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.

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 NaHSO/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).

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.