Freeze-drying modeling and monitoring using a new neuro-evolutive technique

Chemical Engineering Science

Volumn 72, Issue , 2012, Pages 195-204

  Dragoi, Elena Niculina ^a   Curteanu, Silvia ^a   Fissore, Davide ^b  

^a GHEORGHE ASACHI TECHNICAL UNIVERSITY OF IASI   (Romania)

^b POLITECNICO DI TORINO   (Italy)

Author keywords

Artificial neural network; Bioprocessing; Drying; Freeze drying; Product processing; Simulation

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BIOPROCESSING; FREEZE-DRYING; PRODUCT PROCESSING; SIMULATION;

BACKPROPAGATION ALGORITHMS; ELECTRIC NETWORK PARAMETERS; EVOLUTIONARY ALGORITHMS; LOW TEMPERATURE DRYING; NEURAL NETWORKS; PRODUCT DESIGN;

DRYING;

EID: 84856568957     PISSN: 00092509     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ces.2012.01.021     Document Type: Article

References (47)

1. Abbass, H.A., 2001. A memetic Pareto evolutionary approach to artificial neural networks. In: Stumptner, M., Corbett, D., Brooks, M. (Eds.), AI 2001: Advances in Artificial Intelligence, vol. 2256. Springer, Berlin, pp. 113-152.
2. Abbas, H., 2002. The self-adaptive Pareto differential evolution algorithm. In: Proceedings of the 2002 Congress on Evolutionary Computation, Honolulu.
3. Barresi A.A., Pisano R., Rasetto V., Fissore D., Marchisio D.L. Model-based monitoring and control of industrial freeze-drying processes: effect of batch non-uniformity. Drying Technol. 2010, 28:577-590.
4. Bedri Ozer A. CIDE: chaotically initialized differential evolution. Expert Syst. Appl. 2010, 37:4632-4641.
5. Bosca S., Fissore D. Design and validation of an innovative soft-sensor for pharmaceuticals freeze-drying monitoring. Chem. Eng. Sci. 2011, 66:5127-5136.
6. Brest J. Constrained real-parameter optimization with e-self-adaptive differential evolution. Constraint-Handling in Evolutionary Optimization 2009, 73-93. Springer, Berlin. E. Mezura-Montes (Ed.).
7. Brest J., Boskovic B., Greiner S., Zumer V., Maucec M. Performance comparison of self-adaptive and adaptive differential evolution algorithms. Adv. Software Comput. 2007, 11:617-629.
8. Chouvenc P., Vessot S., Andrieu J., Vacus P. Optimization of the freeze-drying cycle: a new model for pressure rise analysis. Drying Technol. 2004, 22:1577-1601.
9. Curteanu S., Cartwright H. Neural networks applied in chemistry. I. Determination of the optimal topology of multilayer perceptron neural networks. J. Chemometrics 2011, 25:527-574.
10. Daraoui N., Dufour P., Hammouri H., Hottot A. Model predictive control during the primary drying stage of lyophilisation. Control Eng. Pract. 2010, 18:483-494.
11. Das S., Suganthan P.N. Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 2011, 15(4-31).
12. Dragoi E.N., Curteanu S., Leon F., Galaction A.I., Cascaval D. Modeling of oxygen mass transfer in the presence of oxygen-vectors using neural networks developed by differential evolution algorithm. Eng. Appl. Artif. Intell. 2011, 24:1214-1226.
13. Durr, P., Mattiussi, C., Floreano, D., 2006. Neuroevolution with analog genetic encoding. In: Proceedings of the Ninth International Conference on Parallel Problem Solving from Nature. Springer-Verlag, Berlin, pp. 671-680.
14. Feoktistov V. Differential Evolution: In Search of Solutions 2006, Springer-Verlag, Berlin.
15. Fissore D., Pisano R., Velardi S.A., Barresi A.A., Galan M. PAT tools for the optimization of the freeze-drying process. Pharm. Eng. 2010, 29:58-70.
16. Fissore D., Pisano R., Barresi A.A. Advanced approach to build the design space for the primary drying of a pharmaceutical freeze-drying process. J. Pharm. Sci. 2011, 100:4922-4933.
17. Fissore D., Pisano R., Barresi A.A. On the methods based on the pressure rise test for monitoring a freeze-drying process. Drying Technol. 2011, 29:73-90.
18. Floreano D., Durr P., Mattiussi C. Neuroevolution: from architecture to learning. Evol. Intell. 2008, 1:47-62.
19. Furtuna R., Curteanu S., Cazacu M. Optimization methodology applied to feed-forward artificial neural network parameter. Int. J. Quatum Chem. 2010, 111:539-553.
20. Gandibleux X., Ehrgott M. 1984-2004-20 Years of multiobjective metaheuristics. But what about the solution of combinatorial problems with multiple objectives?. Evolutionary Multi-Criterion Optimization 2005, 33-46. Springer, Berlin. C. Coello Coello, A.H. Aguirre, E. Zitzler (Eds.).
21. Giordano A., Barresi A.A., Fissore D. On the use of mathematical models to build the design space for the primary drying phase of a pharmaceutical lyophilization process. J. Pharm. Sci. 2011, 100:311-324.
22. Hu C., Yan X. An immune self-adaptive differential evolution algorithm with application to estimate kinetic parameters for homogeneous mercury oxidation. Chin. J. Chem. Eng. 2009, 17:232-240.
23. Koganti V.R., Shalaev E.Y., Berry M.R., Osterberg T., Youssef M., Hiebert D.N., Kanka F.A., Nolan M., Barrett R., Scalzo G., Fitzpatrick G., Fitzgibbon N., Luthra S., Zhang L. Investigation of design space for freeze-drying: use of modeling for primary drying segment of a freeze-drying cycle. AAPS Pharm. Sci. Tech. 2011, 12:854-861.
24. Liapis A.I., Sadikoglu H. Dynamic pressure rise in the drying chamber as a remote sensing method for monitoring the temperature of the product during the primary drying stage of freeze-drying. Drying Technol. 1998, 16:1153-1171.
25. Lu Y., Zhou J., Qin H., Li Y., Zhang Y. An adaptive hybrid differential evolution algorithm for dynamic economic dispatch with valve-point effects. Expert Syst. Appl. 2010, 37:4842-4849.
26. Milton N., Pikal M.J., Roy M.L., Nail S.L. Evaluation of manometric temperature measurement as a method of monitoring product temperature during lyophilisation. PDA J. Pharm. Sci. Technol. 1997, 5:7-16.
27. Mouret J.B., Doncieux S. MENNAG: a modular, regular and hierarchical encoding for neural-networks based on attribute grammars. Evol. Intell. 2008, 1:187-207.
28. Nolfi S., Parisi D. Genotypes for neural networks. The Handbook of Brain Theory and Neural Networks 1995, 431-434. MIT Press, Cambridge. M. Arbib (Ed.).
29. Olafsson S. Metaheuristics. Simulation. Handbooks in Operations Research and Management Science 2006, 633-654. Elsevier, Amsterdam. S.G. Henderson, B.L. Nelson (Eds.).
30. Pisano R., Fissore D., Velardi S.A., Barresi A.A. In-line optimization and control of an industrial freeze-drying process for pharmaceuticals. J. Pharm. Sci. 2010, 99:4691-4709.
31. Pisano R., Fissore D., Barresi A.A. Freeze-drying cycle optimization using model predictive control techniques. Ind. Eng. Chem. Res. 2011, 50:7363-7379.
32. Price K.V., Storn R.M., Lampien J.A. Differential Evolution. A Practical Approach to Global Optimization 2005, Springer, Berlin.
33. Rasetto V., Marchisio D.L., Fissore D., Barresi A.A. On the use of a dual-scale model to improve understanding of a pharmaceutical freeze-drying process. J. Pharm. Sci. 2010, 99:4337-4350.
34. Rojas R. Neural Networks. A Systematic Introduction 1996, Springer, Berlin.
35. Storn, R., Price, K., 1995. Differential Evolution-A Simple and Efficient Adaptive Scheme for Global Optimization Over Continuous Spaces. Technical Report TR-95-012. Berkley.
36. Storn, R., 2008. Differential evolution research - trends and open questions. In: Chakraborty, U. (Ed.), Advances in Differential Evolution. Springer, Berlin, pp. 1-31.
37. Sundaram J., Hsu C.C., Shay Y.M., Sane S.U. Design space development for lyophilization using DOE and process modeling. BioPharm Int. 2010, 23:26-36.
38. Todorov, Y.V., Terziyska, M., Petrov, M., 2007. Nonlinear model predictive controller using a Fuzzy-Neural Wiener-Hammerstein model. In: Proceedings of 16th International Conference on Process Control, article no. 216. Stribske Pleso, Slovakia, pp. 1-7.
39. Todorov, Y.V., Tsvetkov, T.D., 2008. Volterra model predictive control of a lyophilization plant. In: Proceedings of Fourth International IEEE Conference-Intelligent Systems, vol. 3, pp. 2013-2018.
40. Velardi S.A., Barresi A.A. Development of simplified models for the freeze-drying process and investigation of the optimal operating conditions. Chem. Eng. Res. Des. 2008, 87:9-22.
41. Velardi S.A., Rasetto V., Barresi A.A. Dynamic parameters estimation method: advanced manometric temperature measurement approach for freeze-drying monitoring of pharmaceutical. Ind. Eng. Chem. Res. 2008, 47:8445-8457.
42. Velardi S.A., Hammouri H., Barresi A.A. In line monitoring of the primary drying phase of the freeze-drying process in vial by means of a Kalman filter based observer. Chem. Eng. Res. Des. 2009, 87:1409-1419.
43. Velardi S.A., Hammouri H., Barresi A.A. Development of a high gain observer for in-line monitoring of sublimation in vial freeze-drying. Drying Technol. 2010, 28:256-268.
44. Xin Y. Evolving artificial neural networks. Proc. IEEE 1999, 87:1423-1447.
45. Zaharie D. Influence of crossover on the behavior of differential evolution algorithms. Appl. Soft Comput. 2009, 9:1126-1138.
46. Zielinski K., Laur R. Stopping criteria for differential evolution in constrained single-objective optimization. Advances in Differential Evolution 2008, 111-138. Springer, Berlin. U. Chakraborty (Ed.).
47. Zhang X., Chen W., Dai C., Cai W. Dynamic multi-group self-adaptive differential evolution algorithm for reactive power optimization. Int. J. Electr. Power 2010, 32:351-357.