML tools for effective microbial bioprocess design and optimization. This will further help select suitable AI-ML tools for different bioprocesses and their overall impact on enhancing microbial efficiency. © 2026 Sunil Kumar Khare, Ram Karan, Rajeshwari Sinha and R. Hemamalini.