Input variable selection for model-based production control and optimisation

International Journal of Advanced Manufacturing Technology

Volumn 68, Issue 9-12, 2013, Pages 2743-2759

  Glavan, Miha ^a,b   Gradišar, Dejan ^a   Atanasijević Kunc, Maja ^b   Strmčnik, Stanko ^a   Mušič, Gašper ^b  

^a JO EF STEFAN INSTITUTE   (Slovenia)

^b UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

Controllability; Holistic production control; Input variable selection; Model based production control

Indexed keywords

CONTROLLABILITY; INDUSTRIAL ENGINEERING;

CONTROLLABILITY ANALYSIS; HISTORICAL PRODUCTION; INPUT VARIABLE SELECTION; MODEL-BASED OPC; PRODUCTION OPERATORS; PRODUCTION PERFORMANCE; PRODUCTION VARIABLES; TENNESSEE EASTMAN PROCESS;

PRODUCTION CONTROL;

EID: 84887626698     PISSN: 02683768     EISSN: 14333015     Source Type: Journal    
DOI: 10.1007/s00170-013-4840-1     Document Type: Article

References (55)

1. Blum AL, Langley P (1997) Selection of relevant features and examples in machine learning. Artif Intell 97:245-271
2. Breiman L (1995) Better subset regression using the nonnegative Garrote. Technometrics 37(4):373-384
3. Chen CC, Cheng WY (2007) Customer-focused and product-line-based manufacturing performance measurement. Int J Adv Manuf Tech 34:1236-1245
4. Chen R, Liu JS, Tsay RS (1995) Additivity tests for nonlinear autoregression. Biometrika 82(2):369-383
5. Chong IG, Jun CH (2005) Performance of some variable selection methods when multicollinearity is present. Chemometr Intell Lab 78(1-2):103-112
6. Choudhary A, Harding J, Tiwari M (2009) Data mining in manufacturing: a review based on the kind of knowledge. J Intell Manuf 20:501-521
7. Downs JJ, Skogestad S (2011) An industrial and academic perspective on plantwide control. Annu Rev Control 35(1):99-110
8. Downs JJ, Vogel EF (1993) A plant-wide industrial process control problem. Comput Chem Eng 17(3):245-255
9. Engell S (2007) Feedback control for optimal process operation. J Process Contr 17(3):203-219
10. Evans D, Jones AJ (2002) A proof of the gamma test. Proc R Soc Lond (series A) 458:1-41
11. Fernando T, Maier H, Dandy G (2009) Selection of input variables for data driven models: an average shifted histogram partial mutual information estimator approach. J Hydrol 367:165-176
12. Frenzel S, Pompe B (2007) Partial mutual information for coupling analysis of multivariate time series. Phys Rev Lett 99(20):204101
13. Fukuda Y, Patzke R (2010) Standardization of key performance indecator for manufacturing execution system. In: Proceedings of SICE annual conference 2010, pp 263-265
14. Georgakis C, Uztürk D, Subramanian S, Vinson DR (2003) On the operability of continuous processes. Control Eng Pract 11(8):859-869
15. Glavan M, Gradišar D, Strmčnik S, Mušič G (2013) Production modelling for holistic production control. Simul Model Pract Th 30:1-20
16. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157-1182
17. Hassibi B, Stork DG, Com SCR (1993) Second order derivatives for network pruning: optimal brain surgeon. In: Advances in neural information processing systems 5. Morgan Kaufmann, San Francisco, pp 164-171
18. Haykin S (1999) Neural networks: a comprehensive foundation, druga edn. Prentice Hall, Upper Saddle River
19. Hu C, Wan F (2009) Input selection in learning systems: a brief review of some important issues and recent developments. In: IEEE international conference on fuzzy systems, pp 530-535
20. Kadam JV, Marquardt W, Schlegel M, Backx OHBT, Brouwer PJ, Dunnebier G, van Hessem D, Tiagounov A, de Wolf S (2003) Towards integrated dynamic real-time optimization and control of industrial processes. In: Grossmann IE, McDonald CM (eds) Proc. FOCAPO 2003, pp 593-596
21. Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1):273-324
22. Kraskov A, Grassberger P (2008) MIC: mutual information based hierarchical clustering. In: Information theory and statistical learning. Springer, New York, pp 101-123
23. Larsson T, Skogestad S (2000) Plantwide control - a review and a new design procedure. Model Ident Control 21(4):209-240
24. Larsson T, Hestetun K, Hovland E, Skogestad S (2001) Self-optimizing control of a large-scale plant: the Tennessee Eastman process. Ind Eng Chem Res 40(22):4889-4901
25. Li K, Peng JX (2007) Neural input selection-a fast model-based approach. Neurocomputing 70:762-769
26. Lind I, Ljung L (2005) Regressor selection with the analysis of variance method. Automatica 41(4):693-700
27. Lind I, Ljung L (2008) Regressor and structure selection in narx models using a structured anova approach. Automatica 44:383-395
28. Lydon B (2012) Intelligence-based manufacturing: moving to preventative and proactive manufacturing. In: Pharmaceutical automation roundtable (PAR) 2011, Part 6
29. Matlab (2012) Statistics toolbox-anova. http://www.mathworks.com/help/ toolbox/stats/anovan.html. Accessed 30 Aug 2012
30. May R, Dandy G, Maier H (2011) Review of input variable selection methods for artificial neural networks. In: Suzuki K (ed) Artificial neural networks - methodological advances and biomedical applications. InTech, New York
31. May RJ, Maier HR, Dandy GC, Fernando TG (2008) Non-linear variable selection for artificial neural networks using partial mutual information. Environ Modell Softw 23:1312-1326
32. Narendra K, Parthasarathy K (1990) Identification and control of dynamical systems using neural networks. IEEE Trans Neural Netw 1(1):4-27
33. Niska H, Heikkinen M, Kolehmainen M (2006) Genetic algorithms and sensitivity analysis applied to select inputs of a multi-layer perceptron for the prediction of air pollutant time-series. In: Intelligent data engineering and automated learning IDEAL 2006, lecture notes in computer science, vol 4224. Springer, pp 224-231
34. Nørgaard M (2000) Neural network based system identification toolbox. Tech Report 00-E-891, Department of Automation, Technical University of Denmark
35. Nudurupati S, Bititci U, Kumar V, Chan F (2011) State of the art literature review on performance measurement. Comput Ind Eng 60(2):279-290
36. Rawlings J, Amrit R (2009) Optimizing process economic performance using model predictive control. In: Magni L, Raimondo D, Allgöwer F (eds) Nonlinear model predictive control, lecture notes in control and information sciences, vol 384. Springer, Berlin, pp 119-138
37. Ricker N (1995) Optimal steady-state operation of the tennessee eastman challenge process. Comput Chem Eng 19(9):949-959
38. Rutanen K (2011) Tim 1.2.0. http://www.cs.tut.fi/timhome/tim/tim.htm. Accessed 30 Aug 2012
39. Sadoyan H, Zakarian A, Mohanty P (2006) Data mining algorithm for manufacturing process control. Int J Adv Manuf Tech 28:342-350
40. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379-423, 623-656
41. Sharma A (2000) Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: part 1 - a strategy for system predictor identification. J Hydrol 239(1-4):232-239
42. Sindelár R, Babuska R (2004) Input selection for nonlinear regression models. IEEE T Fuzzy Syst 12(5):688-696
43. Skogestad S, Postlethwaite I (2005) Multivariable feedback control: analysis and design. Wiley, New York
44. Smits G, Kordon A, Vladislavleva K, Jordaan E, Kotanchek M (2006) Variable selection in industrial datasets using pareto genetic programming. In: Yu T, Riolo R, Worzel B (eds) Genetic programming theory and practice III, The Kluwer International Series in Engineering and Computer Science, vol 9. Springer, Berlin, pp 79-92
45. Stefánsson A, Koncar N, Jones AJ (1997) A note on the gamma test. Neural Comput Appl 5(3):131-133
46. Stephanopoulos G, Ng C (2000) Perspectives on the synthesis of plant-wide control structures. J Process Contr 10(23):97-111
47. Su M, Rhinehart R (2009) Recursive estimation on spatially rearranged data for dynamic order determination and nonlinear component detection. In: American control conference, 2009. ACC '09, pp 4398-4403
48. Subramanian S, Georgakis C (2005) Methodology for the steady-state operability analysis of plantwide systems. Ind Eng Chem Res 44(20):7770-7786
49. Székely GJ, Rizzo ML, Bakirov NK (2007) Measuring and testing dependence by correlation of distances. Ann Stat 35(6):2769-2794
50. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc (Series B) 58:267-288
51. Vinson DR, Georgakis C (2000) A new measure of process output controllability. J Process Contr 10(2-3):185-194
52. van de Wal M, de Jager A (2001) A review of methods for input/output selection. Automatica 37(4):487-510
53. Yuan Z, Chen B, Zhao J (2011) An overview on controllability analysis of chemical processes. AIChE J 57(5):1185-1201
54. Zorzut S, Gradišar D, Jovan V, Mušič G (2009a) Use of a procedural model in the design of production control for a polymerization plant. Int J Adv Manuf Tech 44:1051-1062
55. Zorzut S, Jovan V, Gradišar D, Mušič G (2009b) Closed-loop control of a polymerisation plant using production performance indicators (PIs). Int J Comput Integ M 22(12):1128-1143