Dynamic simulation and optimization of anaerobic digestion processes using MATLAB. (May 2022)

• Record Type: Journal Article
• Authors: Ganeshan, Prabakaran; Rajendran, Karthik
• Is Part Of: Bioresource technology. Volume 351(2022)
• Publication Date: 2022-05
• Subjects: Anaerobic digestion -- Dynamic modeling -- MATLAB Simulation -- Sensitivity analysis -- Time series modeling; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2022.126970
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: Dynamic behavior of AD process was predicted using MATLAB and modified Hill's model. The developed model had an accuracy of 92%, when validated against literature. Statistically, no significant difference was found between experiment and simulation. Dynamic response of varying OLR predicts the optimum methane production. Abstract: Time series-based modeling provides a fundamental understanding of process fluctuations in an anaerobic digestion process. However, such models are scarce in literature. In this work, a dynamic model was developed based on modified Hill's model using MATLAB, which can predict biomethane production with time series. This model can predict the biomethane production for both batch and continuous process, across substrates and at diverse conditions such as total solids, loading rate, and days of operation. The deviation between literature and the developed model was less than ± 7.6%, which shows the accuracy and robustness of this model. Moreover, statistical analysis showed there was no significant difference between literature and simulation, verifying the null hypothesis. Finding a steady and optimized loading rate was necessary to an industrial perspective, which usually requires extensive experimental data. With the developed model, a stable and optimal methane yield generating loading rate could be identified at minimal input.