Incorporating enriched empirical models into optimization algorithm to enhance biogas production

This paper introduces a novel approach to optimization of the anaerobic co-digestion (AcoD) process by developing enriched versions of first-order kinetic, modified Gompertz, and single-stage combined kinetic models. The key innovation of these enriched models lies in the introduction of new kinetic parameters that depend on both temperature and substrate composition, resulting in a set of new model parameters. These parameters are calibrated simultaneously across various process conditions, unlike existing models where kinetic constants are calibrated for only one operating regime. The enriched models are successfully calibrated and validated with experimental data from a batch AcoD of chicken manure with sawdust and fungal-pretreated Miscanthus; the relative index of agreement is higher than 0.99 for the produced biogas under all considered process conditions. By using the calibrated models to optimize the substrate composition and the AcoD process temperature profile, the results indicate that biogas production can increase by up to 50 %. Moreover, the proposed optimization allows for a favorable cost-benefit ratio; the estimated net energy gain can increase by up to 40 %. The proposed enriched models enable accurate prediction of biogas production at various process conditions and optimization of the AcoD process, representing a significant advancement over existing empirical models.