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Using an SGB Decision Tree Approach to Estimate the Properties of CRM Made by Biomass Pretreated with Ionic Liquids

The use of ionic liquids (ILs) for biomass pretreatment to produce cellulose-rich materials (CRMs) has been well proven. In this research, due to the wide range of applications and ease of using artificial intelligence procedures, on the basis of the algorithm of stochastic gradient boosting (SGB) decision tree, an artificial intelligence approach is proposed to estimate the properties of cellulose-rich materials (CRMs). That being the case, the dataset of the empirical output values was gathered and was randomly broken down into datasets for testing and training. These results show that the best forecasting tool for calculating the properties of CRMs is the developed model. Furthermore, the accuracy of the databank of the biodiesel target values has been examined. In contrast, the influences of model contributed variables on the output have been examined as a new issue. It reveals that the most influencing variable in determining the properties of CRMs is the cellulose enrichment factor. Therefore, this research provides an innovative and accurate tool for predicting the properties of CRMs and sensitivity investigation on effective parameters to help investigators developing the optimized process.