Modeling temperature response in bioenergy production: Novel solution to a common challenge of anaerobic digestion. (1st April 2020) - Kovalovszki, Adam; Treu, Laura; Ellegaard, Lars; Luo, Gang; Angelidaki, Irini
Graphical abstract: Highlights: A dynamic microbial temperature-dependence function was developed for modeling. The novel function was implemented in an advanced anaerobic digestion model. Data from two experiments was used for the validation of the model simulations. In all cases, the simulations showed good agreement with experimental data points. Present extension can potentially improve other, similarly advanced models as well. Abstract: Temperature is one of the most crucial state variables in industrial process control, which is particularly true for the biochemical conversion of biomass, as in anaerobic digestion. However, modeling the effects of temperature changes on anaerobic microbial growth are commonly considered in quasi-steady state, neglecting the timely dynamics of microbial adaptation to such phenomena. To address this inflexibility, the current work presents a new way for temperature effect calculation that improves the simulation efficiency of bioconversion models. The calculation was implemented as a function in a dynamic mathematical model of anaerobic digestion, and was validated via the simulation of experimental data from two laboratory-scale continuous experiments, involving both short- and long-term temperature changes. Model validity was further supported by 16s rRNA gene sequencing data. The bioconversion model extended with the new temperature function showed significant improvements in simulating the most important dependent variables of the digestion process, such as methane production rate and volatile fatty acid concentration during temperature variations. Finally, microbial analysis results shed light on the potential reasons for differences between simulated and experimental results. Overall, the dynamic temperature function was found to be an important addition to the reference model, allowing its user to generate more accurate simulations of digestion processes with changing temperature conditions. Furthermore, it can be seen as a step towards advanced time series forecasting, with potential benefits for integrated process design, process energy optimization and predicting the behavior of full-scale operations affected by ambient temperature conditions.