About The Journal
  Aims and Scope
  Editorial Board
  Submit a Manuscript 
  Subscription Information
  Guidelines for Publication
  Ethics
Articles & Issues
  Search
  Issue
  Online First
  KJChE on
For Authors
  Instructions to Authors
  Ethical Responsibilities of
  Authors in KJChE
  Ethics in Publishing
  Peer Review Process
  Author's Checklist
  Copyright Transfer Form
Contact us
 
 
 
Issue
Korean Journal of Chemical Engineering,
Vol.38, No.1, 8-21, 2021
Hybridized multi-objective optimization approach (HMODE) for lysine fed-batch fermentation process
Ani ZA, Gujarathi AM, Vakili-Nezhaad G
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.
Keywords: Lysine; Multi-objective Optimization; Hybrid Algorithms; Fed-batch Bioreactor; Evolutionary Algorithms
[References]
  1. Singh M, Int. J. Res. Pharm. Sci., 2, 637, 2016
  2. Anastassiadis S, Recent Pat. Biotechnol., 1, 11, 2007
  3. Faurie R, Scheper T, Thommel J, Amino Acids, 79, 1, 2003
  4. Kumar D, Gomes J, Biotechnol. Adv., 23, 41, 2005
  5. Moosavi-Nasab M, Ansari S, Montazer Z, Iran Agric. Res., 25, 99, 2008
  6. Ekwealor I, Obeta J, Afr. J. Biotechnol., 4, 633, 2005
  7. Mitsuhashi S, Curr. Opin. Biotechnol., 26, 38, 2014
  8. Escobet T, Puig V, Quevedo J, Pala-Schonwalder P, Romera J, Adelman W, Comput. Chem. Eng., 124, 228, 2019
  9. Deka D, Datta D, Knowl-Based Syst., 121, 71, 2017
  10. Sendin JOH, Alonso AA, Banga JR, J. Food Eng., 98(3), 317, 2010
  11. Enitan AM, Adeyemo J, Afr. J. Biotechnol., 10, 16120, 2011
  12. Rangaiah GP, Multi-objective optimization: Techniques and applications in chemical engineering, World Scientific, Singapore (2009).
  13. Rangaiah GP, Multi-objective optimization: Techniques and applications in chemical engineering, World Scientific, Singapore (2016).
  14. Seifert T, A contribution to the design of flexible modular chemical plants, Verlag Dr. Hut, Germany (2015).
  15. Parhi SS, Rangaiah GP, Jana AK, Appl. Therm. Eng., 150, 1273, 2019
  16. Garcia S, Trinh CT, Processes, 7, 316, 2019
  17. Rodman AD, Fraga ES, Gerogiorgis D, Comput. Chem. Eng., 108, 448, 2018
  18. Guo Z, Yan X, Chemom. Intell. Lab. Syst., 177, 8, 2018
  19. Albarelli JQ, Onorati S, Caliandro P, Peduzzi E, Meireles MAA, Marechal F, Ensinas AV, Energy, 138, 1281, 2017
  20. Shadbahr J, Zhang Y, Khan F, Hawboldt K, Renew. Energy, 125, 100, 2018
  21. Sharma S, Rangaiah G, Computer Aided Chemical Engineering, Elsevier (2012).
  22. Kim TY, Park JM, Kim HU, Cho KM, Lee SY, Metab. Eng., 28, 63, 2015
  23. Sarkar D, Modak JM, Chem. Eng. Sci., 60(2), 481, 2005
  24. Gujarathi AM, et al., Proceedings of the 10th international conference on thermal engineering, Muscat, Oman, 26-28 Febreuary (2017).
  25. Al-Siyabi B, Gujarathi AM, Sivakumar N, Mater. Manuf. Processes, 32, 1152, 2017
  26. Logist F, Van Erdeghem PMM, Van Impe JF, Chem. Eng. Sci., 64(11), 2527, 2009
  27. Taras S, Woinaroschy A, Comput. Chem. Eng., 43, 10, 2012
  28. Heinzle E, et al., Modeling and assessment, John Wiley & Sons, England (2007).
  29. Gao XD, Chen BZ, He XR, Qiu T, Li JC, Wang CM, Zhang LJ, Comput. Chem. Eng., 32(11), 2801, 2008
  30. Liu T, Gao X, Wang L, J. Taiwan Inst. Chem. Eng., 57, 42, 2015
  31. Yijie S, Gongzhang S, Chin. J. Aeronaut., 21, 540, 2008
  32. Gujarathi AM, Babu BV, Ind. Eng. Chem. Res., 48(24), 11115, 2009
  33. Sharma S, Rangaiah GP, Comput. Chem. Eng., 56, 155, 2013
  34. Huang VL, Suganthan PN, Qin AK, Baskar S, Singapore: Nanyang Technological University, Technical Report (2005).
  35. Babu B, et al., Proceedings of the international conference on emerging mechanical technology: macro to nano, Pilani, India, 16-18 Febreuary (2007).
  36. Gujarathi AM, Babu BV, Mater. Manuf. Processes, 26, 455, 2011
  37. Qu BY, Suganthan PN, J. Zhejiang Uni. SCIE. C, 11, 538, 2010
  38. Qu B, Suganthan PN, Proceedings of the IEEE congress on evolutionary computation, Barcelona, Spain, 18-23 July (2010).
  39. Qu BY, Suganthan PN, Proceedings of the IEEE symposium on differential evolution, Paris, France, 11-15 April (2011).
  40. Chen X, Du W, Qian F, Chemom. Intell. Lab. Syst., 136, 85, 2014
  41. Qu BY, Liang JJ, Zhu YS, Wang ZY, Suganthan PN, Inf. Sci., 351, 48, 2016
  42. Wang YN, Wu LH, Yuan XF, Soft Comput., 14, 193, 2010
  43. Gujarathi AM, Babu B, Proceedings of the 4th indian international conference on artificial intelligence, India, December 16-18 (2009).
  44. Babu B, Anbarasu B, Proceedings of the international symposium 58th annual session of IIChE, New Delhi, India, 14-17 December (2005).
  45. Percus A, Istrate G, Moore C, Computational complexity and statistical physics, Oxford University Press, United States of America (2006).
  46. Zhang M, Wang H, Cui Z, Chen J, Memet. Comput., 10, 199, 2018
  47. Deb K, Pratap A, Agarwal S, Meyarivan T, IEEE Trans. Evol. Comput., 6, 182, 2002
  48. Sundaram A, Particle swarm optimization with applications, InTech, United Kingdom (2018).
  49. Coello CAC, Lechuga MS, Proceedings of the 2002 congress on evolutionary computation, Honolulu, HI, USA, 12-17 May (2002).
  50. Ohno H, Nakanishi E, Takamatsu T, Biotechnol. Bioeng., 18, 847, 1976
  51. Deb K, Thiele L, Laumanns M, Zitzler E, Proceedings of the 2002 congress on evolutionary computation, Honolulu, HI, USA, 12-17 May (2002).
  52. Logist F, Houska B, Diehl M, Van Impe JF, Chem. Eng. Sci., 66(20), 4670, 2011
  53. Gujarathi AM, Babu BV, Chem. Eng. Sci., 65(6), 2009, 2010
  54. Gujarathi AM, Babu BV, Mater. Manuf. Processes, 24, 303, 2009
  55. Gujarathi AM, Babu BV, Int. J. Comput. Int. Stud., 2, 157, 2013
  56. Zitzler E, Evolutionary algorithms for multiobjective optimization: Methods and applications, Citeseer, Switzerland (1999).
  57. Deb K, Multi-objective optimization using evolutionary algorithms, Wiley & Sons, England (2002).
  58. Lee ESQ, Rangaiah GP, Ind. Eng. Chem. Res., 48(16), 7662, 2009
참고문헌정보(참고문헌, 인용문헌)는 화학공학소재연구정보센터에 의해 제공되고 있습니다.