Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application

Belhaj, A.F. and Elraies, K.A. and Alnarabiji, M.S. and Abdul Kareem, F.A. and Shuhli, J.A. and Mahmood, S.M. and Belhaj, H. (2021) Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application. Chemical Engineering Journal, 406 .

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Throughout the application of enhanced oil recovery (EOR), surfactant adsorption is considered the leading constraint on both the successful implementation and economic viability of the process. In this study, a comprehensive investigation on the adsorption behaviour of nonionic and anionic individual surfactants; namely, alkyl polyglucoside (APG) and alkyl ether carboxylate (AEC) was performed using static adsorption experiments, isotherm modelling using (Langmuir, Freundlich, Sips, and Temkin models), adsorption simulation using a state-of-the-art method, binary mixture prediction using the modified extended Langmuir (MEL) model, and artificial neural network (ANN) prediction. Static adsorption experiments revealed higher adsorption capacity of APG as compared to AEC, with sips being the most fitted model with R2 (0.9915 and 0.9926, for APG and AEC respectively). It was indicated that both monolayer and multilayer adsorption took place in a heterogeneous adsorption system with non-uniform surfactant molecules distribution, which was in remarkable agreement with the simulation results. The (APG/AEC) binary mixture prediction depicted contradictory results to the experimental individual behaviour, showing that AEC had more affinity to adsorb in competition with APG for the adsorption sites on the rock surface. The adopted ANN model showed good agreement with the experimental data and the simulated adsorption values for APG and AEC showed a decreasing trend as temperature increases. Simulating the impact of binary surfactant adsorption can provide a tremendous advantage of demonstrating the binary system behaviour with less experimental data. The utilization of ANN for such prediction procedure can minimize the experimental time, operating cost and give feasible predictions compared to other computational methods. The integrated workflow followed in this study is quite innovative as it has not been employed before for surfactant adsorption studies. © 2020 Elsevier B.V.

Item Type:Article
Impact Factor:cited By 8
Uncontrolled Keywords:Adsorption; Anionic surfactants; Binary mixtures; Carboxylation; Computer system recovery; Enhanced recovery; Forecasting; Monolayers; Nonionic surfactants, Adsorption capacities; Enhanced oil recovery; Experimental investigations; Individual behaviour; Multilayer adsorption; State-of-the-art methods; Surfactant adsorption; Temperature increase, Neural networks
ID Code:23776
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:19 Aug 2021 13:10
Last Modified:19 Aug 2021 13:10

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