Cascade Gaussian Process Regression Framework for Biomass Prediction in a Fed-batch Reactor

Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018

Volumn , Issue , 2018, Pages 128-135

  Masampally, Vishnu Swaroopji ^a   Pareek, Aditya ^a   Runkana, Venkataramana ^a  

^a TATA CONSULTANCY SERVICES   (India)

Author keywords

Biomass; Bioreactor; Fedbatch; Gaussian Process; Prediction; Regression

Indexed keywords

BATCH REACTORS; BIOCONVERSION; BIOREACTORS; DISSOLVED OXYGEN; FERMENTATION; FORECASTING; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); REGRESSION ANALYSIS;

BIOREACTOR MODELS; CLOSED-LOOP; FED BATCHES; FED-BATCH BIOREACTORS; FED-BATCH REACTORS; GAUSSIAN PROCESS REGRESSION; GAUSSIAN PROCESSES; OPEN-LOOP; REGRESSION; SUBMODELS;

BIOMASS;

EID: 85062767006     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SSCI.2018.8628937     Document Type: Conference Paper

References (23)

1. K. Shimizu, "A tutorial review on bioprocess systems engineering", Computers & Chemical Engineering, vol. 20, no. 6-7, pp. 915-941, 1996.
2. K. Desai, Y. Badhe, S. Tambe and B. Kulkarni, "Soft-sensor development for fed-batch bioreactors using support vector regression", Biochemical Engineering Journal, vol. 27, no. 3, pp. 225-239, 2006.
3. S. Jayaraman and R. Rhinehart, "Modeling & control policy optimization of algae growth", 2015 American Control Conference (ACC), 2015.
4. Y. Chen and F. Wang, "Crisp and Fuzzy Optimization of a Fed-Batch Fermentation for Ethanol Production", Industrial & Engineering Chemistry Research, vol. 42, no. 26, pp. 6843-6850, 2003.
5. H. Honda and T. Kobayashi, "Fuzzy control of bioprocess", Journal of Bioscience and Bioengineering, vol. 89, no. 5, pp. 401-408, 2000.
6. C. Di Massimo, G. Montague, M. Willis, M. Tham and A. Morris, "Towards improved penicillin fermentation via artificial neural networks", Computers & Chemical Engineering, vol. 16, no. 4, pp. 283-291, 1992.
7. L. Chen, S. Nguang, X. Chen and X. Li, "Modelling and optimization of fed-batch fermentation processes using dynamic neural networks and genetic algorithms", Biochemical Engineering Journal, vol. 22, no. 1, pp. 51-61, 2004.
8. Y. Li and J. Yuan, "Prediction of Key State Variables using Support Vector Machines in Bioprocesses", Chemical Engineering & Technology, vol. 29, no. 3, pp. 313-319, 2006.
9. X. Wang, J. Chen, C. Liu and F. Pan, "Hybrid modeling of penicillin fermentation process based on least square support vector machine", Chemical Engineering Research and Design, vol. 88, no. 4, pp. 415-420, 2010.
10. M. Melcher, T. Scharl, B. Spangl, M. Luchner, M. Cserjan, K. Bayer, F. Leisch and G. Striedner, "The potential of random forest and neural networks for biomass and recombinant protein modeling in Escherichia colifed-batch fermentations", Biotechnology Journal, vol. 10, no. 11, pp. 1770-1782, 2015.
11. C. Rasmussen, "Gaussian Processes in Machine Learning", Advanced Lectures on Machine Learning, pp. 63-71, 2004.
12. T. D. Wang, S. J. Chuang & C. Fyfe, "Comparing Gaussian processes and artificial neural networks for forecasting", Proc of 9th Joint Conf on Information Sciences. Taiwan, pp. 1-4, 2006.
13. F. Di Sciascio and A. Amicarelli, "Biomass estimation in batch biotechnological processes by Bayesian Gaussian process regression", Computers & Chemical Engineering, vol. 32, no. 12, pp. 3264-3273, 2008.
14. H. Jin, X. Chen, L. Wang, K. Yang and L. Wu, "Adaptive Soft Sensor Development Based on Online Ensemble Gaussian Process Regression for Nonlinear Time-Varying Batch Processes", Industrial & Engineering Chemistry Research, vol. 54, no. 30, pp. 7320-7345, 2015.
15. J. Yu, "Online quality prediction of nonlinear and non-Gaussian chemical processes with shifting dynamics using finite mixture model based Gaussian process regression approach", Chemical Engineering Science, vol. 82, pp. 22-30, 2012.
16. C. Mei, Y. Su, G. Liu, Y. Ding and Z. Liao, "Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes", Chinese Journal of Chemical Engineering, vol. 25, no. 1, pp. 116-122, 2017
17. C. Pertev, "Dynamic modeling, sensitivity analysis and parameter estimation of industrial yeast fermenters", Computers & Chemical Engineering, vol. 21, no. 1-2, pp. S739-S744, 1997.
18. B. Sonnleitner and O. Kappeli, "Growth of Saccharomyces cerevisiae is controlled by its limited respiratory capacity: Formulation and verification of a hypothesis", Biotechnology and Bioengineering, vol. 28, no. 6, pp. 927-937, 1986.
19. J. Wang, Y. Xue, T. Yu and L. Zhao, "Run-to-run Optimization for Fed-batch Fermentation Process with Swarm Energy Conservation Particle Swarm Optimization Algorithm", Chinese Journal of Chemical Engineering, vol. 18, no. 5, pp. 787-794, 2010.
20. J. Smith, "Bayesian Theory", Measurement Science and Technology, vol. 12, no. 2, pp. 221-222, 2001.
21. P. Abrahamsen. "A review of Gaussian random fields and correlation functions", 1997.
22. K. Mardia and R. Marshall, "Maximum Likelihood Estimation of Models for Residual Covariance in Spatial Regression", Biometrika, vol. 71, no. 1, p. 135, 1984.
23. Pedregosa, Fabian, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel et al. "Scikit-learn: Machine learning in Python. " Journal of machine learning research 12, no. 2825-2830, 2011.