AI-Driven Optimization Strategies for Enhanced Biobutanol Production

This chapter presents the transformative opportunity through AI integration to make biobutanol production more efficient and sustainable in the context of biofuel. Fermentation processes are optimized by applying AI techniques, such as machine learning models, that analyze historical data in terms of conditions, substrate compositions, and parameters used in the processing. The analytics will have a predictive effect on yields that are improved at low costs with minimal environmental impacts. Machine learning algorithms such as artificial neural networks (ANNs) are useful metamodels for multiobjective optimization in the production of biobutanol. Using ANNs, it is possible to model complex interactions within fermentation systems without needing extensive a priori knowledge of the underlying processes. It is therefore possible to identify optimum operational parameters that maximize butanol yield and productivity, considering cost-effectiveness and scalability for industrial applicability.