Inferential model for industrial polypropylene melt index prediction with embedded priori knowledge and delay estimation

Industrial and Engineering Chemistry Research

Volumn 51, Issue 25, 2012, Pages 8510-8525

  Lou, Haichuan ^a   Su, Hongye ^a   Xie, Lei ^a   Gu, Yong ^a   Rong, Gang ^a  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CONTROL AND OPTIMIZATION; DELAY ESTIMATION; DEPENDENT VARIABLES; DOUBLE-LOOP; HYBRID MODEL; INDEPENDENT VARIABLES; INFERENTIAL MODELS; MECHANISM MODEL; MELT INDEX; MODEL GAIN; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; NETWORK WEIGHTS; NON-LINEAR CONSTRAINTS; NORMALIZED MUTUAL INFORMATION; PARTICLE SWARM; POLYPROPYLENE MELTS; PRIOR KNOWLEDGE; PRIORI KNOWLEDGE; PROPYLENE POLYMERIZATION; REACTION PROCESS; ZERO VALUES;

FORECASTING; INDUSTRY; NEURAL NETWORKS; POLYMERIZATION; PROPYLENE; THERMOPLASTICS;

POLYPROPYLENES;

EID: 84863191342     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie202901v     Document Type: Article

References (40)

1. McAuley, K. B.; MacGregor, J. F. On-Line Inference of Polymer Properties in An Industrial Polyethylene Reactor AIChE J. 1991, 37, 825-835
2. Watanabe, F.; Ogawa, M.; Obshima, M.; Hasbimoto, I. Quality Control System for an Industrial High-Density Polyethylene Process. Presented in AIChE Annual Meeting, St Louis, MO, USA, Nov. 7-12, 1993.
3. Ogawa, M.; Ohshima, M.; Morinaga, K.; Watanabe, F. Quality Inferential Control of an Industrial High Density Polyethylene Process J. Process Control 1999, 9, 51-59
4. Lee, E. H.; Kim, T. Y.; Yeo, Y. K. Prediction and Quality Control of The Melt Index During Production of High-Density Polyethylene Korean J. Chem. Eng. 2008, 25, 613-622
5. Ohshima, M.; Tanigaki, M. Quality Control of Polymer Production Processes J. Process Control 2000, 10, 135-148
6. Shi, J.; Liu, X. G. Melt Index Prediction by Neural Soft-sensor Based on Multiscale Analysis and Principal Component Analysis Chin. J. Chem. Eng. 2005, 13, 849-852
7. Shi, J.; Liu, X. G.; Sun, Y. X. Melt Index Prediction By Neural Networks Based on Independent Component Analysis and Multiscale Analysis Neurocomputing 2006, 70, 280-287
8. Li, J. B.; Liu, X. G. Melt Index Prediction by RBF Neural Network Optimized with an MPSO-SA Hybrid Algorithm Neurocomputing 2011, 74, 735-740
9. Turner, P. Automated Product Grade Transitions, Exposing The Inherent and Latent Dangers of Neural Networks in Manufacturing Process Control-An Industrial Case Study Neural Comput. Applic. 2007, 16, 27-32
10. Kadleca, P.; Gabrys, B.; Strandtb, S. Data-Driven Soft Sensors in the Process Industry Comput. Chem. Eng. 2009, 33, 795-814
11. Chen, L.; Bernard, O.; Bastin, G.; Angelov, P. Hybrid Modelling of Biotechnological Processes Using Neural Networks Control Eng. Practice 2000, 8, 821-827
12. Fortuna, L.; Graziani, S.; Xibilia, M. G. Soft Sensors for Product Quality Monitoring in Debutanizer Distillation Columns Control Eng. Practice 2005, 13, 499-508
13. James, S.; Legge, R.; Budman, H. Comparative Study of Black-Box and Hybrid Estimation Methods in Fed-Batch Fermentation J.Process Control 2002, 12, 113-121
14. Vilim, R. B.; Garcia, H. E.; Chen, F. W. An Estimation Scheme Combining First Principle Knowledge, Neural Networks, and the Likelihood Function IEEE Trans. Control Syst. Technol. 2001, 9, 186-199
15. Bollas, G. M.; Papadokonstadakis, S.; Michalopoulos, J. Using Hybrid Neural Networks in Scaling up an FCC Model from a Pilot Plant to an Industrial Unit Chem. Eng. Process 2003, 42, 697-713
16. Ng, C. W.; Hussain, M. A. Hybrid Neural Network-Prior Knowledge Model in Temperature Control of a Semi-batch Polymerization Process Chem. Eng. Process 2004, 43, 559-570
17. Kim, M.; Lee, Y. H.; Han, I. S.; Han, C. Clustering-Based Hybrid Soft Sensor for a Polypropylene Process with Grade Changeover Operation Ind. Eng. Chem. Res. 2005, 44, 34-42
18. Gnecco, G.; Sanguineti, M. The Weight-Decay Technique in Learning From Data An Optimization Point of View Comput. Manage. Sci 2009, 6, 53-79
19. Minina, A.; Velikova, M. Comparison of Universal Approximators Incorporating Partial Monotonicity by Structure Neural Networks 2010, 23, 471-475
20. Castro, J. R.; Castillo, O.; Melin, P.; Antonio, R. D. A Hybrid Learning Algorithm for a Class of Interval Type-2 Fuzzy Neural Networks Inform. Science 2009, 179, 2175-2193
21. Jansen, P. W.; Perez, R. E. Constrained Structural Design Optimization via a Parallel Augmented Lagrangian Particle Swarm Optimization Approach Comput. Struct. 2011, 89, 1352-1366
22. Coello, C. C. Theoretical and Numerical Constraint-Handling Techniques Used with Evolutionary Algorithms-A Survey of The State of The Art Comput. Methods Appl. Mech. Eng. 2002, 191, 1245-1287
23. Chen, D.; Zhao, C. Particle Swarm Optimization with Adaptive Population Size and Its Application Appl. Soft Comput. 2009, 9, 39-48
24. Schwaab, M.; Biscaia, E. C., Jr.; José, L. M.; Pinto, J. C. Nonlinear Parameter Estimation through Particle Swarm Optimization Chem. Eng. Sci. 2008, 63, 1542-1552
25. Tang, Y.; Wang, Z.; Fang, J. Parameters Identification of Unknown Delayed Genetic Regulatory Networks by a Switching Particle Swarm Optimization Algorithm Expert Syst.Appl. 2011, 38, 2523-2535
26. Zhang, B. J.; Jin, Q.; Xu, Y. Inferential Estimation of Polymer Melt Index Using Sequentially Trained Bootstrap Aggregated Neural Networks Chem. Eng. Technol. 2006, 29, 442-448
27. 2 Using Dynamic Neural Networks Comput. Electr. Eng. 2009, 35, 578-586
28. Ludwig, O., Jr.; Nunes, U.; Araújo, R.; Schnitman, L.; Lepikson, H. A. Applications of Information Theory, Genetic Algorithms, and Neural Models To Predict Oil Flow Commun. Nonlinear Sci. Numer. Simul. 2009, 14, 2870-2885
29. Simon, G.; Verleysen, M. High-Dimensional Delay Selection for Regression Models with Mutual Information and Distance-to-Diagonal Criteria Neurocomputing 2007, 70, 1265-1275
30. Doymaz, F.; Bakhtazad, A.; Romagnoli, J. A.; Palazoglu, A. Wavelet-Based Robust Filtering of Process Data Comput. Chem. Eng. 2001, 25, 1549-1559
31. Lin, B.; Recke, B. K.; J́rgen, K. H.; J́rgensen, S. B. A Systematic Approach for Soft Sensor Development Comput. Chem. Eng. 2007, 31, 419-425
32. Chiang, L. H.; Pell, R. J.; Seasholtz, M. B. Exploring Process Data with the Use of Robust Outlier Detection Algorithms J. Process Control 2003, 13, 437-449
33. Yu, J. A Bayesian Inference Based Two-Stage Support Vector Regression Framework for Soft Sensor Development in Batch Bioprocesses Comput. Chem. Eng. 2012, 41, 134-144
34. EgRiogLu, E.; Aladag, C. A. D. H.; Nay, S. L. G. A New Model Selection Strategy in Artificial Neural Networks Appl. Math. Comput. 2008, 195, 591-597
35. Suykens, J. A. K.; Vandewalle, J. Least Squares Support Vector Machines Classifiers Neural Network Lett. 1999, 19, 293-300
36. Yan, W.; Shao, H.; Wang, X. F. Soft Sensing Modeling Based on Support Vector Machine and Bayesian Model Selection Comput. Chem. Eng. 2004, 28, 1489-1498
37. Yu, J. A Nonlinear Kernel Gaussian Mixture Model Based Inferential Monitoring Approach for Fault Detection and Diagnosis of Chemical Processes Chem. Eng. Sci. 2012, 68, 506-519
38. Yu, J.; Qin, S. J. Multimode Process Monitoring with Bayesian Inference-Based Finite Gaussian Mixture Models AIChE J. 2008, 54, 1811-1829
39. Aguado, D.; Ribes, J.; Montoya, T.; Ferrer, J.; Seco, A. A Methodology for Sequencing Batch Reactor Identification with Artificial Neural Networks: A Case Study Comput. Chem. Eng. 2009, 33, 465-472
40. Gonzaga, J. C. B.; Meleiro, L. A. C.; Kiang, C.; Filho, R. M. ANN-Based Soft-Sensor for Real-Time Process Monitoring and Control of An Industrial Polymerization Process Comput. Chem. Eng. 2009, 33, 43-49