Chemical Industry and Engineering Progress ›› 2023, Vol. 42 ›› Issue (2): 658-668.DOI: 10.16085/j.issn.1000-6613.2022-0744
• Chemical processes and equipment • Previous Articles Next Articles
ZHANG Jianwei(), XU Rui, ZHANG Zhongchuang, DONG Xin(), FENG Ying
Received:
2022-04-25
Revised:
2022-07-10
Online:
2023-03-13
Published:
2023-02-25
Contact:
DONG Xin
通讯作者:
董鑫
作者简介:
张建伟(1964—),男,博士,教授,研究方向为化工过程机械。E-mail:zhangjianwei@syuct.edu.cn。
基金资助:
CLC Number:
ZHANG Jianwei, XU Rui, ZHANG Zhongchuang, DONG Xin, FENG Ying. Mixing characteristics of concentration field in impingement flow reactor based on convolutional neural network[J]. Chemical Industry and Engineering Progress, 2023, 42(2): 658-668.
张建伟, 许蕊, 张忠闯, 董鑫, 冯颖. 基于卷积神经网络的撞击流反应器浓度场混合特性[J]. 化工进展, 2023, 42(2): 658-668.
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URL: https://hgjz.cip.com.cn/EN/10.16085/j.issn.1000-6613.2022-0744
编号 | 类型 | 参数 | 数据维度 | 可学习参数 | 可学习参数总数 |
---|---|---|---|---|---|
1 | 图像输入 | 150×150×3 | 0 | ||
2 | 卷积层 | 卷积核大小:5×5 卷积核数量:64 步幅:1×1 激活函数:Relu | 150×150×64 | 权重:5×5×3×64 偏置:64 | 4864 |
3 | 池化层 | 池化核大小:4×4 步幅:4×4 | 38×38×64 | 0 | |
4 | 卷积层 | 卷积核大小:5×5 卷积核数量:256 步幅:1×1 激活函数:Relu | 38×38×256 | 权重:5×5×64×256 偏置:256 | 409856 |
5 | 池化层 | 池化核大小:4×4 步幅:4×4 | 10×10×256 | 0 | |
6 | 卷积层 | 卷积核大小:3×3 卷积核数量:8 步幅:1×1 激活函数:Relu | 10×10×8 | 权重:3×3×256×8 偏置:8 | 18440 |
7 | 池化层 | 池化核大小:4×4 步幅:4×4 | 3×3×8 | 0 | |
8 | 卷积层 | 卷积核大小:3×3 卷积核数量:64 步幅:1×1 激活函数:Relu | 3×3×64 | 权重:3×3×8×64 偏置:64 | 4672 |
9 | 池化层 | 池化核大小:4×4 步幅:4×4 | 1×1×64 | 0 | |
10 | 扁平层 | 64 | 0 | ||
11 | 全连接层 | 神经元数量:256 激活函数:Relu | 256 | 权重:64×256 偏置:256 | 16640 |
12 | 输出层 | 神经元数量:1 激活函数:Elu | 1 | 权重:256×1 偏置:1 | 257 |
编号 | 类型 | 参数 | 数据维度 | 可学习参数 | 可学习参数总数 |
---|---|---|---|---|---|
1 | 图像输入 | 150×150×3 | 0 | ||
2 | 卷积层 | 卷积核大小:5×5 卷积核数量:64 步幅:1×1 激活函数:Relu | 150×150×64 | 权重:5×5×3×64 偏置:64 | 4864 |
3 | 池化层 | 池化核大小:4×4 步幅:4×4 | 38×38×64 | 0 | |
4 | 卷积层 | 卷积核大小:5×5 卷积核数量:256 步幅:1×1 激活函数:Relu | 38×38×256 | 权重:5×5×64×256 偏置:256 | 409856 |
5 | 池化层 | 池化核大小:4×4 步幅:4×4 | 10×10×256 | 0 | |
6 | 卷积层 | 卷积核大小:3×3 卷积核数量:8 步幅:1×1 激活函数:Relu | 10×10×8 | 权重:3×3×256×8 偏置:8 | 18440 |
7 | 池化层 | 池化核大小:4×4 步幅:4×4 | 3×3×8 | 0 | |
8 | 卷积层 | 卷积核大小:3×3 卷积核数量:64 步幅:1×1 激活函数:Relu | 3×3×64 | 权重:3×3×8×64 偏置:64 | 4672 |
9 | 池化层 | 池化核大小:4×4 步幅:4×4 | 1×1×64 | 0 | |
10 | 扁平层 | 64 | 0 | ||
11 | 全连接层 | 神经元数量:256 激活函数:Relu | 256 | 权重:64×256 偏置:256 | 16640 |
12 | 输出层 | 神经元数量:1 激活函数:Elu | 1 | 权重:256×1 偏置:1 | 257 |
训练图片/张 | 预测图片/张 | 训练及预测环境 | 迭代次数(epoch)/次 | 批次大小(batch size) | 训练时长 /min | 预测时长/s |
---|---|---|---|---|---|---|
4209 | 302 | Anaconda + TensorFlow | 1000 | 64/批 | 332 | 0.1 |
训练图片/张 | 预测图片/张 | 训练及预测环境 | 迭代次数(epoch)/次 | 批次大小(batch size) | 训练时长 /min | 预测时长/s |
---|---|---|---|---|---|---|
4209 | 302 | Anaconda + TensorFlow | 1000 | 64/批 | 332 | 0.1 |
项目 | MSE | MAE | LOSS |
---|---|---|---|
测试集(1263) | 2.55×10-5 | 0.0029 | 1.28×10-5 |
验证集(302) | 2.82×10-6 | 0.0011 | — |
项目 | MSE | MAE | LOSS |
---|---|---|---|
测试集(1263) | 2.55×10-5 | 0.0029 | 1.28×10-5 |
验证集(302) | 2.82×10-6 | 0.0011 | — |
操作参数 | MSE | MAE |
---|---|---|
d=8mm(1504) | 1.88×10-6 | 0.000932 |
d=10mm(2705) | 2.54×10-6 | 0.000978 |
L=3d(3007) | 2.49×10-6 | 0.000972 |
L=4d(1202) | 1.84×10-6 | 0.000933 |
M=0.8(903) | 1.89×10-6 | 0.000935 |
M=0.9(901) | 1.30×10-6 | 0.000808 |
M=1.0(902) | 4.3×10-6 | 0.001095 |
M=1.2(901) | 1.96×10-6 | 0.000983 |
M=1.4(602) | 1.97×10-6 | 0.000996 |
操作参数 | MSE | MAE |
---|---|---|
d=8mm(1504) | 1.88×10-6 | 0.000932 |
d=10mm(2705) | 2.54×10-6 | 0.000978 |
L=3d(3007) | 2.49×10-6 | 0.000972 |
L=4d(1202) | 1.84×10-6 | 0.000933 |
M=0.8(903) | 1.89×10-6 | 0.000935 |
M=0.9(901) | 1.30×10-6 | 0.000808 |
M=1.0(902) | 4.3×10-6 | 0.001095 |
M=1.2(901) | 1.96×10-6 | 0.000983 |
M=1.4(602) | 1.97×10-6 | 0.000996 |
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