# Greenhouse Residues' Potential for Biogas Production
## 3 Results
### BMP Simulation  
The BMP test is performed in batch reactors using an inoculum of microorganisms suitable to degrade the substrate added for evaluation anaerobically. The typical sources of inoculum are anaerobic digesters treating sewage sludge or a variety of substrates (e.g., energy crops, food wastes, manures, etc.) so that the microorganisms contained in the inoculum are well adapted to a variety of organic matter. In this way, the initial lag phase, which is typical in microbial growth, is minimized. However, it cannot be diminished  
Figure 4: Methane production of residual biomass. Figure 3: Average values of BMP per residue type expressed in terms of (**a**) mass of VS and (**b**) mass of fresh matter (FM). since the microorganisms need a particular amount of time to adapt to the conditions of a new environment when exposed to a specific substrate. Since biogas production is assumed to be proportional to the microorganisms' growth, the Gompertz model or the MGM, developed to fit growth data, are frequently used. However, a two-phase biogas production is often observed, which includes a temporal cease or slowdown of the biogas after start-up and a subsequent resumption of the production at a lower rate most times. This \"diauxic type\" of biogas or methane production can be attributed to various reasons, including the degradation of readily biodegradable and difficult-to-degrade substrates. In this case, the MGM cannot accurately fit the experimental data. Therefore, a TGM has been proposed to estimate the lag time, the methane production, and the maximum specific growth production rate at the two phases. Since most BMP tests in the present study exhibited this type of profile (Supplementary Materials, Figures S1-S5), the TGM (Equation (1)) was applied to fit the experimental data and compare the model parameter values for each biomass type studied. The model's goodness of fit was evaluated based on the NSE and PBIAS coefficients. The NSE coefficient represents the residual over the experimental variance [54], while the PBIAS coefficient expresses the average tendency of the model to overestimate or underestimate the experimental data [55]. The model fitting was evaluated according to the limits set in literature [56]. In all cases, the NSE was close to 1, and PBIAS was close to 0, (Tables S1-S5) indicating a very good fit. In all cases, the first lag time constant was close to zero, meaning that there was readily biodegradable organic matter which yielded the highest portion of the BMP (more than 50% of the methane was obtained until the first plateau, as indicated via A1 compared to the sum of A\({}_{1}\) and A\({}_{2}\)), except for the cucumber suckers collected in August. When both lag time constants were close, there was no apparent diauxic behavior; however, MGM did not fit the data well. It is evident that the slope of the methane production rate curve was initially high and then decreased long before it reached a plateau, indicating the need to use a TGM. Comparisons of the kinetic parameters for the same type of biomass during the different sampling times showed that no tendency for different kinetics could be identified. For example, the parameter \(\mu_{\text{max1}}\) was lower in the May samples for tomato leaves but higher for tomato suckers and cucumber leaves, while no statistical difference can be identified for \(\mu_{\text{max2}}\). Comparing the maximum specific methane production rate constants for the different types of biomass showed that the tomato suckers were degraded faster during the first phase (23.9 \(\pm\) 4.9 NL kg\({}^{-1}\) VS d\({}^{-1}\)). At the same time, there was no statistical difference in the \(\upmu_{\text{max1}}\) for the other biomass types (15.4 \(\pm\) 6.2, 16. \(\pm\)6.0, 19.1 \(\pm\) 4.9 NL kg\({}^{-1}\) VS d\({}^{-1}\) for the tomato leaves, cucumber leaves, and suckers, respectively).