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

ABSTRACT: 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.

SUBMITTER: Belhaj AF

PROVIDER: S-EPMC7511199 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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

Belhaj Ahmed F AF Elraies Khaled A KA Alnarabiji Mohamad S MS Abdul Kareem Firas A FA Shuhli Juhairi A JA Mahmood Syed M SM Belhaj Hadi H

Chemical engineering journal (Lausanne, Switzerland : 1996) 20200923

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 ...[more]

PMID: 32989375

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Project description:Surfactant flooding is adjudged one of the most promising chemicals enhanced oil recovery (cEOR) methods due to its high microscopic sweep efficiency. This surfactant shows high potential in mobilizing trapped residual oil (ganglia) through excellent lowering of the interfacial tension (IFT) between the crude oil-aqueous interface to ultra-low values while favorably altering the wettability (oil-wet to water-wet). Surfactant adsorption is a critical factor that determines how successful this cEOR method will be as well as the project economics. Surfactant retention due to adsorption caused majorly by electrostatic forces of attraction between hydrophilic head, and the positive and negative charges of the adsorbent solid surface leading to insufficiency of the remaining surfactant concentration in the injected slug to achieve the supposed ultralow IFT needed for mobilization. This article describes the experimental data on the adsorption of a natural surfactant derived from linseed oil and the results from its adsorption isotherm modelling. This anionic surfactant (LSO-derived) has a CMC value of 2500 ppm, average fractional removal of 0.60 under a range of concentrations (500, 1000, 2000, 4000, 8000, and 12000 ppm), with the adsorption kinetics revealing that adsorption density rises as a function of time with increasing adsorbate concentrations. Five different classical adsorption isotherm models were explored- in the form three (Redlich-Peterson or R-P), two (Langmuir, Freundlich, Temkin), one (Linear-Henry) parameters models. Their characteristics adsorption parameters were calculated, with highest adsorption capacity value of 2.955mg/g obtained from the simulation using OriginPro 2021 Software. The analysis demonstrates that the R-P model provided the greatest fit as a hybrid model with the highest correlation coefficient value. The kinetic adsorption models Pseudo-First Order (PFO), Pseudo-Second Order (PSO), Pseudo-Nth Order (PNO), and Intra-Particle Diffusion (IPD), as well as their thermodynamic property model, were also examined in addition to static isotherm models. On average, using non-linear regression approach, PSO and PNO provided the best appropriate fit models under this hypothesis, with correlation values of the nth order ranging from 0.443 to 2.122 (excluding 5.847 the non-converged fit value). Prior to thermodynamic analysis, it was confirmed by the IPD with multi-linear graphical characteristics that intra-particle transport was not the only rate-limiting step in adsorption processes and proceeded spontaneously by the This model can be utilized to design a template for LSO surfactant-rock adsorption in chemical flooding schemes for EOR applications.

Project description:There is a rising interest in biotechnology for the compartmentalization of biochemical reactions in water droplets. Several applications, such as the widely used digital PCR, seek to encapsulate a single molecule in a droplet to be amplified. Directed evolution, another technology with growing popularity, seeks to replicate what happens in nature by encapsulating a single gene and the protein encoded by this gene, linking genotype with phenotype. Compartmentalizing reactions in droplets also allows the experimentalist to run millions of different reactions in parallel. Compartmentalization requires a fluid that is immiscible with water and a surfactant to stabilize the droplets. While there are fluids and surfactants on the market that have been used to accomplish encapsulation, there are reported concerns with these. Span® 80, for example, a commonly used surfactant, has contaminants that interfere with various biochemical reactions. Similarly, synthetic fluids distributed by the cosmetic industry allow some researchers to produce experimental results that can be published, but then other researchers fail to reproduce some of these protocols due to the unreliable nature of these products, which are not manufactured with the intent of being used in biotechnology. The most reliable fluids, immiscible with water and suitable for biochemical reactions, are fluorinated fluids. Fluorinated compounds have the peculiar characteristic of being immiscible with water while at the same time not mixing with hydrophobic molecules. This peculiar characteristic has made fluorinated fluids attractive because it seems to be the basis of their being biologically inert. However, commercially available fluorinated fluids have densities between 1.4 to 1.6 g/mL. The higher-than-water density of fluorinated oils complicates handling of the droplets since these would float on the fluid since the water droplets would be less dense. This can cause aggregation and coalescence of the droplets. Here, we report the synthesis, characterization, and use of fluorinated polysiloxane oils that have densities similar to the one of water at room temperature, and when mixed with non-ionic fluorinated surfactants, can produce droplets encapsulating biochemical reactions. We show how droplets in these emulsions can host many biological processes, including PCR, DNA origami, rolling circle amplification (RCA), and Taqman® assays. Some of these use unnatural DNA built from an Artificially Expanded Genetic Information System (AEGIS) with six nucleotide "letters".

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

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

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