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Production optimization for concentration and volume-limited fed-batch reactors in biochemical processes

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Abstract

Since a very slight violation of constraint could cause process safety and product quality problems in biochemical processes, an adaptive approach of fed-batch reactor production optimization that can strictly satisfy constraints over the entire operating time is presented. In this approach, an improved smooth function is proposed such that the inequality constraints can be transformed into smooth constraints. Based on this, only an auxiliary state is needed to monitor violations in the augmented performance index. Combined with control variable parameterization (CVP), the dynamic optimization is executed and constraint violations are examined by calculating the sensitivities of states to ensure that the inequality constraints are satisfied everywhere inside the time interval. Three biochemical production optimization problems, including the manufacturing of ethanol, penicillin and protein, are tested as illustrations. Meanwhile, comparisons with pure penalty CVP method, famous dynamic optimization toolbox DOTcvp and literature results are carried out. Research results show that the proposed method achieves better performances in terms of optimization accuracy and computation cost.

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References

  1. Schürer J, Bersch D, Schlicker S, Thiele R, Ziogas OWA, Zapf R, Kolb G (2016) Operation of a small scale demonstration plant for biodiesel synthesis under supercritical conditions. Chem Eng Technol 39(11):2151–2163. https://doi.org/10.1002/ceat.201600269

    Article  Google Scholar 

  2. Xiao L, Liu X, He S (2016) An adaptive pseudospectral method for constrained dynamic optimization problems in chemical engineering. Chem Eng Technol 39(10):1884–1894. https://doi.org/10.1002/ceat.201600281

    Article  CAS  Google Scholar 

  3. Salehmin M, Annuar M, Chisti Y (2013) High cell density fed-batch fermentations for lipase production: feeding strategies and oxygen transfer. Bioprocess Biosyst Eng 36(11):1527–1543. https://doi.org/10.1007/s00449-013-0943-1

    Article  CAS  Google Scholar 

  4. Hanly TJ, Henson MA (2013) Dynamic metabolic modeling of a microaerobic yeast co-culture: predicting and optimizing ethanol production from glucose/xylose mixtures. Biotechnol Biofuels 6(1):532–538. https://doi.org/10.1186/1754-6834-6-44

    Article  Google Scholar 

  5. Prieto CA, Guadix A, González-Tello P, Guadix EM (2005) A cyclic batch membrane reactor for the hydrolysis of whey protein. J Food Eng 78(1):257–265. https://doi.org/10.1016/j.jfoodeng.2005.09.024

    Article  Google Scholar 

  6. Henson MA (2003) Dynamic modeling and control of yeast cell populations in continuous biochemical reactors. Comput Chem Eng 27(8):1185–1199. https://doi.org/10.1016/S0098-1354(03)00046-2

    Article  CAS  Google Scholar 

  7. Amribt Z, Niu H, Bogaerts P (2013) Macroscopic modelling of overflow metabolism and model based optimization of hybridoma cell fed-batch cultures. Biochem Eng J 70:196–209. https://doi.org/10.1016/j.bej.2012.11.005

    Article  CAS  Google Scholar 

  8. García MSG, Balsa-Canto E, Alonso AA, Banga JR (2006) Computing optimal operating policies for the food industry. J Food Eng 74(1):13–23. https://doi.org/10.1016/j.jfoodeng.2005.02.011

    Article  Google Scholar 

  9. Mir M, Ghoreishi SM (2015) Response surface optimization of biodiesel production via catalytic transesterification of fatty acids. Chem Eng Technol 38(5):835–834. https://doi.org/10.1002/ceat.201300328

    Article  Google Scholar 

  10. Pajaie HS, Taghizadeh M (2015) Statistical optimization for production of light olefins in a fluidized-bed reactor. Chem Eng Technol 38(5):931–940. https://doi.org/10.1002/ceat.201400681

    Article  CAS  Google Scholar 

  11. Luus R (1992) On the application of iterative dynamic programming to singular optimal control problems. IEEE Trans Automat Contr 37(11):1802–1806. https://doi.org/10.1109/9.173155

    Article  Google Scholar 

  12. Wei Q, Liu D (2014) Adaptive dynamic programming for optimal tracking control of unknown nonlinear systems with application to coal gasification. IEEE Trans Automat Sci Eng 11(4):1020–1036. https://doi.org/10.1109/TASE.2013.2284545

    Article  Google Scholar 

  13. Herrera F, Zhang J (2009) Optimal control of batch processes using particle swam optimisation with stacked neural network models. Comput Chem Eng 33(10):1593–1601. https://doi.org/10.1016/j.compchemeng.2009.01.009

    Article  CAS  Google Scholar 

  14. Hadiyanto H, Esveld D, Boom R, Van Straten G, Van Boxtel A (2008) Control vector parameterization with sensitivity based refinement applied to baking optimization. Food Bioprod Process 86(2):130–141. https://doi.org/10.1016/j.fbp.2008.03.007

    Article  Google Scholar 

  15. Conway BA (2012) A survey of methods available for the numerical optimization of continuous dynamic systems. J Optim Theory Appl 152(2):271–306. https://doi.org/10.1007/s10957-011-9918-z

    Article  Google Scholar 

  16. Biegler LT, Cervantes AM, Wächter A (2002) Advances in simultaneous strategies for dynamic process optimization. Chem Eng Sci 57(4):575–593. https://doi.org/10.1016/S0009-2509(01)00376-1

    Article  CAS  Google Scholar 

  17. Loxton RC, Lin Q, Rehbock V, Teo KL (2012) Control parameterization for optimal control problems with continuous inequality constraints: new convergence results. Numer Algebra Contr Optim 2(3):571–599. https://doi.org/10.3934/naco.2012.2.571

    Article  Google Scholar 

  18. Assassa F, Marquardt W (2014) Dynamic optimization using adaptive direct multiple shooting. Comput Chem Eng 60(1):242–259. https://doi.org/10.1016/j.compchemeng.2013.09.017

    Article  CAS  Google Scholar 

  19. Zhang P, Chen H, Liu X, Zhang Z (2015) An iterative multi-objective particle swarm optimization-based control vector parameterization for state constrained chemical and biochemical engineering problems. Biochem Eng J 103:138–151. https://doi.org/10.1016/j.bej.2015.07.004

    Article  CAS  Google Scholar 

  20. Gao X, Yang F, Huang D, Ding Y (2014) An iterative two-level optimization method for the modeling of Wiener structure nonlinear dynamic soft sensors. Ind Eng Chem Res 53(3):1172–1178. https://doi.org/10.1021/ie4020793

    Article  CAS  Google Scholar 

  21. Bartl M, Li P, Biegler LT (2011) Improvement of state profile accuracy in nonlinear dynamic optimization with the quasi-sequential approach. AIChE J 57(57):2185–2197. https://doi.org/10.1002/aic.12437

    Article  CAS  Google Scholar 

  22. Hong W, Wang S, Li P, Wozny G, Biegler LT (2006) A quasi-sequential approach to large-scale dynamic optimization problems. AIChE J 52(1):255–268. https://doi.org/10.1002/aic.10625

    Article  CAS  Google Scholar 

  23. Lin Q, Loxton R, Teo KL (2014) The control parameterization method for nonlinear optimal control: a survey. J Ind Manag Optim 10(1):275–309. https://doi.org/10.3934/jimo.2014.10.275

    Article  Google Scholar 

  24. Biegler LT (2010) Nonlinear programming: concepts, algorithms, and applications to chemical processes. SIAM, Philadelphia

    Book  Google Scholar 

  25. Sundaralingam R (2015) Two step method for dynamic optimization of inequality state constrained systems using iterative dynamic programming. Ind Eng Chem Res 54(31):7658–7667. https://doi.org/10.1021/ie503836h

    Article  CAS  Google Scholar 

  26. Zhao Y, Stadtherr MA (2011) Rigorous global optimization for dynamic systems subject to inequality path constraints. Ind Eng Chem Res 50(22):12678–12693. https://doi.org/10.1021/ie200996f

    Article  CAS  Google Scholar 

  27. Vlassenbroeck J (1988) A Chebyshev polynomial method for optimal control with state constraints. Automatica 24(4):499–506. https://doi.org/10.1016/0005-1098(88)90094-5

    Article  Google Scholar 

  28. Jacobson DH, Lele MM (1969) A transformation technique for optimal control problems with a state variable inequality constraint. IEEE T Automat Contr 14(5):457–464. https://doi.org/10.1109/TAC.1969.1099283

    Article  Google Scholar 

  29. Vassiliadis V, Sargent R, Pantelides C (1994) Solution of a class of multistage dynamic optimization problems. 2. Problems with path constraints. Ind Eng Chem Res 33(9):2123–2133. https://doi.org/10.1021/ie00033a015

    Article  CAS  Google Scholar 

  30. Kameswaran S, Biegler LT (2008) Advantages of nonlinear-programming-based methodologies for inequality path-constrained optimal control problems-a numerical study. SIAM J Sci Comput 30(2):957–981. https://doi.org/10.1137/050644938

    Article  Google Scholar 

  31. Liu X, Hu Y, Feng J, Liu K (2014) A novel penalty approach for nonlinear dynamic optimization problems with inequality path constraints. IEEE Trans Automat Contr 59(10):2863–2867. https://doi.org/10.1109/TAC.2014.2317293

    Article  Google Scholar 

  32. Fu J, Faust JM, Chachuat B, Mitsos A (2015) Local optimization of dynamic programs with guaranteed satisfaction of path constraints. Automatica 62:184–192. https://doi.org/10.1016/j.automatica.2015.09.013

    Article  Google Scholar 

  33. Lin Q, Loxton R, Teo KL, Wu YH (2014) Optimal feedback control for dynamic systems with state constraints: an exact penalty approach. Optim Lett 8(4):1535–1551. https://doi.org/10.1007/s1159-13-0657-y

    Article  Google Scholar 

  34. Loxton R, Teo KL, Rehbock V, Yiu KFC (2009) Optimal control problems with a continuous inequality constraint on the state and the control. Automatica 45(10):2250–2257. https://doi.org/10.1016/j.automatica.2009.05.029

    Article  Google Scholar 

  35. Liu J, Teo KL, Wang X, Wu C (2015) An exact penalty function-based differential search algorithm for constrained global optimization. Soft Comput 20(4):1305–1313. https://doi.org/10.1007/s00500-015-1588-6

    Article  Google Scholar 

  36. Mekarapiruk W, Luus R (1997) Optimal control of inequality state constrained systems. Ind Eng Chem Res 36(5):1686–1694. https://doi.org/10.1021/ie960583e

    Article  CAS  Google Scholar 

  37. Bojkov B, Luus R (1996) Optimal control of nonlinear systems with unspecified final times. Chem Eng Sci 51(6):905–919. https://doi.org/10.1016/0009-2509(95)00340-1

    Article  CAS  Google Scholar 

  38. Syed S, Alhazzaa MI, Asif M (2011) Treatment of oily water using hydrophobic nano-silica. Chem Eng J 167(1):99–103. https://doi.org/10.1016/j.cej.2010.12.006

    Article  CAS  Google Scholar 

  39. Nielsen MG, Vesborg PC, Hansen O, Chorkendorff I (2015) Removal of low concentration contaminant species using photocatalysis: elimination of ethene to sub-ppm levels with and without water vapor present. Chem Eng J 262:648–657. https://doi.org/10.1016/j.cej.2014.10.008

    Article  CAS  Google Scholar 

  40. Polyzoidis A, Altenburg T, Schwarzer M, Loebbecke S, Kaskel S (2016) Continuous microreactor synthesis of ZIF-8 with high space–time-yield and tunable particle size. Chem Eng J 283:971–977. https://doi.org/10.1016/j.cej.2015.08.071

    Article  CAS  Google Scholar 

  41. Luus R (1976) A discussion on optimization of an alkylation process. Int J Numer Meth Eng 10(5):1187–1190. https://doi.org/10.1002/nme.1620100518

    Article  Google Scholar 

  42. Gupta YP (1995) Semiexhaustive search for solving nonlinear optimal control problems. Ind Eng Chem Res 34(11):3878–3884. https://doi.org/10.1021/ie00038a027

    Article  CAS  Google Scholar 

  43. Yu C, Teo KL, Zhang L, Bai Y (2012) On a refinement of the convergence analysis for the new exact penalty function method for continuous inequality constrained optimization problem. J Ind Manag Optim 8:485–491. https://doi.org/10.3934/jimo.2012.8.485

    Article  Google Scholar 

  44. Hirmajer T, Balsa-Canto E, Banga JR (2010) DOTcvp: dynamic optimization toolbox with control vector parameterization approach for handling continuous and mixed-integer dynamic optimization problems. Instituto de investigaciones marinas, Spain

    Google Scholar 

  45. Hong J (1986) Optimal substrate feeding policy for a fed batch fermentation with substrate and product inhibition kinetics. Bioprocess Biosyst Eng 28(9):1421–1431. https://doi.org/10.1002/bit.260280916

    CAS  Google Scholar 

  46. Jayaraman VK, Kulkarni BD, Gupta K, Rajesh J, Kusumaker HS (2001) Dynamic optimization of fed-batch bioreactors using the ant algorithm. Biotechnol Prog 17(1):81–88. https://doi.org/10.1021/bp000133o

    Article  CAS  Google Scholar 

  47. Egea JA, Balsacanto E, García MG, Banga JR (2009) Dynamic optimization of nonlinear processes with an enhanced scatter search method. Ind Eng Chem Res 48(9):4388–4401. https://doi.org/10.1021/ie801717t

    Article  CAS  Google Scholar 

  48. Ochoa S (2016) A new approach for finding smooth optimal feeding profiles in fed-batch fermentations. Biochem Eng J 105:177–188. https://doi.org/10.1016/j.bej.2015.09.004

    Article  CAS  Google Scholar 

  49. Banga JR, Balsa-Canto E, Moles CG, Alonso AA (2005) Dynamic optimization of bioprocesses: efficient and robust numerical strategies. J Biotechnol 117(4):407–419. https://doi.org/10.1016/j.jbiotec.2005.02.013

    Article  CAS  Google Scholar 

  50. Chen CL, Sun DY, Chang CY (2015) Numerical solution of time-delayed optimal control problems by iterative dynamic programming. Optim Contr Appl Met 21(3):91–105. https://doi.org/10.1002/1099-1514

    Article  Google Scholar 

  51. Xiao L, Liu X, Ma L, Zhang Z (2017) An effective pseudospectral method for constraint dynamic optimisation problems with characteristic times. Int J Cont. https://doi.org/10.1080/00207179.2017.1286534

    Google Scholar 

  52. Luus R (1993) Piecewise linear continuous optimal control by iterative dynamic programming. Ind Eng Chem Res 32(5):859–865. https://doi.org/10.1021/ie00017a014

    Article  CAS  Google Scholar 

  53. Upreti SR (2004) A new robust technique for optimal control of chemical engineering processes. Comput Chem Eng 28(8):1325–1336. https://doi.org/10.1016/j.compchemeng.2003.09.003

    Article  CAS  Google Scholar 

  54. Lee J, Ramirez WF (1994) Optimal fed-batch control of induced foreign protein production by recombinant bacteria. AIChE J 40(5):899–907. https://doi.org/10.1002/aic.690400516

    Article  CAS  Google Scholar 

  55. Logist F, Telen D, Houska B, Diehl M, Van Impe J (2013) Multi-objective optimal control of dynamic bioprocesses using ACADO toolkit. Bioprocess Biosyst Eng 36(2):151–164. https://doi.org/10.1007/s00449-012-0770-9

    Article  CAS  Google Scholar 

  56. Liu P, Li G, Liu X, Zhang Z (2016) Novel non-uniform adaptive grid refinement control parameterization approach for biochemical processes optimization. Biochem Eng J 111:63–74. https://doi.org/10.1016/j.bej.2016.03.006

    Article  CAS  Google Scholar 

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Acknowledgements

This work is supported by National Natural Science Foundation of China (Grant No. 61590921, 61603336), Zhejiang Province Natural Science Foundation (Y16B040003), Shanghai Aerospace Science and Technology Innovation Fund (E11501) and Aerospace Science and Technology Innovation Fund of China Aerospace Science and Technology Corporation (E81601), and their supports are thereby acknowledged.

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Correspondence to Ping Liu or Xinggao Liu.

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Liu, P., Liu, X., Zhang, Z. et al. Production optimization for concentration and volume-limited fed-batch reactors in biochemical processes. Bioprocess Biosyst Eng 41, 407–422 (2018). https://doi.org/10.1007/s00449-017-1875-y

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