Correlation between hydrolysis rate constant and chemical composition of energy crops

• Published in: Renewable energy Vol. 118; pp. 34 - 42
• Main Authors: Dandikas, Vasilis, Heuwinkel, Hauke, Lichti, Fabian, Eckl, Thomas, Drewes, Jörg E., Koch, Konrad
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2018
• Subjects: Biogas potential; Hydrolysis rate constant; Kinetic model; Lignocellulosic biomass; Linear regression; Kinetic model; Linear regression; Lignocellulosic biomass; Biogas potential; Hydrolysis rate constant
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2017.10.100

Besides biogas yield, the kinetic of biogas production in a biomethane potential (BMP) test also provides important information for feedstock characterization. In this study, fodder analysis and BMP tests with high temporal resolution were performed in order to identify statistical correlations between the hydrolysis rate constant (kh) and the chemical composition of various energy crops. Different species and cultivars of energy crops were analyzed in order to develop a broadly applicable regression model for the prediction of kh. Two independent datasets (222 samples in total) were used, one for the calibration of the model and one for its validation. The results indicated that the analytical parameters non-fiber carbohydrates and crude protein were statistically suitable for a multiple linear regression model for the prediction of kh. Furthermore, a first-order kinetic model and the proposed regression models can be utilized for the prediction of the biogas production in a BMP test. The proposed approach offers a fast and reliable prediction of the biogas production rate and allows a feedstock assessment according to their biogas potential. •222 energy crops were analyzed for chemical composition and biomethane potential.•A novel approach for hydrolysis rate constant prediction is presented.•Crude protein and non-fiber carbohydrates were chosen as model regressors.•Biogas production rate and yield can now be predicted simultaneously.