This paper deals with modeling hydrogen contents of bio-oil (H-BO) as a function of pyrolysis conditions and biomass compositions of feedstock. The support vector machine algorithm optimized by the grey wolf optimization method has been used in modeling this end. Comprehensive data for this purpose were aggregated from previous sources and reports. The results of various analyses showed that this algorithm has a high ability to predict actual results. The calculated values of R², MRE (%), MSE, and RMSE were obtained as 0.973, 1.98, 0.0568, and 0.241, respectively. According to the results of various analyses, the high performance of this model in predicting the output values was proved. Also, by comparing this model with the previously proposed models in terms of accuracy, it was observed that this model had a better performance. This algorithm can be a good alternative to costly and time-consuming laboratory data.

中文翻译：

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监督学习机在生物油氢含量准确预测中的应用

本文涉及将生物油 (H-BO) 的氢含量建模为热解条件和原料生物质组成的函数。通过灰狼优化方法优化的支持向量机算法已经用于建模。为此目的，综合数据是从以前的来源和报告中汇总的。各种分析的结果表明，该算法具有较高的预测实际结果的能力。R ²的计算值、MRE (%)、MSE 和 RMSE 分别为 0.973、1.98、0.0568 和 0.241。根据各种分析的结果，证明了该模型在预测输出值方面的高性能。此外，通过在准确性方面将该模型与先前提出的模型进行比较，可以观察到该模型具有更好的性能。该算法可以很好地替代昂贵且耗时的实验室数据。