Advanced modelling in anaerobic co-digestion: the multiscale aspects of substrate composition, hydrogen transfer and microbial growth

Abstract

A growing human population and the improvement of living standards both require that more food, animal feed and industrial goods are being produced worldwide. This continuous development sets high demands on energy generation, which in a climate-conscious society must be provided from renewable resources to an ever greater extent. Anaerobic digestion that is a prominent bioenergy technology and involves the multistep microbial conversion of complex organic matter to the versatile energy carrier called biogas, has long been considered as a viable solution for sustainable energy generation. Due to the difficulties in operating and optimizing the digestion, however, interest in advanced information technologies to provide reliable process monitoring, forecasting and control solutions has been rising steadily. The focus of present thesis was therefore to implement and evaluate the effects of various functional extensions in a detailed kinetic bioconversion model, and by doing so, assess its potential for being applied in diverse industrial applications. Moreover, a detailed literature review of the past, present and future of anaerobic digestion modelling was also included in the document. As a central part of the project, model development involved the optimization of its most sensitive kinetic and hydrolysis yield constants; its adaptation to simulating co-digestion experiments with an increased number of substrates and under various operation conditions; the implementation of model functionalities for simulating in situ and hybrid biogas upgrading processes; the extension of microbial growth equations to account for dynamic temperature effects; and the inclusion of additional model microbial groups to enable bioaugmentation process simulation. Accordingly, the optimization process was carried out following a systematic method, which comprised of the initial selection of parameters to be analysed, the sensitivity analysis of selected parameters using calibration study simulations, the numerical estimation of the most influential parameters and the evaluation of the estimated parameter set through the simulation of validation studies. Thorough analysis of the results showed that based on their sensitivity, the initially selected 44 parameters could be reduced to 13, and by estimating their values numerically, simulation fits could be improved significantly. By introducing a standardized protocol for parameter estimation and a universal set of optimized parameters, this work therefore provided a simplified solution for simulating anaerobic co-digestion scenarios. With regards to the model extension for simulating complex co-digestion processes, the tool was suited for the simulation of experimental scenarios both in batch and continuous operation, while concurrently the number of potential model substrates was increased. Subsequent simulations of a series of experiments done in batch and continuous operation showed that the model could fit measured data points with high accuracy. Furthermore, analysis concerning total volatile fatty acid simulations also indicated that large regression errors might arise, in case the absolute scale of measured values is small. In the next step, the model extension with in situ and hybrid biogas upgrading functionalities involved the addition of hydrogenotrophic methanogenic archaea and syntrophic acetate oxidizing bacteria as new model microbial groups, along with the amendment of gas-liquid equilibrium calculations by considering externally provided and internally produced hydrogen flows. Following model calibration with non-upgraded experimental process data, in situ biogas upgrading simulations were successfully validated with upgraded process data, showing high correlation between measurements and simulations. As far as hybrid biogas upgrading functionalities are concerned, these were also validated with sets of experimental reactor data and indicated good model performance, although with higher statistical errors. In both cases, however, changes in volatile fatty acid concentration were captured to a smaller degree and highlighted the need for a more detailed modelling of the acidogenesis step in anaerobic digestion. A further model improvement concerned the extension of temperature effect calculation on microbial growth, specifically by separating long-term and short-term dynamics. The mathematical implementation was also validated using data from two experiments, where long-duration and short-duration temperature disturbances were investigated, respectively. The results proved to describe experimental trends exceptionally well. Simulating volatile fatty acid concentrations, nonetheless, was found to be challenging and supported earlier findings, while the simulation of short-duration disturbance dynamics were shown to require more research. Lastly, the model was adapted for the simulation of bioaugmentation scenarios and was therefore extended with ten bioaugmentative microbial groups: those being responsible for the same conversion pathways as their native counterparts, although with potentially different kinetics parameters. The enhanced model was then evaluated with data originating from two different experimental setups, both focusing on ammonia inhibited operation. Simulation results were in good agreement with measured data points and showed the overall robustness of the model in simulating such scenarios, with additional emphasis on the need for better short-duration disturbance modelling. The overall assessment of results obtained during the thesis work showed that the above described individual extensions contributed to the improvement of model performance, and by addressing the identified challenges, a further extended model can become a valuable tool in monitoring, controlling and forecasting full-scale anaerobic digestion processes.