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.

中文翻译：

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使用 SGB 决策树方法估计由离子液体预处理的生物质制成的 CRM 的特性

使用离子液体 (IL) 进行生物质预处理以生产富含纤维素的材料 (CRM) 已得到充分证明。在本研究中，由于人工智能程序的广泛应用和易用性，在随机梯度提升（SGB）决策树算法的基础上，提出了一种人工智能方法来估计富含纤维素材料的性质。 （客户关系管理）。在这种情况下，经验输出值的数据集被收集并随机分解为用于测试和训练的数据集。这些结果表明，计算 CRM 属性的最佳预测工具是开发的模型。此外，还检查了生物柴油目标值数据库的准确性。相比之下，模型贡献变量对输出的影响已作为一个新问题进行了研究。结果表明，决定 CRMs 特性的最大影响变量是纤维素富集因子。因此，本研究为预测​​ CRMs 的特性和对有效参数的敏感性调查提供了一种创新和准确的工具，以帮助研究人员开发优化过程。