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.