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An approximate probability graphical modelling method for complex industrial fermentation processes


Wang, H and Chen, B and Lu, Z and Khan, FI, An approximate probability graphical modelling method for complex industrial fermentation processes, Proceedings of the 9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2015, 2-4 September 2015, Paris, France, pp. 826-831. ISSN 2405-8963 (2015) [Refereed Conference Paper]

Copyright Statement

Copyright 2015 IFAC

DOI: doi:10.1016/j.ifacol.2015.09.629


Fed-batch fermentation process is an effective method for production. Due to the various feeding streams and operational conditions in different fed-batches, usually it is difficult to formulate a kinetics-based ordinary differential equations model for industrial fed-batch fermentation process. On the other hand, there are plenty of historical data collected during the fermentation process. In this paper, we firstly applied the graphical modeling method to model the fed-batch fermentation process. In this proposed method, the missing data within records are imputed, and then, the correlations between variables are determined by the low order conditional independence method, after that, the parameters of these related variables are learned by the multivariate auto regressive method. The calculation of L-lysine fed-batch fermentation process demonstrates the effectiveness of the proposed approximate model method.

Item Details

Item Type:Refereed Conference Paper
Keywords:dynamic Bayesian network, Gaussian approximate, missing data imputation, process modelling, fed-batch fermentation
Research Division:Biological Sciences
Research Group:Industrial biotechnology
Research Field:Fermentation
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in engineering
UTAS Author:Khan, FI (Professor Faisal Khan)
ID Code:120533
Year Published:2015
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2017-08-28
Last Modified:2017-10-17

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