Re-optimisation Control of a Fed-batch Fermentation Process using Bootstrap Aggregated Extreme Learning Machine

Carolina Maria Cardona Baron, Jie Zhang

2017

Abstract

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.

Paper Citation

in Harvard Style

Cardona Baron C. and Zhang J. (2017). Re-optimisation Control of a Fed-batch Fermentation Process using Bootstrap Aggregated Extreme Learning Machine . In Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-263-9, pages 165-176. DOI: 10.5220/0006477601650176

in Bibtex Style

@conference{icinco17,
author={Carolina Maria Cardona Baron and Jie Zhang},
title={Re-optimisation Control of a Fed-batch Fermentation Process using Bootstrap Aggregated Extreme Learning Machine},
booktitle={Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2017},
pages={165-176},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006477601650176},
isbn={978-989-758-263-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Re-optimisation Control of a Fed-batch Fermentation Process using Bootstrap Aggregated Extreme Learning Machine
SN - 978-989-758-263-9
AU - Cardona Baron C.
AU - Zhang J.
PY - 2017
SP - 165
EP - 176
DO - 10.5220/0006477601650176