Understanding error propagation in anaerobic co-digestion synergy assessment: A review and methodological framework

In the light of energy crisis, anaerobic digestion (AD) is within the candidacy of biotechnology by transforming organic substrate into biogas. However, some substrates present compositions that limit methane production. Therefore, anaerobic co-digestion (ACoD) is a variant of AD that mixes substrates (co-substrates) to improve methane production conditions. The biochemical methane potential (BMP) test is an experimental method to estimate the methane yield of substrates during AD, with applications in evaluating synergistic effects on co-substrates. The protocols describe the implementation of the BMP test, identify variants in the experimental setup, characterizations of substrates and inocula, and variable responses obtained. However, the literature is scarce regarding estimating errors during BMP tests and their influence on synergistic effects. Experimental errors are undeniable in research, but high errors lead to false conclusions. Therefore, this research proposes a model for estimating errors in BMP tests and analyzes the influence in synergistic effects identification. A search for information in studies published between 2022 and 2023 presented 39 studies analyzing co-substrates, starting from the BMPs of the mono-substrates and continuing with at least three mixing ratios. The critical review of the literature identified coincidences and differences in implementing the BMP test. The model established factors for diminishing experimental error, including following protocols, the experience of research personnel, using triplicates, and defining the mixing ratio based on VS, among others. These factors improve accuracy, reproducibility, and reliability in synergy assessments. Estimating errors strengthens synergy validation and enhances BMP test repeatability. • Synergies strengthen ACoD as a technology for producing renewable energy. • Following experimentation protocols is essential to ensure reliable results. • Researchers with previous scientific publications on BMP tests produced fewer errors. • Estimating experimental errors validates the BMP test results.