Abstract
A major drawback of Biochemical Methane Potential (BMP) tests is their long test duration, which could be reduced substantially if the final gas production could be predicted at an earlier stage. For this purpose, this study evaluates 61 different algorithms for their capability to predict the final BMP and required degradation time based on data from 138 BMP tests of various substrate types. By combining the best algorithms it was possible to predict the BMP with a relative root mean squared error (rRMSE) of less than 10% just 6days after initiation of the experiment. The results from this study indicate that there is a possibility to shorten the test length substantially by combining laboratory tests and intelligent prediction algorithms. Shorter test duration may widen the possible applications for BMP tests in full-scale biogas plants, allowing for a better selection and proper pricing of biomass.
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