Application of hybrid neural particle swarm optimisation algorithm to predict solubility of carbon dioxide in blended aqueous amine-based solvents Online publication date: Wed, 30-Mar-2016
by Reza Taherdangkoo; Mohammad Taherdangkoo
International Journal of Software Engineering, Technology and Applications (IJSETA), Vol. 1, No. 2/3/4, 2015
Abstract: In this study, we proposed a new artificial neural network (ANN) trained with particle swarm optimisation (PSO) to predict solubility of CO2 in aqueous amine-based solvents over wide range of pressure, temperature, overall concentration, and MWa. The model was developed with mixtures that consisted of methyldiethanolamine (MDEA), monoethanolamine (MEA), diethanolamine (DEA), 2-amino-2-methyl-1-propanol (AMP), diisopropanolamine (DIPA), piperazine (PZ), triethanolamine (TEA), and tetramethylen sulfone (TMS). PSO is used to find best initial weights and biases of neural network. As input parameters, neural network considered the overall solute's concentration, temperature, CO2 pressure, and MWa. The PSO-ANN model was trained, and tested using 75%, and 25% of all experimental data points, respectively. The results show that the proposed model provides predictions in acceptable agreement with experimental data.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Software Engineering, Technology and Applications (IJSETA):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook