Enhancing bioethanol production: Synergistic effects of nano-Fe3O4, inoculum, large language model-assisted additive optimization and economic implications

Abstract

This study presents a novel approach that enhanced bioethanol production through nano-based S. cerevisiae inoculum development, antioxidant (ascorbic acid) and surfactant (Tween-80) incorporation. Response Surface Methodology (RSM) was initially used to model Fe₃O₄ NPs (0–0.1 wt%), temperature (28–30 °C), and exposure time (12–18 h) towards high S. cerevisiae specific growth rate and biomass concentration for improved bioethanol production. Experimental validation yielded 0.234 ± 0.019 h⁻¹ and 3.395 ± 0.102 g/L respectively, corresponding to a 13% and 8% enhancement over the control. The fermentation performance of the optimized inoculum on pretreated potato residues was enhanced (>55%). Moreover, the use of context-driven retrieval-augmented generation (CD-RAG) large language model (LLM) assisted the optimization of Tween-80 and ascorbic acid inclusion for improved specific growth rate (1.16-fold) and biomass concentration (1.11-fold). Economic assessment of the inoculum development strategy points to a potential profitable approach for producing bioethanol with shorter payback time (<10 years) at higher processing capacity. These findings have provided valuable insights to improve bioethanol yield using Fe₃O₄ NPs and CD-RAG LLM in inoculum development.