Identification of Critical Problems in Biochemical Methane Potential (BMP) Tests From Methane Production Curves - Koch, Konrad; Hafner, Sasha D.; Weinrich, Sören; Astals, Sergi (Frontiers in Environmental Science, 2019)
Biochemical methane potential (BMP) tests are routinely conducted in academia and industry to determine the methane potential of a given substrate. Although many guidelines have been proposed to standardize BMP tests, results published in the peer-reviewed literature show that critical flaws in experimental design or execution are still common. Therefore, a powerful but simple method for evaluating the quality of BMP measurements is proposed in this work: The specific methane production (SMP) curve from any BMP trial should have a similar shape for most substrates, and significant deviation from this typical response is usually associated with a lack of reliability in resulting BMP estimates. In this study, some common experimental mistakes were reproduced to demonstrate this concept and establish relationships between flaws in experiments, SMP curves, and BMP values. The studied flaws were inoculum storage (2 weeks and at different temperatures), inoculum dilution with water (no dilution to 1:2 dilution), and inoculum-to-substrate ratio (from 2.00 to 0.05). Common kinetic models were used to better assess SMP curves. All flaws exhibited an impact on the SMP curves, but excessive dilution and extremely low inoculum-to-substrate ratio had the largest impacts on both SMP curves and BMP. In the most extreme case (ISR of 0.05), there was a clear lag phase of more than 10 days, and the resulting BMP was 15% lower than for the reference case. Together these results demonstrate the utility of the proposed method. The ultimate goal of this research is to help less-experienced researchers identify and address problems in their experimental procedure, ultimately contributing to more accurate BMP measurements. The general approach is both simple and useful, and should always be part of a quality check for BMP tests.