Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models

Chemical Engineering Research and Design

Volumn 95, Issue , 2015, Pages 113-132

  Shao, Weiming ^a   Tian, Xuemin ^a  

^a CHINA UNIVERSITY OF PETROLEUM   (China)

Author keywords

Adaptive localization; Adaptive soft sensor; Bayesian inference; Local partial least squares; Selective ensemble learning

Indexed keywords

BAYESIAN NETWORKS; FORECASTING; INFERENCE ENGINES; TESTING;

ADAPTIVE LOCALIZATIONS; ADAPTIVE SOFT-SENSOR; BAYESIAN INFERENCE; PARTIAL LEAST SQUARE (PLS); SELECTIVE ENSEMBLES;

LEAST SQUARES APPROXIMATIONS;

EID: 84961290207     PISSN: 02638762     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cherd.2015.01.006     Document Type: Article

References (55)

1. Chen K., Ji J., Wang H.Q., Liu Y., Song Z.H. Adaptive local kernel-based learning for soft sensor modeling of nonlinear processes. Chem. Eng. Res. Design 2011, 89(10):2117-2124.
2. Dayal B.S., MacGregor J.F. Recursive exponentially weighted PLS and its applications to adaptive control and prediction. J. Process Control 1997, 7(3):169-179.
3. Deng J., Xie L., Chen L., Khatibisepehr S., Huang B., Xu F.W., Espejo A. Development and industrial application of soft sensors with on-line Bayesian model updating strategy. J. Process Control 2013, 22(3):317-325.
4. Fu Y.F., Su H.Y., Zhang Y., Chu J. Adaptive soft-sensor modeling algorithm based on FCMISVM and its application in PX adsorption separation process. Chin. J. Chem. Eng. 2008, 16(5):746-751.
5. Fujiwara K., Kano M., Hasebe S., Takinami A. Soft-sensor development using correlation-based just-in-time modeling. AIChE J. 2009, 55(7):1754-1764.
6. Fortuna L., Graziani S., Rizzo A., Xibilia G.M. Soft Sensors for Monitoring and Control of Industrial Processes 2007, Springer-Verlag, London.
7. Galicia H.J., He Q.P., Wang J. A reduced order soft sensor approach and its application to continuous digester. J. Process Control 2011, 21(4):489-500.
8. Ge Z.Q., Song Z.H. Semisupervised Bayesian method for soft sensor modeling with unlabeled data samples. AIChE J. 2011, 57(8):2109-2118.
9. Ge Z.Q., Song Z.Q. Ensemble independent component regression models and soft sensing application. Chemom. Intell. Lab. Syst. 2014, 130(15):115-122.
10. Ge Z.Q., Song Z.H., Wang P.L. Probabilistic combination of local independent component regression model for multimode quality prediction in chemical processes. Chem. Eng. Res. Design 2014, 92(3):501-512.
11. Geman S., Bienenstock E., Doursat R. Neural networks and the bias/variance dilemma. Neural Comput. 1992, 4:1-58.
12. Grbić R., Slišković D., Kadlec P. Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models. Comput. Chem. Eng. 2013, 58(11):84-97.
13. Himmelblau D.M. Accounts of experiences in the application of artificial neural networks in chemical engineering. Ind. Eng. Chem. Res. 2008, 47(16):5782-5796.
14. Kadlec. On Robust and Adaptive Soft Sensors 2009, Bournemouth University, Poole, (Ph.D. dissertation).
15. Kadlec P., Gabrys B., Strandt S. Data-driven soft sensors in the process industry. Comput. Chem. Eng. 2009, 33(4):795-814.
16. Kadlec P., Grbić R., Gabrys B. Review of adaptation mechanisms for data-driven soft sensors. Comput. Chem. Eng. 2011, 35(1):1-24.
17. Kadlec P., Gabrys B. Local learning based adaptive soft sensor for catalyst activation prediction. AIChE J. 2011, 57(5):1288-1301.
18. Kaneko H., Arakawa M., Funatsu K. Development of a new soft sensor method using independent component analysis and partial least squares. AIChE J. 2009, 55(1):87-98.
19. Kaneko H., Funatsu K. Maintenance-free soft sensor models with time difference of process variables. Chemom. Intell. Lab. Syst. 2011, 107(2):312-317.
20. Kaneko H., Funatsu K. Development of soft sensor models based on time difference of process variables with accounting for nonlinear relationship. Ind. Eng. Chem. Res. 2011, 58(18):10643-10651.
21. Kaneko H., Funatsu K. Classification of the degradation of soft sensor models and discussion on adaptive models. AIChE J. 2013, 59(7):2339-2347.
22. Kaneko H., Funatsu K. Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants. Chemom. Intell. Lab. Syst. 2014, 137(15):57-66.
23. Kaneko H., Funatsu K. Database monitoring index for adaptive soft sensors and the application to industrial process. AIChE J. 2014, 60(1):160-169.
24. Kano M., Ogawa M. The state of the art in chemical process control in Japan: good practice and questionnaire survey. J. Process Control 2010, 20(9):969-982.
25. Kano M., Fujiwara K. Virtual sensing technology in process industries: trends and challenges revealed by recent industrial applications. J. Chem. Eng. Jpn. 2013, 46(1):1-17.
26. Khatibisepehr S., Huang B., Xu F.W., Espejo A. A Bayesian approach to design of adaptive multi-model inferential sensors with application in oil sand industry. J. Process Control 2012, 22(10):1913-1929.
27. Kim S., Kano M., Hasebe S., Takinami A., Seki T. Long-term industrial applications of inferential control based on just-in-time soft sensors: economical impact and challenges. Ind. Eng. Chem. Res. 2013, 52(35):12346-12356.
28. Kim S., Okajima R., Kano M., Hasebe S. Development of soft sensor using locally weighted PLS with adaptive similarity measure. Chemom. Intell. Lab. Syst. 2013, 124(5):43-49.
29. Liu J.L. On-line soft sensor for polyethylene process with multiple production grades. Control Eng. Pract. 2007, 15(7):769-778.
30. Liu J.L., Chen D.S., Shen J.F. Development of self-validating soft sensors using fast moving window partial least squares. Ind. Eng. Chem. Res. 2010, 49(22):11530-11546.
31. Liu Y., Gao Z.L., Li P., Wang H.Q. Just-in-time kernel learning with adaptive parameter selection for soft sensor modeling of batch processes. Ind. Eng. Chem. Res. 2012, 51(11):4313-4327.
32. Liu Y., Chen J.H. Integrated soft sensor using just-in-time support vector regression and probabilistic analysis for quality prediction of multi-grade processes. J. Process Control 2013, 33(6):793-804.
33. Liu Y., Li C.L., Gao Z.L. A novel unified correlation model using ensemble support vector regression for prediction of flooding velocity in randomly packed towers. J. Ind. Eng. Chem. 2014, 22(3):1109-1118.
34. x emission prediction of a coal-fired boiler. Energy 2013, 55(15):319-329.
35. Ni W.D., Tan S.K., Ng W.J., Brown S.D. Moving-window GRP for nonlinear dynamic system modeling with dual updating and dual preprocessing. Ind. Eng. Chem. Res. 2012, 51(8):6416-6428.
36. Ni W.D., Tan S.K., Ng W.J., Brown S.D. Localized, adaptive recursive partial least squares regression for dynamic system modeling. Ind. Eng. Chem. 2012, 55(23):8025-8039.
37. Ni W.D., Brown S.D., Man R.L. A localized adaptive soft sensor for dynamic system modeling. Chem. Eng. Sci. 2014, 111(24):250-363.
38. Pani A.K., Mohanta H.K. A survey of data treatment techniques for soft sensor design. Chem. Product Process Model. 2011, 6(1):1-21.
39. Qin S.J. Recursive PLS algorithms for adaptive data modeling. Comput. Chem. Eng. 1998, 22(4-5):503-514.
40. Shao W.M., Tian X.M., Wang P. Online learning soft sensor method based on recursive kernel algorithm for PLS. J. Chem. Ind. Eng. Soc. China 2012, 63(9):2887-3289.
41. Shao W.M., Tian X.M., Chen H.L. Adaptive anti-over-fitting soft sensing method based on local learning. Prepr. 10th IFAC Int. Symp. Dyn. Control Process Syst. 2013, 10(1):415-420. ((Mumbai, India), Dec. 18-20).
42. Shao W.M., Tian X.M., Wang P. Local partial least squares based online soft sensing method for multi-output processes with adaptive process states division. Chin. J. Chem. Eng. 2014, 22(7):828-836.
43. Tang J., Yu W., Chai T.Y., Zhao L.J. On-line principal component analysis with application to process modeling. Neurocomputing 2012, 82(1):167-178.
44. Tang J., Chai T.Y., Zhao L.J., Yu W., Yue H. Soft sensor for parameters of mill load based on multi-spectral segments PLS sub-models and on-line adaptive weighted fusion algorithm. Neurocomputing 2012, 78(1):38-47.
45. Tang J., Chai T.Y., Zhao L.J. Modeling load parameters of ball mill in grinding process based on selective ensemble multisensory information. IEEE Trans. Autom. Sci. Eng. 2013, 10(3):726-740.
46. Wu F.H., Chai T.Y. Soft sensing method for magnetic tube recovery ratio via fuzzy systems and neural networks. Neurocomputing 2010, 73(13-15):2489-2497.
47. Xie L., Zeng J.S., Gao C.H. Novel just-in-time learning-based soft sensor utilizing non-Gaussian information. IEEE Trans. Control Syst. Technol. 2014, 22(1):360-369.
48. Xu S.Q., Liu X.G. Melt index prediction by fuzzy functions with dynamic fuzzy neural networks. Neurocomputing 2014, 142(22):191-198.
49. Yu J. A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses. Comput. Chem. Eng. 2012, 41(11):134-144.
50. Yu J. Online quality prediction of nonlinear and non-Gaussian chemical processes with shifting dynamics using finite mixture model based Gaussian process regression approach. Chem. Eng. Sci. 2012, 82(12):22-30.
51. Yu J., Chen K.L., Rashid M.M. A Bayesian model averaging based multi-kernel Gaussian process regression framework for nonlinear state estimation and quality prediction of multiphase batch processes with transient dynamics and uncertainty. Chem. Eng. Sci. 2013, 93(19):96-109.
52. Zhang S.N., Wang F.L., He D.K., Jia R.D. Real-time product quality control for batch processes based on stacked least squared support vector regression models. Comput. Chem. Eng. 2012, 36(10):217-226.
53. Zhang S.N., Wang F.L., He D.K., Jia R.D. Online quality prediction for cobalt oxalate synthesis process using least squares support vector regression approach with dual updating. Control Eng. Pract. 2013, 21(10):1267-1276.
54. Zhao, L.J., Chai, T.Y., Yuan, D.C., 2012. Selective ensemble extreme learning machine modeling of effluent quality in wastewater treatment plants. 9(6), 627-633.
55. Zhou Z.H., Wu J.X., Tang W. Ensembling neural networks: many could be better than all. Artif. Intell. 2002, 137(1-2):239-263.