Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization

doi:10.1016/j.cjche.2016.08.013

Abstract

Abstract: Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid model. This may result in decreasing the generalization ability of the derived hybridmodel. Therefore, a simultaneous hybridmodeling approach is presented in this paper. It transforms the training of the empiricalmodel part into a dynamic system parameter identification problem, and thus allows training the empiricalmodel partwith only measured data. An adaptive escaping particle swarm optimization (AEPSO) algorithm with escaping and adaptive inertia weight adjustment strategies is constructed to solve the resulting parameter identification problem, and thereby accomplish the training of the empirical model part. The uniform design method is used to determine the empirical model structure. The proposed simultaneous hybrid modeling approach has been used in a lab-scale nosiheptide batch fermentation process. The results show that it is effective and leads to a more consistent model with better generalization ability when compared to existing ones. The performance of AEPSO is also demonstrated.

Key words: Bioprocess, Dynamic modeling, Neural networks, Optimization

摘要： Hybrid modeling approaches have recently been investigated as an attractive alternative to model fermentation processes. Normally, these approaches require estimation data to train the empirical model part of a hybrid model. This may result in decreasing the generalization ability of the derived hybridmodel. Therefore, a simultaneous hybridmodeling approach is presented in this paper. It transforms the training of the empiricalmodel part into a dynamic system parameter identification problem, and thus allows training the empiricalmodel partwith only measured data. An adaptive escaping particle swarm optimization (AEPSO) algorithm with escaping and adaptive inertia weight adjustment strategies is constructed to solve the resulting parameter identification problem, and thereby accomplish the training of the empirical model part. The uniform design method is used to determine the empirical model structure. The proposed simultaneous hybrid modeling approach has been used in a lab-scale nosiheptide batch fermentation process. The results show that it is effective and leads to a more consistent model with better generalization ability when compared to existing ones. The performance of AEPSO is also demonstrated.

关键词: Bioprocess, Dynamic modeling, Neural networks, Optimization

Qiangda Yang, Hongbo Gao, Weijun Zhang, Huimin Li. Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization[J]. Chin.J.Chem.Eng., 2016, 24(11): 1631-1639.

Qiangda Yang, Hongbo Gao, Weijun Zhang, Huimin Li. Simultaneous hybrid modeling of a nosiheptide fermentation process using particle swarm optimization[J]. Chinese Journal of Chemical Engineering, 2016, 24(11): 1631-1639.

References

[1] Y. Yu, L. Duan, Q. Zhang, et al., Nosiheptide biosynthesis featuring a unique indole side ring formation on the characteristic thiopeptide framework, ACS Chem. Biol. 4(10) (2009) 855-864.
[2] M.N. Kashani, S. Shahhosseini, A methodology for modeling batch reactors using generalized dynamic neural networks, Chem. Eng. J. 159(1-3) (2010) 195-202.
[3] L. Chen, O. Bernard, G. Bastin, P. Angelov, Hybrid modelling of biotechnological processes using neural networks, Control. Eng. Pract. 8(7) (2000) 821-827.
[4] M. Ławryńczuk, Online set-point optimization cooperating with predictive control of a yeast fermentation process:A neural network approach, Eng. Appl. Artif. Intell. 24(6) (2011) 968-982.
[5] J.L.Wang, X.Y. Feng, T. Yu, A geometric approach to support vector regression and its application to fermentation process fast modeling, Chin. J. Chem. Eng. 20(4) (2012) 715-722.
[6] L.H.P. Harada, A.C.D. Costa, R.M. Filho, Hybrid neuralmodeling of bioprocesses using functional link networks, Appl. Biochem. Biotechnol. 98(1) (2002) 1009-1023.
[7] J.A.Wilson, L.E.M. Zorzetto, A generalised approach to process state estimation using hybrid artificial neural network/mechanistic models, Comput. Chem. Eng. 21(9) (1997) 951-963.
[8] S.Ö. Laursen, D. Webb, W.F. Ramirez, Dynamic hybrid neural network model of an industrial fed-batch fermentation process to produce foreign protein, Comput. Chem. Eng. 31(3) (2007) 163-170.
[9] X.F.Wang, J.D. Chen, C.B. Liu, F. Pan, Hybridmodeling of penicillin fermentation process based on least square support vector machine, Chem. Eng. Res. Des. 88(4) (2010) 415-420.
[10] D.C. Psichogios, L.H. Ungar, A hybrid neural network-first principles approach to process modeling, AIChE J. 38(10) (1992) 1499-1511.
[11] D. Beluhan, S. Beluhan, Hybrid modeling approach to on-line estimation of yeast biomass concentration in industrial bioreactor, Biotechnol. Lett. 22(8) (2000) 631-635.
[12] S. James, L. Legge, H. Budman, Comparative study of black-box and hybrid estimation methods in fed-batch fermentation, J. Process Control 12(1) (2002) 113-121.
[13] A. Saraceno, S. Curcio, V. Calabrò, G. Iorio, Hybrid neural approach to model batch fermentation of "ricotta cheese whey" to ethanol, Comput. Chem. Eng. 34(10) (2010) 1590-1596.
[14] O. Kahrs, W. Marquardt, Incremental identification of hybrid process models, Comput. Chem. Eng. 32(4-5) (2008) 694-705.
[15] C.H. Yang, S.W. Tsai, L.Y. Chuang, C.H. Yang, An improved particle swarm optimization with double-bottom chaotic maps for numerical optimization, Appl. Math. Comput. 219(1) (2012) 260-279.
[16] M.S. Li, X.Y. Huang, H.S. Liu, et al., Prediction of gas solubility in polymers by back propagation artificial neural network based on self-adaptive particle swarm optimization algorithm and chaos theory, Fluid Phase Equilib. 356(2013) 11-17.
[17] A. Alfi, H.Modares, Systemidentification and control using adaptive particle swarm optimization, Appl. Math. Model. 35(3) (2011) 1210-1221.
[18] D.L. Jia, G.X. Zheng, B.Y. Qu, M.K. Khan, A hybrid particle swarm optimization algorithm for high-dimensional problems, Comput. Ind. Eng. 61(4) (2011) 1117-1122.
[19] G. Birol, C. Ündey, A. Çinar, A modular simulation package for fed-batch fermentation:Penicillin production, Comput. Chem. Eng. 26(11) (2002) 1553-1565.
[20] S. Ruchi, C. Subhash, K.S. Ashok, Batch kinetics and modeling of gibberellic acid production by Gibberella fujikuroi, Enzym. Microb. Technol. 36(4) (2005) 492-497.
[21] J. Kennedy, R.C. Eberhart, Particle swarm optimization, Proceedings of the 1995 IEEE International Conference on Neural Networks, Perth, Australia, 1995.
[22] Y.B. Meng, J.H. Zou, X.S. Gan, L. Zhao, Research onWNN aerodynamicmodeling from flight data based on improved PSO algorithm, Neurocomputing 83(2012) 212-221.
[23] K. Hornik,M. Stinchcombe, H.White,Multilayer feedforward networks are universal approximators, Neural Netw. 2(5) (1989) 359-366.
[24] K.T. Fang, C.X. Ma, Orthogonal and Uniform Experimental Design, first ed. Science Press, Beijing, 2001(in Chinese).
[25] A.A. Koutinas, R. Wang, I.K. Kookos, C. Webb, Kinetic parameters of Aspergillus awamori in submerged cultivations on whole wheat flour under oxygen limiting conditions, Biochem. Eng. J. 16(1) (2003) 23-34.
[26] S.K. Noor, M.M. Indra, R.P. Singh, Modeling the growth of Corynebacterium glutamicum under product inhibition in L-glutamic acid fermentation, Biochem. Eng. J. 25(2) (2005) 173-178.
[27] V.V. Vesselinov, D.R. Harp, Adaptive hybrid optimization strategy for calibration and parameter estimation of physical process models, Comput. Geosci. 49(2012) 10-20.
[28] J. Sun, W. Fang, V. Palade, X.J. Wu, W.B. Xu, Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point, Appl. Math. Comput. 218(7) (2011) 3763-3775.