State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University, No.28, Xianning West Road, Xi'an 710049, China.
State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University, No.28, Xianning West Road, Xi'an 710049, China.
Bioresour Technol. 2024 Oct;410:131275. doi: 10.1016/j.biortech.2024.131275. Epub 2024 Aug 14.
This article reveals the basic laws of straw supercritical water gasification (SCWG) and provides basic experimental data for the effective utilization of straw. The paper studied the impact of three operational conditions on the production of high-calorific value hydrogen-rich combustible gases through SCWG of straw within a quartz tube reactor. The findings reveal that elevated reaction temperatures, extended residence times, and reduced feedstock concentrations favor the SCWG of straw. When combustible gas contains carbon dioxide, the maximum low heating value (LHV) of the gas is 21 MJ/Nm. Upon removing carbon dioxide, the LHV of the gas reached 38 MJ/Nm. Subsequently, a machine learning (ML) model was developed to forecast gas yield and LHV during the SCWG process. The results demonstrate that the model exhibits robust generalization capabilities. ML can be extensively applied to forecast biomass SCWG processes across various operational conditions.
本文揭示了秸秆超临界水气化（SCWG）的基本规律，为秸秆的有效利用提供了基础实验数据。本文通过在石英管反应器中进行秸秆的超临界水气化实验，研究了三种操作条件对高发热值富氢可燃气体生成的影响。研究结果表明，升高反应温度、延长停留时间和降低原料浓度有利于秸秆的超临界水气化。当可燃气体中含有二氧化碳时，气体的低热值（LHV）最大值为 21 MJ/Nm。除去二氧化碳后，气体的 LHV 达到 38 MJ/Nm。随后，开发了一个机器学习（ML）模型来预测 SCWG 过程中的气体产率和 LHV。结果表明，该模型具有较强的泛化能力。ML 可以广泛应用于预测各种操作条件下的生物质 SCWG 过程。
