CFD-based reduced-order modeling of fluidized-bed biomass fast pyrolysis using artificial neural network
CFD-based reduced-order modeling of fluidized-bed biomass fast pyrolysis using artificial neural network
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DOI：
10.1016/j.renene.2020.01.057
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发表时间：
2020-06
期刊：
影响因子：
8.7
通讯作者：
Hanbin Zhong;Q. Xiong;L. Yin;Juntao Zhang;Yuqin Zhu;Shengrong Liang;B. Niu;Xinyu Zhang
中科院分区：
文献类型：
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作者：
Hanbin Zhong;Q. Xiong;L. Yin;Juntao Zhang;Yuqin Zhu;Shengrong Liang;B. Niu;Xinyu Zhang
为了减少生物质快速热解反应器设计和优化的计算量，基于鼓泡流化床反应器生物质快速热解多流体模型（MFM）模拟的CFD数据，应用降阶建模技术开发降阶模型（ROM）。对9个不同热解温度下的CFD进行了模拟，产物收率以及温度对产物收率的影响与实验吻合较好，充分验证了CFD方法。使用反向传播（BP）人工神经网络（ANN）将CFD模拟的物质质量分数数据映射到热解温度和反应器中每个计算节点的坐标。人工神经网络中的神经元数量和主动功能得到了优化。研究了所开发的 ROM 在训练和测试温度下预测物种分布的能力。还研究了样本方法和输出数量的影响。
In order to reduce the computational effort of design and optimization for biomass fast pyrolysis reactor, the reduced-order modeling technology was applied to develop reduced-order models (ROMs) based on the CFD data from multi-fluid model (MFM) simulation of biomass fast pyrolysis in a bubbling fluidized bed reactor. The CFD simulations at nine different pyrolysis temperatures were performed, and the product yields and the influence of temperature on product yields were in a good agreement with experiments, which fully validated the CFD approach. The back-propagation (BP) artificial neural network (ANN) was used to map the species mass fraction data of CFD simulation to pyrolysis temperature and coordinates of each computational node in the reactor. The number of neurons and active function in the ANN was optimized. The ability of the developed ROMs to predict the species distributions at both training and testing temperature was investigated. The influence of sample method and number of outputs was also studied.