Skip to main content
SpringerLink
Log in

Hybridized multi-objective optimization approach (HMODE) for lysine fed-batch fermentation process

  • Process Systems Engineering, Process Safety
  • Published:
Korean Journal of Chemical Engineering Aims and scope Submit manuscript

Abstract

A new hybrid multi-objective differential evolution (MODE) algorithm is proposed that combines the MODE algorithm for the global space search with a dynamical local search (DLS) method for the local space search. HMODE-DLS algorithm was validated using the tri-objective DTLZ7 test problem and the results were compared with MODE algorithm with respect to four performance metrics. In addition to HMODE-DLS, another three algorithms were used to solve two multi-objective optimization cases in an industrial lysine bioreactor at different feeding conditions. Case 1 considers maximizing lysine’s productivity and yield. While case 2 studies the maximization of productivity along with minimization of total operating time. In all cases, theoretical and industrial, HMODE-DLS showed a better performance with a better quality Pareto set of solutions. The Pareto front of case 1 found by HMODE-DLS was compared with a recent study trade-off, and the current non-dominated solutions values were found to be improved. This indicates that the lysine production process is enhanced. For case 2, the switching time from fed-batch to batch was found to be the key decision variable. Generally, these findings indicate the effectiveness of HMODE-DLS for the studied cases and its potential in solving real world complex problems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Abbreviations

c1:

personal learning coefficient

c2:

Global learning coefficient

Conv:

convergence metric

CR:

crossover

CS,F :

concentration of S in the feed [g/L]

Dl :

local scaling factor

DLS:

dynamical local search

F:

feeding flow rate [g/h]

Mf :

mutation factor

GD:

generational distance metric

i:

number of decision variables

MODE:

multi-objective differential evolution algorithm

MOEA’s:

multi-objective evolutionary algorithms

MOO:

multi-objective optimization

n:

number of decision variables

NSGA-II:

non-dominated sorting genetic algorithm-II

ob:

number of objective functions for DTLZ7 test problem

P:

product mass [g]

Q:

number of population handled by DLS

S:

substrate mass [g]

SP:

spread metric

SPC:

spacing metric

str1 & str2:

strategies of MODE algorithm

tf :

maximum operating time [h]

ts 1 :

first switch time [h]

ts 2 :

second switch time [h]

u:

volume flow rate of the feed [L/h]

V:

reactor volume [L]

w:

inertia weight

wDamp :

inertia damping rate

X:

biomass mass [g]

X DLS_new :

new population point generated with DLS

xMODE_new :

new population point generated with MODE algorithm

Xo, Xm, Xa, Xb, Xc, Xd & Xe :

random points selected from the generation

π :

rate of S consumed [g/gh]

σ :

rate of P formation [g/gh]

μ :

growth rate [1/h]

ω :

frequency factor

ω l :

lower ω limit

ω u :

upper ω limit

References

  1. M. Singh, Int. J. Res. Pharm. Sci. (Madurai, India), 2, 637 (2016).

    Google Scholar 

  2. S. Anastassiadis, Recent Pat. Biotedmol, 1, 11 (2007).

    Article  CAS  Google Scholar 

  3. R. Faurie, T. Scheper and J. Thommel, Amino Acids, 79, 1 (2003).

    Google Scholar 

  4. D. Kumar and J. Gomes, Biotechnol. Adv., 23, 41 (2005).

    Article  CAS  PubMed  Google Scholar 

  5. M. Moosavi-Nasab, S. Ansari and Z. Montazer, Iran Agric. Res, 25, 99 (2008).

    Google Scholar 

  6. I. Ekwealor and J. Obeta, Afr. J. Biotechnol., 4, 633 (2005).

    Article  CAS  Google Scholar 

  7. S. Mitsuhashi, Curr. Opin. Biotechnol., 26, 38 (2014).

    Article  CAS  PubMed  Google Scholar 

  8. T. Escobet, V Puig, J. Quevedo, P. Palá-Schönwälder, J. Romera and W Adelman, Comput. Chem. Eng, 124, 228 (2019).

    Article  CAS  Google Scholar 

  9. D. Deka and D. Datta, Knowl-Based Syst, 121, 71 (2017).

    Article  Google Scholar 

  10. J. O. H. Sendín, A. A. Alonso and J. R. Banga, J. Food Eng, 98, 317 (2010).

    Article  Google Scholar 

  11. A. M. Enitan and J. Adeyemo, Afr. J. Biotechnol., 10, 16120 (2011).

    Google Scholar 

  12. G. P. Rangaiah, Multi-objective optimization: Techniques and applications in chemical engineering, World Scientific, Singapore (2009).

    Google Scholar 

  13. G. P. Rangaiah, Multi-objective optimization: Techniques and applications in chemical engineering, World Scientific, Singapore (2016).

    Google Scholar 

  14. T. Seifert, A contribution to the design of flexible modular chemical plants, Verlag Dr. Hut, Germany (2015).

    Google Scholar 

  15. S. S. Parhi, G. P. Rangaiah and A. K. Jana, Appl. Therm. Eng., 150, 1273 (2019).

    Article  CAS  Google Scholar 

  16. S. Garcia and C. T. Trinh, Processes, 7, 316 (2019).

    Article  Google Scholar 

  17. A. D. Rodman, E. S. Fraga and D. Gerogiorgis, Comput. Chem. Eng., 108, 448 (2018).

    Article  CAS  Google Scholar 

  18. Z. Guo and X. Yan, Chemom. Intell. Lab. Syst., 177, 8 (2018).

    Article  CAS  Google Scholar 

  19. J. Q. Albarelli, S. Onorati, P. Caliandro, E. Peduzzi, M. A. A. Meireles, F. Marechal and A. V Ensinas, Energy, 138, 1281 (2017).

    Article  CAS  Google Scholar 

  20. J. Shadbahr, Y Zhang, F. Khan and K. Hawboldt, Renew. Energy, 125, 100 (2018).

    Article  CAS  Google Scholar 

  21. S. Sharma and G. Rangaiah, Multi-objective optimization of a fermentation process integrated with cell recycling and inter-stage extraction, Computer Aided Chemical Engineering, Elsevier (2012).

  22. T. Y. Kim, J. M. Park, H. U. Kim, K. M. Cho and S. Y. Lee, Metab. Eng., 28, 63 (2015).

    Article  CAS  PubMed  Google Scholar 

  23. D. Sarkar and J. M. Modak, Chem. Eng. Sci., 60, 481 (2005).

    Article  CAS  Google Scholar 

  24. A. M. Gujarathi, B. Al-Siyabi, N. Sivakumar and M. Mathew, Proceedings of the 10th international conference on thermal engineering, Muscat, Oman, 26–28 Febreuary (2017).

  25. B. Al-Siyabi, A.M. Gujarathi and N. Sivakumar, Mater. Manuf. Processes, 32, 1152 (2017).

    Article  CAS  Google Scholar 

  26. F. Logist, P. Van Erdeghem and J. Van Impe, Chem. Eng. Sci., 64, 2527 (2009).

    Article  CAS  Google Scholar 

  27. S. Taras and A. Woinaroschy, Comput. Chem. Eng., 43, 10 (2012).

    Article  CAS  Google Scholar 

  28. E. Heinzle, A. P. Biwer and C. L. Cooney, Development of sustainable bioprocesses: Modeling and assessment, John Wiley & Sons, England (2007).

    Google Scholar 

  29. X. Gao, B. Chen, X. He, T. Qiu, J. Li, C. Wang and L. Zhang, Comput. Chem. Eng., 32, 2801 (2008).

    Article  CAS  Google Scholar 

  30. T. Liu, X. Gao and L. Wang, J. Taiwan Inst. Chem. Eng., 57, 42 (2015).

    Article  CAS  Google Scholar 

  31. S. Yijie and S. Gongzhang, Chin. J. Aeronaut., 21, 540 (2008).

    Article  Google Scholar 

  32. A. M. Gujarathi and B. Babu, Ind. Eng. Chem. Res., 48, 11115 (2009).

    Article  CAS  Google Scholar 

  33. S. Sharma and G. P. Rangaiah, Comput. Chem. Eng., 56, 155 (2013).

    Article  CAS  Google Scholar 

  34. V. L. Huang, P. N. Suganthan, A. K. Qin and S. Baskar, Singapore: Nanyang Technological University, Technical Report (2005).

  35. B. Babu, A. M. Gujarathi, P. Katla and V. Laxmi, Proceedings of the international conference on emerging mechanical technology: macro to nano, Pilani, India, 16–18 Febreuary (2007).

  36. A. M. Gujarathi and B. V. Babu, Mater. Manuf. Processes, 26, 455 (2011).

    Article  CAS  Google Scholar 

  37. B.-y. Qu and P.-N. Suganthan, J. Zhejiang Uni. SCIE. C, 11, 538 (2010).

    Article  Google Scholar 

  38. B. Qu and P. N. Suganthan, Proceedings of the IEEE congress on evolutionary computation, Barcelona, Spain, 18–23 July (2010).

  39. B.Y. Qu and P.N. Suganthan, Proceedings of the IEEE symposium on differential evolution, Paris, France, 11–15 April (2011).

  40. X. Chen, W Du and F. Qian, Chemom. Intell. Lab. Syst., 136, 85 (2014).

    Article  CAS  Google Scholar 

  41. B. Y. Qu, J. J. Liang, Y. S. Zhu, Z. Y. Wang and P. N. Suganthan, Inf. Sci., 351, 48 (2016).

    Article  Google Scholar 

  42. Y.-N. Wang, L.-H. Wu and X.-F. Yuan, Soft Comput., 14, 193 (2010).

    Article  Google Scholar 

  43. A. M. Gujarathi and B. Babu, Proceedings of the 4th indian international conference on artificial intelligence, India, December 16–18 (2009).

  44. B. Babu and B. Anbarasu, Proceedings of the international symposium 58th annual session of IIChE, New Delhi, India, 14–17 December (2005).

  45. A. Percus, G. Istrate and C. Moore, Computational complexity and statistical physics, Oxford University Press, United States of America (2006).

    Google Scholar 

  46. M. Zhang, H. Wang, Z. Cui and J. Chen, Memet. Comput., 10, 199 (2018).

    Article  Google Scholar 

  47. K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, IEEE Trans. Evol. Comput., 6, 182 (2002).

    Article  Google Scholar 

  48. A. Sundaram, Particle swarm optimization with applications, InTech, United Kingdom (2018).

  49. C. A. C. Coello and M. S. Lechuga, Proceedings of the 2002 congress on evolutionary computation, Honolulu, HI, USA, 12–17 May (2002).

  50. H. Ohno, E. Nakanishi and T. Takamatsu, Biotechnol. Bioeng., 18, 847 (1976).

    Article  CAS  PubMed  Google Scholar 

  51. K. Deb, L. Thiele, M. Laumanns and E. Zitzler, Proceedings of the 2002 congress on evolutionary computation, Honolulu, HI, USA, 12–17 May (2002).

  52. F. Logist, B. Houska, M. Diehl and J. F. Van Impe, Chem. Eng. Sci., 66, 4670 (2011).

    Article  CAS  Google Scholar 

  53. A.M. Gujarathi and B.V. Babu, Chem. Eng. Sci., 65, 2009 (2010).

    Article  CAS  Google Scholar 

  54. A.M. Gujarathi and B.V. Babu, Mater. Manuf. Processes, 24, 303 (2009).

    Article  CAS  Google Scholar 

  55. A. M. Gujarathi and B. V. Babu, Int. J. Comput. Int. Stud, 2, 157 (2013).

    Google Scholar 

  56. E. Zitzler, Evolutionary algorithms for multiobjective optimization: Methods and applications, Citeseer, Switzerland (1999).

    Google Scholar 

  57. K. Deb, Multi-objective optimization using evolutionary algorithms, Wiley & Sons, England (2002).

    Google Scholar 

  58. E. S.-Q. Lee and G. Rangaiah, Ind. Eng. Chem. Res., 48, 7662 (2009).

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Petroleum & Chemical Engineering, College of Engineering, Sultan Qaboos University, P. O. Box 33, Al-Khod, P.C. 123, Muscat, Sultanate of Oman

    Zainab Al Ani, Ashish Madhukar Gujarathi & Gholamreza Vakili-Nezhaad

Authors
  1. Zainab Al Ani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ashish Madhukar Gujarathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gholamreza Vakili-Nezhaad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashish Madhukar Gujarathi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Al Ani, Z., Gujarathi, A.M. & Vakili-Nezhaad, G. Hybridized multi-objective optimization approach (HMODE) for lysine fed-batch fermentation process. Korean J. Chem. Eng. 38, 8–21 (2021). https://doi.org/10.1007/s11814-020-0642-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11814-020-0642-y

Keywords

Search

Navigation