Abstract
Anaerobic digestion is a complex biological process widely used for organic waste treatment and biogas production. Understanding the intermediate stages and biochemicals is essential for effective process management. This study uses ANN modeling as well as genetic algorithm optimization to explore and predict how these intermediates behave. By scrutinizing the interactions between VFAs and CH4 production, within the context of our VFA Complex Feed characterized by unique concentrations, this model underscores the paramount significance of three VFAs: acetate, propionate, and butyrate. Notably, in this distinctive study, contrary to prior research, acetate manifests a deleterious influence on biogas production (CI = -1.92), whereas propionate (CI = +1.22) and butyrate (CI = +1.14) exhibit a favorable impact. Notably, acetate exerts the most substantial absolute influence (AAS = +4.7) when juxtaposed with other VFAs. These results support prior research, supporting its validity. By combining machine learning with theoretical knowledge, our study advances our comprehension of anaerobic digestion intermediates and offers valuable insights for optimizing the process.
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