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Modelling of a Post-combustion CO2 Capture Process Using Extreme Learning Machine

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



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


This paper presents modelling of a post-combustion CO2 capture process using bootstrap aggregated extreme learning machine. Extreme learning machine (ELM) randomly assigns the weights between input and hidden layers and obtains the weights between the hidden layer and output layer using regression type approach in one step. This feature allows an ELM model being developed very quickly. This paper proposes using principal component regression to obtain the weights between the hidden and output layers to address the collinearity issue among hidden neuron outputs. Due to the weights between input and hidden layers are randomly assigned, ELM models could have variations in performance. This paper proposes combining multiple ELM models to enhance model prediction accuracy and reliability. To predict the CO2 production rate and CO2 capture level, eight parameters in the process were utilized as model input variables: inlet gas flow rate, CO2 concentration in inlet flow gas, inlet gas temperature, inlet gas pressure, lean solvent flowrate, lean solvent temperature, lean loading and reboiler duty. The bootstrap re-sampling of training data was applied for building each single ELM and then the individual ELMs are stacked, thereby enhancing the model accuracy and reliability. The bootstrap aggregated extreme learning machine (BA-ELM) can provide fast learning speed and good generalization performance, which will be used to optimize the CO2 capture process.

Publication metadata

Author(s): Li F, Zhang J, Oko E, Wang M

Publication type: Article

Publication status: Published

Journal: International Journal of Coal Science & Technology

Year: 2017

Volume: 4

Issue: 1

Pages: 33-40

Print publication date: 01/03/2017

Online publication date: 23/02/2017

Acceptance date: 13/02/2017

Date deposited: 14/02/2017

ISSN (print): 2095-8293

ISSN (electronic): 2198-7823

Publisher: Springer


DOI: 10.1007/s40789-017-0158-1


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