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Modelling of a Post-combustion CO2 Capture Process Using Bootstrap Aggregated Extreme Learning Machines

Lookup NU author(s): Fei Li, Dr Jie ZhangORCiD, Dr Eni OkoORCiD, Dr Danping Huang


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© 2016 Elsevier B.V. This paper presents a study of modelling post-combustion CO2 capture process using bootstrap aggregated ELMs. The dynamic ELM models predict CO2 capture rate and CO2 capture level using the following variables as model inputs: inlet flue gas flow rate, CO2 concentration in inlet flue gas, pressure of flue gas, temperature of flue gas, lean solvent flow rate, MEA concentration and temperature of lean solvent. In order to enhance model accuracy and reliability, multiple ELM models are developed from bootstrap re-sampling replications of the original training data and combined. Bootstrap aggregated ELM model can offer more accurate and reliable predictions than a single ELM model, as well as provide model prediction confidence bounds. The developed models can be used in the optimisation of CO2 capture processes.

Publication metadata

Author(s): Bai Z, Li F, Zhang J, Oko E, Wang M, Xiong Z, Huang D

Publication type: Book Chapter

Publication status: Published

Book Title: Computer Aided Chemical Engineering

Year: 2016

Volume: 38

Pages: 2007-2012

Online publication date: 25/06/2016

Acceptance date: 02/04/2016

Publisher: Elsevier BV

Place Published: Amsterdam, Netherlands


DOI: 10.1016/B978-0-444-63428-3.50339-8

Notes: Proceedings of 26th European Symposium on Computer Aided Process Engineering

Library holdings: Search Newcastle University Library for this item

ISBN: 9780444634283