Intelligent Modeling of Temperature Profile for Sustainable Food Waste Composting

The composting process exhibits dynamic changes in environmental variables due to underlying biological and physiochemical mechanisms, adding complexity to its modeling. This work focuses on developing a mathematical model for food waste composting in forced continuous aeration conditions. The model incorporates substrate degradation with microbial kinetics, heat balance, and mass balance to address the complexity. In this study, we compare the performance of n ^th order kinetics against first-order kinetics in predicting temperature variations during food waste composting. Additionally, a comparison is made between nth order kinetics and Monod kinetics for forecasting temperature variations in manure waste composting. The six-stage with fifth order ODE solver (ode45) in MATLAB is employed to numerically solve the differential equations and generate temperature profiles. The developed model successfully predicts peak temperature and the time taken to reach it, enabling better temperature control during the thermophilic phase. This control is vital for avoiding excessive temperatures that can inhibit microbial activities. Comparison of experimental and simulation data, using RRMSE and MAE validation, demonstrates good agreement in the performance of the n ^th order model when utilizing experimental heat of reaction and proposed kinetics. To further enhance the predictive model for composting processes, it is suggested to expand the n ^th order model to incorporate other factors such as oxygen concentration, pH, and moisture content. This expansion can contribute to improved prediction accuracy and performance. By employing intelligent and resilient digital innovations, this research strives to advance sustainable living practices in the domain of food waste composting.