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Modeling and Optimization Techniques with Applications in Food Processes, Bio-processes and Bio-systems

  • Eva Balsa-CantoEmail author
  • Antonio A. Alonso
  • Ana Arias-Méndez
  • Miriam R. García
  • A. López-Núñez
  • Maruxa Mosquera-Fernández
  • C. Vázquez
  • Carlos Vilas
Chapter
Part of the SEMA SIMAI Springer Series book series (SEMA SIMAI, volume 9)

Abstract

Food processes, bio-processes and bio-systems are coupled systems that may involve heat, mass and momentum transfer together with kinetic processes. This work illustrates, with a number of examples, how model-based techniques—i.e. simulation, optimization and control—offer the possibility to improve our knowledge about the system at hand and facilitate process design and optimisation even in real time. The contribution is mainly based on the authors experience and illustrates concepts with several examples such as biofilm formation, gluconic acid production, deep-fat frying of potato chips and the thermal processing of packaged foods.

Keywords

Model identification Reduced order modelling Real-time optimization Food processing Bio-processes Bio-systems 

Notes

Acknowledgements

The authors acknowledge financial support from CSIC [PIE201270E075]. A. Arias-Méndez and M. Mosquera-Fernández acknowledge financial support from the JAE-CSIC program, A. López-Nuñez acknowledges financial support from Xunta de Galicia.

References

  1. 1.
    Alonso, A.A., Frouzakis, C., Kevrekidis, I.: Optimal sensor placement for state reconstruction of distributed process systems. AIChE J. 50 (7), 1438–1452 (2004)CrossRefGoogle Scholar
  2. 2.
    Alonso, A., Arias-Méndez, A., Balsa-Canto, E., García, M., Molina, J., Vilas, C., Villafin, M.: Real time optimization for quality control of batch thermal sterilization of prepackaged foods. Food Control 32 (2), 392–403 (2013)CrossRefGoogle Scholar
  3. 3.
    Arias-Méndez, A., Warning, A., Datta, A., Balsa-Canto, E.: Quality and safety driven optimal operation of deep-fat frying of potato chips. J. Food Eng. 119 (1), 125–134 (2013)CrossRefGoogle Scholar
  4. 4.
    Balsa-Canto, E., Banga, J.: AMIGO, a toolbox for advanced model identification in systems biology using global optimization. Bioinformatics 27 (16), 2311–2313 (2011)CrossRefGoogle Scholar
  5. 5.
    Balsa-Canto, E., Alonso, A.A., Banga, J.R.: A novel, efficient and reliable method for thermal process design and optimization. Part i: theory. J. Food Eng. 52, 227–234 (2002)Google Scholar
  6. 6.
    Balsa-Canto, E., Alonso, A.A., Banga, J.R.: Reduced-order models for nonlinear distributed process systems and their application in dynamic optimization. Ind. Eng. Chem. Res. 43, 3353–3363 (2004)CrossRefGoogle Scholar
  7. 7.
    Balsa-Canto, E., Vassiliadis, V., Banga, J.: Dynamic optimization of single- and multi-stage systems using a hybrid stochastic-deterministic method. Ind. Eng. Chem. Res. 44 (5), 1514–1523 (2005)CrossRefGoogle Scholar
  8. 8.
    Balsa-Canto, E., Alonso, A.A., Banga, J.: Computational procedures for optimal experimental design in biological systems. IET Syst. Biol. 2 (4), 163–172 (2008)CrossRefGoogle Scholar
  9. 9.
    Balsa-Canto, E., Alonso, A., Banga, J.: An iterative identification procedure for dynamic modeling of biochemical networks. BMC Syst. Biol. 4 (11) (2010). doi: 10.1186/1752-0509-4-11Google Scholar
  10. 10.
    Balsa-Canto, E., López-Núñez, A., Vázquez, C.: Numerical solution of a biofilm model based on pdes. Poster Presented in the XVI Jacques-Louis Lions Spanish-French School on Numerical Simulation in Physics and Engineering, EHF2014, Pamplona (2014)Google Scholar
  11. 11.
    Beyenal, H., Lewandowski, Z., Harkin, G.: Quantifying biofilm structure: facts and fiction. Biofouling 20, 1–23 (2004)CrossRefGoogle Scholar
  12. 12.
    Biegler, L., Cervantes, A., Wätcher, A.: Advances in simulaneous strategies for dynamic process optimization. Chem. Eng. Sci. 57 (4), 575–593 (2002)CrossRefGoogle Scholar
  13. 13.
    Bock, H., Plitt, K.: A multiple shooting algorithm for direct solution of optimal control problems. In: Proceedings of the 9th IFAC World Congress, pp. 242–247. Pergamon Press, New York (1984)Google Scholar
  14. 14.
    Eberl, H.J., Parker, D.F., Loosdrecht, M.C.V.: A new deterministic spatio temporal continuum model for biofilm development. J. Theor. Med. 3, 161–175 (2001)CrossRefzbMATHGoogle Scholar
  15. 15.
    Egea, J., Rodríguez-Fernández, M., Banga, J., Marti, R.: Scatter search for chemical and bio-process optimization. J. Glob. Optim. 37 (3), 481–503 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Egea, J., Balsa-Canto, E., García, M., Banga, J.: Dynamic optimization of nonlinear processes with an enhanced scatter search method. Ind. Eng. Chem. Res. 48 (9), 4388–4401 (2009)CrossRefGoogle Scholar
  17. 17.
    Egea, J., Martí, R., Banga, J.: An evolutionary method for complex-process optimization. Comput. Oper. Res. 37 (2), 315–324 (2010)CrossRefzbMATHGoogle Scholar
  18. 18.
    Floudas, C.: Deterministic Global Optimization: Theory, Methods and Applications. Kluwer Academics, Dordrecht (2000)CrossRefGoogle Scholar
  19. 19.
    García, M.R., Vilas, C., Banga, J., Alonso, A.: Optimal field reconstruction of distributed process systems from partial measurements. Ind. Eng. Chem. Res. 46 (2), 530–539 (2007)CrossRefGoogle Scholar
  20. 20.
    García, M.R., Vilas, C., Banga, J.R., Alonso, A.A.: Exponential observers for distributed tubular (bio)reactors. AIChE J. 54 (11), 2943–2956 (2008)CrossRefGoogle Scholar
  21. 21.
    García, M., Vilas, C., Balsa-Canto, E., Lyubenovac, V., Ignatovac, M., Alonso, A.: On-line estimation in a distributed parameter bioreactor: application to the gluconic acid production. Comput. Chem. Eng. 35 (1), 84–91 (2011)CrossRefGoogle Scholar
  22. 22.
    Gottlieb, D., Orszag, S.A.: Numerical Analysis of Spectral Methods: Theory and Applications. Society for Industrial and Applied Mathematics, Philadelphia (1977)CrossRefzbMATHGoogle Scholar
  23. 23.
    Lapidus, L., Pinder, G.: Numerical Solution of Partial Differential Equations in Science and Engineering. Wiley, New York (1999)CrossRefzbMATHGoogle Scholar
  24. 24.
    Ljung, L.: System identification: theory for the user. Prentice Hall, Englewood Cliffs (1999)CrossRefzbMATHGoogle Scholar
  25. 25.
    Mosquera-Fernández, M., Rodríguez-López, P., Cabo, M., Balsa-Canto, E.: Numerical spatio-temporal characterization of listeria monocytogenes biofilms. Int. J. Food Microbiol. 182, 26–36 (2014)CrossRefGoogle Scholar
  26. 26.
    Palazoglu, T., Erdogdu, F., Uyar, R.: Experimental comparison of natural convection and conduction heat transfer. J. Food Process Eng. 33, 85–100 (2010)CrossRefGoogle Scholar
  27. 27.
    Pardalos, P., Romeijn, H., Tuyb, H.: Recent developments and trends in global optimization. J. Comput. App. Math. 124, 209–228 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Pinter, J.: Global Optimization in Action. Continuous and Lipschitz Optimization: Algorithms, Implementations and Applications. Kluwer Academics, Dordrecht (1996)CrossRefzbMATHGoogle Scholar
  29. 29.
    Reddy, J.: An Introduction to the Finite Element Method. McGraw-Hill, New York (1993)Google Scholar
  30. 30.
    Rodríguez-Fernández, M., Egea, J., Banga, J.: Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems. BMC Bioinformatics 7, 483 (2006)CrossRefGoogle Scholar
  31. 31.
    Sirovich, L.: Turbulence and the dynamics of coherent structures. Part I: coherent structures. Q. Appl. Math. 45 (3), 561–571 (1987)MathSciNetzbMATHGoogle Scholar
  32. 32.
    Srey, S., Jahid, I., Ha, S.D.: Biofilm formation in food industries: a food safety concern. Food Control 31 (2), 572–585 (2013)CrossRefGoogle Scholar
  33. 33.
    Talbi, E.G.: Metaheuristics: From Design to Implementation. Wiley, Hoboken (2009)CrossRefzbMATHGoogle Scholar
  34. 34.
    Vande Wouwer, A., Saucez, P., Vilas, C.: Simulation of ODE/PDE Models with MATLAB, OCTAVE and SCILAB: Scientific and Engineering Applications. Springer, Cham (2014)zbMATHGoogle Scholar
  35. 35.
    Vassiliadis, V.S., Pantelides, C.C., Sargent, R.W.H.: Solution of a class of multistage dynamic optimization problems. 1. Problems without path constraints. Ind. Eng. Chem. Res. 33 (9), 2111–2122 (1994)Google Scholar
  36. 36.
    Walter, E., Pronzato, L.: Identification of Parametric Models from Experimental Data. Springer, Masson (1997)zbMATHGoogle Scholar
  37. 37.
    Wanner, O., Eberl, H.J., Morgenroth, E., Noguera, D.R., Picioreanu, C., Rittmann, B.E., van Loosdrecht, M.C.: Mathematical modeling of biofilms. Technical report, IWA Task Group on Biofilm Modeling (2006)Google Scholar
  38. 38.
    Warning, A., Dhall, A., Mitrea, D., Datta, A.: Porous media based model for deep-fat vacuum frying potato chips. J. Food Eng. 110 (3), 428–440 (2012)CrossRefGoogle Scholar
  39. 39.
    Yang, X., Beyenal, H., Harkin, G., Lewandowski, Z.: Quantifying biofilm structure using image analysis. J. Microbiol. Methods 39, 109–119 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Eva Balsa-Canto
    • 1
    Email author
  • Antonio A. Alonso
    • 1
  • Ana Arias-Méndez
    • 1
  • Miriam R. García
    • 1
  • A. López-Núñez
    • 2
  • Maruxa Mosquera-Fernández
    • 1
  • C. Vázquez
    • 2
  • Carlos Vilas
    • 1
  1. 1.(Bio)Process Engineering GroupIIM-CSICVigoSpain
  2. 2.Department of MathematicsUniversity A CoruñaA CoruñaSpain

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