Recently, biomass sources are important for energy applications. There is need for analyzing of the biomass model based on different components such as carbon, ash, and moisture content since the biomass sources are important for energy applications. In this paper, an extreme learning machine (ELM) is used to estimate efficiency. ELM was implemented for single-layer feed-forward neural network (SLFN) architectures. Because biomass modeling could be a very challenging task for conventional mathematical, it is suitable to apply machine learning models which could overcome nonlinearities of the process. The main attempt in this study was to develop a machine learning model for prediction of the higher heating values of biomass based on proximate analysis. According the prediction accuracy (coefficient of determination and root mean square error) of the higher heating value of the biomass, the inputs’ influence was determined on the higher heating value. According to the obtained results, fixed carbon has less moderate coefficient, ash has less correlation coefficient, and volatile matter has the most correlation coefficient. Therefore, the volatile matter percentage weight has the highest relevance on the higher heating value of the biomass. On the contrary, the ash has the smallest relevance on the higher heating value of the biomass based on machine learning approach.

最近，生物质资源对于能源应用很重要。由于生物质源对于能源应用很重要，因此需要基于不同的成分（例如碳，灰分和水分含量）分析生物质模型。在本文中，极限学习机（ELM）用于估计效率。ELM是为单层前馈神经网络（SLFN）体系结构实现的。由于生物量建模对于常规数学而言可能是一项非常具有挑战性的任务，因此适合应用可以克服过程非线性的机器学习模型。这项研究的主要尝试是开发一种机器学习模型，用于基于近距离分析来预测生物质的较高热值。根据生物质较高发热量的预测精度（确定系数和均方根误差），确定输入对较高发热量的影响。根据获得的结果，固定碳的中度系数较小，灰分的相关系数较小，挥发性物质的相关系数最大。因此，挥发性物质的重量百分比与生物质的较高的热值具有最高的相关性。相反，基于机器学习方法，灰分与生物质的较高热值的相关性最小。挥发性物质的相关系数最大。因此，挥发性物质的重量百分比与生物质的较高的热值具有最高的相关性。相反，基于机器学习方法，灰分与生物质的较高热值的相关性最小。挥发性物质的相关系数最大。因此，挥发性物质的重量百分比与生物质的较高的热值具有最高的相关性。相反，基于机器学习方法，灰分与生物质的较高热值的相关性最小。