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Biogeography-Based Optimization for Dynamic Optimization of Chemical Reactors

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Applications of Metaheuristics in Process Engineering

Abstract

Large scale fed-batch operations are industrially important in agricultural and pharmaceutical sectors. In fed-batch operations, we need to optimize the flow rates of rate limiting substrates to maximize the required performance index. Dynamic optimization of the feed rate profiles renders the problem quite complex and requires intelligent techniques. In this context, evolutionary algorithms have proven to be very useful. We introduce the application of a fairly recent nature-inspired evolutionary optimization technique—biogeography-based optimization (BBO), not so widely known as other established evolutionary optimization techniques—for the fed-batch bioreactor-based dynamic optimization problems. We demonstrate with the help of two case studies that BBO can achieve performance indices in close agreement or better than the results in the literature for the considered problems.

(work done while the author was at Centre for Modeling and Simulation, University of Pune, India)

(work done while the author was at C-DAC, Pune, India)

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References

  1. Balsa-Canto, E., Alonso, A.A., Banga, J.R.: Dynamic optimization of bioprocesses: deterministic and stochastic strategies. ACoFoP IV (Automatic Control of Food and Biological Processes), pp. 21–23. Göteborg, Sweden (1998)

    Google Scholar 

  2. Balsa-Canto, E., Banga, J.R., Alonso, A.A., Vassiliadis, V.S.: Dynamic optimization of chemical and biochemical processes using restricted second-order information. Comput. Chem. Eng. 25(4–6), 539–546 (2001)

    Article  Google Scholar 

  3. Banga, J.: Optimization in computational systems biology. BMC Syst. Biol. 2, 47 (2008), http://www.biomedcentral.com/1752-0509/2/47

  4. Banga, J.R., Seider, W.D.: State of the art in global optimization: Computational methods and applications. In: Floudas C.A., Pardalos P. (eds.), Nonconvex Optimization and Its Applications, vol. 7 (1996). ISBN 978-1-4613-3437-8

    Google Scholar 

  5. Banga, J.R., Alonso, A.A., Singh, R.P.: Stochastic optimal control of fed-batch bioreactors. American Institute of Chemical Engineers (AIChE) Annual Meeting, San Francisco, 1994

    Google Scholar 

  6. Banga, J.R., Alonso, A.A., Singh, R.P.: Stochastic dynamic optimi-zation of batch and semi-continuous bioprocesses. Biotech. Prog. 13, 326–335 (1997)

    Article  Google Scholar 

  7. Banga, J.R., IrizarryRivera, R., Seider, W.D.: Stochastic optimiza-tion for optimal and model-predictive control. Comput. Chem. Eng. 22, 603–612 (1998)

    Article  Google Scholar 

  8. Banga, J.R., Alonso, A.A., Moles, C.G., Balsa-Canto, E.: Efficient and robust numerical strategies for the optimal control of non-linear bio-processes. Mediterranean Conference on Control and Automation (MED2002), Lisbon, Portugal, 9–12 (2002)

    Google Scholar 

  9. Banga, J., Balsa-Canto, E., Moles, E., Alonso, A.: Dynamic optimization of bioprocesses: efficient and robust numerical strategies. J. Biotechnol. 117, 407–419 (2005)

    Article  Google Scholar 

  10. Carrasco, E.F., Banga, J.R.: Dynamic optimization of batch reactors using adaptive stochastic algorithms. Ind. Eng. Chem. Res. 36(6), 2252–2261 (1997)

    Article  Google Scholar 

  11. Chen, C.T., Hwang, C.: Optimal control computation for differential-algebraic process systems with general constraints. Chem. Eng. Commun. 97, 9–26 (1990)

    Google Scholar 

  12. Chen, C.T., Hwang, C.: Optimal on-off control for fed-batch fermentation processes. Ind. Eng. Chem. Res. 29, 1869–1875 (1990)

    Article  Google Scholar 

  13. Chen, L.Z., Nguang, S.K., Chen, X.D.: Online identification and optimization of feed rate profiles for high productivity fed-batch culture of hybridoma cells using genetic algorithms. In: Proceedings of the American Control Conference 5, 3811–3816, vol. 5, pp. 3811–3816 (2001)

    Google Scholar 

  14. Chiou, J.P., Wang, F.S.: Hybrid method of evolution algorithms for static and dynamic optimization problems with application to a fedbatch fermentation process. Comput. Chem. Eng. 23, 1277–1291 (1999)

    Article  Google Scholar 

  15. Gujarathi, A.M., Babu, B.V.: Multi-objective optimization of industrial processes using elitist multi-objective differential evolution. Mater. Manuf. Process. 26(3), 455–463 (2011)

    Article  Google Scholar 

  16. Guo, W., Lei, W., Qidi, W.: An analysis of the migration rates for biogeography-based optimization. Inf. Sci. 254(1), 111–140 (2014), ISSN 0020-0255, http://dx.doi.org/10.1016/j.ins.2013.07.018

  17. Jayaraman, V.K., Kulkarni, B.D., Gupta, K., Rajesh, J., Kusumaker, H.S.: Dynamic optimization of fed-batch bioreactors using the ant algorithm. Biotechnol. Prog. 17, 81–88 (2001)

    Article  Google Scholar 

  18. Lee, J., Ramirez, W.F.: Optimal fed-batch control of induced foreign protein production by recombinant bacteria. AIChE J. 40(5), 899–907 (1994)

    Article  Google Scholar 

  19. Lim, H.C., Tayeb, Y.J., Modak, J.M., Bonte, P.: Computational algorithms for optimal feed rates for a class of fed-batch fermentation: Numerical results for penicillin and cell mass production. Biotechnol. Bioeng. 28, 1408–1420 (1986)

    Article  Google Scholar 

  20. Lopez Cruz, I.L., van Willigenburg, L.G., van Straten, G.: Evolutionary algorithms for optimal control of chemical processes. In: Proceedings of (IASTED) International Conference on Control Applications (2000)

    Google Scholar 

  21. Lozovyy, P., Thomas, G., Simon, D.: Biogeography-based optimization for robot controller tuning, in: Igelnik, K. (ed.) Comput. Model. Simulation Intellect, Current State and Future Perspectives, IGI Global, 162–181 (2011)

    Google Scholar 

  22. Luss, R.: Application of dynamic programming to differential algebraic process systems. Comput. Chem. Eng. 17, 373–377 (1993)

    Article  Google Scholar 

  23. Luus, R.: IEEE Trans. Autom. Control 37(11), 1802–1806 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  24. Luus, R.: Sensitivity of a control policy on yield of a fed-batch reactor. In: (IASTED) International Conference on Modelling and Simulation, Pittsburg (1995)

    Google Scholar 

  25. Matsuura, K., Shiba, H., Nunokawa, Y., Shimizu, S.K., Kaishi, H.: Calculation of optimal trajectories for fermentation processes by genetic algorithm. J. Soc. Ferm. Bioeng. 71,171–178 (1993)

    Google Scholar 

  26. Mendes, P., Kell, D.: Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation. Bioinformatics 14(10), 869–883 (1998)

    Article  Google Scholar 

  27. Mo, H., Xu, L.: Biogeography based optimization for traveling salesman problem. In: Sixth International Conference on Natural Computation (ICNC), vol. 6, pp. 3141–3147 (2010). doi:10.1109/ICNC.2010.5584489

    Google Scholar 

  28. Na, J.G., Chang, Y.K., Chung, B.H., Lim, H.C.: Adaptive optimization of fed-batch culture of yeast by using genetic algorithms. Bioproc. Biosyst. Eng. 24, 299–308 (2002)

    Article  Google Scholar 

  29. Nguang, S.K., Chen, L., Chen, X.D.: Optimisation of fed-batch culture of hybridoma cells using genetic algorithm. ISA Trans. 40, 381–389 (2001)

    Article  Google Scholar 

  30. Nikumbh, S.: Bbo: Biogeography-Based Optimization, R package for continuous BBO, developed and maintained by Sarvesh Nikumbh. Available online at [http://cran.r-project.org/web/packages/bbo/] (2013)

  31. Nikumbh, S., Ghosh, S., Jayaraman, V.K.: Biogeography-based informative gene selection and cancer classification using svm and random forests. In: Proceedings of the IEEE World Congress on Computational Intelligence (IEEE WCCI), pp. 187–192 (2012)

    Google Scholar 

  32. Ovreiu, M., Simon, D.: Biogeography-based optimization of neuro-fuzzy system parameters for diagnosis of cardiac disease. In: Proceedings of Genetic and Evolutionary Computation Conference, vol. 12, pp. 135–1242 (2010)

    Google Scholar 

  33. Panchal, V.K., Singh, P., Kaur, N., Kundra, H.: Biogeography based satellite image classification. Int. J. Comput. Sci. Inf. Secur. 6, 269–274 (2009)

    Google Scholar 

  34. Park, S., Ramirez, W.F.: Optimal production of secreted protein in fed-batch reactors. AIChE J. 34(9), 1550 (1988)

    Article  Google Scholar 

  35. Ronen, M., Shabtai, Y., Guterman, H.: Optimization of feeding profile for a fed-batch bioreactor by an evolutionary algorithm. J. Biotechnol. 97, 253–263 (2002)

    Article  Google Scholar 

  36. Roubos, J.A., van Straten, G., van Boxtel, A.: Numerical computational method using genetic algorithm for the optimal control problem with terminal constraints and free parameters. J. Biotechnol. 67, 173–187 (1999)

    Article  Google Scholar 

  37. Sarkar, D., Modak, J.M.: Optimization of fed-batch bioreactors using genetic algorithm: multiple control variables. Comput. Chem. Eng. 28(5), 789–798 (2004)

    Article  Google Scholar 

  38. Shelokar, P.S., Jayaraman, V.K., Kulkarni, B.D.: Multicanonical jump walk annealing assisted by tabu for dynamic optimization of chemical engineering processes. Eur. J. Oper. Res. 185(3), 1213–1229 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  39. Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12, 702–713 (2008). (doi:10.1109/TEVC.2008.919004)

    Article  Google Scholar 

  40. Simon, D., Rarick, R., Ergezer, M., Du, D.: Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms. Inf. Sci. 181(7), 1224–1248 (2011)

    Article  MATH  Google Scholar 

  41. Song, Y., Liu, M., Wang, Z.: Biogeography-based optimization for the traveling salesman problems. 2010 Third Int. Joint Conf. Comput. Sci. Optim. (CSO) 1, 295–299 (2010). doi:10.1109/CSO.2010.79

    Google Scholar 

  42. Tholudur, A., Ramirez, W.F.: Optimization of fed-batch bioreactors using neural network parameter function models. Biotech. Prog. 12, 302–309 (1996)

    Article  Google Scholar 

  43. Tholudur, A., Ramirez, W.F.: Obtaining smoother singular arc policies using a modified iterative dynamic programming algorithm. Int. J. Control 68(5), 1115–1128 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  44. Tholudur, A., Ramirez, W.F.: Obtaining smoother singular arc policies using a modified iterative dynamic programming algorithm. Int. J. Control 68(5), 1115–1128 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  45. Vassiliadis, V.S.: Computational solution of dynamic optimization problems with general differential-algebraic constraints. PhD thesis, University of London, Imperial College (1993)

    Google Scholar 

  46. Wang, F.S., Sheu, J.W.: Multiobjective parameter estimation problems of ferementation processes using a high ethanol tolerance yeast. Chem. Eng. Sci. 55, 3685–3695 (2000)

    Article  Google Scholar 

  47. Yang, R.L., Wu, C.P.: Global optimal control by accelerated simu-lated annealing. In: First Asian Control Confence, Tokyo (1994)

    Google Scholar 

  48. Zuo, K., Wu, W.T.: Semi-realtime optimization and control of a fed-batch fermentation system. Comput. Chem. Eng. 24, 1105–1109 (2000)

    Article  Google Scholar 

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Acknowledgments

The authors acknowledge the Centre for Modeling and Simulation, University of Pune, India, and the Centre for Development of Advanced Computing, India, for their support. Also, VKJ gratefully acknowledges the Council of Scientific and Industrial Research (CSIR), New Delhi, India, for financial support.

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Authors and Affiliations

  1. Computational Biology and Applied Algorithmics, Max Planck Institut für Informatik, Saarbrücken, Germany

    Sarvesh Nikumbh

  2. Advanced Analytics Institute (AAI), University of Technology, Sydney, Australia

    Shameek Ghosh

  3. Evolutionary Computing and Image Processing (ECIP), Center for Development of Advanced Computing (C-DAC), Pune, India

    Valadi K. Jayaraman

  4. Shiv Nadar University, Tehsil Dadri, District Gautam Buddha Nagar, UP, 201314, India

    Valadi K. Jayaraman

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  1. Sarvesh Nikumbh
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Correspondence to Sarvesh Nikumbh or Valadi K. Jayaraman .

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Editors and Affiliations

  1. Evol. Comput. & Image Proc. Group, Center for Develop. of Adv. Computing, Pune, India

    Jayaraman Valadi

  2. Lab. LiSSi (EA 3956), Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

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Nikumbh, S., Ghosh, S., Jayaraman, V.K. (2014). Biogeography-Based Optimization for Dynamic Optimization of Chemical Reactors. In: Valadi, J., Siarry, P. (eds) Applications of Metaheuristics in Process Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-06508-3_8

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  • DOI: https://doi.org/10.1007/978-3-319-06508-3_8

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  • Online ISBN: 978-3-319-06508-3

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