RESUMEN
Inter-laboratory reproducibility of biomethane potential (BMP) is dismal, with differences in BMP values for the same sample exceeding a factor of two in some cases. A large group of BMP researchers directly addressed this problem during a workshop held in Leysin, Switzerland, in June 2015. The workshop resulted in a new set of guidelines for BMP tests published in 2016, which is the subject of the present commentary. The work has continued with two international inter-laboratory studies and one additional workshop held in Freising, Germany, in 2018. The dataset generated by the two inter-laboratory studies were used to refine the validation criteria for BMP tests. Based on these new results an update to the original guidelines is proposed here.
Asunto(s)
Metano , Alemania , Metano/análisis , Estándares de Referencia , Reproducibilidad de los Resultados , SuizaRESUMEN
Production of biogas from different organic materials is a most interesting source of renewable energy. The biomethane potential (BMP) of these materials has to be determined to get insight in design parameters for anaerobic digesters. Although several norms and guidelines for BMP tests exist, inter-laboratory tests regularly show high variability of BMPs for the same substrate. A workshop was held in June 2015, in Leysin, Switzerland, with over 40 attendees from 30 laboratories around the world, to agree on common solutions to the conundrum of inconsistent BMP test results. This paper presents the consensus of the intense roundtable discussions and cross-comparison of methodologies used in respective laboratories. Compulsory elements for the validation of BMP results were defined. They include the minimal number of replicates, the request to carry out blank and positive control assays, a criterion for the test duration, details on BMP calculation, and last but not least criteria for rejection of the BMP tests. Finally, recommendations on items that strongly influence the outcome of BMP tests such as inoculum characteristics, substrate preparation, test setup, and data analysis are presented to increase the probability of obtaining validated and reproducible results.