Optimizing the early-stage of composting process emissions - artificial intelligence primary tests

Although composting has many advantages in treating organic waste, many problems and challenges are still associated with emissions, like NH₃, CO and H₂S, as well as greenhouse gases such as CO₂. One promising approach to enhancing composting conditions is using novel analytical methods based on artificial intelligence. To predict and optimize the emissions (CO, CO₂, H₂S, NH₃) during the early-stage of composting process machine learning (ML) models were utilized. Data about emissions from laboratory composting with compost's biochar with different incubation (50, 60, 70 °C) and biochar doses (0, 3, 6, 9, 12, 15% dry mass) were used for ML models selections and training. ML models such as acritical neural network (ANN, Bayesian Regularized Neural Network; R² accuracy CO:0.71, CO₂:0.81, NH₃:0.95, H₂S:0.72) and decision tree (DT, RPART; R² accuracy CO:0.69, CO₂:0.80, NH₃:0.93, H₂S:0.65) have demonstrated satisfactory results. The ML models to predict CO and H₂S during composting were demonstrated for the first time. Utilizing emission data to predict other noxious gases presents a cost-effective and expeditious alternative to the empirical analysis of compost properties.