Modelling of a post-combustion CO2 capture process using neural networks

FUEL

卷:151

Article; Proceedings Paper

Li, Fei¹  Zhang, Jie¹  Oko, Eni²  Wang, Meihong² 

[1] Newcastle Univ, Sch Chem Engn & Adv Mat, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England

[2] Univ Hull, Sch Engn, Kingston Upon Hull HU6 7RX, N Humberside, England

关键词: CO2 capture;    Chemical absorption;    Neural networks;    Data-driven modelling;   

DOI  :  10.1016/j.fuel.2015.02.038

来源: Elsevier

【 摘 要 】

This paper presents a study of modelling post-combustion CO2 capture process using bootstrapaggregated neural networks. The neural network 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 feedforward neural network models are developed from bootstrap re-sampling replications of the original training data and are combined. Bootstrap aggregated model can offer more accurate predictions than a single neural network, as well as provide model prediction confidence bounds. Simulated CO2 capture process operation data from gPROMS simulation are used to build and verify neural network models. Both neural network static and dynamic models are developed and they offer accurate predictions on unseen validation data. The developed neural network models can then be used in the optimisation of the CO2 capture process. (C) 2015 Elsevier Ltd. All rights reserved.

【 授权许可】

Free