化工学报 ›› 2024, Vol. 75 ›› Issue (8): 2886-2896.DOI: 10.11949/0438-1157.20240228
丁家琦(), 刘海涛(), 赵普, 朱香凝, 王晓放, 谢蓉
收稿日期:
2024-02-29
修回日期:
2024-04-09
出版日期:
2024-08-25
发布日期:
2024-08-21
通讯作者:
刘海涛
作者简介:
丁家琦(1999—),男,博士研究生,jqding@mail.dlut.edu.cn
基金资助:
Jiaqi DING(), Haitao LIU(), Pu ZHAO, Xiangning ZHU, Xiaofang WANG, Rong XIE
Received:
2024-02-29
Revised:
2024-04-09
Online:
2024-08-25
Published:
2024-08-21
Contact:
Haitao LIU
摘要:
煤炭超临界水制氢技术在高温高压条件下利用超临界水充分气化煤炭,实现了高效低排放的转化和制氢过程。为解决因反应器内复杂多相流行为导致的仿真耗时问题,以及常见代理模型时序预测时间短、精度下降快等问题,提出基于本征正交分解(proper orthogonal decomposition,POD)和Koopman理论的深度学习模型POD-Koopman,用于捕捉和学习反应器内复杂流场的长时时空演变特征,实现数据驱动的长时滚动预测。测试结果表明其能在较小计算开销下准确滚动预测反应器内多相流场时变行为,助力下游制氢反应器工业化设计及优化任务。
中图分类号:
丁家琦, 刘海涛, 赵普, 朱香凝, 王晓放, 谢蓉. 煤炭超临界水制氢反应器内多相流场智能滚动预测研究[J]. 化工学报, 2024, 75(8): 2886-2896.
Jiaqi DING, Haitao LIU, Pu ZHAO, Xiangning ZHU, Xiaofang WANG, Rong XIE. Study on intelligent rolling prediction of the multiphase flows in coal-supercritical water fluidized bed reactor for hydrogen production[J]. CIESC Journal, 2024, 75(8): 2886-2896.
项目 | H2 | CO2 | CO | CH4 |
---|---|---|---|---|
仿真值/(kg/mol) | 37.82 | 20.86 | 2.67 | 2.22 |
实验值/(kg/mol) | 39.83 | 21.52 | 3.21 | 0.84 |
表1 反应器仿真产气量与实验值对比
Table 1 Comparison of simulated gas production of the reactor with experimental values
项目 | H2 | CO2 | CO | CH4 |
---|---|---|---|---|
仿真值/(kg/mol) | 37.82 | 20.86 | 2.67 | 2.22 |
实验值/(kg/mol) | 39.83 | 21.52 | 3.21 | 0.84 |
区块 | 子模型 | 超参数 | 张量维度 |
---|---|---|---|
输入 | — | — | [500, 512, 32] |
Reshape | — | [500, 16384] | |
降维 | POD | Rank=200 | [500, 200] |
编码 | MLP (ReLU) | [200, 128] | [500, 128] |
MLP (ReLU) | [128, 128] | [500, 128] | |
MLP | [128, 64] | [500, 64] | |
Koopman递推 | MLP (b = False) | [64, 64] | [500, 64] |
解码 | MLP (ReLU) | [64, 128] | [500, 128] |
MLP (ReLU) | [128, 128] | [500, 128] | |
MLP | [128, 200] | [500, 200] | |
还原 | POD | [500, 16384] | |
输出 | Reshape | — | [500, 512, 32] |
表2 POD-Koopman模型训练配置
Table 2 The POD-Koopman model training configuration
区块 | 子模型 | 超参数 | 张量维度 |
---|---|---|---|
输入 | — | — | [500, 512, 32] |
Reshape | — | [500, 16384] | |
降维 | POD | Rank=200 | [500, 200] |
编码 | MLP (ReLU) | [200, 128] | [500, 128] |
MLP (ReLU) | [128, 128] | [500, 128] | |
MLP | [128, 64] | [500, 64] | |
Koopman递推 | MLP (b = False) | [64, 64] | [500, 64] |
解码 | MLP (ReLU) | [64, 128] | [500, 128] |
MLP (ReLU) | [128, 128] | [500, 128] | |
MLP | [128, 200] | [500, 200] | |
还原 | POD | [500, 16384] | |
输出 | Reshape | — | [500, 512, 32] |
模型处理器 | POD-Koopman | MLP | LSTM |
---|---|---|---|
损失函数 | 潜空间线性误差+重构误差 | 重构误差 | 重构误差 |
递推格式 | RK-4 | —/— | —/— |
训练耗时/s | 593 | 272 | 437 |
表3 3种预测模型基本配置比较
Table 3 The configuration comparison of three predictive models
模型处理器 | POD-Koopman | MLP | LSTM |
---|---|---|---|
损失函数 | 潜空间线性误差+重构误差 | 重构误差 | 重构误差 |
递推格式 | RK-4 | —/— | —/— |
训练耗时/s | 593 | 272 | 437 |
图8 不同模型滚动预测精度衰减至PSNR=30 dB阈值时所预测的H2摩尔分数场及时刻
Fig.8 The comparison of predicted H2 mole fractional field as well as the time stamp of different models when the rolling prediction PSNR is 30 dB
PSNR | POD-Koopman | MLP | LSTM |
---|---|---|---|
H2 | |||
CO2 | |||
CO | |||
CH4 | |||
Vol |
表4 不同模型在外推测试集(48~70 s)上滚动预测的PSNR指标的均值和标准差
Table 4 Mean and standard deviation of the PSNR metric of predictions of different models on the test set (48—70 s)
PSNR | POD-Koopman | MLP | LSTM |
---|---|---|---|
H2 | |||
CO2 | |||
CO | |||
CH4 | |||
Vol |
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