Prediction of char yield and nitrogen fixation rate from pyrolysis of sewage sludge based on machine learning

Based on sewage sludge composition and pyrolysis processing conditions, machine learning was employed as a tool to predict the nitrogen fixation rate and char yield of sewage sludge pyrolysis. Multiple linear regression, decision tree, support vector machine, random forest, and gradient boosting tree methods were used in the research to model the collected data, and the model results were further discussed. The required data set is sorted by the existing articles, and the missing values are filled by k-Nearest Neighbors method. Predicting the char yield and nitrogen fixation rate by considering the ultimate and proximate composition of the sewage sludge can give good results. The results showed the gradient boosting tree method is the most accurate in predicting nitrogen fixation rate and char yield, with the coefficient of determination for char yield and nitrogen fixation rate reaching 0.864 and 0.860, respectively. Fusion of existing models by voting or stacking methods revealed promising further improvement in prediction accuracy, but it was limited. In further analysis, using the mean decrease impurity and SHapely Additive exPlanations methods to more accurately evaluate the feature importance. For example, HTT was the most important feature affecting char yield and nitrogen fixation rate, especially when predicting the nitrogen fixation rate, with a feature importance of 0.625. This study also quantified the effects of different features on char yield and nitrogen fixation rate using partial dependence and individual condition expectation plots to provide a reference for the utilization and research of sewage sludge pyrolysis.