Browse by author
Lookup NU author(s): Dr Jie ZhangORCiD
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
This paper presents a reliable optimal control strategy for a fed-batch fermentation process using ant colony optimisation and bootstrap aggregated neural network models. Bootstrap aggregated neural networks are used to enhance model accuracy and reliability. A further advantage of bootstrap aggregated neural network is that model prediction confidence bounds can be calculated from individual network predictions. The objective function of fed-batch fermentation process optimisation based on neural network models typically contains multiple local minima and traditional gradient based optimisation may be trapped in a local minimum. In order to overcome this problem, ant colony optimisation is used. The optimisation objective function is modified to incorporate model prediction confidence in order to enhance the reliability of the calculated "optimal" control policy. Application results on a simulated fed-batch fermentation process demonstrate that the proposed strategy is very effective.
Author(s): Zhang J, Feng YT, Al-Mahrouqi MH
Editor(s): Pistikopoulos, E.N., Georgiadis, M.C., Kokossis, A.C.
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 21st European Symposium on Computer Aided Process Engineering
Year of Conference: 2011
Publisher: Elsevier BV
Library holdings: Search Newcastle University Library for this item
Series Title: Computer-Aided Chemical Engineering