AI-driven optimization of biosurfactant production from agricultural waste

India is an agricultural country. A significant amount of agricultural waste is generated annually, which is burnt by farmers to clear fields for the next crop. The negative externality of burning agricultural waste is not only air and soil pollution but also health hazards. The probable solution to this problem is to find the utilization of agricultural waste in a manner that not only resolves the problem of pollution but also generates beneficial, measurable, and sustainable outcomes. Agricultural waste can serve as an excellent substrate for biosurfactant production. Biosurfactants are known to contribute to the enhancement of soil fertility. The chapter proposes the optimization of biosurfactant production from agricultural waste by employing artificial intelligence. This encompasses the enhancement of parameters such as biomass, product yield, and prediction and control of critical process parameters. The relationship and variability in different parameters of bioprocess can be streamlined by training the system, in several experimental runs, with variations in batches of agricultural waste. This shall help in modeling and analyzing complex relationships between inputs and variables, thus optimizing the biosurfactant quality, and shall offer predictions for large-scale agricultural waste utilization. Thus, the integration of artificial intelligence in optimizing biosurfactant production from agricultural waste presents a transformative solution to the pressing issues of pollution and waste management.