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.

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.

A novel method has been explored based on the recovery of two industrial wastes V2O5 and phosphogypsum from the fertilizer production industry, which also provide a significant challenge due to their toxicity and environmental impact. To solve the problem of these two harmful wastes, these...

This study investigates a controlled synthesis and particle size optimization of nanocalcite particles using phosphogypsum, a waste byproduct from the phosphate fertilizer industry, and cesium carbonate (Cs₂CO₃), a common carbonate source. The effects of synthesis parameters, including temperature and pH, on the size, morphology, and crystallinity of the synthesized nanocalcite particles were systematically examined. The optimized synthesis conditions for obtaining nanocalcite particles with desired properties are discussed. The synthesized nanocalcite particles were characterized using various techniques, such as XRD, FTIR, and SEM, to analyze their crystal structure, morphology, and elemental composition. Particle sizes were determined using the Debye–Scherrer method, and accordingly, nanometric sizes were achieved. The potential applications of the synthesized nanocalcite particles in cementitious materials, agriculture, and drug delivery are highlighted. This research provides valuable insights into the sustainable synthesis and size optimization of nanocalcite particles from phosphogypsum and Cs₂CO₃ at a controlled temperature and pH.

In the present study, nano-calcium carbonate (NCC) was prepared from phosphogypsum (PG) as waste material from phosphate industry using Na₂CO₃ and Al(HCO₃)₃ as carbonates precursors. The physicochemical characteristics of the prepared nano-calcite CaCO₃ were studied using various methods, including X-ray diffraction (XRD), scanning electron microscope (SEM), chemical analysis, plasma spectrometry with inductive coupling (ICP), as well as the Bernard calcimeter. The size of the pure nanocalcite particles produced differs according to the nature of the carbonate precursor; they are 51 nm and 68 with the use of sodium carbonate and aluminum hydrogen carbonate, respectively.