Static and dynamic adsorption of a gemini surfactant on a carbonate rock in the presence of low salinity water.

In chemical enhanced oil recovery (cEOR) techniques, surfactants are extensively used for enhancing oil recovery by reducing interfacial tension and/or modifying wettability. However, the effectiveness and economic feasibility of the cEOR process are compromised due to the adsorption of surfactants on rock surfaces. Therefore, surfactant adsorption must be reduced to make the cEOR process efficient and economical. Herein, the synergic application of low salinity water and a cationic gemini surfactant was investigated in a carbonate rock. Firstly, the interfacial tension (IFT) of the oil-brine interface with surfactant at various temperatures was measured. Subsequently, the rock wettability was determined under high-pressure and high-temperature conditions. Finally, the study examined the impact of low salinity water on the adsorption of the cationic gemini surfactant, both statically and dynamically. The results showed that the low salinity water condition does not cause a significant impact on the IFT reduction and wettability alteration as compared to the high salinity water conditions. However, the low salinity water condition reduced the surfactant's static adsorption on the carbonate core by four folds as compared to seawater. The core flood results showed a significantly lower amount of dynamic adsorption (0.11 mg/g-rock) using low salinity water conditions. Employing such a method aids industrialists and researchers in developing a cost-effective and efficient cEOR process.

Artificial Intelligence Techniques and Response Surface Methodology for the Optimization of Methyl Ester Sulfonate Synthesis from Used Cooking Oil by Sulfonation.

Herein, the impacts of sulfonation temperature (100-120 °C), sulfonation time (3-5 h), and NaHSO3/methyl ester (ME) molar ratio (1:1-1.5:1 mol/mol) on methyl ester sulfonate (MES) yield were studied. For the first time, MES synthesis via the sulfonation process was modeled using the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and response surface methodology (RSM). Moreover, particle swarm optimization (PSO) and RSM methods were used to improve the independent process variables that affect the sulfonation process. The RSM model (coefficient of determination (R2) = 0.9695, mean square error (MSE) = 2.7094, and average absolute deviation (AAD) = 2.9508%) was the least efficient in accurately predicting MES yield, whereas the ANFIS model (R2 = 0.9886, MSE = 1.0138, and AAD = 0.9058%) was superior to the ANN model (R2 = 0.9750, MSE = 2.6282, and AAD = 1.7184%). The results of process optimization using the developed models revealed that PSO outperformed RSM. The ANFIS model coupled with PSO (ANFIS-PSO) achieved the best combination of sulfonation process factors (96.84 °C temperature, 2.68 h time, and 0.92:1 mol/mol NaHSO3/ME molar ratio) that resulted in the maximum MES yield of 74.82%. Analysis of MES synthesized under optimum conditions using FTIR, 1H NMR, and surface tension determination showed that MES could be prepared from used cooking oil.

Pumice-supported ZnO-photocatalyzed degradation of organic pollutant in textile effluent: optimization by response surface methodology, artificial neural network, and adaptive neural-fuzzy inference system.

A heterogeneous photocatalysis was adopted to treat textile industry effluent using a combination of pumice-supported ZnO (PUM-ZnO) photocatalyst and solar irradiation. The visible light-responsive PUM-ZnO photocatalyst was prepared via the impregnation method and characterized using various spectroscopic techniques. The photocatalytic degradation process was modeled via response surface methodology (RSM), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS), while the optimization of the three independent parameters significant to the photocatalytic process was carried out by a genetic algorithm (GA) and RSM methods. The low standard error of prediction (SEP) of 0.56-1.75% and high coefficient of determination (R2) greater than 0.96 for the models developed indicated that they adequately predicted the photodegradation process with high accuracy in the order of ANFIS > ANN > RSM. The process optimization results from the developed models showed that GA performed better than RSM. The best optimal condition (3.29 g/L catalyst dosage, 45.85 min irradiation time, and 3.13 effluent pH) that resulted in maximum degradation efficiency of 99.46% was achieved by the ANFIS model coupled with GA (ANFIS-GA).