A simple mass balance tool to predict carbon and nitrogen fluxes in anaerobic digestion systems. (November 2021) - Bareha, Y.; Affes, R.; Moinard, V.; Buffet, J.; Girault, R.
Graphical abstract: Highlights: A mass-balance tool enables estimation of anaerobic digestion systems outputs. All inputs have significant impact on, at least, one tool output. Model evaluation shows an accurate prediction for raw digestates and liquid phases. Phase separation and storage leads to prediction uncertainty for solid phase. Suitability of the tool for technical, economic and environmental applications. Abstract: The increase in anaerobic digestion systems has profoundly affected the waste management of territories, particularly for agricultural systems. Changes in cultural practices and imports of organic waste modify the carbon (C) and nitrogen (N) fluxes on territories where anaerobic digestion is implemented. Successful anaerobic digestion can increase the economic and ecological efficiency of the waste management system. Conversely, poor anaerobic digestion leads to low economic and environmental efficiency due to greenhouse gas emissions and nutrient loss. Modeling the impact of anaerobic digestion on the systems integrating anaerobic digestion can improve the efficiency of these practices. The aim of this study was to develop, analyze, and evaluate a simple mass balance tool able to predict carbon and nitrogen fluxes in anaerobic digestion systems. The tool is composed of an exhaustive substrate database used by three models: (i) an anaerobic digestion model that predicts C and N contents in biogas and digestate; (ii) a phase separation model that predicts C and N content in liquid and solid phase digestates; and (iii) a storage model that predicts C and N content in raw, liquid phase, and solid phase digestates, as well as C and N emissions during storage. Sensitivity analyses were performed on the tool to determine critical inputs. Sensitivity analysis showed that outputs were highly sensitive to their respective inputs and to total inputs of solids. Performance evaluation showed that the tool can provide good quality predictions with R 2 correlations between observation and prediction varying from 0.72 to 0.99 with the best predictions obtained for raw digestate.