Modeling and optimization of process parameters in co-composting of tea waste and food waste: Radial basis function neural networks and genetic algorithm

In this study, the effects of co-composting of food waste (FW) and tea waste (TW) on the losses of total nitrogen (TN), total organic carbon (TOC), and moisture content (MC) were investigated. TW and FW were composted separately and compared with the co-composting of FW and TW at different ratios. While the MC losses were close to each other in all processes, the lowest TN and TOC losses were found in the composting process containing 25% TW as 26.80% and 40.11%, respectively. Moreover, Radial Basis Function Neural Networks (RBFNNs) were used to predict the losses of TN, TOC, and MC. The outputs of RBFNN were compared with Response Surface Methodology (RSM), Support Vector Regression (SVR), and Feed Forward Neural Network (FF-NN). In addition, the optimal parameter values were determined by Genetic algorithm (GA). As a result, it will be possible to simulate and improve different co-composting processes with obtained data.