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Re-optimisation control of a fed-batch fermentation process using bootstrap aggregated extreme learning machine

Lookup NU author(s): Dr Jie ZhangORCiD


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Copyright © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. This paper presents using bootstrap aggregated extreme learning machine for the on-line re-optimisation control of a fed-batch fermentation process. In order to overcome the difficulty in developing mechanistic model, data driven models are developed using extreme learning machine (ELM). ELM has the advantage of fast training in that the hidden layer weights are randomly assigned. A single ELM model can lack of robustness due the randomly assigned hidden layer weights. To overcome this problem, multiple ELM models are developed from bootstrap re-sampling replications of the original training data and are then combined. In addition to enhanced model accuracy, bootstrap aggregated ELM can also give model prediction confidence bounds. A reliable optimal control policy is achieved by means of the inclusion of model prediction confidence bounds within the optimisation objective function to penalise wide model prediction confidence bounds which are associated with uncertain predictions as a consequence of plant model-mismatch. Finally, in order to deal with process disturbances, an on-line re-optimisation strategy is developed and successfully implemented.

Publication metadata

Author(s): Baron CMC, Zhang J

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: ICINCO 2017 - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics

Year of Conference: 2017

Pages: 165-176

Online publication date: 28/07/2017

Acceptance date: 02/04/2016

Publisher: SciTePress


Library holdings: Search Newcastle University Library for this item

ISBN: 9789897582639