Optimization of Ethanol Fermentation in a Bubble Column Bioreactor Using Response Surface Methodology with Ferric Oxide Nanoparticle-Modified Supports

Abstract Fermentation using Saccharomyces cerevisiae in bubble column bioreactors is widely recognized for its high sugar-to-ethanol conversion efficiency and scalability. However, optimizing fermentation parameters is critical for maximizing ethanol yield. This study employed a two-stage optimization strategy, beginning with a Factorial Design to evaluate the effects of seven independent variables on ethanol yield (%). The generated Pareto chart ranked these factors based on standardized effects, assessed against the Bonferroni limit (3.4558) and t-value threshold (2.0395). The results identified Time (hr), Air Flow Rate (L/min), Cell Loading (OD600), and Mass of Bead (g) as statistically significant contributors to ethanol yield, with Time (hr) exhibiting the highest standardized effect. The most significant factors identified in the Pareto analysis were then used for further optimization via Response Surface Methodology with Central Composite Design (CCD). The Analysis of Variance (ANOVA) confirmed that the quadratic model was highly significant (F = 14.14, p < 0.0001), with cell loading (F = 48.48, p < 0.0001) and mass of bead (F = 26.53, p = 0.0001) identified as the most influential factors. Air flow rate also played a significant role (F = 5.88, p = 0.0284). The interaction between cell loading and bead mass (F = 18.27, p = 0.0007) significantly enhanced ethanol yield. The highest ethanol yield of 47.9% was achieved at 52.70 hours, 0.01 L/min air flow, 1.51 g/L cell loading, and 47.20 g bead mass, with a minimal prediction error (0.84%). These findings demonstrate a reliable approach to optimizing fermentation efficiency, providing valuable insights for industrial bioethanol production.