Antonina Andreevna Filimonova

The article presents the results of the development of multicomponent adsorption materials based on industrial waste sludge from the water treatment plant of a thermal power plant. Activation of the sludge at 1000 °C makes it possible to obtain a porous matrix. It has been experimentally established that compositions with 50% activated sludge content in combination with oxides of Zn, Fe, Mn, Cu and NaOH have an optimal sorption capacity with respect to hydrogen sulfide. Kinetic studies have shown that the optimal contact time of the adsorbent with the adsorbate is 15–20 min, and the operating temperature should not exceed 300 K. The calculated thermodynamic parameters confirm the exothermic chemical mechanism of sorption. The materials have the ability to regenerate and display a color change upon contact with hydrogen sulfide. With respect to mercaptan sulfur, the maximum capacity was achieved for the sorption composition with 74.7% ZnO content. The logarithmic dependence of the mercaptan sulfur capacity on the percentage of zinc oxide in the composition has also been established. The environmental significance of the work lies in the utilization of large-tonnage waste and the absence of liquid effluents during regeneration. The proposed materials show promise as potentially cost-effective alternatives for gas purification, though comprehensive economic analysis remains the subject of future work.

Electrochemical treatment of sulfide‑containing industrial effluents and gas emissions remains a pressing challenge for the development of clean technologies, whose solution requires anodes with low operating potential, stable performance, and facile, cost‑effective fabrication. The paper studies the influence of the substrate nature on the structural and electrochemical characteristics of lead dioxide anodes in sulfide-containing aqueous systems using the electrolysis of an aqueous solution of sodium sulfide Na₂S as an example. The substrates used were composite materials based on polylactic acid with the addition of 7 and 10 wt.% multiwalled carbon nanotubes, designated as PLA (7% mCNT)/PbO₂ and PLA (10% mCNT)/PbO₂, as well as a PLA (Graphite)/PbO₂ composite containing dispersed graphite as a conducting phase; PbO₂ coatings were applied to all of the above polymer substrates by electrochemical deposition. For comparison, a traditional Ti/PbO₂ anode and a graphite electrode without a lead dioxide coating, used as a standalone anode, were also considered. Cyclic voltammetry in a Na₂S solution was used to evaluate the anodic reaction overpotential, specific current densities, and current-voltage curve shapes on various substrates, as well as to analyze the influence of the conductive composite composition on the electrochemical behavior of the PbO₂ layer. It was shown that the PLA (7% mCNT)/ PbO₂, PLA (10% mCNT)/PbO₂, and especially PLA (Graphite)/PbO₂ anodes provide operating potentials, ohmic resistance, and cyclic stability comparable to or superior to those of the traditional Ti/PbO₂ anode in a sulfide-containing electrolyte, whereas the uncoated graphite electrode is inferior to the composite PbO₂ electrodes in terms of a combination of parameters. The obtained results allow us to conclude that the nature and composition of the substrate play a key role in the formation of the morphology, conductivity and electrochemical properties of PbO₂ anodes and confirm their high potential for use in electrochemical systems for treating sulfide-containing aqueous media and gas emissions.

Sulfide-alkaline wastewater (SAW) from petrochemical plants, particularly from pyrolysis and hydrotreating units, presents a significant environmental challenge due to its high toxicity, extreme alkalinity (pH > 12), and high concentrations of sulfides and organic pollutants. Traditional treatment methods like acid neutralization or air oxidation are often inefficient, generate secondary waste, or fail to recover valuable components. This study investigates the effectiveness of a novel electrochemical system for the simultaneous treatment of SAW and recovery of valuable products. A lab-scale four-chamber electrodialyzer, equipped with cation-exchange membranes and nickel bipolar electrodes, was designed and tested using real industrial wastewater. The wastewater was characterized by a pH of 13.06, chemical oxygen demand of 12,600 mg/L, and a sulfide content of approximately 5000 mg/L. The process leverages anodic oxidation to convert sulfide ions into elemental sulfur, while sodium cations migrate through cation-exchange membranes to the cathodic compartments. There, water reduction generates high-purity hydrogen (≥99.9%) and a concentrated, purified sodium hydroxide solution. The results demonstrate the ineffectiveness of electrodialysis with anion-exchange membranes due to rapid membrane degradation. In contrast, the proposed electrodialyzer with bipolar electrodes achieved excellent performance: a caustic soda solution with a concentration of 2.3–2.5% was recovered with a current efficiency of 83–85%, containing only trace amounts of sulfides (0.0052%) and organic impurities (0.053%). The process completely removed the original sulfide alkalinity. The study confirms the chemical and mechanical stability of the cation-exchange membranes under harsh SAW conditions. The proposed technology offers a path towards a closed-loop system in refineries by enabling the reuse of recovered caustic, utilization of hydrogen, and potential recovery of sulfur, aligning with the principles of green chemistry and circular economy.

Sulfide-alkaline wastewater (SAW) from petrochemical plants, particularly from pyrolysis and hydrotreating units, presents a significant environmental challenge due to its high toxicity, extreme alkalinity (pH > 12), and high concentrations of sulfides and organic pollutants. Traditional treatment methods like acid neutralization or air oxidation are often inefficient, generate secondary waste, or fail to recover valuable components. This study investigates the effectiveness of a novel electrochemical system for the simultaneous treatment of SAW and recovery of valuable products. A lab-scale four-chamber bipolar electrolyzer, equipped with cation-exchange membranes and nickel electrodes, was designed and tested using real industrial wastewater. The wastewater was characterized by a pH of 13.06, chemical oxygen demand of 12600 mg/l, and a sulfide content of approximately 5000 mg/l. The process leverages anodic oxidation to convert sulfide ions into elemental sulfur, while sodium cations migrate through cation-exchange membranes to the cathodic compartments. There, water reduction generates high-purity hydrogen (≥99.9%) and a concentrated, purified sodium hydroxide solution. The results demonstrate the ineffectiveness of conventional electrodialysis with anion-exchange membranes due to rapid membrane degradation. In contrast, the proposed bipolar membrane electrolysis achieved excellent performance: a caustic soda solution with a concentration of 2.3–2.5% was recovered with a current efficiency of 83–85%, containing only trace amounts of sulfides (0.0052%) and organic impurities (0.053%). The process completely removed the original sulfide alkalinity. The study confirms the chemical and mechanical stability of the cation-exchange membranes under harsh SAW conditions. The proposed technology offers a path towards a closed-loop system in refineries by enabling the reuse of recovered caustic, utilization of hydrogen, and potential recovery of sulfur, aligning with the principles of green chemistry and circular economy.

Forecasting electricity consumption is one of the most important scientific and practical tasks in the field of electric power engineering. The forecast accuracy directly impacts the operational efficiency of the entire power system and the performance of electricity markets. This paper proposes algorithms for source data preprocessing and tuning XGBoost models to obtain the most accurate forecast profiles. The initial data included hourly electricity consumption volumes and meteorological conditions in the power system of the Republic of Tatarstan for the period from 2013 to 2025. The novelty of the study lies in defining and justifying the optimal model training period and developing a new evaluation metric for assessing model efficiency—financial losses in Balancing Energy Market operations. It was shown that the optimal depth of the training dataset is 10 years. It was also demonstrated that the use of traditional metrics (MAE, MAPE, MSE, etc.) as loss functions during training does not always yield the most effective model for market conditions. The MAPE, MAE, and financial loss values for the most accurate model, evaluated on validation data from the first 5 months of 2025, were 1.411%, 38.487 MWh, and 16,726,062 RUR, respectively. Meanwhile, the metrics for the most commercially effective model were 1.464%, 39.912 MWh, and 15,961,596 RUR, respectively.

A solid oxide fuel cell (SOFC) is an electrochemical energy conversion device that provides higher thermoelectric efficiency than traditional cogeneration systems. Current research in this field highlights a variety of mathematical models. These models are based on complex physicochemical and electrochemical reactions, enabling accurate simulation and optimal control of fuel cells. However, these models require substantial computational resources, leading to high processing times. White box and gray box models are unable to achieve real-time optimization of control parameters. A potential solution involves using data-driven machine learning (ML) black-box models. This study examines three ML models: artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGB). The training dataset consisted of experimental results from SOFC laboratory experiments, comprising 32,843 records with 47 control parameters. The study evaluated the effectiveness of input matrix dimensionality reduction using the following feature importance evaluation methods: mean decrease in impurity (MDI), permutation importance (PI), principal component analysis (PCA), and Shapley additive explanations (SHAP). The application of ML models revealed a complex nonlinear relationship between the SOFC output voltage and the control parameters of the system. The default XGB model achieved the optimal balance between accuracy (MSE = 0.9940) and training speed (τ = 0.173 s/it), with performance capabilities that enable real-time enhancement of SOFC thermoelectric characteristics during system operation.

Currently, the key challenge of the oil-refining industry worldwide is to produce environmentally friendly fuel in large volumes to meet market demand, which is due to strict environmental standards governing the permissible sulfur content in fuel. Natural gas, refinery gas, and coal gas contain acid gases such as hydrogen sulfide and carbon dioxide. These compounds must be removed from the gas stream because of the toxicity of H₂S and to prevent the acid gas-induced corrosion of pipelines and facilities. Hydrogen sulfide is released as a result of various industrial processes, and its removal is critical because this compound can cause corrosion and environmental damage even at low concentrations. Sulfur compounds are also present in natural gas, biofuels and other fuel gases used in power plants. This article proposes new adsorbents of natural and waste origin and presents the results of their testing for the removal of acid gases. This paper also considers methods for the preparation of adsorbents from waste and procedures for the removal of sulfur-containing compounds. Using agricultural, industrial waste to produce activated sorbents not only solves the problem of waste disposal but also reduces the cost of desulfurization, contributing to the creation of sustainable and environmentally friendly technologies. The Review Section comprehensively summarizes current research on hydrogen sulfide removal in gas cleaning processes using agricultural and industrial waste as highly efficient adsorbents. In the Experimental Section, 10 composite materials based on natural raw materials and wastes, as well as 6 commercial adsorbents, were synthesized and tested under laboratory conditions. The choice of materials for the adsorbent production was based on the principles of environmental friendliness, availability, and cost-effectiveness. The developed materials based on modified sludge from water treatment plants of thermal power plants are effective sorbents for the purification of gas emissions from petrochemical enterprises. For industrial use, it is necessary to solve the problems of increasing the economic attractiveness of sorbents from waste, the ability of regeneration, the competitive adsorption of pollutants, the use of indicator sorbents, the optimization of operating conditions, and safe waste disposal.

Natural gas, refinery gas, and coal gas contain acid gases such as hydrogen sulfide and carbon dioxide. These compounds must be removed from the gas stream because of the toxicity of H2S and to prevent acid gas-induced corrosion of pipelines and facilities. Hydrogen sulfide is released as a result of various industrial processes, and its removal is critical because this compound can cause corrosion and environmental damage even at low concentrations. Sulfur compounds are also present in natural gas, biofuels and other fuel gases used in power plants. The article proposes new adsorbents and presents the results of their testing for the removal of acid gases. The review section comprehensively summarizes current research on hydrogen sulfide removal in gas cleaning processes using agricultural and industrial waste as highly efficient adsorbents. In the experimental section, 10 composite materials based on natural raw materials and wastes, as well as 6 commercial adsorbents, were synthesized and tested under laboratory conditions. The choice of materials for the adsorbents production was based on the principles of environmental friendliness, availability, and cost-effectiveness. The developed materials based on modified sludge from water treatment plants of thermal power plants are effective sorbents for the purification of gas emissions from petrochemical enterprises.

Fuel gas desulfurization is an important technological step toward achieving environmental neutrality in industrial production. The presence of sulfur compounds in fuel not only significantly increases the rate of corrosion processes, but also increases the amount of sulfur oxides in flue gases. Adsorption technologies are actively used to purify fuel. Materials that are mainly chosen as sorbents are determined by economic feasibility. These materials include bentonite clays. The paper presents an analysis of scientific works on the possible methods of using bentonite clays in industry. Due to its natural properties, this material has high adsorption properties. The authors present experimental laboratory studies to determine the adsorption efficiency of bentonite clay with a carbon substrate. Results on the adsorption capacity after regeneration of the composition are also presented. Based on the data obtained, the adsorption unit was calculated, and the process flow diagram was designed.

The correct prediction of heating network pipeline failure rates can increase the reliability of the heat supply to consumers in the cold season. However, due to the large number of factors affecting the corrosion of underground steel pipelines, it is difficult to achieve high prediction accuracy. The purpose of this study is to identify connections between the failure rate of heating network pipelines and factors not taken into account in traditional methods, such as residual pipeline wall thickness, soil corrosion activity, previous incidents on the pipeline section, flooding (traces of flooding) of the channel, and intersections with communications. To achieve this goal, the following machine learning algorithms were used: random forest, gradient boosting, support vector machines, and artificial neural networks (multilayer perceptron). The data were collected on incidents related to the breakdown of heating network pipelines in the cities of Kazan and Ulyanovsk. Based on these data, four intelligent models have been developed. The accuracy of the models was compared. The best result was obtained for the gradient boosting regression tree, as follows: MSE = 0.00719, MAE = 0.0682, and MAPE = 0.06069. The feature «Previous incidents on the pipeline section» was excluded from the training set as the least significant.