Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks

Yadollah Abdollahi; Nor Asrina Sairi; Suhana Binti Mohd Said; Ebrahim Abouzari-lotf; Azmi Zakaria; Mohd Faizul Bin Mohd Sabri; Aminul Islam; Yatimah Alias

doi:10.1016/j.saa.2015.06.036

Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks

Spectrochim Acta A Mol Biomol Spectrosc. 2015 Nov 5:150:892-901. doi: 10.1016/j.saa.2015.06.036. Epub 2015 Jun 17.

Authors

Yadollah Abdollahi¹, Nor Asrina Sairi², Suhana Binti Mohd Said³, Ebrahim Abouzari-lotf⁴, Azmi Zakaria⁵, Mohd Faizul Bin Mohd Sabri⁶, Aminul Islam⁷, Yatimah Alias⁸

Affiliations

¹ Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia. Electronic address: yadollah@um.edu.my.
² Department of Chemistry, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia. Electronic address: asrina@um.edu.my.
³ Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
⁴ Institute of Hydrogen Economy, Energy Research Alliance, International Campus, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia.
⁵ Material Synthesis and Characterization Laboratory, Institute of Advanced Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia.
⁶ Department of Mechanical Engineering, Faculty of Engineering, 50603 Kuala Lumpur, Malaysia.
⁷ Catalysis and Science Research Center, Faculty of Science, University Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia.
⁸ Department of Chemistry, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia.

PMID: 26119355
DOI: 10.1016/j.saa.2015.06.036

Abstract

It is believe that 80% industrial of carbon dioxide can be controlled by separation and storage technologies which use the blended ionic liquids absorber. Among the blended absorbers, the mixture of water, N-methyldiethanolamine (MDEA) and guanidinium trifluoromethane sulfonate (gua) has presented the superior stripping qualities. However, the blended solution has illustrated high viscosity that affects the cost of separation process. In this work, the blended fabrication was scheduled with is the process arranging, controlling and optimizing. Therefore, the blend's components and operating temperature were modeled and optimized as input effective variables to minimize its viscosity as the final output by using back-propagation artificial neural network (ANN). The modeling was carried out by four mathematical algorithms with individual experimental design to obtain the optimum topology using root mean squared error (RMSE), R-squared (R(2)) and absolute average deviation (AAD). As a result, the final model (QP-4-8-1) with minimum RMSE and AAD as well as the highest R(2) was selected to navigate the fabrication of the blended solution. Therefore, the model was applied to obtain the optimum initial level of the input variables which were included temperature 303-323 K, x[gua], 0-0.033, x[MDAE], 0.3-0.4, and x[H2O], 0.7-1.0. Moreover, the model has obtained the relative importance ordered of the variables which included x[gua]>temperature>x[MDEA]>x[H2O]. Therefore, none of the variables was negligible in the fabrication. Furthermore, the model predicted the optimum points of the variables to minimize the viscosity which was validated by further experiments. The validated results confirmed the model schedulability. Accordingly, ANN succeeds to model the initial components of the blended solutions as absorber of CO2 capture in separation technologies that is able to industries scale up.

Keywords: Back-propagation-ANN; Blended-viscosity; CO(2)-absorber; CO(2)-separation; Ionic-liquids; N-methyldiethanolamine.

Publication types

Research Support, Non-U.S. Gov't