Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007, Oviedo, Spain.
Department of Energy, College of Mining, Energy and Materials Engineering, University of Oviedo, 33004, Oviedo, Spain.
Environ Sci Pollut Res Int. 2023 Jul;30(31):76977-76991. doi: 10.1007/s11356-023-27805-5. Epub 2023 May 30.
In the context of Industry 4.0, hydrogen gas is becoming more significant to energy feedstocks in the world. The current work researches a novel artificial smart model for characterising hydrogen gas production (HGP) from biomass composition and the pyrolysis process based on an intriguing approach that uses support vector machines (SVMs) in conjunction with the artificial bee colony (ABC) optimiser. The main results are the significance of each physico-chemical parameter on the hydrogen gas production through innovative modelling and the foretelling of the HGP. Additionally, when this novel technique was employed on the observed dataset, a coefficient of determination and correlation coefficient equal to 0.9464 and 0.9751 were reached for the HGP estimate, respectively. The correspondence between observed data and the ABC/SVM-relied approximation showed the suitable effectiveness of this procedure.
在工业 4.0 的背景下，氢气作为世界能源原料的重要性日益凸显。本研究提出了一种基于支持向量机（SVM）与人工蜂群（ABC）优化器相结合的新型人工智能模型，用于描述生物质组成和热解过程中氢气产量（HGP）的特征。主要结果是通过创新建模，确定了每个物理化学参数对氢气产量的重要性，并对氢气产量进行了预测。此外，当将这项新技术应用于实际数据集时，氢气产量的估计达到了 0.9464 的决定系数和 0.9751 的相关系数。观察数据与基于 ABC/SVM 的近似值之间的一致性表明了该方法的有效性。
