The importance of inspecting the inoculum's methane production for estimating kinetic parameters in biochemical methane potential tests.

The aim of this study was to improve the quality of estimations of the first-order kinetic constant k, in Biochemical Methane Potential (BMP) tests. The results showed that existing guidelines for BMP tests are not sufficient to improve the estimation of k. The methane production of the inoculum itself exerted a major influence on the estimation of k. A flawed value in k was correlated with a high endogenous methane production. Excluding blanks that showed a distinct lag-phase of >1 day and a mean relative standard deviation >10% during the first ten days of a BMP test helped to retrieve more consistent estimates for k. For improving the repeatability in the determination of k in BMP tests, it is strongly recommended to inspect the methane production rate of the blanks. The proposed threshold values may be applied by other researchers but need further verification with different data.

Predicting methane yield by linear regression models: A validation study for grassland biomass.

The objectives of this study were to assess and validate previously published prediction models with an independent dataset and to expose the power and limitations of linear regression models for predicting biomethane potential. Two datasets were used for the validation, one with all individual samples and one with the average values of each cultivar. The results revealed similar performances of all four models for the individual samples. The methane yields of the cultivars were predicted more accurately than the methane yields of the individual samples. The grassland specific model predicted the variation in the dataset with an R2 of 0.84 and the slope of the regression line was equal to 1.0. Linear regression models are suitable to depict the variation in methane yield and for substrate ranking. However, the prediction error of the absolute values may be high since systematic external effects cannot be determined by a regression model.

Near-infrared spectroscopy (NIRS) for a real time monitoring of the biogas process.

In this research project Near-infrared spectroscopy (NIRS) was applied to monitor the content of specific process parameters in anaerobic digestion. A laboratory scaled biogas digester was constantly fed every four hours with maize- and grass silage to keep a base load with an organic loading rate (OLR) of 2.5 kg oDM/m3 ∗ d. Daily impact loads with shredded wheat up to an OLR of 8 kg oDM/m3 ∗ d were added in order to generate peaks at the parameters tested. The developed calibration models are capable to show changes in process parameters like volatile fatty acids (VFA), propionic acid, total inorganic carbon (TIC) and the ratio of the volatile fatty acids to the carbonate buffer (VFA/TIC). Based on the calibration of the models for VFA and TIC, the values could be predicted with an R2 of 0.94 and 0.97, respectively. Moreover, the residual prediction deviations were 4.0 and 6.0 for VFA and TIC, respectively.