罐式批次成品汽油调和配方集成建模方法

doi:10.16085/j.issn.1000-6613.2021-2262

化工进展 ›› 2022, Vol. 41 ›› Issue (9): 4701-4712.DOI: 10.16085/j.issn.1000-6613.2021-2262

罐式批次成品汽油调和配方集成建模方法

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

^1.兰州理工大学电气工程与信息工程学院，甘肃兰州 730050
^2.甘肃省工业过程先进控制重点实验室，甘肃兰州 730050
^3.兰州理工大学电气与控制工程国家级实验教学示范中心，甘肃兰州 730050
^4.中国石化兰州石化分公司油品储运厂，甘肃兰州 730060

收稿日期:2021-11-05 修回日期:2021-12-16 出版日期:2022-09-25 发布日期:2022-09-27
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:
甘肃省青年博士基金(2021QB-044)

Integrated modelling method for tank-batch finished gasoline blending formulations

LI Wei¹^,²^,³(), RUAN Chenglong¹^,²^,³(), WANG Xiaoming¹^,²^,³, LI Yajie¹^,²^,³, LIANG Chenglong⁴

^1.College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, Gansu, China
^2.Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, Gansu, China
^3.National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, Gansu, China
^4.Oil Storage and Transportation Plant, Petrochina Lanzhou Petrochemical Company, Lanzhou 730060, Gansu, China

Received:2021-11-05 Revised:2021-12-16 Online:2022-09-25 Published:2022-09-27
Contact: RUAN Chenglong

摘要/Abstract

摘要：

考虑罐式批次成品汽油调和过程中罐底余油对产品质量指标的影响，本文提出了一种基于改进多核模糊C均值（multi-kernel fuzzy C-means，MKFCM）与极端梯度提升树（extreme gradient boosting，XGBoost）集成的成品汽油调和的通用配方建模方法。该方法首先考虑各调和组分的差异性，提出一种自适应核参数计算方法对MKFCM改进，并将其用于罐底油聚类分析，旨在最大程度划分出性质相近的罐底油批次类型；在此基础上择优选用XGBoost算法，以各批次罐底余油的组分比例和产品预期质量指标作为输入，建立各批次子配方模型；在配方生成时，基于改进MKFCM求得当前罐底余油的隶属度向量，并以此为权值对子配方模型进行加权融合，最终以多模型集成的方式得到了成品汽油调和的通用配方。经使用某企业实际工业数据进行实验分析，结果表明，较单一模型或未改进MKFCM的集成模型，基于改进MKFCM-XGBoost的多模型集成配方，预测精度和泛化能力均更优，更适合罐式批次成品汽油调和过程。

关键词: 调和配方, 预测, 改进多核模糊C均值, 极端梯度提升树, 集成, 罐底油, 优化

Abstract:

During the blending process of tank batches of finished gasoline,an integrated modeling method for finished gasoline blending general formula was proposed by considering the influence of the remaining oil at the bottom of the tank on product quality indicators, which based on an improved multi-kernel fuzzy C-means (MKFCM) and an extreme gradient boosting tree (XGBoost). Firstly, considering the differences of the blending components, an adaptive kernel parameter calculation method was presented to improve MKFCM, and then it was used for tank bottom oil cluster analysis in order to classify tank bottom oils with similar properties to the greatest extent batch-type. Secondly, XGBoost algorithm was selected on this basis. The sub-formulation model of each batch was established by taking the component proportion of residual oil at the bottom of each batch of tank and the expected quality index of the product as the input. When generating the formula, the membership vector of the current tank bottom residual oil was obtained based on the improved MKFCM and the above membership vectors were used to weight and fuse the sub formula model. Finally, the blended general formula of finished gasoline was obtained by multi-model integration. The experimental analysis by using the actual industrial data of an enterprise showed that compared with a single model or an integrated model without the improved MKFCM, the multi-model integrated formula based on the improved MKFCM-XGBoost possessed better prediction accuracy and generalization ability, and it was more suitable for tank type process of blending batches of finished gasoline.

Key words: blending formula, prediction, improve MKFCM, extreme gradient boosting (XGBoost), integration, remaining oil at the bottom of the tank, optimization

中图分类号:

TP273

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.

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.

图/表 12

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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
XGBoost1/	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
XGBoost2/	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
XGBoost1/	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
XGBoost2/	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
样本编号	Ⅰ类	Ⅱ类	Ⅲ类	Ⅰ类	Ⅱ类	Ⅲ类	所属类别
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

罐式批次成品汽油调和配方集成建模方法

Integrated modelling method for tank-batch finished gasoline blending formulations

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图/表 12

参考文献 39

相关文章 15

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Metrics

本文评价

项目	MSE
项目	XGBoost1	KNN1	XGBoost2	KNN2	XGBoost3	KNN3	模型1^①
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
项目	XGBoost1	KNN1	XGBoost2	KNN2	XGBoost3	KNN3	模型1^①
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1^①	0.0361	100.0342
模型2^②	0.1160	100.0680
模型3^③	0.0300	100.0350
本文模型	0.0180	99.9934

项目	MSE				R²
项目	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

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