An approach to parameters estimation of a chromatography model using a clustering genetic algorithm based inverse model

Soft Computing

Volumn 15, Issue 5, 2011, Pages 963-973

  Mesa, Mirtha Irizar ^a   Llanes Santiago, Orestes ^a   Fernández, Francisco Herrera ^b   Rodríguez, David Curbelo ^c   da Silva Neto, Antônio José ^d   Câmara, Leôncio Diógenes T ^d  

^a UNIVERSITY OF HAVANA   (Cuba)

^b CENTRAL UNIVERSITY OF LAS VILLAS   (Cuba)

^c CENTER OF MOLECULAR IMMUNOLOGY   (Cuba)

^d IPRJ/UERJ   (Brazil)

Author keywords

Adsorption chromatography; Genetic algorithms; Inverse problem; Parameter estimation

Indexed keywords

ADSORPTION CHROMATOGRAPHY; BATCH CHROMATOGRAPHY; CHROMATOGRAPHY MODELS; CLUSTERING GENETIC ALGORITHMS; COMPLEX SPACE; COMPUTATIONAL TIME; DIRECT SOLUTION; EFFECTIVE SOLUTION; INVERSE MODELS; NON-LINEAR PARAMETERS; PARAMETERS ESTIMATION; PROTEIN ADSORPTION; SYSTEM IDENTIFICATIONS; SYSTEMS OF PARTIAL DIFFERENTIAL EQUATIONS;

ADSORPTION; CHROMATOGRAPHY; COMPUTATIONAL FLUID DYNAMICS; GENETIC ALGORITHMS; IMAGE SEGMENTATION; INVERSE PROBLEMS; PARAMETER ESTIMATION; PARTIAL DIFFERENTIAL EQUATIONS;

CLUSTERING ALGORITHMS;

EID: 79954896037     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-010-0638-3     Document Type: Article

References (48)

1. Altenhöner U, Meurer M, Strube J, Schmidt-Traub H (1997) Parameter estimation for the simulation of liquid chromatography. J Chromatogr 769: 59-69.
2. Andrés-Toro B, Besada-Portas E, Fernández-Blanco P, López-Orozco J, Girón-Sierra J (2002) Multiobjective optimization of dynamic processes by evolutionary methods. In: Proceedings of the 15th IFAC World Congress on Automatic Control, pp 342-348. Barcelona, Spain.
3. Angelov P, Guthke R (1997) A ga-based approach to optimization of bioprocesses described by fuzzy rules. J Bioprocess Eng 16: 299-301.
4. Au W, Chan K, Yao X (2003) A novel evolutionary data mining algorithm with applications to churn prediction. IEEE Trans Evol Comput 7(6): 532-545.
5. Back T (1997) Handbook of evolutionary computation. Oxford University Press, Oxford.
6. Benini E, Toffolo A (2002) Optimal design of horizontal-axis wind turbines using blade-element theory and evolutionary computation. J Solar Energy Eng 124(4): 357-363.
7. Davis L (1991) The handbook of genetic algorithms. van Nostrand Reinholdm, New York.
8. de Vasconcellos J, Silva Neto A, Santana C (2003) An inverse mass transfer problem in solid-liquid adsorption systems. Inverse Probl Eng 11(5): 391-408.
9. de Vasconcellos J, Silva Neto A, Santana C, Soeiro F (2002) Parameter estimation in adsorption columns with stochastic global optimization methods. In: 4th international conference on inverse problems in engineering, Rio de Janeiro, Brazil, pp 45-51.
10. Deb K, Anand A, Joshi D (2002) A computationally efficient evolutionary algorithm for real-parameter optimization. Evol Comput 10: 371-395.
11. Dennis J, Schnabel R (1983) Numerical methods for unconstrained optimization and nonlinear equations. Prentice Hall, Englewood Cliffs.
12. Eykhoff P (1974) System identification. Parameter and state estimation. Wiley, New York.
13. Fletcher R (1987) Practical methods of optimization. Wiley, New York.
14. Fu R, Xu T, Pan Z (2005) Modelling of the adsorption of bovine serum albumin on porous polyethylene membrane by back-propagation artificial neural network. J Membr Sci 251: 137-144.
15. Garcia S, Fernández A, Luengo J, Herrera F (2009) A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Computing 13: 959-977. Published online 20th December 2008. Springer.
16. Georgieva O, Hristozov I, Pencheva T, Tzonkov S, Hitzmann B (2003) Mathematical modelling and variable structure control systems for fed-batch fermentation of Escherichia coli. Biochem Eng Quart 17(4): 293-299.
17. Giro R, Cyrillo M, Galvâo D (2002) Designing conducting polymers using genetic algorithms. Chem Phys Lett 366(1-2): 170-175.
18. Goldberg D (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading.
19. Gu T (1995) Mathematical modelling and scale-up of liquid chromatography. Springer, New York.
20. Guiochon G, Shirazi D, Felinger A, Katti A (2006) Fundamentals of preparative and nonlinear chromatography, 2nd edn. Academic Press, London.
21. Hee-Su K, Sung-Bae C (2001) An efficient genetic algorithm with less fitness evaluation by clustering. In: Proceedings of the 2001 IEEE congress on evolutionary computation. IEEE.
22. Herrera F, Lozano M (2005) Editorial note: real coded genetic algorithms. special issue on real coded genetic algorithms: foundations, models and operators. Softcomputing 9(4): 223-224.
23. Herrera F, Lozano M, Sánchez AM (2003) A taxonomy for the crossover operator for real-coded genetic algorithms. an experimental study. Int J Intell Syst 18(3): 309-338.
24. Herrera F, Lozano M, Verdegay J (1998) Tackling real-coded genetic algorithms: operators and tools for the behavioral analysis. Artif Intell Rev 12(4): 265-319.
25. Horstmann BJ, Chase HA (1989) Modelling the affinity adsorption of immunoglobulin g to protein a immobilized to agarose matrices. Chem Eng Res Des 67.
26. Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice Hall, Upper Saddle River.
27. Jin Y (2002) Fitness approximation in evolutionary computation: A survey. In: Proceedings of the 2002 genetic and evolutionary computation conference, pp 1105-1112.
28. Jin Y (2005) A comprehensive survey of fitness approximation in evolutionary computation. Soft Comput 9(1): 3-9. Special Issue on "approximation and learning in evolutionary computation".
29. Karr C, Yakushin I, Nicolosi K (2000) Solving inverse initial-value, boundary-value problems via genetic algorithm. Eng Appl Artif Intell 13: 625-633.
30. Kewley R, Embrechts M (2002) Computational military tactical planning system. IEEE Trans Syst Man Cybernet Part C Appl Rev 32(2): 161-171.
31. Ljung L (1999) Model validation and model error modeling. Tech. Rep. LiTH-ISY-R-215, Lund University, Sweden.
32. Ljung L (1999) System identification Theory for the user, 2nd edn. Prentice Hall, Upper Saddle River.
33. Ljung L, Glad T (1994) Modeling of dynamic systems. Prentice Hall, Upper Saddle River.
34. Manterea T, Alanderb J (2005) Evolutionary software engineering, a review. Appl Soft Comput 5(3): 315-331.
35. Michalewicz Z (1992) Genetic algorithms + data structures = evolution programs. Springer, Berlin.
36. Montiel O, Castillo O, Melin P, Sepulveda R (2003) The evolutionary learning rule for system identification. Appl Soft Comput 3(4): 343-352.
37. Nagata Y, Chu K (2003) Optimization of a fermentation medium using neural networks and genetic algorithms. Biotechnol Lett 25(21): 1837-1842.
38. Nelder J, Mead R (1965) A simplex method for function minimization. Comput J 7(4): 308-313.
39. Oduguwa V, Tiwari A, Roy R (2005) Evolutionary computing in manufacturing industry: an overview of recent applications. Appl Soft Comput 5(3): 281-299.
40. ONeil P (1983) Advanced engineering mathematics. Wadsworth Publishing Company, Belmont.
41. Persson P, Nilsson B (2001) Parameter estimation of protein chromatographic processes based on breakthrough curves. In: Dochain D, Perrier M (eds) Proceedings of the 8th international conference on computer applications in biotechnology. Quebec City, Canada.
42. Roeva O, Pencheva T, et al (2004) A genetic algorithms based approach for identification of escherichia coli fed-batch fermentation. Bioautomation 1: 30-41.
43. Sheskin D (2006) Handbook of parametric and nonparametric statistical procedures, vol. 1736. Chapman & Hall//CRC, Londos/West Palm Beach.
44. Söderstrom T, Stoica P (1994) System identification. Prentice Hall International, Hemel Hempstead, Paperback Edition.
45. Tarantola A (2005) Inverse problem theory and model parameter estimation. SIAM.
46. Zar J (1999) Biostatiscal analysis. Prentice Hall, Englewood Cliffs.
47. Zhou Z, Ong Y, Nair P (2004) Hierarchical surrogate-assisted evolutionary optimization framework. In: Proceedings of IEEE congress evolutionary computation CEC'04, special session on learning and approximation in design optimization, Portland, USA, pp 1586-1593.
48. Zhou Z, Ong Y, Nair P, Keane AJ, Lum K (2007) Combining global and local surrogate models to accelerate evolutionary optimization. IEEE Trans Syst Man Cybernet Part C Appl Rev 37(1): 66-76.