A novel synthesis process and characterization of nano-Calcium fluoride(n-CaF2) single crystal prepared from phosphogypsum waste. The phosphogypsum (CaSO4,•2H2O) powder has been mechanically mixed with NH4F in presence of a controlled amount of water. The mixture still sintered for 48 hours...

Membrane fouling remains a major obstacle to the performance of the reverse osmosis (RO) desalination processes. Artificial intelligence (AI) is now a promising approach for the reliable modeling of these complex systems. This study evaluates three modeling techniques—multiple linear regression (MLR), artificial neural networks (ANNs), and support vector regression (SVR)—for predicting transmembrane pressure (TMP) at the Boujdour desalination plant, based on five input parameters: temperature, turbidity, pH, conductivity, and feedflow. The analysis is based on an original dataset of 195 daily measurements, and due to the absence of timestamps, the study focuses on state-to-TMP prediction rather than chronological forecasting, with no temporal generalization claimed. Approximately 2000 augmented training samples generated using a conservative SMOGN approach were used for model development, while performance evaluation relied exclusively on 39 independent real test observations. Two modeling strategies were adopted: (i) a minimalist approach based on significant variables identified by an ordinary least squares (OLS) model (pH and conductivity), and (ii) a multivariate approach integrating all parameters to capture non-linear interactions. A rigorous validation framework was put in place to avoid information leakage and ensure the robustness and generalizability of the models. Performance was evaluated using R², RMSE, and MAE metrics, supplemented by robustness and significance analyses including bootstrap confidence intervals, paired statistical comparisons, and interpretability analyses based on permutation importance, partial dependence plots (PDPs), and individual conditional expectation (ICE) curves. The results indicate that the SVR model achieves the best average predictive accuracy among the tested models, albeit with moderate explanatory power.

In the present study, phosphogypsum waste (PG) was utilized to synthesize fluorapatite (FAP) via a simple precipitation method. The FAP was prepared using a novel mixing technique with anhydrite obtained from PG and phosphoric acid solution in the presence of pure fluoride ammonium in a Teflon reactor. The characteristics of the prepared FAP were investigated with various methods including Scanning Electron Microscopy (SEM) coupled with Energy-Dispersive Spectroscopy (EDS), X-Ray Diffraction (XRD), and Fourier Transform Infrared Spectroscopy (FTIR). The FAP derived from PG exhibited a hexagonal structure with a particle size ranging from 1.5 to 9 μm along with a high purity. FAP also showed an excellent Ca/P ratio equals to 1.67. These results proved that the FAP was successfully synthesized from phosphogypsum waste.

Nowadays, reverse osmosis (RO) desalination has become a highly effective and economical solution to address water scarcity worldwide. The membranes used in this type of separation are influenced by both pre-treatment operations and feed water quality, leading to fouling, a complex phenomenon responsible for reducing treatment performance and shortening membrane lifespan. In this study, an autopsy of a RO membrane from the Boujdour plant was performed, and a fouling prediction tool based on transmembrane pressure (TMP) was developed using MATLAB/Simulink (R2015a) with an artificial neural network (ANN) model. The impact of membrane fouling on treatment performance was also examined through one year of monitoring. A detailed analysis of the fouled membrane was conducted using SEM/EDS techniques to characterize the fouling on the membrane’s surface and cross-section. The results revealed significant fractures on the membrane surface, with fouling predominantly consisting of organic deposits (characterized by a high oxygen concentration of 39.69%) and inorganic fouling, including Si (7.99%), Al (2.79%), Mg (1.56%), Fe (1.27%), and smaller quantities of K (0.87%), S (0.36%), and Ca (0.12%). The ANN model for predicting transmembrane pressure was successfully developed, achieving a high R² value of 92.077% and a low mean square error (MSE) of 0.005657. This predictive model demonstrates the ability to forecast future TMP cycles based on historical data. The research provides a detailed understanding of the types of fouling affecting RO membranes and contributes to the development of preventive strategies to mitigate membrane fouling.

This study explores the corrosion inhibition of C38 steel in a 1 M hydrochloric acid (HCl) solution using a novel benzoxazole-2-thione compound. The inhibitor was synthesized and structurally characterized by both ¹H NMR (DMSO-d₆/TMS) and ¹³C NMR spectroscopy. Electrochemical techniques, including Tafel polarization and electrochemical impedance spectroscopy, were employed to evaluate the inhibition performance. The results indicate that the benzoxazole-2-thione significantly reduces the corrosion rate, achieving a maximum inhibition efficiency of 95.25% at a concentration of 10⁻⁴ M. To gain deeper insights into the inhibition mechanism, theoretical methods such as density functional theory, Monte Carlo simulations, and molecular dynamics were applied to investigate the adsorption behavior of the compound on the steel surface. The adsorption process follows the Langmuir isotherm model, suggesting the coexistence of physisorption and chemisorption interactions. Surface morphology and elemental composition analyses using scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDX) confirm the formation of a protective inhibitor film on the steel surface.