一种分步约简的炼油生产敏感变量选择方法

doi:10.11949/0438-1157.20191499

摘要/Abstract

摘要：

变量筛选是现代工业过程产品质量预测研究中的热点问题之一。过滤式变量选择方法因其计算速度快且不易造成过拟合得到了广泛应用，但其存在容易忽略变量相关性且不能准确反映工况信息的问题，在高维数据维度灾难问题日渐突出的当今不再适用。针对这一问题，提出一种分步约简的敏感变量选择方法。该方法在明确敏感变量和关键敏感变量的基础上，根据变量对工况的描述能力和辅助变量与主导变量的净相关性定义了敏感性指标，实现敏感变量的初选；接着，构建加权余弦马田系统以解决变量冗余性问题，实现敏感变量的精选。所提方法应用于加氢裂化产品质量预测，实际工业应用结果表明，该方法不仅可以提高模型的预测精度，而且可以有效地降低模型复杂性。

关键词: 敏感性指标, 变量选择, 预测, 系统工程, 石油

Abstract:

Variable selection has always been a hotspot in product quality prediction of modern industrial production. Due to the fast calculation speed and do not easily cause overfitting, filter methods have been widely used.However, because the correlation of variables is easily ignored and no information of working condition reflected in the filter methods,it is no longer suitable for solving the disaster problems of high-dimensional data. To cope with these problems, a fractional step reduction method for sensitive variable selection is proposed in the paper. First, two concepts of sensitive variables (SV) and key sensitive variables (KSV) are first identified. Then, a sensitivity index (SI) is developed to select the sensitive variables preliminarily. After that, a weighted cosine Mahalanobis-Taguchi system (WCMTS) is proposed to precisely select the key sensitive variables. Finally, the proposed method is applied to an industry hydrocracking process. Practical industrial application results show that the method can not only improve the prediction accuracy of the model, but also effectively reduce the complexity of the model.

Key words: sensitivity index, variable selection, prediction, systems engineering, petroleum

中图分类号:

TP 274

李灵, 王雅琳, 孙备. 一种分步约简的炼油生产敏感变量选择方法[J]. 化工学报, 2020, 71(5): 2173-2181.

Ling LI, Yalin WANG, Bei SUN. Fractional step reduction method for sensitive variable selection of refining processes[J]. CIESC Journal, 2020, 71(5): 2173-2181.

图/表 14

参考文献 31

1	Guyon I, Elisseeff A. An introduction to variable and feature selection[J]. J. Mach. Learn. Res., 2003, 3: 1157-1182.
2	Lazar C, Taminau J, Meganck S, et al. A survey on filter techniques for feature selection in gene expression microarray analysis[J]. IEEE ACM. T. Comput. Bi., 2012, 9(4): 1106-1119.
3	Girish C, Ferat S. A survey on feature selection methods[J]. Comput. Electr. Eng., 2014, 40: 16-28.
4	Ron K, George H J. Wappers for feature subset selection[J]. Artif. Intell., 1997, 97: 273-324.
5	Liu C, Wang W Y, Zhao Q, et al. A new feature selection method based on a validity index of feature subset[J]. Pattern Recogn. Lett., 2017, 92: 1-8.
6	Blum A L, Langley P. Selection of relevant features and examples in machine learning[J]. Artif. Intell., 1997, 97: 245-270.
7	Chen Q, Zhang M J, Xue B. Feature selection to improve generalization of genetic programming for high-dimensional symbolic regression[J]. IEEE T. Evolut. Comput., 2017, 21(5): 792-806.
8	Battiti R. Using mutual information for selecting features in supervised neural net learning[J]. IEEE T. Neural Networ., 1994, 5(4): 537-550.
9	卢新国, 林亚平, 陈志平. 一种改进的互信息特征选取预处理算法[J]. 湖南大学学报(自然科学版), 2005, 32(1): 104-107.
	Lu X G, Lin Y P, Chen Z P. An improved feature selection preprocessing algorithm based on mutual information[J]. Journal of Hunan University (Natural Sciences), 2005, 32(1): 104-107.
10	童楚东, 史旭华. 基于互信息的PCA方法及其在过程监测中的应用[J]. 化工学报, 2015, 66(10): 4101-4106.
	Tong C D, Shi X H. Mutual information based PCA algorithm with application in process monitoring[J]. CIESC Journal, 2015, 66(10): 4101-4106.
11	光俊叶, 邵伟, 孙亮, 等. 基于融合欧式距离与Kendall Tau距离度量的谱聚类算法[J]. 控制理论与应用, 2017, 34(6): 783-789.
	Guang J Y, Shao W, Sun L, et al. Spectral clustering with mixed Euclidean and Kendall Tau metrics[J]. Control Theory & Applications, 2017, 34(6): 783-789.
12	Vrieze S I. Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC)[J]. Psychological Methods, 2012, 17(2): 228-243.
13	卢春红, 熊伟丽, 顾晓峰. 基于贝叶斯推理的PKPCAM的非线性多模态过程故障监测与诊断方法[J]. 化工学报, 2014, 65(12): 4866-4874.
	Lu C H, Xiong W L, Gu X F. Fault detection and diagnosis for nonlinear and multimode processes using Bayesian inference based PKPCAM approach[J]. CIESC Journal, 2014, 65(12): 4866-4874.
14	Liu J Y, Li R Z, Wu R L. Feature selection for varying coefficient models with ultrahigh-dimensional covariates[J]. J. Am. Stat. Assoc., 2014, 109(505): 266-274.
15	Sun X, Liu Y H, Xu M T, et al. Feature selection using dynamic weights for classification[J]. Knowl-based Syst., 2013, 37: 541-549.
16	Jiang F, Sui Y F, Zhou L. A relative decision entropy-based feature selection approach[J]. Pattern Recogn., 2015, 48: 2151-2163.
17	Dong Y W, Yang S Q, Xu C Y, et al. Determination of soil parameters in apple-growing regions by near- and mid-infrared spectroscopy[J]. Pedosphere, 2011, 21(5): 591-602.
18	Vohland M, Besold J, Hill J, et al. Comparing different multivariate calibration methods for the determination of soil organic carbon pools with visible to near infrared spectroscopy[J]. Geoderma, 2011, 166(1): 198-205.
19	吴佳, 谢永芳, 阳春华, 等. 一种无监督约简的浮选泡沫图像特征选择方法及应用[J]. 信息与控制, 2014, 43(3): 314-317.
	Wu J, Xie Y F, Yang C H, et al. An unsupervised reduction method for the selection of flotation froth image characters and its application[J]. Information and Control, 2014, 43(3): 314-317.
20	Tham M T, Montague G A, Julian M A, et al. Soft-sensors for process estimation and inferential control[J]. J. Process Contr., 1991, 1(1): 3-14.
21	李秋美, 田学民, 尚林源. 基于偏相关性分析的MPC控制器模型失配检测[J]. 化工学报, 2016, 67(3): 852-857.
	Li Q M, Tian X M, Shang L Y. Detection of model-plant mismatch based on partial correlation analysis of MPC controllers[J]. CIESC Journal, 2016, 67(3): 852-857.
22	常志朋, 陈龙生, 崔立志. 基于马田系统的区间Choquet 模糊积分多属性决策方法[J]. 控制与决策, 2016, 31(1): 180-186.
	Chang Z P, Cheng L S, Cui L Z. Interval Choquet fuzzy integral multi-attribute decision making method based on Mahalanobis-Taguchi system[J]. Control and Decision, 2016, 31(1): 180-186.
23	Wang H J, Huo N, Li J H, et al. A road quality detection method based on the Mahalanobis-Taguchi System[J]. IEEE Access, 2018, 6: 29078-29087.
24	马贺贺, 胡益, 侍洪波. 基于马氏距离局部离群因子方法的复杂化工过程故障监测[J]. 化工学报, 2013, 64(5): 1674-1682.
	Ma H H, Hu Y, Shi H B. Fault detection of complex chemical processes using Mahalanobis distance based local outlier factor[J]. CIESC Journal, 2013, 64(5): 1674-1682.
25	刘晓凤, 栾小丽, 刘飞. 基于隐变量空间载荷余弦相似度的间歇过程递推优化[J]. 化工学报, 2018, 69(3): 1167-1172.
	Liu X F, Luan X L, Liu F. Recursive optimization of batch processes based on load cosine similarity in latent variable space[J]. CIESC Journal, 2018, 69(3): 1167-1172.
26	王雅琳, 夏海兵, 袁小峰, 等. 基于趋势相似度分析的多重时滞辨识及其在加氢裂化流程中的应用[J]. 化工学报, 2018, 69(3): 1149-1157.
	Wang Y L, Xia H B, Yuan X F, et al. Multi-delay identification by trend-similarity analysis and its application to hydrocracking process[J]. CIESC Journal, 2018, 69(3): 1149-1157.
27	Zhao J S， Chen B Z, Shen J Z. A hybrid ANN-ES system for dynamic fault diagnosis of hydrocracking process[J]. Comput. Chem. Eng., 1997, 21: 5929-5933.
28	Yuan X, Ge Z, Song Z. Spatio-temporal adaptive soft sensor for nonlinear time-varying and variable drifting processes based on moving window LWPLS and time difference model[J]. Asia-Pac. J. Chem. Eng., 2016, 11(2): 209-219.
29	Chen J, Yang C, Zhou C, et al. Multivariate regression model for industrial process measurement based on double locally weighted partial lease squares[J]. IEEE Trans. Instrum. Meas., 2019, DOI: 10.1109/TIM.2019.2943824. DOI URL
30	Ren L, Lv W, Jiang S, et al. Fault diagnosis using a joint model based on sparse representation and SVM[J]. IEEE Trans. Instrum. Meas., 2016, 65(10): 2313-2320.
31	Yuan X F, Ge Z Q, Song Z H. Locally weighted kernel principal component regression model for soft sensing of nonlinear time-variant processes[J]. Ind. Eng. Chem. Res., 2014, 53(35): 13736-13749.

编辑推荐 0

Metrics

阅读次数

全文

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	2	6	0	90

来源	本网站	其他网站

次数	65	33
比例	66%	34%

摘要

562

最新录用	在线预览	正式出版

11	0	551

来源	本网站	其他网站

次数	435	127
比例	77%	23%

序号	过程参数	偏相关系数	离散程度	敏感性指数
1	原料油总流量	0.5784	0.1211	0.0700
2	反应加热炉总入口温度	0.4035	0.0408	0.0165
…	…	…	…	…
11	精制反应器三床层底部温度	0.4132	0.2690	0.1112
12	精制反应器塔底温度指示	0.2873	0.0107	0.0031
13	精制反应器压差	0.2312	0.0258	0.0060
…	…	…	…	…
21	冷低压分离器冷低分油流量	0.4756	0.1239	0.0589
22	新氢至加裂流量	0.4829	1.5620	0.7543
23	脱硫化氢汽提塔塔顶回流量	0.0931	0.0834	0.0078
…	…	…	…	…
30	主分馏塔中段抽出量	0.4561	0.0365	0.0166
31	主分馏塔中段返回温度	0.2578	0.0171	0.0044
…	…	…	…	…
37	柴油汽提塔塔顶温度	0.3006	0.0311	0.0093
38	柴油汽提塔底部温度	0.1722	0.0205	0.0035

序号	过程参数	偏相关系数	离散程度	敏感性指数
1	原料油总流量	0.5784	0.1211	0.0700
2	反应加热炉总入口温度	0.4035	0.0408	0.0165
…	…	…	…	…
11	精制反应器三床层底部温度	0.4132	0.2690	0.1112
12	精制反应器塔底温度指示	0.2873	0.0107	0.0031
13	精制反应器压差	0.2312	0.0258	0.0060
…	…	…	…	…
21	冷低压分离器冷低分油流量	0.4756	0.1239	0.0589
22	新氢至加裂流量	0.4829	1.5620	0.7543
23	脱硫化氢汽提塔塔顶回流量	0.0931	0.0834	0.0078
…	…	…	…	…
30	主分馏塔中段抽出量	0.4561	0.0365	0.0166
31	主分馏塔中段返回温度	0.2578	0.0171	0.0044
…	…	…	…	…
37	柴油汽提塔塔顶温度	0.3006	0.0311	0.0093
38	柴油汽提塔底部温度	0.1722	0.0205	0.0035

样本序号	马氏距离	余弦相似度	余弦马氏距离
1	1.2643	0.4000	1.1260
2	0.5608	0.3437	0.5261
3	0.8299	0.3559	0.7540
4	1.0096	0.4000	0.9121
…	…	…	…
29	1.0354	0.3401	0.9241
30	1.3146	0.3460	1.1597
31	0.9665	0.4000	0.8759
32	1.2449	0.4000	1.1097

样本序号	马氏距离	余弦相似度	余弦马氏距离
1	1.2643	0.4000	1.1260
2	0.5608	0.3437	0.5261
3	0.8299	0.3559	0.7540
4	1.0096	0.4000	0.9121
…	…	…	…
29	1.0354	0.3401	0.9241
30	1.3146	0.3460	1.1597
31	0.9665	0.4000	0.8759
32	1.2449	0.4000	1.1097

样本序号	马氏距离	余弦相似度	余弦马氏距离
1	4.919	0.3437	4.1870
2	276.954	0.3437	232.6964
3	1.6571	5.3472	2.2475
4	227.402	0.3460	191.0370
5	49.435	0.4000	41.5894
6	572.315	0.4000	480.8086
7	562.881	0.4000	472.8840
8	167.429	0.3401	140.6948
9	14.732	0.3401	12.4293
10	335.268	0.4000	281.6891
11	134.409	0.3390	112.9578
12	551.747	0.4000	463.5315