Análise da produção de biogás considerando um modelo computacional adaptado: um estudo para diferentes composições de substrato

Abstract

The growing demand for renewable energy sources and the need to mitigate intensified greenhouse gas emissions have led to research on anaerobic digestion as a sustainable technology for waste treatment and energy generation. In this context, this work proposes an adaptation of a mathematical model representing the dynamics of a biodigester, based on the model presented by Oro et al. (2017), through the inclusion of a simplified differential innovation and a state variable representing the substrate volume. The modified model was developed to simulate batch operating conditions, directly describing biogas production curves over time. Validation was performed using data from two laboratory experiments. The first, dealing with co-digestion, involved dairy bovine wastewater (DBW) and Arundo donax L., while the second dealt with co-digestion between DBW and elephant grass broth (ECB). Both experiments were conducted under different substrate compositions and temperature conditions. The interruption of interruptions was avoided through the Differential Evolution optimization algorithm, minimizing the error in the L 2 norm between simulated and experimental data. Furthermore, stability and sensitivity analyses were performed to evaluate the influence of the parameters on the system’s behavior. The results presented show that the proposed model reflects the cumulative biogas production profiles observed experimentally, exhibiting low L 2 error values, ranging from 0.0006-0.0013 and a maximum error of 0.0015-0.0025 in the experiment with Arundo donax L., varying in the range of 0.0067-0.0359 for the L 2 error and 0.0020-0.0710 for the maximum error. The analysis also indicated that the substrate composition significantly influences the system’s performance, highlighting possible inhibitory effects and variations in the imposed proportions. Overall, the presented model proved to be a consistent and reliable computational tool for describing the dynamics of anaerobic digestion in batch-operated biodigesters, contributing to process optimization studies and supporting decision-making in biogas production systems.