A two-level measurement-based dynamic optimization strategy for a bioreactor in penicillin fermentation process

doi:10.1016/j.cjche.2014.10.011

Chin.J.Chem.Eng. ›› 2015, Vol. 23 ›› Issue (1): 112-120.DOI: 10.1016/j.cjche.2014.10.011

• PROCESS SYSTEMS ENGINEERING • Previous Articles Next Articles

A two-level measurement-based dynamic optimization strategy for a bioreactor in penicillin fermentation process

Qinghua Chi¹, Weijie Zhang¹, Zhengshun Fei², Jun Liang¹

1 State Key Laboratory of Industrial Control Technology, Department of Control Science & Engineering, Zhejiang University, Hangzhou 310027, China;
2 Department of Measurement Technology and Instruments, Zhejiang University of Science and Technology, Hangzhou 310023, China

Received:2013-05-04 Revised:2013-08-25 Online:2015-01-24 Published:2015-01-28
Contact: Jun Liang
Supported by:
Supported by the National Natural Science Foundation of China (61174114), the Research Fund for the Doctoral Program of Higher Education in China (20120101130016), and the Scholarship Award for Excellent Doctoral Student granted byMinistry of Education.

A two-level measurement-based dynamic optimization strategy for a bioreactor in penicillin fermentation process

Qinghua Chi¹, Weijie Zhang¹, Zhengshun Fei², Jun Liang¹

1 State Key Laboratory of Industrial Control Technology, Department of Control Science & Engineering, Zhejiang University, Hangzhou 310027, China;
2 Department of Measurement Technology and Instruments, Zhejiang University of Science and Technology, Hangzhou 310023, China

通讯作者: Jun Liang
基金资助:
Supported by the National Natural Science Foundation of China (61174114), the Research Fund for the Doctoral Program of Higher Education in China (20120101130016), and the Scholarship Award for Excellent Doctoral Student granted byMinistry of Education.

Abstract

Abstract: One measurement-based dynamic optimization scheme can achieve optimality under uncertainties by tracking the necessary condition of optimality (NCO-tracking), with a basic assumption that the solution model remains invariant in the presence of all kinds of uncertainties. This assumption is not satisfied in some cases and the standard NCO-tracking scheme is infeasible. In this paper, a novel two-level NCO-tracking scheme is proposed to deal with this problem. A heuristic criterion is given for triggering outer level compensation procedure to update the solution model once any change is detected via online measurement and estimation. The standard NCO-tracking process is carried out at the inner level based on the updated solutionmodel. The proposed approach is illustrated via a bioreactor in penicillin fermentation process.

Key words: Dynamic optimization, Uncertainty, NCO-tracking, Solution model

摘要： One measurement-based dynamic optimization scheme can achieve optimality under uncertainties by tracking the necessary condition of optimality (NCO-tracking), with a basic assumption that the solution model remains invariant in the presence of all kinds of uncertainties. This assumption is not satisfied in some cases and the standard NCO-tracking scheme is infeasible. In this paper, a novel two-level NCO-tracking scheme is proposed to deal with this problem. A heuristic criterion is given for triggering outer level compensation procedure to update the solution model once any change is detected via online measurement and estimation. The standard NCO-tracking process is carried out at the inner level based on the updated solutionmodel. The proposed approach is illustrated via a bioreactor in penicillin fermentation process.

关键词: Dynamic optimization, Uncertainty, NCO-tracking, Solution model

Qinghua Chi, Weijie Zhang, Zhengshun Fei, Jun Liang. A two-level measurement-based dynamic optimization strategy for a bioreactor in penicillin fermentation process[J]. Chin.J.Chem.Eng., 2015, 23(1): 112-120.

References

[1] L.T. Biegler, I.E. Grossmann, Retrospective on optimization, Comput. Chem. Eng. 28 (8) (2004) 1169-1192.

[2] D. Bonvin, Optimal operation of batch reactors—Apersonal view, J. Process Control 8 (5-6) (1998) 355-368.

[3] Y. Nie, L.T. Biegler, C.M. Villa, J.M. Wassick, Reactor modeling and recipe optimization of polyether polyol processes: polypropylene glycol, AIChE J. 59 (7) (2013) 2515-2529.

[4] A.W. Dowling, S.R.R. Vetukuri, L.T. Biegler, Large-scale optimization strategies for pressure swing adsorption cycle synthesis, AIChE J 58 (12) (2012) 3777-3791.

[5] F. Sun, W.L. Du, R.B. Qi, F. Qian, W.M. Zhong, A hybrid improved genetic algorithm and its application in dynamic optimization problems of chemical processes, Chin. J. Chem. Eng. 21 (2) (2013) 144-154.

[6] F. Sun,W.M. Zhong, H. Cheng, F. Qian, Novel control vector parameterization method with differential evolution algorithmand its application in dynamic optimization of chemical processes, Chin. J. Chem. Eng. 21 (1) (2013) 64-71.

[7] B. Srinivasan, D. Bonvin, E. Visser, S. Palanki, Dynamic optimization of batch processes: II. Role of measurements in handling uncertainty, Comput. Chem. Eng. 27 (1) (2003) 27-44.

[8] B. Srinivasan, S. Palanki, D. Bonvin, Dynamic optimization of batch processes: I. Characterization of the nominal solution, Comput. Chem. Eng. 27 (1) (2003) 1-26.

[9] O. Abel, W. Marquardt, A model predictive control scheme for safe and optimal operation of exothermic semi-batch reactors, 5th IFAC Symposium on Dynamics and Control of Process Systems, Greece, 1998, pp. 761-766.

[10] J.W. Eaton, J.B. Rawlings, Feedback control of nonlinear processes using on-line optimization techniques, Comput. Chem. Eng. 14 (4-5) (1990) 469-479.

[11] D. Ruppen, D. Bonvin, D.W.T. Rippin, Implementation of adaptive optimal operation for a semi-batch reaction systems, Comput. Chem. Eng. 22 (1-2) (1998) 185-189.

[12] D. Bonvin, L. Bodizs, B. Srinivasan, Optimal grade transition for polyethylene reactors via NCO tracking, Chem. Eng. Res. Des. 83 (A6) (2005) 692-697.

[13] W. Zhang, J. Liang, Z. Fei, L. Ye, Optimal grade transition in polymerization processes under uncertainty, CIESC J. 62 (10) (2011) 2797-2804.

[14] B. Srinivasan, L.T. Biegler, D. Bonvin, Tracking the necessary conditions of optimality with changing set of active constraints using a barrier-penalty function, Comput. Chem. Eng. 32 (3) (2008) 572-579.

[15] D. Bonvin, B. Srinivasan, On the role of the necessary conditions of optimality in structuring dynamic real-time optimization schemes, Comput. Chem. Eng. 51 (2013) 172-180.

[16] J. Jäschke, S. Skogestad, NCO tracking and self-optimizing control in the context of real-time optimization, J. Process Control 21 (10) (2011) 1407-1416.

[17] B. Srinivasan, D. Bonvin, Real-time optimization of batch processes by tracking the necessary conditions of optimality, Ind. Eng. Chem. Res. 46 (2) (2007) 492-504.

[18] J.V. Kadam, W. Marquardt, B. Srinivasan, D. Bonvin, Optimal grade transition in industrial polymerization processes via NCO tracking, AIChE J 53 (3) (2007) 627-639.

[19] J.H. Lee, N.L. Ricker, Extended Kalman filter based nonlinear model predictive control, Ind. Eng. Chem. Res. 33 (6) (1994) 1530-1541.

[20] D.G. Robertson, J.H. Lee, J.B. Rawlings, A moving horizon based approach for least squares estimation, AIChE J 42 (8) (1996) 2209-2224.

[21] J.F. Forbes, T.E. Marlin, J.F. MacGregor,Model adequacy requirements for optimizing plant operations, Comput. Chem. Eng. 18 (6) (1994) 497-510.

[22] B. Chachuat, B. Srinivasan, D. Bonvin, Adaptation strategies for real-time optimization, Comput. Chem. Eng. 33 (10) (2009) 1557-1567.

[23] P. Seferlis, A.N. Hrymak, Sensitivity analysis for chemical process optimization, Comput. Chem. Eng. 20 (10) (1996) 1177-1200.

[24] B.N. Lundberg, A.B. Poore, Numerical continuation and singularity detection methods for parametric nonlinear programming, SIAM J. Optim. 3 (1) (1993) 134-154.

[25] E.T. Hale, S.J. Qin, Multi-parametric nonlinear programming and the evaluation of implicit optimization model adequacy, Proceedings of the 7th International Symposium on the Dynamics and Control of Process Systems, 2004.

[26] J.V. Kadam, M. Schlegel, B. Srinivasan, D. Bonvin,W. Marquardt, Dynamic optimization in the presence of uncertainty: from off-line nominal solution to measurementbased implementation, J. Process Control 17 (5) (2007) 389-398.

A two-level measurement-based dynamic optimization strategy for a bioreactor in penicillin fermentation process

A two-level measurement-based dynamic optimization strategy for a bioreactor in penicillin fermentation process

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Wenhui Yang, Wuxi Qian, Zhihong Yuan, Bingzhen Chen. Perspectives on the flexibility analysis for continuous pharmaceutical manufacturing processes [J]. Chinese Journal of Chemical Engineering, 2022, 41(1): 29-41.
[2]	Mohammad Sadegh Sharafi, Mehdi Ghasemi, Mohammad Ahmadi, Alireza Kazemi. An experimental approach for measuring carbon dioxide diffusion coefficient in water and oil under supercritical conditions [J]. Chinese Journal of Chemical Engineering, 2021, 34(6): 160-170.
[3]	Markus Enekvist, Xiaodong Liang, Xiangping Zhang, Kim Dam-Johansen, Georgios M. Kontogeorgis. Estimating Hansen solubility parameters of organic pigments by group contribution methods [J]. Chinese Journal of Chemical Engineering, 2021, 29(3): 186-197.
[4]	Bo Liu, Yufei Wang, Xiao Feng. Optimization of circulating cooling water systems based on chance constrained programming [J]. Chinese Journal of Chemical Engineering, 2021, 40(12): 167-178.
[5]	Ying Chen, Zhihong Yuan, Bingzhen Chen. Process optimization with consideration of uncertainties-An overview [J]. Chin.J.Chem.Eng., 2018, 26(8): 1700-1706.
[6]	Xu Chen, Wenli Du, Feng Qian. Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms [J]. Chin.J.Chem.Eng., 2016, 24(11): 1600-1608.
[7]	Baocang Ding, Hongguang Pan. Output feedback robust model predictive control with unmeasurable model parameters and bounded disturbance [J]. , 2016, 24(10): 1431-1441.
[8]	Lingjun Tan, Chen Yang, Nana Zhou. Synthesis/design optimization of SOFC-PEM hybrid system under uncertainty [J]. Chin.J.Chem.Eng., 2015, 23(1): 128-137.
[9]	HU Yunqing, LIU Xinggao, XUE Anke. An Improved Control Vector Iteration Approach for Nonlinear Dynamic Optimization. II. Problems with Path Constraints [J]. Chin.J.Chem.Eng., 2014, 22(2): 141-145.
[10]	Juwari Purwo sutikno, Badhrulhisham Abdul aziz, Chin Sim yee, Rosbi Mamat. A New Tuning Method for Two-Degree-of-Freedom Internal Model Control under Parametric Uncertainty [J]. Chin.J.Chem.Eng., 2013, 21(9): 1030-1037.
[11]	SUN Fan, DU Wenli, QI Rongbin, QIAN Feng, ZHONG Weimin. A Hybrid Improved Genetic Algorithm and Its Application in Dynamic Optimization Problems of Chemical Processes [J]. Chin.J.Chem.Eng., 2013, 21(2): 144-154.
[12]	SUN Fan, ZHONG Weimin, CHENG Hui, QIAN Feng. Novel Control Vector Parameterization Method with Differential Evolution Algorithm and Its Application in Dynamic Optimization of Chemical Processes [J]. Chin.J.Chem.Eng., 2013, 21(1): 64-71.
[13]	LIU Xinggao, CHEN Long, HU Yunqing. Solution of Chemical Dynamic Optimization Using the Simultaneous Strategies [J]. Chin.J.Chem.Eng., 2013, 21(1): 55-63.
[14]	HU Yunqing, LIU Xinggao, XUE Anke. An Improved Control Vector Iteration Approach for Nonlinear Dynamic Optimization (Ⅰ) Problems Without Path Constraints^* [J]. Chin.J.Chem.Eng., 2012, 20(6): 1053-1058.
[15]	FEI Zhengshun, HU Bin, YE Lubin, LIANG Jun. ARX-NNPLS Model Based Optimization Strategy and Its Application in Polymer Grade Transition Process^* [J]. Chin.J.Chem.Eng., 2012, 20(5): 971-979.