Augmenting Biogas Process Modeling by Resolving Intracellular Metabolic Activity

The process of anaerobic digestion in which waste biomass is transformed to methane by complex microbial communities has been modeled for more than 16 years by parametric gray box approaches that simplify process biology and do not resolve intracellular microbial activity. Information on such activity, however, has become available in unprecedented detail by recent experimental advances in metatranscriptomics and metaproteomics. The inclusion of such data could lead to more powerful process models of anaerobic digestion that more faithfully represent the activity of microbial communities. We augmented the Anaerobic Digestion Model No. 1 (ADM1) as the standard kinetic model of anaerobic digestion by coupling it to Flux-Balance-Analysis (FBA) models of methanogenic species. Steady-state results of coupled models are comparable to standard ADM1 simulations if the energy demand for non-growth associated maintenance (NGAM) is chosen adequately. When changing a constant feed of maize silage from continuous to pulsed feeding, the final average methane production remains very similar for both standard and coupled models, while both the initial response of the methanogenic population at the onset of pulsed feeding as well as its dynamics between pulses deviates considerably. In contrast to ADM1, the coupled models deliver predictions of up to 1,000s of intracellular metabolic fluxes per species, describing intracellular metabolic pathway activity in much higher detail. Furthermore, yield coefficients which need to be specified in ADM1 are no longer required as they are implicitly encoded in the topology of the species’ metabolic network. We show the feasibility of augmenting ADM1, an ordinary differential equation-based model for simulating biogas production, by FBA models implementing individual steps of anaerobic digestion. While cellular maintenance is introduced as a new parameter, the total number of parameters is reduced as yield coefficients no longer need to be specified. The coupled models provide detailed predictions on intracellular activity of microbial species which are compatible with experimental data on enzyme synthesis activity or abundance as obtained by metatranscriptomics or metaproteomics. By providing predictions of intracellular fluxes of individual community members, the presented approach advances the simulation of microbial community driven processes and provides a direct link to validation by state-of-the-art experimental techniques

Modelling start-up performance of anaerobic digestion of saline-rich macro-algae

Some of the key factors affecting the adaptation of anaerobic digestion processes to increasing levels of salinity were determined in batch tests using brown seaweed as a feedstock. It was found that cultures seeded with non-saline anaerobic inoculum required an adaptation period of up to two months to reach the same level of methane production rate as in those cultures seeded with saline adapted inoculum. The anaerobic digestion model N.1 (ADM1) was modified to include an extra inhibition function to account for the effect of salinity and calibrated using a set of experimental data obtained from batch biochemical methane potential tests. After calibration, the model was able to accurately predict methane production rates. The results thus show that, in the absence of saline-adapted inoculum, non-saline inoculum can be used for the start-up of anaerobic digestion systems treating saline-rich feedstocks

Modelling anaerobic digestion during temperature and load variations

Experimental results and simulations based on the Anaerobic Digestion Model No.1 (ADM1) with temperature effects on kinetics were used to evaluate rate limiting steps in sludge bed anaerobic digestion (AD) during load and temperature variations. Simulations were carried out in Aquasim. The model is compared to data from a pilot experiment in a 220 liter AD sludge bed reactor treating diary manure for 16 months of various loads; 0–13 kg COD L−1 d−1 and various temperatures; 25°C, 30°C and 35°C. Methane and CO2 production were monitored on-line while soluble and particulate organic carbon, pH and volatile fatty acids were measured on regularly collected inlet and effluent samples. Simulated overall soluble and particulate organic carbon removal, methane and CO2 production, pH and acetate are close to measured values while propionate is underestimated during some transitions. The fit is mainly sensitive to the composition of the feed in terms of relative amounts of lipids, proteins and carbohydrates especially at simultaneously high load and low temperature. During such conditions, the model predicts accumulation of long chained fatty acids (LCFA), suggesting that the degradation of LCFA is the rate-limiting step at low temperatures. This effect is not explained by reduced LCFA solubility at lower temperature. The model predicts that sludge bed AD efficiency on substrates with little or no LCFA is independent of temperature between 25°C and 35°C while LCFA degradation is favoured by higher temperature.The project was supported by the Norwegian Agricultural Agency, Innovation Norway, The Research Council of Norway, Ministry of Education and Research and Telemark University College

A compartmental model of anaerobic digester for improved description of the process performance

Understanding anaerobic digester (AD) performance relates to the complex interplay between hydrodynamics and kinetics. The latter is not straightforward and has been tackled by means of models. However, the computational burden to run such a model in a dynamic way is still too large. Here, a simplified compartmental model (CM) is derived from a CFD model (hydrodynamics). Compatibility of the CM and CFD model was tested by comparing the RTD curve of a virtual pulse tracer test. Subsequently, the CM was integrated with ADM1 in each compartment and the steady state performance was compared with that of a CSTR model with ADM1

Predictive control of anaerobic digestion of wastewater sludge: a feasibility study

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Observability and Identifiability Analyses of Process Models for Agricultural Anaerobic Digestion Plants

Dynamic operation of anaerobic digestion plants requires advanced process monitoring and control. Different simplifications of the Anaerobic Digestion Model No. 1 (ADM1) have been proposed recently, which appear promising for model-based process automation and state estimation. As a fundamental requirement, observability and identifiability of these models are analyzed in this work, which was pursued through algebraic and geometric analysis. Manual algebraic assessment was successfull for small models such as the ADM1-R4 and simplified versions of the ADM1-R3, which were derived in this context. However, for larger model classes the algebraic approach showed to be insufficient. By contrast, the geometric approach, implemented in the STRIKE_GOLDD toolbox, allowed to show observability for more complex models (including ADM1-R4 and ADM1-R3), employing two independent algorithms. The present study lays the groundwork for state observer design, parameter estimation and advanced control resting upon ADM1-based models.Comment: 34 pages, 3 figures. Extended version. A shortened version was submitted to and accepted for the 24th International Conference on Process Control on April 4, 202