Early prediction of BMP tests: A step response method for estimating first-order model parameters

The Biochemical Methane Potential (BMP) test is an essential tool for supporting real-scale facilities, for instance to derive practical knowledge about a digester performance. However, its broader application is limited by long test duration and high cost. This work proposes a new method for early prediction of BMP first-order kinetic parameters (the maximum methane yield, B0, and the kinetic constant rate k), based on the analysis of a part of data collected from the experiment. Akaike and Bayesian information criteria were used to verify that the prevailing degradation kinetics is that of first-order, for many substrates. An algorithm was developed, providing good early estimates within a short time (4e10 days): in 92.5% of cases, the relative error of the final BMP estimate was found to be in the 1e13% range, with a relative Root Mean Squared Errors (rRMSE) of below 10%. Results suggest that it’s possible to shorten BMP test duration by leveraging data collected in the first part of the experiment.