This study investigates the adsorption of surfactants on Algerian reservoir rock from Hassi Messaoud. A new data generation method based on a design of experiments (DOE) approach has been developed to improve the accuracy of adsorption modeling using artificial neural networks (ANNs). Unlike traditional data acquisition methods, this approach enables a methodical and structured exploration of adsorption behavior while reducing the number of required experiments, leading to improved prediction accuracy, optimization, and cost-effectiveness. The modeling is based on three key parameters: surfactant type (SDS and EOR ASP 5100), concentration, and temperature. The dataset required for ANN training was generated from a polynomial model derived from a full factorial design (DOE) established in a previous study. Before training, 32 different ANN configurations were evaluated by varying learning algorithms, adaptation functions, and transfer functions. The best-performing model was a cascade-type network employing the Levenberg–Marquardt learning function, learngdm adaptation, tansig activation function for the hidden layer, and purelin for the output layer, achieving an R² of 0.99 and an MSE of 6.84028 × 10⁻⁹. Compared to DOE-based models, ANN exhibited superior predictive accuracy, with a performance factor (PF/3) of 0.00157 and the same MSE. While DOE showed a slight advantage in relative error (9.10 × 10⁻⁵% vs. 1.88 × 10⁻⁴% for ANN), ANN proved more effective overall. Three optimization approaches—ANN-GA, DOE-GA, and DOE-DF (desirability function)—were compared, all converging to the same optimal conditions (SDS at 200 ppm and 25 °C). This similarity between the various optimization techniques confirms the strength of genetic algorithms for optimization in the field of EOR and that they can be reliably applied in practical field operations. However, ANN-GA exhibited slightly better convergence, achieving a fitness value of 2.3247.

A Invasive emerging pollutants from wastewater effluent discharge, such as dyes, pesticides, etc., pose a serious threat to the ecosystem. Advanced oxidation processes (AOPs) under visible light irradiation have emerged as a promising technology to overcome toxic and recalcitrant organic compounds. In this work, the impact of operational factors, including the concentration of Fe3+ or Fe2+, [H2O2], the molar ratio (Oxalate/ Fe3+ or Fe2+), and the initial pH, was studied to obtain high efficiency in the degradation and mineralization of such a food dye to reduce their pollution. The study deals with the comparison of the efficiency of UV, LED, and sunlight irradiation on the photocatalytic degradation of the dye using Fe3+/Lig and Fe2+/Lig complexes. The results showed that sunlight irradiation gave a very rapid kinetic and higher degradation efficiency of over 70%. The optimized conditions for the maximum elimination of the dye with a photocatalytic degradation efficiency (98%) and a mineralization rate of 96% were obtained with the Fe3+/Lig complex, based on the Box Benhken surface design analysis. In the presence of H2O2, the degradation reached an equilibrium stage after 15 min (97.57%) for the Fe(III)/Lig system. Moreover, the inhibition effect of inorganic ions on the photo-Fenton performance of Fe3+/Lig and Fe2+/Lig was studied. The study suggests that the use of nanocrystals of hematite as a Fenton reagent for treating textile effluents needs further investigation. The results showed that the proposed models were well-suited to batch treatment under sunlight. This study not only proposes a Fe3+/Lig and Fe2+/Lig system for the elimination of a food dye without adjusting the pH of the medium, but it also provides insight into the best source of light irradiation for the photocatalytic degradation process.