Numerical simulation of organic waste aerobic biodegradation: A new way to correlate respiration kinetics and organic matter fractionation. (February 2015) - Denes, Jeremy; Tremier, Anne; Menasseri-Aubry, Safya; Walter, Christian; Gratteau, Laurette; Barrington, Suzelle
Highlights: A model was developed to simulate the aerobic biodegradation of organic wastes. Evolution of organic matter (OM) fractions was monitored during respirometric tests. Model was calibrated on analytical characteristics of wastes and their digestates. The model successfully predicts the evolution of OM fractions and respiration rate. A unique set of parameters was obtained except those related to microbial activity. Abstract: Composting wastes permits the reuse of organic matter (OM) as agricultural amendments. The fate of OM during composting and the subsequent degradation of composts in soils largely depend on waste OM quality. The proposed study aimed at developing a model to predict the evolution in organic matter quality during the aerobic degradation of organic waste, based on the quantification of the various OM fractions contained in the wastes. The model was calibrated from data gathered during the monitoring of four organic wastes (two non-treated wastes and their digestates) exposed to respirometric tests. The model was successfully fitted for all four wastes and permitted to predict respiration kinetics, expressed as CO2 production rates, and the evolution of OM fractions. The calibrated model demonstrated that hydrolysis rates of OM fractions were similar for all four wastes whereas the parameters related to microbial activity (eg. growth and death rates) were specific to each substrate. These later parameters have been estimated by calibration on respirometric data, thus demonstrating that coupling analyses of OM fractions in initial wastes and respirometric tests permit the simulation of the biodegradation of various type of waste. The biodegradation model presented in this paper could thereafter be integrated in a composting model by implementing mass and heat balance equations.