CIESC Journal ›› 2020, Vol. 71 ›› Issue (3): 1278-1287.DOI: 10.11949/0438-1157.20190934
• Process system engineering • Previous Articles Next Articles
Yuhao DU,Gaowei YAN(),Rong LI,Fang WANG
Received:
2019-08-14
Revised:
2019-11-03
Online:
2020-03-05
Published:
2020-03-05
Contact:
Gaowei YAN
通讯作者:
阎高伟
基金资助:
CLC Number:
Yuhao DU, Gaowei YAN, Rong LI, Fang WANG. Multiple working conditions soft sensor modeling method of geodesic flow kernel based on locally linear embedding[J]. CIESC Journal, 2020, 71(3): 1278-1287.
杜宇浩, 阎高伟, 李荣, 王芳. 基于局部线性嵌入的测地线流式核多工况软测量建模方法[J]. 化工学报, 2020, 71(3): 1278-1287.
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工况 | G/H比率 | 产品等级/(kg/h) |
---|---|---|
1 | 50/50 | 14076 |
2 | 10/90 | 14077 |
3 | 90/10 | 11111 |
Table 1 Data of three working conditions
工况 | G/H比率 | 产品等级/(kg/h) |
---|---|---|
1 | 50/50 | 14076 |
2 | 10/90 | 14077 |
3 | 90/10 | 11111 |
Item | 成分A | 成分B | 成分C | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
PLSR | LLE | GFK | LLEGFK | PLSR | LLE | GFK | LLEGFK | PLSR | LLE | GFK | LLEGFK | |
1—2 | 1.739 | 0.989 | 0.890 | 0.460 | 1.192 | 1.089 | 0.956 | 0.719 | 0.620 | 0.543 | 0.537 | 0.425 |
1—3 | 2.635 | 2.201 | 1.464 | 0.839 | 5.219 | 4.864 | 4.793 | 4.179 | 0.901 | 0.859 | 0.744 | 0.476 |
2—1 | 1.670 | 1.577 | 0.808 | 0.437 | 1.851 | 1.618 | 1.135 | 0.625 | 0.711 | 0.646 | 0.599 | 0.510 |
2—3 | 2.151 | 1.920 | 1.597 | 0.983 | 4.845 | 4.251 | 4.570 | 3.963 | 0.937 | 0.875 | 0.776 | 0.613 |
3—1 | 1.629 | 1.397 | 0.356 | 0.328 | 4.682 | 4.472 | 4.314 | 4.095 | 0.746 | 0.725 | 0.695 | 0.501 |
3—2 | 1.484 | 1.057 | 0.704 | 0.655 | 3.873 | 4.902 | 3.924 | 3.783 | 0.661 | 0.621 | 0.635 | 0.550 |
Table 2 Comparison of RMSE of soft sensor of different algorithm parameters under different working conditions
Item | 成分A | 成分B | 成分C | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
PLSR | LLE | GFK | LLEGFK | PLSR | LLE | GFK | LLEGFK | PLSR | LLE | GFK | LLEGFK | |
1—2 | 1.739 | 0.989 | 0.890 | 0.460 | 1.192 | 1.089 | 0.956 | 0.719 | 0.620 | 0.543 | 0.537 | 0.425 |
1—3 | 2.635 | 2.201 | 1.464 | 0.839 | 5.219 | 4.864 | 4.793 | 4.179 | 0.901 | 0.859 | 0.744 | 0.476 |
2—1 | 1.670 | 1.577 | 0.808 | 0.437 | 1.851 | 1.618 | 1.135 | 0.625 | 0.711 | 0.646 | 0.599 | 0.510 |
2—3 | 2.151 | 1.920 | 1.597 | 0.983 | 4.845 | 4.251 | 4.570 | 3.963 | 0.937 | 0.875 | 0.776 | 0.613 |
3—1 | 1.629 | 1.397 | 0.356 | 0.328 | 4.682 | 4.472 | 4.314 | 4.095 | 0.746 | 0.725 | 0.695 | 0.501 |
3—2 | 1.484 | 1.057 | 0.704 | 0.655 | 3.873 | 4.902 | 3.924 | 3.783 | 0.661 | 0.621 | 0.635 | 0.550 |
工况 | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
介质充填率 | 0.3 | 0.35 | 0.4 | 0.45 | 0.5 |
实验次数 | 139 | 103 | 88 | 95 | 102 |
Table 3 MFR and number of experiments under different working conditions
工况 | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
介质充填率 | 0.3 | 0.35 | 0.4 | 0.45 | 0.5 |
实验次数 | 139 | 103 | 88 | 95 | 102 |
Item | MBVR | PD | CVR | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
PLSR | LLE | GFK | LLEGFK | PLSR | LLE | GFK | LLEGFK | PLSR | LLE | GFK | LLEGFK | |
1—2 | 0.534 | 0.208 | 0.302 | 0.074 | 0.056 | 0.130 | 0.062 | 0.016 | 0.087 | 0.132 | 0.131 | 0.102 |
1—3 | 0.746 | 0.406 | 0.356 | 0.133 | 0.196 | 0.175 | 0.056 | 0.041 | 0.296 | 0.207 | 0.182 | 0.134 |
1—4 | 1.835 | 0.491 | 0.397 | 0.102 | 0.596 | 0.235 | 0.075 | 0.049 | 0.397 | 0.115 | 0.356 | 0.109 |
1—5 | 2.151 | 1.920 | 0.541 | 0.242 | 1.326 | 0.185 | 0.157 | 0.061 | 0.822 | 0.430 | 0.746 | 0.296 |
Table 4 Comparison of RMSE of soft sensor of different algorithm parameters under different working conditions
Item | MBVR | PD | CVR | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
PLSR | LLE | GFK | LLEGFK | PLSR | LLE | GFK | LLEGFK | PLSR | LLE | GFK | LLEGFK | |
1—2 | 0.534 | 0.208 | 0.302 | 0.074 | 0.056 | 0.130 | 0.062 | 0.016 | 0.087 | 0.132 | 0.131 | 0.102 |
1—3 | 0.746 | 0.406 | 0.356 | 0.133 | 0.196 | 0.175 | 0.056 | 0.041 | 0.296 | 0.207 | 0.182 | 0.134 |
1—4 | 1.835 | 0.491 | 0.397 | 0.102 | 0.596 | 0.235 | 0.075 | 0.049 | 0.397 | 0.115 | 0.356 | 0.109 |
1—5 | 2.151 | 1.920 | 0.541 | 0.242 | 1.326 | 0.185 | 0.157 | 0.061 | 0.822 | 0.430 | 0.746 | 0.296 |
1 | Wang J G,Xie Z,Yao Y,et al.Soft sensor development for improving economic efficiency of the coke dry quenching process[J].Journal of Process Control,2019,77:20-28. |
2 | 曹鹏飞,罗雄麟.化工过程软测量建模方法研究进展[J].化工学报,2013,64(3):788-800. |
Cao P F,Luo X L.Modeling of soft sensor for chemical process[J].CIESC Journal,2013,64(3):788-800. | |
3 | 刘毅,王海清,李平.用于发酵过程在线建模的自适应局部最小二乘支持向量机回归方法[J].化工学报,2008,59(8):2052-2057. |
Liu Y,Wang H Q,Li P.Adaptive local learning based least squares support vector regression with application to online modeling for fermentation processes[J].Journal of Chemical Industry and Engineering(China),2008,59(8):2052-2057. | |
4 | 刘毅,王海清,李平.局部最小二乘支持向量机回归在线建模方法及其在间歇过程的应用[J].化工学报,2007,58(11):2846-2851. |
Liu Y,Wang H Q,Li P.Local least squares support vector regression with application to online modeling for batch processes[J].Journal of Chemical Industry and Engineering(China),2007,58(11):2846-2851. | |
5 | Zheng W,Liu Y,Gao Z,et al.Just-in-time semi-supervised soft sensor for quality prediction in industrial rubber mixers[J].Chemometrics and Intelligent Laboratory Systems,2018,180:36-41. |
6 | Jin H,Chen X,Li W,et al.Adaptive soft sensor development based on online ensemble gaussian process regression for nonlinear time-varying batch processes[J].Industrial & Engineering Chemistry Research,2015,54(30):7320-7345. |
7 | Jin H,Chen X,Yang J,et al.Multi-model adaptive soft sensor modeling method using local learning and online support vector regression for nonlinear time-variant batch processes[J].Chemical Engineering Science,2015,131:282-303. |
8 | Liu Y,Yang C,Gao Z,et al.Ensemble deep kernel learning with application to quality prediction in industrial polymerization processes[J].Chemometrics and Intelligent Laboratory Systems,2018,174:15-21. |
9 | Liu J X,Tao L,Chen J H.Quality prediction for multi-grade processes by just-in-time latent variable modeling with integration of common and special features[J].Chemical Engineering Science,2018,191:31-41. |
10 | 杜永贵,李思思,阎高伟,等.基于流形正则化域适应湿式球磨机负荷参数软测量[J].化工学报,2018,69(3):1244-1251. |
Du Y G,Li S S,Yan G W,et al.Soft sensor of wet ball mill load parameter based on domain adaptation with manifold regularization[J].CIESC Journal,2018,69(3):1244-1251. | |
11 | Lv X B,Guan Y,Deng B Y.Transfer learning based clinical concept extraction on data from multiple sources[J].Journal of biomedical informatics,2014,52:55-64. |
12 | Grubinger T,Chasparis G C,Natschläger T.Generalized online transfer learning for climate control in residential buildings[J].Energy and Buildings,2017,139:63-71. |
13 | Sun S,Shi H,Wu Y.A survey of multi-source domain adaptation[J].Information Fusion,2015,24:84-92. |
14 | Liu Y,Yang C,Liu K,et al.Domain adaptation transfer learning soft sensor for product quality prediction[J].Chemometrics and Intelligent Laboratory Systems,2019,192:103813. |
15 | 贺敏,汤健,郭旭琦,等.基于流形正则化域适应随机权神经网络的湿式球磨机负荷参数软测量[J].自动化学报,2019,45(2):398-406. |
He M,Tang J,Guo X Q,et al.Soft sensor of wet ball mill load parameter based on domain adaptation with manifold regularization[J].Acta Automatica Sinica,2019,45(2):398-406. | |
16 |
Li J,Liu W,Zhou Y,et al.Domain adaptation with few labeled source samples by graph regularization[J].Neural Processing Letters,2019.doi:10.1007/s11063-019-10075-z.
DOI |
17 | Wang C.A Geometric Framework for Transfer Learning Using Manifold Alignment[D].University of Massachusetts Amherst,2010. |
18 | Shrivastava A,Shekhar S,Patel V M.Unsupervised domain adaptation using parallel transport on Grassmann manifold[C]//2014 IEEE Winter Conference on Applications of Computer Vision (WACV).Steamboat Springs, CO, USA:IEEE,2014:277-284. |
19 | 王存睿,张庆灵,段晓东,等.基于流形结构的人脸民族特征研究[J].自动化学报,2018,44(1):140-159. |
Wang C R,Zhang Q L,Duan X D,et al.Research of face ethnic features from manifold structure[J].Acta Automatica Sinica,2018,44(1):140-159. | |
20 | Gopalan R,Li R,Chellappa R.Domain adaptation for object recognition: an unsupervised approach[C]//International Conference on Computer Vision.Barcelona, Spain:IEEE,2011:999-1006. |
21 | Gong B,Shi Y,Sha F,et al.Geodesic flow kernel for unsupervised domain adaptation[C]//IEEE Conference on Computer Vision and Pattern Recognition.Providence, RI, USA:IEEE,2012:2066-2073. |
22 | Roweis S T,Saul L K.Nonlinear dimensionality reduction by locally linear embedding[J].Science,2000,290(5500):2323-2326. |
23 | Zhang Y,Wang C.Modeling and monitoring of multimodes process[C]//International Symposium on Neural Networks.Berlin, Germany:Springer,2012:159-168. |
24 | Samat A,Gamba P,Abuduwaili J,et al.Geodesic flow kernel support vector machine for hyperspectral image classification by unsupervised subspace feature transfer[J].Remote Sensing,2016,8(3):234. |
25 | Wang J,Feng W,Chen Y,et al.Visual domain adaptation with manifold embedded distribution alignment[C]//2018 ACM Multimedia Conference on Multimedia Conference.Seoul, South Korea:ACM,2018:402-410. |
26 | Zou W,Xia Y,Li H.Fault diagnosis of Tennessee-Eastman process using orthogonal incremental extreme learning machine based on driving amount[J].IEEE Transactions on Cybernetics,2018,48(12):3403-3410. |
27 | Hsu C C,Chen L S,Liu C H.A process monitoring scheme based on independent component analysis and adjusted outliers[J].International Journal of Production Research,2010,48(6):1727-1743. |
28 | Wang X,Ren J,Liu S.Distribution adaptation and manifold alignment for complex processes fault diagnosis[J].Knowledge-Based Systems,2018,156:100-112 |
29 | 阎高伟,贺敏,汤健,等.基于最大均值差异多源域迁移学习的湿式球磨机负荷参数软测量[J].控制与决策,2018,33(10):70-75. |
Yan G W,He M,Tang J,et al.Soft sensor of wet ball mill load based on maximum mean discrepancy multi-source domain transfer learning[J].Control and Decision,2018,33(10):70-75. | |
30 | 汤健.基于频谱数据驱动的旋转机械设备负荷软测量[M].北京:国防工业出版社,2015. |
Tang J.Soft Sensing of Rotating Machinery Equipment Load based on Spectrum Data Drive[M].Beijing:National Defense Industry Press,2015. |
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