Chemical Industry and Engineering Progress ›› 2022, Vol. 41 ›› Issue (9): 4701-4712.DOI: 10.16085/j.issn.1000-6613.2021-2262
• Chemical processes and equipment • Previous Articles Next Articles
LI Wei1,2,3(), RUAN Chenglong1,2,3(), WANG Xiaoming1,2,3, LI Yajie1,2,3, LIANG Chenglong4
Received:
2021-11-05
Revised:
2021-12-16
Online:
2022-09-27
Published:
2022-09-25
Contact:
RUAN Chenglong
李炜1,2,3(), 阮成龙1,2,3(), 王晓明1,2,3, 李亚洁1,2,3, 梁成龙4
通讯作者:
阮成龙
作者简介:
李炜(1963—),女,硕士,教授,研究方向为复杂系统建模、故障诊断与容错控制、寿命预测与延寿控制。E-mail:liwei@lut.edu.cn。
基金资助:
CLC Number:
LI Wei, RUAN Chenglong, WANG Xiaoming, LI Yajie, LIANG Chenglong. Integrated modelling method for tank-batch finished gasoline blending formulations[J]. Chemical Industry and Engineering Progress, 2022, 41(9): 4701-4712.
李炜, 阮成龙, 王晓明, 李亚洁, 梁成龙. 罐式批次成品汽油调和配方集成建模方法[J]. 化工进展, 2022, 41(9): 4701-4712.
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项目 | 改进MKFCM算法 |
---|---|
输入 | 整体核参数、罐底油各组分添加比例 |
输出 | 隶属度矩阵 U |
步骤 | 1.计算输入组分的各个核函数; 2.初始化核权重向量; 3.根据 4.初始隶属度矩阵; 5.根据 6.根据 7.根据 8.根据 9.根据 10.重复步骤5~步骤9,直到满足终止条件隶属度矩阵变化波动小于一定范围; 11.结束。 |
项目 | 改进MKFCM算法 |
---|---|
输入 | 整体核参数、罐底油各组分添加比例 |
输出 | 隶属度矩阵 U |
步骤 | 1.计算输入组分的各个核函数; 2.初始化核权重向量; 3.根据 4.初始隶属度矩阵; 5.根据 6.根据 7.根据 8.根据 9.根据 10.重复步骤5~步骤9,直到满足终止条件隶属度矩阵变化波动小于一定范围; 11.结束。 |
算法 | 主要参数 | 参数设置 |
---|---|---|
改进 MKFCM | 整体核参数σ | 0.005 |
聚类个数C | 3 | |
平滑因子m | 2 | |
迭代结束条件 | ‖ U (i)- U (i-1)‖<0.0001 |
算法 | 主要参数 | 参数设置 |
---|---|---|
改进 MKFCM | 整体核参数σ | 0.005 |
聚类个数C | 3 | |
平滑因子m | 2 | |
迭代结束条件 | ‖ U (i)- U (i-1)‖<0.0001 |
模型 | 主要参数 | MTBE | 乙苯 | 甲苯 | 搜索范围 |
---|---|---|---|---|---|
XGBoost1/ | n_estimators | 2182/2855/7902/2300 | 200/1200/15002300 | 350/1300/2960/2300 | 200~10000 |
colsample_bytree | 0.77/0.71/0.71/1 | 1/1/0.77/1 | 0.25/0.3/0.9/1 | 0~1 | |
XGBoost2/ | colsample_bylevel | 1 | 1 | 1/1/0.9/1 | 0~1 |
colsample_bynode | 1 | 1 | 1/1/0.5/1 | 0~1 | |
XGBoost3/ | η | 0.14/0.05/0.05/0.3 | 0.16/0.05/0.14/0.3 | 0.3/0.3/0.7/0.3 | 0~1 |
XGBoost | max_depth | 7/8/10/6 | 15/13/16/6 | 8/8/6/6 | 6~20 |
模型 | 主要参数 | MTBE | 乙苯 | 甲苯 | 搜索范围 |
---|---|---|---|---|---|
XGBoost1/ | n_estimators | 2182/2855/7902/2300 | 200/1200/15002300 | 350/1300/2960/2300 | 200~10000 |
colsample_bytree | 0.77/0.71/0.71/1 | 1/1/0.77/1 | 0.25/0.3/0.9/1 | 0~1 | |
XGBoost2/ | colsample_bylevel | 1 | 1 | 1/1/0.9/1 | 0~1 |
colsample_bynode | 1 | 1 | 1/1/0.5/1 | 0~1 | |
XGBoost3/ | η | 0.14/0.05/0.05/0.3 | 0.16/0.05/0.14/0.3 | 0.3/0.3/0.7/0.3 | 0~1 |
XGBoost | max_depth | 7/8/10/6 | 15/13/16/6 | 8/8/6/6 | 6~20 |
样本编号 | MKFCM算法隶属度 | 改进MKFCM算法隶属度 | 所属类别 | ||||
---|---|---|---|---|---|---|---|
Ⅰ类 | Ⅱ类 | Ⅲ类 | Ⅰ类 | Ⅱ类 | Ⅲ类 | ||
6 | 0.33301 | 0.33329 | 0.33368 | 0.36035 | 0.54604 | 0.09359 | 3/2 |
55 | 0.33309 | 0.33369 | 0.33320 | 0.30645 | 0.34325 | 0.35028 | 2/3 |
116 | 0.33367 | 0.33315 | 0.33316 | 0.38301 | 0.47266 | 0.14431 | 1/2 |
147 | 0.33332 | 0.33338 | 0.33328 | 0.35798 | 0.34124 | 0.30077 | 2/1 |
样本编号 | MKFCM算法隶属度 | 改进MKFCM算法隶属度 | 所属类别 | ||||
---|---|---|---|---|---|---|---|
Ⅰ类 | Ⅱ类 | Ⅲ类 | Ⅰ类 | Ⅱ类 | Ⅲ类 | ||
6 | 0.33301 | 0.33329 | 0.33368 | 0.36035 | 0.54604 | 0.09359 | 3/2 |
55 | 0.33309 | 0.33369 | 0.33320 | 0.30645 | 0.34325 | 0.35028 | 2/3 |
116 | 0.33367 | 0.33315 | 0.33316 | 0.38301 | 0.47266 | 0.14431 | 1/2 |
147 | 0.33332 | 0.33338 | 0.33328 | 0.35798 | 0.34124 | 0.30077 | 2/1 |
项目 | MSE | ||||||
---|---|---|---|---|---|---|---|
XGBoost1 | KNN1 | XGBoost2 | KNN2 | XGBoost3 | KNN3 | 模型1① | |
加氢汽油 | 0.2586 | 0.3335 | 0.2583 | 0.3319 | 0.2318 | 0.3335 | 0.3595 |
醚化汽油 | 0.0865 | 0.1406 | 0.0728 | 0.1402 | 0.0721 | 0.1408 | 0.1009 |
MTBE | 0.0402 | 0.1188 | 0.0376 | 0.1171 | 0.0383 | 0.1183 | 0.0569 |
车用异辛烷 | 0.1547 | 0.2415 | 0.1850 | 0.2387 | 0.1821 | 0.2414 | 0.1846 |
汽油重芳烃 | 0.1765 | 0.2024 | 0.1739 | 0.1997 | 0.1623 | 0.1971 | 0.1897 |
生成油 | 0.0301 | 0.0506 | 0.0335 | 0.0475 | 0.0384 | 0.0508 | 0.0665 |
乙苯 | 0.1353 | 0.2378 | 0.1275 | 0.2426 | 0.1464 | 0.2395 | 0.2184 |
甲苯 | 0.2095 | 0.3754 | 0.2126 | 0.3799 | 0.2010 | 0.3817 | 0.2552 |
二甲苯 | 0.1655 | 0.2048 | 0.1433 | 0.2075 | 0.1503 | 0.2101 | 0.1660 |
项目 | R2 | ||||||
XGBoost1 | KNN1 | XGBoost2 | KNN2 | XGBoost3 | KNN3 | 模型1① | |
加氢汽油 | 0.9926 | 0.9904 | 0.9926 | 0.9905 | 0.9933 | 0.9904 | 0.9897 |
醚化汽油 | 0.9940 | 0.9903 | 0.9949 | 0.9903 | 0.9950 | 0.9903 | 0.9930 |
MTBE | 0.9893 | 0.9687 | 0.9900 | 0.9691 | 0.9899 | 0.9688 | 0.9845 |
车用异辛烷 | 0.9676 | 0.9495 | 0.9613 | 0.9501 | 0.9619 | 0.9495 | 0.9617 |
汽油重芳烃 | 0.9388 | 0.9299 | 0.9398 | 0.9308 | 0.9437 | 0.9317 | 0.9353 |
生成油 | 0.9867 | 0.9776 | 0.9851 | 0.9790 | 0.9830 | 0.9775 | 0.9711 |
乙苯 | 0.9458 | 0.9048 | 0.9490 | 0.9029 | 0.9414 | 0.9042 | 0.9119 |
甲苯 | 0.9654 | 0.9380 | 0.9649 | 0.9373 | 0.9668 | 0.9370 | 0.9569 |
二甲苯 | 0.9753 | 0.9694 | 0.9786 | 0.9690 | 0.9776 | 0.9686 | 0.9754 |
项目 | MSE | ||||||
---|---|---|---|---|---|---|---|
XGBoost1 | KNN1 | XGBoost2 | KNN2 | XGBoost3 | KNN3 | 模型1① | |
加氢汽油 | 0.2586 | 0.3335 | 0.2583 | 0.3319 | 0.2318 | 0.3335 | 0.3595 |
醚化汽油 | 0.0865 | 0.1406 | 0.0728 | 0.1402 | 0.0721 | 0.1408 | 0.1009 |
MTBE | 0.0402 | 0.1188 | 0.0376 | 0.1171 | 0.0383 | 0.1183 | 0.0569 |
车用异辛烷 | 0.1547 | 0.2415 | 0.1850 | 0.2387 | 0.1821 | 0.2414 | 0.1846 |
汽油重芳烃 | 0.1765 | 0.2024 | 0.1739 | 0.1997 | 0.1623 | 0.1971 | 0.1897 |
生成油 | 0.0301 | 0.0506 | 0.0335 | 0.0475 | 0.0384 | 0.0508 | 0.0665 |
乙苯 | 0.1353 | 0.2378 | 0.1275 | 0.2426 | 0.1464 | 0.2395 | 0.2184 |
甲苯 | 0.2095 | 0.3754 | 0.2126 | 0.3799 | 0.2010 | 0.3817 | 0.2552 |
二甲苯 | 0.1655 | 0.2048 | 0.1433 | 0.2075 | 0.1503 | 0.2101 | 0.1660 |
项目 | R2 | ||||||
XGBoost1 | KNN1 | XGBoost2 | KNN2 | XGBoost3 | KNN3 | 模型1① | |
加氢汽油 | 0.9926 | 0.9904 | 0.9926 | 0.9905 | 0.9933 | 0.9904 | 0.9897 |
醚化汽油 | 0.9940 | 0.9903 | 0.9949 | 0.9903 | 0.9950 | 0.9903 | 0.9930 |
MTBE | 0.9893 | 0.9687 | 0.9900 | 0.9691 | 0.9899 | 0.9688 | 0.9845 |
车用异辛烷 | 0.9676 | 0.9495 | 0.9613 | 0.9501 | 0.9619 | 0.9495 | 0.9617 |
汽油重芳烃 | 0.9388 | 0.9299 | 0.9398 | 0.9308 | 0.9437 | 0.9317 | 0.9353 |
生成油 | 0.9867 | 0.9776 | 0.9851 | 0.9790 | 0.9830 | 0.9775 | 0.9711 |
乙苯 | 0.9458 | 0.9048 | 0.9490 | 0.9029 | 0.9414 | 0.9042 | 0.9119 |
甲苯 | 0.9654 | 0.9380 | 0.9649 | 0.9373 | 0.9668 | 0.9370 | 0.9569 |
二甲苯 | 0.9753 | 0.9694 | 0.9786 | 0.9690 | 0.9776 | 0.9686 | 0.9754 |
模型 | GE | PBR/% |
---|---|---|
模型1① | 0.0361 | 100.0342 |
模型2② | 0.1160 | 100.0680 |
模型3③ | 0.0300 | 100.0350 |
本文模型 | 0.0180 | 99.9934 |
模型 | GE | PBR/% |
---|---|---|
模型1① | 0.0361 | 100.0342 |
模型2② | 0.1160 | 100.0680 |
模型3③ | 0.0300 | 100.0350 |
本文模型 | 0.0180 | 99.9934 |
项目 | MSE | R2 | ||||||
---|---|---|---|---|---|---|---|---|
模型1 | 模型2 | 模型3 | 本文模型 | 模型1 | 模型2 | 模型3 | 本文模型 | |
加氢汽油 | 0.6121 | 0.3595 | 0.2945 | 0.2228 | 0.9825 | 0.9897 | 0.9915 | 0.9937 |
醚化汽油 | 0.2714 | 0.1009 | 0.0712 | 0.0621 | 0.9811 | 0.9930 | 0.9951 | 0.9958 |
MTBE | 0.0735 | 0.0569 | 0.0509 | 0.0344 | 0.9800 | 0.9845 | 0.9865 | 0.9909 |
车用异辛烷 | 0.1952 | 0.1846 | 0.1676 | 0.1562 | 0.9595 | 0.9617 | 0.9654 | 0.9674 |
汽油重芳烃 | 0.2918 | 0.1897 | 0.1835 | 0.1534 | 0.9006 | 0.9353 | 0.9345 | 0.9469 |
生成油 | 0.1012 | 0.0665 | 0.0586 | 0.0289 | 0.9560 | 0.9711 | 0.9758 | 0.9873 |
乙苯 | 0.2426 | 0.2184 | 0.1745 | 0.1281 | 0.9021 | 0.9119 | 0.9317 | 0.9487 |
甲苯 | 0.3015 | 0.2552 | 0.2193 | 0.1734 | 0.9491 | 0.9569 | 0.9637 | 0.9714 |
二甲苯 | 0.1918 | 0.1660 | 0.1420 | 0.0943 | 0.9716 | 0.9754 | 0.9788 | 0.9864 |
项目 | MSE | R2 | ||||||
---|---|---|---|---|---|---|---|---|
模型1 | 模型2 | 模型3 | 本文模型 | 模型1 | 模型2 | 模型3 | 本文模型 | |
加氢汽油 | 0.6121 | 0.3595 | 0.2945 | 0.2228 | 0.9825 | 0.9897 | 0.9915 | 0.9937 |
醚化汽油 | 0.2714 | 0.1009 | 0.0712 | 0.0621 | 0.9811 | 0.9930 | 0.9951 | 0.9958 |
MTBE | 0.0735 | 0.0569 | 0.0509 | 0.0344 | 0.9800 | 0.9845 | 0.9865 | 0.9909 |
车用异辛烷 | 0.1952 | 0.1846 | 0.1676 | 0.1562 | 0.9595 | 0.9617 | 0.9654 | 0.9674 |
汽油重芳烃 | 0.2918 | 0.1897 | 0.1835 | 0.1534 | 0.9006 | 0.9353 | 0.9345 | 0.9469 |
生成油 | 0.1012 | 0.0665 | 0.0586 | 0.0289 | 0.9560 | 0.9711 | 0.9758 | 0.9873 |
乙苯 | 0.2426 | 0.2184 | 0.1745 | 0.1281 | 0.9021 | 0.9119 | 0.9317 | 0.9487 |
甲苯 | 0.3015 | 0.2552 | 0.2193 | 0.1734 | 0.9491 | 0.9569 | 0.9637 | 0.9714 |
二甲苯 | 0.1918 | 0.1660 | 0.1420 | 0.0943 | 0.9716 | 0.9754 | 0.9788 | 0.9864 |
1 | 宋倩倩, 慕彦君, 侯雨璇, 等. 中美两国石油化工产业实力对比分析[J]. 化工进展, 2020, 39(5): 1607-1619. |
SONG Qianqian, MU Yanjun, HOU Yuxuan, et al. Comparative analysis of the strength of petrochemical industry between China and USA[J]. Chemical Industry and Engineering Progress, 2020, 39(5): 1607-1619. | |
2 | 李炜, 王晓明, 蒋栋年, 等. 基于SHPSO-GA-BP的成品汽油调和中加氢汽油组分辛烷值的预测[J]. 化工学报, 2020, 71(7): 3191-3200. |
LI Wei, WANG Xiaoming, JIANG Dongnian, et al. Prediction of octane number of finished gasoline blend based on SHPSO-GA-BP[J]. CIESC Journal, 2020, 71(7): 3191-3200. | |
3 | CASTRO P M. Source-based discrete and continuous-time formulations for the crude oil pooling problem[J]. Computers & Chemical Engineering, 2016, 93: 382-401. |
4 | TAIFOURIS M, MARTÍN M, MARTÍNEZ A, et al. Simultaneous optimization of the design of the product, process, and supply chain for formulated product[J]. Computers & Chemical Engineering, 2021, 152: 107384. |
5 | CASTRO P M. New MINLP formulation for the multiperiod pooling problem[J]. AIChE Journal, 2015, 61(11): 3728-3738. |
6 | LIU L Y. Molecular characterisation and modelling for refining processes[D]. University of Manchester, 2015 |
7 | JAMRI M AL, LI J, SMITH R. Molecular characterisation of biomass pyrolysis oil and petroleum fraction blends[J]. Computers & Chemical Engineering, 2020, 140: 106906. |
8 | 王通, 段泽文. 基于模糊评估自适应更新的油井动液面软测量建模[J]. 化工学报, 2019, 70(12): 4760-4769. |
WANG Tong, DUAN Zewen. Soft sensor modeling for dynamic liquid level of oil well based on fuzzy inference adaptive updating[J]. CIESC Journal, 2019, 70(12): 4760-4769. | |
9 | 何新礼, 谢莉, 杨慧中. 基于DP-RFR的多模型软测量建模[J]. 控制工程, 2020, 27(1): 64-69. |
HE Xinli, XIE Li, YANG Huizhong. Multi-model soft sensor modeling based on the DP-RFR method[J]. Control Engineering of China, 2020, 27(1): 64-69. | |
10 | TARIQ T, KADDOUR M. A novel cluster head selection method based on HAC algorithm for energy efficient wireless sensor network[C]// IPAC ’15: Proceedings of the International Conference on Intelligent Information Processing, Security and Advanced Communication. 2015: 1-6. |
11 | 刘聪, 谢莉, 杨慧中. 基于改进DPC的青霉素发酵过程多模型软测量建模[J]. 化工学报, 2021, 72(3): 1606-1615. |
LIU Cong, XIE Li, YANG Huizhong. Multi-model soft sensor development for penicillin fermentation process based on improved density peak clustering[J]. CIESC Journal, 2021, 72(3): 1606-1615. | |
12 | 吴小燕, 陈松灿. 联机核模糊C均值聚类方法[J]. 系统工程与电子技术, 2012, 34(12): 2599-2606. |
WU Xiaoyan, CHEN Songcan. Online kernel fuzzy C-means clustering algorithm[J]. Systems Engineering and Electronics, 2012, 34(12): 2599-2606. | |
13 | 张敏, 于剑. 基于划分的模糊聚类算法[J]. 软件学报, 2004, 15(6): 858-868. |
ZHANG Min, YU Jian. Fuzzy partitional clustering algorithms[J]. Journal of Software, 2004, 15(6): 858-868. | |
14 | 任喜伟, 何立风, 姚斌, 等. 油水界测量过程中自适应阈值聚类优化算法[J]. 化工进展, 2019, 38(2): 779-789. |
REN Xiwei, HE Lifeng, YAO Bin, et al. Clustering optimization algorithm with adaptive threshold for oil-water interface detection process[J]. Chemical Industry and Engineering Progress, 2019, 38(2): 779-789. | |
15 | KUMAR A, SHARMA K, SHARMA A. Genetically optimized fuzzy C-means data clustering of IoMT-based biomarkers for fast affective state recognition in intelligent edge analytics[J]. Applied Soft Computing, 2021, 109: 107525. |
16 | 徐洋, 方洋旺, 伍友利, 等. 相似分布特性准则下的高斯混合项聚类-合并算法[J]. 国防科技大学学报, 2019, 41(4): 156-164. |
XU Yang, FANG Yangwang, WU Youli, et al. Cluster-merge method for the Gaussian mixture components based on the similarity distribution criterion[J]. Journal of National University of Defense Technology, 2019, 41(4): 156-164. | |
17 | SILVA SOUZA M P DA, DE MENEZES E SILVA FILHO T, AMARAL G J A D, et al. Investigating different fitness criteria for swarm-based clustering[J]. International Journal of Business Intelligence and Data Mining, 2019, 15(1): 117. |
18 | 田睿, 范祥祥, 戴影, 等. 核模糊C均值聚类算法优选BDS-3三频组合观测值[J]. 系统工程与电子技术, 2020, 42(3): 686-697. |
TIAN Rui, FAN Xiangxiang, DAI Ying, et al. Optimal triple-frequency combination observations for BDS-3 derived from a modified kernel-based fuzzy C-means clustering algorithm[J]. Systems Engineering and Electronics, 2020, 42(3): 686-697. | |
19 | 刘云, 刘富, 侯涛, 等. 优化核参数的模糊C均值聚类算法[J]. 吉林大学学报(工学版), 2016, 46(1): 246-251. |
LIU Yun, LIU Fu, HOU Tao, et al. Kernel-based fuzzy C-means clustering method based on parameter optimization[J]. Journal of Jilin University (Engineering and Technology Edition), 2016, 46(1): 246-251. | |
20 | HUANG H C, CHUANG Y Y, CHEN C S. Multiple kernel fuzzy clustering[J]. IEEE Transactions on Fuzzy Systems, 2012, 20(1): 120-134. |
21 | ZENG S, WANG Z Y, HUANG R, et al. A study on multi-kernel intuitionistic fuzzy C-means clustering with multiple attributes[J]. Neurocomputing, 2019, 335: 59-71. |
22 | 孙建平, 苑一方. 复杂过程的多模型建模方法研究[J]. 仪器仪表学报, 2011, 32(1): 132-137. |
SUN Jianping, YUAN Yifang. Multi-model modeling approach for complex process[J]. Chinese Journal of Scientific Instrument, 2011, 32(1): 132-137. | |
23 | 牛培峰, 刘超, 李国强, 等. 基于双层聚类与GSA-LSSVM的汽轮机热耗率多模型预测[J]. 电机与控制学报, 2016, 20(3): 90-95. |
NIU Peifeng, LIU Chao, LI Guoqiang, et al. Multi-model for turbine heat rate forecasting based on double layer clustering algorithm and GSA-LSSVM[J]. Electric Machines and Control, 2016, 20(3): 90-95. | |
24 | 张孙力, 杨慧中. 一种基于改进扩张搜索聚类算法的软测量建模方法[J]. 南京理工大学学报, 2017, 41(5): 574-580. |
ZHANG Sunli L, YANG Huizhong. Soft sensor modeling method based on improved expanding searching clustering algorithm[J]. Journal of Nanjing University of Science and Technology, 2017, 41(5): 574-580. | |
25 | 葛祥振. 基于仿射传播聚类和高斯过程回归的软测量建模研究[D]. 无锡: 江南大学, 2017. |
GE Xiangzhen. Research on soft sensor modeling based on affinity propagation clustering and Gaussian process regression[D]. Wuxi: Jiangnan University, 2017. | |
26 | 许言路, 张建森, 吉星, 等. 基于多模型融合神经网络的短期负荷预测[J]. 控制工程, 2019, 26(4): 619-624. |
XU Yanlu, ZHANG Jiansen, JI Xing, et al. Research on short-term load forecasting method based on multi-model fusion neural network[J]. Control Engineering of China, 2019, 26(4): 619-624. | |
27 | ACCARINO G, CHIARELLI M, FIORE S, et al. A multi-model architecture based on long short-term memory neural networks for multi-step sea level forecasting[J]. Future Generation Computer Systems, 2021, 124: 1-9. |
28 | CHEN T Q, GUESTRIN C. XGBoost: a scalable tree boosting system[C]// KDD ’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016: 785-794. |
29 | DING Y, FAN L X, LIU X. Analysis of feature matrix in machine learning algorithms to predict energy consumption of public buildings[J]. Energy and Buildings, 2021, 249: 111208. |
30 | 朱礼涛, 欧阳博, 张希宝, 等. 机器学习在多相反应中的应用进展[J]. 化工进展, 2021, 40(4): 1699-1714. |
ZHU Litao, OUYANG Bo, ZHANG Xibao, et al. Progress on application of machine learning to multiphase reactors[J]. Chemical Industry and Engineering Progress, 2021, 40(4): 1699-1714. | |
31 | QIU Y, GARG D, KIM S M, et al. Machine learning algorithms to predict flow boiling pressure drop in mini/micro-channels based on universal consolidated data[J]. International Journal of Heat and Mass Transfer, 2021, 178: 121607. |
32 | 陈延展, 胡浩, 任紫畅, 等. 基于XGBoost和改进灰狼优化算法的催化裂化汽油精制装置的辛烷值损失模型分析[J/]. 石油学报(石油加工), 2022, 38(1): 208-219. |
CHEN Yanzhan, HU Hao, REN Zichang, et al. Model analysis of gasoline octane loss in catalytic cracking post-refining unit based on XGBoost and improved gray wolf optimization algorithm[J]. Acta Petrolei Sinica (Petroleum Processing Section), 2022, 38(1): 208-219. | |
33 | 何仁初, 陈海泉, 杨超文. 面向国Ⅵ标准的汽油调合优化技术探讨与分析[J]. 化工进展, 2018, 37(3): 962-969. |
HE Renchu, CHEN Haiquan, YANG Chaowen. Discussion and analysis on optimization technology of gasoline blending for Chinese national standard Ⅵ[J]. Chemical Industry and Engineering Progress, 2018, 37(3): 962-969. | |
34 | RUSIN M H, CHUNG H S, MARSHALL J F. A “transformation” method for calculating the research and motor octane numbers of gasoline blends[J]. Industrial & Engineering Chemistry Fundamentals, 1981, 20(3): 195-204. |
35 | 李春艳. 基于信息熵与多核学习改进的多视角FCM聚类的研究[D]. 苏州: 苏州大学, 2020. |
LI Chunyan. Research on improved multi-view fuzzy C-means clustering based on information entropy and multiple kernel learning[D]. Suzhou: Soochow University, 2020. | |
36 | 周绍磊, 廖剑, 史贤俊. 基于Fisher准则和最大熵原理的SVM核参数选择方法[J]. 控制与决策, 2014, 29(11): 1991-1996. |
ZHOU Shaolei, LIAO Jian, SHI Xianjun. SVM parameters selection method based on Fisher criterion and maximum entropy principle[J]. Control and Decision, 2014, 29(11): 1991-1996. | |
37 | ZHANG Yaling, HAN Jin. Differential privacy fuzzy C-means clustering algorithm based on Gaussian kernel function[J]. PLoS one, 2021, 16(3): e0248737. |
38 | ZHOU S H, ZHU E, LIU X W, et al. Subspace segmentation-based robust multiple kernel clustering[J]. Information Fusion, 2020, 53: 145-154. |
39 | TIBSHIRANI R, WALTHER G, HASTIE T. Estimating the number of clusters in a data set via the gap statistic[J]. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2001, 63(2): 411-423. |
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