title: Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks.
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.
authors.affiliation: Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia. Electronic address: yadollah@um.edu.my.
authors.forename: Yadollah
authors.initials: Y
authors.lastname: Abdollahi
authors.affiliation: Department of Chemistry, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia. Electronic address: asrina@um.edu.my.
authors.forename: Nor Asrina
authors.initials: NA
authors.lastname: Sairi
authors.affiliation: Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia.
authors.forename: Suhana Binti Mohd
authors.initials: SB
authors.lastname: Said
authors.affiliation: Institute of Hydrogen Economy, Energy Research Alliance, International Campus, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia.
authors.forename: Ebrahim
authors.initials: E
authors.lastname: Abouzari-lotf
authors.affiliation: Material Synthesis and Characterization Laboratory, Institute of Advanced Technology, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia.
authors.forename: Azmi
authors.initials: A
authors.lastname: Zakaria
authors.affiliation: Department of Mechanical Engineering, Faculty of Engineering, 50603 Kuala Lumpur, Malaysia.
authors.forename: Mohd Faizul Bin Mohd
authors.initials: MF
authors.lastname: Sabri
authors.affiliation: Catalysis and Science Research Center, Faculty of Science, University Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia.
authors.forename: Aminul
authors.initials: A
authors.lastname: Islam
authors.affiliation: Department of Chemistry, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia.
authors.forename: Yatimah
authors.initials: Y
authors.lastname: Alias
keywords: Back-propagation-ANN; Blended-viscosity; CO(2)-absorber; CO(2)-separation; Ionic-liquids; N-methyldiethanolamine