Urgench State University

This study focuses on the production of sulfate-resistant Portland cement clinker using local raw materials found in the Aral Sea region of the Republic of Uzbekistan and on its properties. The obtained results highlight the technological, economic, and environmental advantages. The raw material mixtures consisted of limestone, barren sand, kaolin, and iron-containing industrial waste, resulting in a clinker with an optimized oxide composition (LSF = 0.82, SR = 2.15, AP = 0.94). X-ray phase analysis reveals a high content of alite (C3S = 38.6%) and belite (C2S = 41.0%) phases, which ensures high mechanical strength. Low tricalcium aluminate content (tricalcium aluminate < 5%) and sufficient ferrite content (C(4)AF = 16.2%) increased sulfate resistance and phase dominance. Sulfate resistance tests conducted in accordance with ASTM C1012/C1012M showed that sulfate-resistant Portland cement had the lowest swelling index among samples immersed in a 5% Na2SO4 solution, demonstrating its superior stability compared to conventional Portland cement and mixed cement types. The experimental results demonstrate the feasibility of producing high-quality sulfate-resistant Portland cement clinker using local raw materials and demonstrate its potential for use in the sulfate-rich conditions of the Aral Sea region. From an economic perspective, the use of local raw materials reduces production costs, the need for imports, and increases the competitiveness of the cement industry in our region. From an environmental perspective, the long service life of sulfate-resistant Portland cement and the ability to utilize industrial waste align with the principles of a "green economy" and promote waste reduction and sustainable growth.

In this study, using mathematical modeling, the change in Al2O3 content as a function of acid concentration during hydrochloric acid activation of alkaline-earth bentonite from the Navbahor deposit is investigated. During the experiment, the HCl concentration varied from 5% to 20%, and the change in Al2O3 content was nonlinear. To describe the experimental data obtained during hydrochloric acid activation of alkaline-earth bentonite from the Navbahor deposit, an exponential decay model was proposed, and its parameters were estimated by regression analysis. The accuracy of this approach is supported by the coefficient of determination (R2 = 0.964) and the root mean square error (RMSE = 0.231%), indicating high accuracy and stability. The results obtained show that the decrease in Al2O3 content under acid activation conditions exhibits a nonlinear dependence on hydrochloric acid concentration rather than on time, and they enable formulation of a mathematical expression for the quantitative description of the process and for evaluating the effect of concentration.

This study investigates the neutralization behavior of an acidic wastewater (AWW)-mineralized mass (MM) system at mass ratios ranging from 100:10 to 100:40, processed at 333 K for 30 min. The evolution of pH as a function of MM dosage and the corresponding CaO content (%) in the solid phase were quantitatively evaluated. The solution pH increased sigmoidally from 4.10 to 7.30, while the CaO content rose from 23.92% to 36.96%, approaching a saturation plateau at higher MM dosages. The pH-dose relationship was described using four-parameter logistic (4PL), Gompertz, and Weibull models, all showing a high goodness of fit (R2 >= 0.97). Model comparison based on AICc and BIC criteria indicated that the Gompertz model provided the best statistical performance, whereas the 4PL model ensured clearer physicochemical interpretability. A strong positive correlation between pH and CaO content was established (Pearson r = 0.9649, n = 7, p < 0.001), enabling estimation of CaO content from pH values. Numerical inversion of the 4PL model combined with a multi-model ensemble approach was used to determine optimal MM dosages for target pH levels. The recommended operating conditions were identified as 100:32 for pH 6.5, 100:36 for pH 6.8, and 100:38 for pH 7.0, with a stabilization zone observed at 100:37-100:40.

In this study, the acid activation process of the bentonite clay, which was conducted for producing a bleaching sorbent for the oil and fat industry, was mathematically analyzed. Increase in SiO2 content under different concentrations of HCl was analyzed using the different mathematical models. During acid activation, increasing the acid concentration from 5% to 20% resulted in an increase in the SiO2 content from 61.94% to 65.12%. During the activation process, a moderate increase in HCl concentration caused the improvement of the sorption properties of the clay by dissolving some components and restructuring the active sites. An excessive increase in the concentration of HCl leads to degradation of the mineral structure and partial breakdown of the silica framework, which negatively influences sorption performance. Analysis of the obtained results using the different mathematical approaches showed that an increase in SiO2 content during activation corresponds fully to a linear model. According to this, a linear model was described by the equation y = 60.785 + 0.2088X. Accuracy of the results obtained from the linear equation was confirmed by a coefficient of determination, R2 = 0.9845, indicating a high accordance with the experimental data. This model mathematically predicts the increase in SiO2 content and proves that the activation process proceeds as a linear function. A mathematical approach to the activation process enables one to calculate in advance the properties of sorption of the clay, to reduce the consumption of acid and water, and to calculate the eventual demands of other reagents.

This study addresses the challenge of accurately predicting oil–water interfacial tension through the integration of surfactant physicochemical descriptors and machine learning algorithms. The main objective was to establish quantitative relationships between surfactant structure, concentration, and oil characteristics to determine their collective effect on interfacial energy minimization. A dataset of 260 experimentally reported points was compiled from peer-reviewed studies, encompassing seven inputs (Surfactant MW, Charge, HLB value, CMC, Concentration, oil ZPC, and Oil API) against measured IFT as output. After ensuring dataset uniformity through leverage-based outlier detection, six algorithms (DT, AdaBoost, RF, KNN, CNN, and MLP-ANN) and one hybrid framework were trained and evaluated using 5-fold cross-validation with performance indices R2, MSE, and AARE%. The Ensemble Learning model achieved the highest accuracy (R2test = 0.986, MSEtest = 4.09), demonstrating superior generalization compared with single learners. SHAP analysis confirmed surfactant concentration as the dominant factor with a strong negative association to IFT, followed by Oil API and ZPC, consistent with Gibbs adsorption theory. The results emphasize that interfacial behavior is mainly dictated by surfactant molecular architecture and concentration rather than oil composition. This unified data-driven approach provides a reproducible framework for evaluating and optimizing surfactant formulations to minimize IFT effectively in industrial applications.