Chemical Industry and Engineering Progress ›› 2023, Vol. 42 ›› Issue (1): 148-158.DOI: 10.16085/j.issn.1000-6613.2022-0539
• Chemical processes and equipment • Previous Articles Next Articles
WANG Lu(), ZHANG Lei(), DU Jian
Received:
2022-04-01
Revised:
2022-05-12
Online:
2023-02-20
Published:
2023-01-25
Contact:
ZHANG Lei
通讯作者:
张磊
作者简介:
王璐(1997—),女,硕士研究生,研究方向为化工系统工程。E-mail:877907576@qq.com。
基金资助:
CLC Number:
WANG Lu, ZHANG Lei, DU Jian. High-throughput screening of zeolite materials for CO2/N2 selective adsorption separation by machine learning[J]. Chemical Industry and Engineering Progress, 2023, 42(1): 148-158.
王璐, 张磊, 都健. 机器学习高效筛选用于CO2/N2选择性吸附分离的沸石材料[J]. 化工进展, 2023, 42(1): 148-158.
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URL: https://hgjz.cip.com.cn/EN/10.16085/j.issn.1000-6613.2022-0539
评估指标 | 公式 |
---|---|
摆动能力 | |
吸附选择性 | |
吸附材料选择参数 | |
吸附材料性能分数 | |
再生能力R | R |
评估指标 | 公式 |
---|---|
摆动能力 | |
吸附选择性 | |
吸附材料选择参数 | |
吸附材料性能分数 | |
再生能力R | R |
参数 | 值 | |
---|---|---|
模拟类型 | MonteCarlo | |
平衡数 | 50000 | |
循环周期数 | 50000 | |
力场类型 | GarciaPerez2006 | |
晶格单位 | 3×3×3 | |
组分 | CO2 | N2 |
分子类型 | TraPPE | TraPPE |
摩尔分数 | 0.14 | 0.86 |
平移概率 | 0.5 | 0.5 |
旋转概率 | 0.5 | 0.5 |
重插概率 | 0.5 | 0.5 |
交换概率 | 1 | 1 |
参数 | 值 | |
---|---|---|
模拟类型 | MonteCarlo | |
平衡数 | 50000 | |
循环周期数 | 50000 | |
力场类型 | GarciaPerez2006 | |
晶格单位 | 3×3×3 | |
组分 | CO2 | N2 |
分子类型 | TraPPE | TraPPE |
摩尔分数 | 0.14 | 0.86 |
平移概率 | 0.5 | 0.5 |
旋转概率 | 0.5 | 0.5 |
重插概率 | 0.5 | 0.5 |
交换概率 | 1 | 1 |
数据集 | CO2 | N2 |
---|---|---|
训练集 | 4704 | 4848 |
验证集 | 588 | 606 |
测试集 | 589 | 604 |
可用 | 5881 | 6058 |
空白值 | 257 | 80 |
统计 | 6138 | 6138 |
数据集 | CO2 | N2 |
---|---|---|
训练集 | 4704 | 4848 |
验证集 | 588 | 606 |
测试集 | 589 | 604 |
可用 | 5881 | 6058 |
空白值 | 257 | 80 |
统计 | 6138 | 6138 |
神经元个数 | 训练集 | 验证集 | 外部测试集 | RMSE统计 | R2统计 | |||
---|---|---|---|---|---|---|---|---|
RMSE | R2 | RMSE | R2 | RMSE | R2 | |||
5 | 0.4227 | 0.9968 | 0.1995 | 0.9940 | 0.1601 | 0.9963 | 0.7823 | 0.9957 |
7 | 0.3028 | 0.9992 | 0.1281 | 0.9968 | 0.1252 | 0.9984 | 0.5562 | 0.9981 |
0.1216 | 0.9996 | 0.1256 | 0.9972 | 0.0834 | 0.9989 | 0.3305 | 0.9986 | |
11 | 0.3681 | 0.9993 | 0.2662 | 0.9973 | 0.1219 | 0.9979 | 0.7562 | 0.9982 |
8 | 0.2360 | 0.9980 | 0.2051 | 0.9913 | 0.1171 | 0.9974 | 0.5581 | 0.9956 |
10 | 0.3638 | 0.9966 | 0.2632 | 0.9941 | 0.2611 | 0.9948 | 0.8881 | 0.9952 |
神经元个数 | 训练集 | 验证集 | 外部测试集 | RMSE统计 | R2统计 | |||
---|---|---|---|---|---|---|---|---|
RMSE | R2 | RMSE | R2 | RMSE | R2 | |||
5 | 0.4227 | 0.9968 | 0.1995 | 0.9940 | 0.1601 | 0.9963 | 0.7823 | 0.9957 |
7 | 0.3028 | 0.9992 | 0.1281 | 0.9968 | 0.1252 | 0.9984 | 0.5562 | 0.9981 |
0.1216 | 0.9996 | 0.1256 | 0.9972 | 0.0834 | 0.9989 | 0.3305 | 0.9986 | |
11 | 0.3681 | 0.9993 | 0.2662 | 0.9973 | 0.1219 | 0.9979 | 0.7562 | 0.9982 |
8 | 0.2360 | 0.9980 | 0.2051 | 0.9913 | 0.1171 | 0.9974 | 0.5581 | 0.9956 |
10 | 0.3638 | 0.9966 | 0.2632 | 0.9941 | 0.2611 | 0.9948 | 0.8881 | 0.9952 |
神经元个数 | 训练集 | 验证集 | 外部测试集 | RMSE统计 | R2统计 | |||
---|---|---|---|---|---|---|---|---|
RMSE | R2 | RMSE | R2 | RMSE | R2 | |||
7 | 0.0130 | 0.9991 | 0.0571 | 0.9972 | 0.0316 | 0.9966 | 0.1018 | 0.9976 |
8 | 0.0791 | 0.9983 | 0.0507 | 0.9959 | 0.0565 | 0.9958 | 0.1863 | 0.9967 |
9 | 0.0326 | 0.9996 | 0.0219 | 0.9978 | 0.0270 | 0.9970 | 0.0814 | 0.9981 |
10 | 0.0522 | 0.9997 | 0.0332 | 0.9984 | 0.0429 | 0.9975 | 0.1283 | 0.9985 |
11 | 0.1516 | 0.9979 | 0.0703 | 0.9962 | 0.0514 | 0.9955 | 0.2733 | 0.9965 |
12 | 0.0317 | 0.9997 | 0.0372 | 0.9985 | 0.0271 | 0.9975 | 0.0960 | 0.9986 |
0.0176 | 0.9997 | 0.0228 | 0.9983 | 0.0284 | 0.9978 | 0.0689 | 0.9986 | |
14 | 0.0289 | 0.9998 | 0.0214 | 0.9985 | 0.0270 | 0.9975 | 0.0773 | 0.9986 |
15 | 0.1264 | 0.9990 | 0.0710 | 0.9941 | 0.0680 | 0.9958 | 0.2654 | 0.9963 |
神经元个数 | 训练集 | 验证集 | 外部测试集 | RMSE统计 | R2统计 | |||
---|---|---|---|---|---|---|---|---|
RMSE | R2 | RMSE | R2 | RMSE | R2 | |||
7 | 0.0130 | 0.9991 | 0.0571 | 0.9972 | 0.0316 | 0.9966 | 0.1018 | 0.9976 |
8 | 0.0791 | 0.9983 | 0.0507 | 0.9959 | 0.0565 | 0.9958 | 0.1863 | 0.9967 |
9 | 0.0326 | 0.9996 | 0.0219 | 0.9978 | 0.0270 | 0.9970 | 0.0814 | 0.9981 |
10 | 0.0522 | 0.9997 | 0.0332 | 0.9984 | 0.0429 | 0.9975 | 0.1283 | 0.9985 |
11 | 0.1516 | 0.9979 | 0.0703 | 0.9962 | 0.0514 | 0.9955 | 0.2733 | 0.9965 |
12 | 0.0317 | 0.9997 | 0.0372 | 0.9985 | 0.0271 | 0.9975 | 0.0960 | 0.9986 |
0.0176 | 0.9997 | 0.0228 | 0.9983 | 0.0284 | 0.9978 | 0.0689 | 0.9986 | |
14 | 0.0289 | 0.9998 | 0.0214 | 0.9985 | 0.0270 | 0.9975 | 0.0773 | 0.9986 |
15 | 0.1264 | 0.9990 | 0.0710 | 0.9941 | 0.0680 | 0.9958 | 0.2654 | 0.9963 |
Ⅰ组 | Ⅱ组 | Ⅲ组 | Ⅳ组 |
---|---|---|---|
ABW、APC、LOV、MER、MON、NAB、PHI、PUN | AEI、AFY、RHO、SAV | AEN、AFG、AFI、AFN、ASV、ATT、BIK、ESV、HEU、MTW、MVY、NSI、RRO、RWR、UEI、YUG | GIS、SIV、WEI |
Ⅰ组 | Ⅱ组 | Ⅲ组 | Ⅳ组 |
---|---|---|---|
ABW、APC、LOV、MER、MON、NAB、PHI、PUN | AEI、AFY、RHO、SAV | AEN、AFG、AFI、AFN、ASV、ATT、BIK、ESV、HEU、MTW、MVY、NSI、RRO、RWR、UEI、YUG | GIS、SIV、WEI |
沸石 | R | ||||
---|---|---|---|---|---|
MON | 4.494542 | 79.02956 | 191.4463 | 5732.045 | 860.4634 |
ABW | 3.279881 | 81.96941 | 133.9630 | 2715.545 | 439.3827 |
APC | 3.089023 | 81.20606 | 131.6266 | 2570.451 | 406.5975 |
PHI | 3.488620 | 81.73907 | 102.9267 | 2181.423 | 359.0721 |
PUN | 4.489790 | 89.73103 | 78.15195 | 1797.634 | 350.8858 |
NAB | 4.182718 | 94.98367 | 76.82922 | 1403.537 | 321.3550 |
MER | 3.277813 | 83.61190 | 89.87160 | 1762.165 | 294.5823 |
LOV | 3.614247 | 86.22495 | 63.68264 | 770.8370 | 230.1648 |
机器学习模型补充数据筛选得到的沸石: | |||||
VSV | 4.588631 | 94.35575 | 83.24993 | 1971.854 | 382.0032 |
RSN | 4.073608 | 91.19332 | 72.17017 | 1260.089 | 293.9930 |
KFI | 3.447833 | 89.05406 | 62.91092 | 871.5884 | 216.9064 |
沸石 | R | ||||
---|---|---|---|---|---|
MON | 4.494542 | 79.02956 | 191.4463 | 5732.045 | 860.4634 |
ABW | 3.279881 | 81.96941 | 133.9630 | 2715.545 | 439.3827 |
APC | 3.089023 | 81.20606 | 131.6266 | 2570.451 | 406.5975 |
PHI | 3.488620 | 81.73907 | 102.9267 | 2181.423 | 359.0721 |
PUN | 4.489790 | 89.73103 | 78.15195 | 1797.634 | 350.8858 |
NAB | 4.182718 | 94.98367 | 76.82922 | 1403.537 | 321.3550 |
MER | 3.277813 | 83.61190 | 89.87160 | 1762.165 | 294.5823 |
LOV | 3.614247 | 86.22495 | 63.68264 | 770.8370 | 230.1648 |
机器学习模型补充数据筛选得到的沸石: | |||||
VSV | 4.588631 | 94.35575 | 83.24993 | 1971.854 | 382.0032 |
RSN | 4.073608 | 91.19332 | 72.17017 | 1260.089 | 293.9930 |
KFI | 3.447833 | 89.05406 | 62.91092 | 871.5884 | 216.9064 |
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