Assessment and parameter identification of simplified models to describe the kinetics of semi-continuous biomethane production from anaerobic digestion of green and food waste

Abstract

Biochemical reactions occurring during anaerobic digestion have been modelled using reaction kinetic equations such as first-order, Contois and Monod which are then combined to form mechanistic models. This work considers models which include between one and three biochemical reactions to investigate if the choice of the reaction rate equation, complexity of the model structure as well as the inclusion of inhibition plays a key role in the ability of the model to describe the methane production from the semi-continuous anaerobic digestion of green waste (GW) and food waste (FW). A parameter estimation method was used to investigate the most important phenomena influencing the biogas production process. Experimental data were used to numerically estimate the model parameters and the quality of fit was quantified. Results obtained reveal that the model structure (i.e. number of reactions, inhibition) has a much stronger influence on the quality of fit compared with the choice of kinetic rate equations. In the case of GW there was only a marginal improvement when moving from a one to two reaction model, and none with inclusion of inhibition or three reactions. However, the behaviour of FW digestion was more complex and required either a two or three reaction model with inhibition functions for both ammonia and volatile fatty acids. Parameter values for the best fitting models are given for use by other authors.

Characterising and modelling free ammonia and ammonium inhibition in anaerobic systems

“…Most studies have used the non-competitive inhibition functions to describe inhibition by NH and other biostatic inhibitors (Batstone et al 2002, Fitamo et al 2016, Hierholtzer and Akunna 2012, Owhondah et al 2016. However, the experimental results of the present study suggested that the non-competitive inhibition function is not suitable to describe the inhibition extent and pattern for NH 3 and NH 4 + on acetoclastic methanogenesis (see Section…”

Treatment of the Supernatant of Anaerobically Digested Organic Fraction of Municipal Solid Waste in a Demo-Scale Mesophilic External Anaerobic Membrane Bioreactor

“…For model 1, this is the case of X bo and Y only, with broad and non-sense 95% CIs for the other parameters and, hence, model 1 cannot explain results, despite the good fitting of experimental values (Figure 2). With the simplification done with model 2, only µ m and B optimal values fail in the statistical significance t-test, with broad 95% CIs, including negative values due to the linearity of the estimator, although the correlation coefficient is relatively low (0.74) and the estimated values are of the same order of magnitude of those obtained in simplified models using Contois kinetics (Chen, 1983;Tomei et al, 2008;Owhondah et al, 2016). The rest of the estimated parameters are highly statistically significant, with realistic CIs, although three pairs of parameters present high correlation coefficients: β and S bo (0.97), α and k d (−0.95), and G o and Y (−0.97).…”

“…Vavilin et al (2004) found that this kinetics is as good at fitting experimental data as the more complex models related to organic particles surface enzymatic reactions, but with less unknown parameters. Owhondah et al (2016) tested different kinetics combinations for describing the anaerobic digestion of food waste using simplified models with one, two, or three reaction steps. When testing the model with one single reaction, the best experimental data fitting was obtained with the Contois kinetics and also was the result when expressing hydrolysisacidogenesis when testing the process with two or three reactions.…”

Early warning indicators for mesophilic anaerobic digestion of corn stalk: a combined experimental and simulation approach

“…3 and 4. where and , respectively, represent a single simulated or measured (experimental) data value, while is the average of all experimental data values and is the total number of experimental data points available. RMSE is commonly used for AD model evaluation [58, 59] and in certain cases, offers advantages over other measures, especially when sensitivity to large errors between experimental and simulated data points is required [60]. After dividing RMSE by the mean of the experimental data points involved, the resulting rRMSE can be compared through different variable datasets, without having to consider its specific units.…”