Prediction of NH 3 and HCN yield from biomass fast pyrolysis: Machine learning modeling and evaluation.

Rapid pyrolysis is a promising technique to convert biomass into fuel oil, where NO _X emission remains a substantial environmental risk. NH ₃ and HCN are top precursors for NO _X emission. In order to clarify their migration path and provide appropriate strategies for their controlling, six up-to-date machine learning (ML) models were established to predict the NH ₃ and HCN yield during rapid pyrolysis of 26 biomass feedstocks. Cross-validation and grid search methods were used to determine the optimal hyperparameters for these ML models. The support vector regression (SVR) model achieved optimal accuracy among them. The optimal root means square error (%), mean absolute error (%), and R ² of test set for NH ₃ /HCN yield were 1.2901/1.1531, 1.0501/0.84712, and 0.98253/0.96152, respectively. In addition, based on the results of Pearson correlation analysis, the input variables with a weak linear correlation with the target product were eliminated, which was found capable of improving the prediction accuracy of almost all ML models except SVR. While after input variables elimination, the SVR model still showed the optimal NH ₃ and HCN yield prediction accuracy. It reflects SVR's great significance and potential for predicting the yield of NO _X precursors during rapid biomass pyrolysis.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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