Safeguarding drinking water is a major public health and environmental concern because it is essential to human life but may contain pollutants that can cause illness or harm the environment. Therefore, continuous research is necessary to improve water treatment methods and guarantee its quality. As part of this study, the effectiveness of coagulation–flocculation treatment using aluminum sulfate (Al₂(SO₄)₃) was evaluated on a very polluted site. Samplings were taken almost every day for a month from the polluted site, and the samples were characterized by several physicochemical properties, such as hydrogen potential (pH), electrical conductivity, turbidity, organic matter, ammonium (NH⁺₄), phosphate (PO₄³⁻), nitrate (NO₃⁻), nitrite (NO₂⁻), calcium (Ca²⁺), magnesium (Mg²⁺⁾, total hardness (TH), chloride (Cl⁻⁾, bicarbonate (HCO₃⁻), sulfate (SO₄²⁻), iron (Fe³⁺), manganese (Mn²⁺), aluminum (Al³⁺), potassium (K⁺), sodium (Na⁺), complete alkalimetric titration (TAC), and dry residue (DR). Then, these samples were treated with Al₂(SO₄)₃ using the jar test method, which is a common method to determine the optimal amount of coagulant to add to the water based on its physicochemical characteristics. A mathematical model had been previously created using the support vector machine method to predict the dose of coagulant according to the parameters of temperature, pH, TAC, conductivity, and turbidity. This Al₂(SO₄)₃ treatment step was repeated at the end of each month for a year, and a second characterization of the physicochemical parameters was carried out in order to compare them with those of the raw water. The results showed a very effective elimination of the various pollutions, with a very high rate, thus demonstrating the effectiveness of the Al₂(SO₄)₃. The physicochemical parameters measured after the treatment showed a significant reduction in the majority of the physicochemical parameters. These results demonstrated that the coagulation–flocculation treatment with Al₂(SO₄)₃ was very effective in eliminating the various pollutions present in the raw water. They also stress the importance of continued research in the field of water treatment to improve the quality of drinking water and protect public health and the environment.

This research aimed to assess the adsorption properties of raw walnut shell powder (WNSp) for the elimination of methylene blue (MB) from an aqueous medium. The initial MB concentration (2–50 mg/L), the mass of the biomaterial (0.1–1 g/L), the contact time (10–120 min), the medium’s pH (2–12), and the temperature (25–55 °C) were optimized as experimental conditions. A maximum adsorption capacity of 19.99 mg/g was obtained at an MB concentration of 50 mg/L, a medium pH of 6.93 and a temperature of 25 °C, using 0.2 g/L of WNSp. These conditions showed that the MB dye elimination process occurred spontaneously. Different analytical approaches were used to characterize the WNSp biomaterial, including functional groups involved in MB adsorption, the surface characteristics and morphological features of the WNSp before and after MB uptake, and identification of WNSp based on their diffraction pattern. The experimental isotherm data were analyzed by the Langmuir and Freundlich models for the adsorption of MB dye. The corresponding values of parameter R_L of Langmuir were between 0.51 and 0.172, which confirmed the WNSp’s favorable MB dye adsorption. The experimental kinetic data were examined, and the pseudo-second-order model was shown to be more suitable for describing the adsorption process, with an excellent determination coefficient (R² = 0.999). The exchanged standard enthalpy (H° = −22.456 KJ.mol⁻¹) was calculated using the van ‘t Hoff equation, and it was proven that the adsorption process was exothermic. The spontaneous nature and feasibility of the MB dye adsorption process on WNSp were validated by negative standard enthalpy values (G°) ranging from −2.580 to −0.469 at different temperatures. It was established that WNSp may be employed as a novel, effective, low-cost adsorbent for the elimination of methylene blue in aqueous solutions.

This study conducts a comprehensive investigation to optimize the degradation of crystal violet (CV) dye using the Fenton process. The main objective is to improve the efficiency of the Fenton process by optimizing various physicochemical factors such as the Fe²⁺ concentration, H₂O₂ concentration, and pH of the solution. The results obtained show that the optimal dosages of Fe²⁺ and H₂O₂ giving a maximum CV degradation (99%) are 0.2 and 3.13 mM, respectively. The optimal solution pH for CV degradation is 3. The investigation of the type of acid for pH adjustment revealed that sulfuric acid is the most effective one, providing 100% yield, followed by phosphoric acid, hydrochloric acid, and nitric acid. Furthermore, the examination of sulfuric acid concentration shows that an optimal concentration of 0.1 M is the most effective for CV degradation. On the other hand, an increase in the initial concentration of the dye leads to a reduction in the hydroxyl radicals formed (HO^•), which negatively impacts CV degradation. A concentration of 10 mg/L of CV gives complete degradation of dye within 30 min following the reaction. Increasing the solution temperature and stirring speed have a negative effect on dye degradation. Moreover, the combination of ultrasound with the Fenton process resulted in a slight enhancement in the CV degradation, with an optimal stirring speed of 300 rpm. Notably, the study incorporates the use of Gaussian process regression (GPR) modeling in conjunction with the Improved Grey Wolf Optimization (IGWO) algorithm to accurately predict the optimal degradation conditions. This research, through its rigorous investigation and advanced modeling techniques, offers invaluable insights and guidelines for optimizing the Fenton process in the context of CV degradation, thereby achieving the twin goals of cost reduction and environmental impact minimization.

Monitoring stations have been established to combat water pollution, improve the ecosystem, promote human health, and facilitate drinking water production. However, continuous and extensive monitoring of water is costly and time-consuming, resulting in limited datasets and hindering water management research. This study focuses on developing an optimized K-nearest neighbor (KNN) model using the improved grey wolf optimization (I-GWO) algorithm to predict dry residue quantities. The model incorporates 20 physical and chemical parameters derived from a dataset of 400 samples. Cross-validation is employed to assess model performance, optimize parameters, and mitigate the risk of overfitting. Four folds are created, and each fold is optimized using 11 distance metrics and their corresponding weighting functions to determine the best model configuration. Among the evaluated models, the Jaccard distance metric with inverse squared weighting function consistently demonstrates the best performance in terms of statistical errors and coefficients for each fold. By averaging predictions from the models in the four folds, an estimation of the overall model performance is obtained. The resulting model exhibits high efficiency, with remarkably low errors reflected in the values of R, R², R²_ADJ, RMSE, and EPM, which are reported as 0.9979, 0.9958, 0.9956, 41.2639, and 3.1061, respectively. This study reveals a compelling non-linear correlation between physico-chemical water attributes and the content of dry tailings, indicating the ability to accurately predict dry tailing quantities. By employing the proposed methodology to enhance water quality models, it becomes possible to overcome limitations in water quality management and significantly improve the precision of predictions regarding critical water parameters.

This work aimed to formulate xanthan gum microspheres for the encapsulation of metformin hydrochloride, according to the process of ionotropic gelation. The obtained microparticles, based on various fractions of xanthan gum (0.5–1.25), were subjected to different physico-chemical tests and a drug release study. Microspheres with an average size varying between 110.96 μm and 208.27 μm were obtained. Encapsulation efficiency reached 93.11% at a 1.25% biopolymer concentration. The swelling study showed a swelling rate reaching 29.8% in the gastric medium (pH 1.2) and 360% in the intestinal medium (pH 6.8). The drug release studies showed complete metformin hydrochloride release from the beads, especially those prepared from xanthan gum at the concentration of 1.25%, in intestinal medium at 90.00% after 6 h. However, limited and insignificant drug release was observed within the gastric medium (32.50%). The dissolution profiles showed sustained release kinetics.

The main objective of this study was to create a mathematical tool that could be used with experimental data to predict the rheological flow behavior of functionalized xanthan gum according to the types of chemical groups grafted onto its backbone. Different rheological and physicochemical analyses were applied to assess six derivatives synthesized via the etherification of xanthan gum by hydrophobic benzylation with benzyl chloride and carboxymethylation with monochloroacetic acid at three (regent/polymer) ratios R equal to 2.4 and 6. Results from the FTIR study verified that xanthan gum had been modified. The degree of substitution (DS) values varying between 0.2 and 2.9 for carboxymethylxanthan gum derivatives were found to be higher than that of hydrophobically modified benzyl xanthan gum for which the DS ranged from 0.5 to 1. The molecular weights of all the derivatives were found to be less than that of xanthan gum for the two types of derivatives, decreasing further as the degree of substitution (DS) increased. However, the benzyl xanthan gum derivatives presented higher molecular weights varying between 1,373,146 (g/mol) and 1,262,227 (g/mol) than carboxymethylxanthan gum derivatives (1,326,722–1,015,544) (g/mol). A shear-thinning behavior was observed in the derivatives, and the derivatives’ viscosity was found to decrease with increasing DS. The second objective of this research was to create an ANN model to predict one of the rheological properties (the apparent viscosity). The significance of the ANN model (R² = 0.99998 and MSE = 5.95 × 10⁻³) was validated by comparing experimental results with the predicted ones. The results showed that the model was an efficient tool for predicting rheological flow behavior.