Toggle Main Menu Toggle Search

Open Access padlockePrints

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

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


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


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 paper proposes using principal component regression to obtain the weights between the hidden and output layers. Due to the weights between input and hidden layers are randomly assigned, ELM could have variations in performance. This paper proposes combining multiple ELMs to enhance model prediction accuracy and reliability. To predict the CO2 production rate and CO2 capture level, seven parameters in the process were regarded as 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 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 MH

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR)

Year of Conference: 2016

Pages: 1252-1257

Print publication date: 01/01/2016

Online publication date: 26/09/2016

Acceptance date: 01/01/1900

ISSN: 9781509017157

Publisher: IEEE


DOI: 10.1109/MMAR.2016.7575318