Enhancing biogenic carbon dioxide conversion to acetic acid using support materials and machine learning-based optimization

Amid escalating environmental challenges and the global pursuit of carbon neutrality, sustainable pathways for platform chemical synthesis are a critical research priority. Acetic acid, a high-demand compound with extensive industrial applications, is still predominantly produced via petroleum-based processes, raising environmental and economic concerns. Considering these challenges, this study explores the biological conversion of biogenic carbon dioxide (CO₂) into acetic acid, promoting a circular carbon economy. Specifically, it investigates the role of packing and conductive materials in influencing biological CO₂-to-acetic acid conversion, demonstrating that their effects are material-specific, with some enhancing and others inhibiting process performance. To enhance acetogenic activity, 2-bromoethanesulfonic acid was used to inhibit methanogenesis, thereby favoring the Wood-Ljungdahl pathway. The experimental design incorporated a multilevel categorical design (MCD) to assess the individual and combined effects of support materials, namely packing and conductive types, on key process parameters: pH, yield (g acetic acid/g CO₂), and efficiency (%). Zero-valent iron was identified as the most effective conductive material, achieving a yield of 0.68 g acetic acid/g CO₂ and an efficiency up to 99.8 %. The statistical models developed through MCD exhibited strong predictive performance (R² > 0.80; prediction errors < 3 %) and were validated experimentally. Furthermore, an artificial neural network (ANN) was trained and integrated with a genetic algorithm (GA) to optimize system performance. The ANN-GA model demonstrated slightly higher predictive accuracy compared to MCD. Overall, this study confirms the significant role of support material selection in enhancing biological CO₂ conversion and presents an advanced modeling framework for process optimization.