News from Azerbaijan Higher Technical Schools

In today’s complex, data-intensive business environment, organizations are increasingly relying on knowledge-based decision-making (KBDM) to achieve a competitive advantage. This article investigates how integrating systematic knowledge management processes into the decisionmaking framework enhances organizational performance. Using a mixed-methods approach, including comprehensive surveys, in-depth interviews, and document analysis, the study identifies key enablers of KBDM. These include a collaborative organizational culture, supportive and visionary leadership, and the deployment of advanced technological systems such as decision support systems and artificial intelligence. The findings reveal that organizations that effectively capture both explicit and tacit knowledge can make more informed, evidence-based decisions that improve innovation, operational efficiency, and strategic adaptability. Moreover, the research underscores the importance of aligning technology with human processes to prevent information overload and ensure that knowledge is shared and applied effectively. The practical implications of these findings suggest that firms must invest in building robust knowledge infrastructures and fostering a culture that rewards learning and continuous improvement. The study also outlines future research directions for developing quantitative metrics to evaluate decision quality and further exploring cross-cultural differences in knowledge management practices.

Evaluating the productivity of oil wells plays an important role in the effective management of oil production. This process is based on studying the technical and geological characteristics of wells, accurately determining the production potential and formulating effective exploitation strategies. This article analyzes traditional and modern approaches used to assess the productivity of oil wells. The main methods include hydrodynamic studies, geophysical measurements, model-based forecasting and approaches based on artificial intelligence technologies. The advantages and disadvantages of various approaches are compared and their effectiveness in terms of industrial application is examined. Within the framework of this study, the history of development of approaches to assessing the productivity of oil wells, existing methods and prospects are examined. Among the traditional methods, pressure-utility analyses, flow measurements and downhole pressure surveys occupy an important place. In addition, modern approaches include innovative technologies such as artificial intelligence, machine learning and big data analysis. The study shows that modern models and digital technologies allow for more accurate prediction of the production process. In particular, the application of artificial intelligence creates conditions for fast and effective processing of large volumes of data, which optimizes the operational decisionmaking process. At the same time, a comparative analysis of various methods allows determining their suitability in terms of industrial practice. Thus, while traditional methods are in some cases more stable and tested, innovative approaches provide faster and more adaptive results. The results of the study show that the optimal approach is formed using complex methods. For an accurate assessment of the productivity of oil wells, the application of multi-level analysis methods that take into account geological, technological and economic factors is recommended. This approach ensures the sustainable development of oil production, improves strategic planning and resource management. The integration of traditional and modern methods offers a comprehensive view of well productivity, enhancing the accuracy of assessments. Emerging technologies like real-time monitoring and predictive analytics are increasingly being adopted in field operations. These tools help identify underperforming wells quickly and enable timely interventions. Moreover, the growing availability of high-resolution subsurface data further strengthens model-based predictions. As the oil industry continues to digitize, collaboration between data scientists and petroleum engineers becomes crucial for maximizing the effectiveness of these productivity evaluation techniques.

In the digital age, the management of electronic documents is crucial for organizational efficiency. However, the increasing volume and complexity of electronic documents present challenges in terms of processing, organization, and retrieval. This article explores how machine learning algorithms can be leveraged to optimize electronic document workflows, improving efficiency and unlocking insights. From text classification to sentiment analysis, we delve into various applications of machine learning in document management and discuss real-world case studies and future trends.

Polymer impaction is one of the modern technologies widely used to enhance oil recovery. The aim of this method is to increase the recovery efficiency by adjusting the heterogeneity of the oil reservoir and the differences in oil-water mobility. Various types of polymers, especially high molecular weight polymers, improve the filtration and oil recovery coefficient by adjusting the viscosity under reservoir conditions. However, the correct selection of the optimal concentration of the polymer solution and application parameters is of critical importance. This study aimed to determine the optimal polymer amount to increase the efficiency of polymer impaction through laboratory tests and hydrodynamic simulation. Polymer solutions of different concentrations were prepared in laboratory conditions and their rheological properties under temperature and pressure conditions were investigated. During the experimental studies, the dynamic viscosity, filtration properties, distribution within the reservoir and the effect on phase balance of the polymer were studied. In addition, the adsorption and molecular interactions of the polymer on rocks with different mineralogical compositions were also investigated.
Experimental data were incorporated into hydrodynamic modeling to simulate the flow dynamics and filtration properties of the polymer solution in oil reservoir conditions. The modeling results showed that the optimal concentration of polymer impact allows for an increase in the recovery factor by 15-25%, reduces early production water release, and balances pressure differences between wells. In addition, uneven distribution and high adsorption of the polymer can cause blockages within the reservoir, reducing production efficiency. Therefore, the rheological and filtration parameters of the polymer should be carefully determined according to the reservoir conditions. As a result of the studies conducted, the effectiveness of different polymer concentrations was compared and the optimal amount of impact was determined according to the geological characteristics of the reservoir. It was found that maintaining the polymer concentration between 0,5-1,0 % both increases production efficiency and prevents blockages and adsorption within the reservoir. The results of this study provide new approaches to the effective use of the polymer impact method in oil recovery, creating practical application opportunities for reservoir engineers and oil production specialists.

This article substantiates the necessity of applying a systematic approach, taking into account the complex and multi-parameter nature of the oil field development process, and its practical significance is scientifically analyzed. In order to overcome the local and sectoral limitations of traditional approaches, the application of systems theory, complex systems management models and multi-dimensional decision-making algorithms in the oil industry is discussed.
The purpose of the study is to establish a flexible management structure that takes into account the interaction and dynamics of processes at each stage of oil field development, supported by information technologies and mathematical modeling tools. For this purpose, a multi-level analysis was conducted on the geological-structural structure of the field, reservoir properties, physico-chemical indicators of oil, production technology, exploitation systems and economicparametric indicators.
The article explains the main components of the systematic approach - structuring, functional analysis, feedback mechanisms and optimization algorithms - based on real field development examples. The results obtained show that the systematic approach provides the following advantages: more rational management of resources; increasing the technological and economic efficiency of production; detecting and preventing risks at an early stage; formulating an exploitation policy in accordance with the principles of preserving ecological balance and sustainable development.
The integrated decision-making platform presented by the authors is enriched with real-time monitoring and analysis of information flows, the application of predictive models working with artificial intelligence and machine learning algorithms. This approach promises effective results, especially for fields operating under conditions of high uncertainty.
As a result, the systematic approach model proposed in the article is considered an important scientific and methodological basis that can contribute not only to making optimal decisions during the exploitation of an oil field, but also to the development of energy strategies at the regional and national levels.
During the development of oil fields, many problems arise: reduced productivity, changes in reservoir pressure, increased water levels, as well as economic losses and environmental problems. These problems cannot be fully solved by traditional management methods. The application of a systems approach can provide innovative solutions for the analysis, management and elimination of these difficulties.
The systems approach is the principle of complex analysis and management of various processes, phenomena and objects. This approach is based on the study of the interrelationships and mechanisms of influence of the system as a whole, rather than individual aspects of a problem. The main goal of the systems approach is to analyze problems, make optimal decisions and develop appropriate strategies for the implementation of these decisions.
The study classified the main stages of field development based on systems theory, functional structuring and modeling methods, and identified the main decision-making criteria for each stage. At the same time, internal and external factors affecting the development process -including productivity dynamics, pressure regime, water pressure, maintenance regime and environmental risks - were assessed using an integrative approach.
The article shows that a systematic approach allows for more accurate and effective implementation of strategic planning, operational management and forecasting of results during the development of oil fields. This approach also contributes to the efficient use of energy resources, environmental protection and optimization of operating costs.
The application of a systematic approach to oil field development provides the following advantages: • Process optimization: Ensures efficient use of resources at each stage of field operation. • Complex problem solving: Enables effective solution of various problems by analyzing the interrelationships between geological, technological and economic factors. • Long-term planning: Develops optimal strategies to increase the field's operational life and ensure production stability. • Environmental sustainability: Minimizes environmental impacts and increases compliance of oil production with environmental standards. • Application of innovations: The quality and volume of production are increased by applying new technologies and digital solutions.

Drilling through depleted formations presents a complex set of geomechanical and operational challenges, including narrowed pressure margins, unpredictable fracture gradients, and heightened risks of fluid losses and borehole instability. Traditional drilling approaches frequently lack the flexibility needed to respond effectively to the dynamic and unpredictable downhole conditions that arise in depleted reservoirs—particularly in high-angle wells or formations characterized by interbedded or naturally fractured lithologies. In response to these limitations, Managed Pressure Drilling (MPD) has emerged as a key enabling technology, offering real-time control of bottomhole pressure through the application of surface backpressure and automated regulation systems.
This paper provides a comprehensive review of drilling operations in depleted formations, highlighting key geomechanical constraints and real-world case studies from fields such as Brent, South Louisiana, and New Mexico. Comparative analysis of drilling fluid losses with and without MPD demonstrates the significant impact of pressure management on operational efficiency. In addition, the paper explores how MPD integrates with intelligent data systems, including realtime monitoring, automated control algorithms, and digital twin models, to enhance safety and precision during drilling.
The study concludes that MPD is not merely a supplementary technology, but a foundational component of modern drilling strategy in depleted and high-risk environments. Its ability to reduce non-productive time, minimize formation damage, and adapt to complex reservoir conditions makes it essential for maximizing recovery and improving well performance.

The widespread presence of industrial facilities in Azerbaijan and the country’s geological and geographical conditions make the management of risks arising from the combination of technological and natural disasters (NATECH) a highly relevant issue. Within the framework of SEVESO directives, minimizing these risks, assessing potential hazards, and implementing economically efficient measures are among the key priorities in ensuring industrial safety.
This article identifies areas in Azerbaijan with high NATECH risks and examines the implementation of SEVESO measures aimed at mitigating these risks. Special attention is given to the oil and gas industry, chemical plants, oil refineries, port infrastructures, and other strategic facilities.
The study investigates the primary causes and potential impacts of NATECH incidents, analyzes methods for risk reduction through the application of SEVESO measures in local industrial facilities, and evaluates costs and benefits to ensure economic efficiency in risk management.
The results indicate that the effective implementation of preventive measures can be economically feasible for managing NATECH risks. Establishing automated monitoring systems, developing disaster-resistant infrastructures, and training personnel play a crucial role in risk mitigation.
The proposed measures not only help reduce potential human casualties and environmental consequences caused by technological and natural disasters but also enhance the resilience of industrial enterprises, minimizing economic losses.
This study provides recommendations for the advancement of SEVESO directive implementation and the development of improved approaches to NATECH risk management in Azerbaijan.

Geological and technical measures (GTM) play a key role in increasing hydrocarbon production by improving interaction with productive layers. These measures are aimed at increasing the permeability of the formation, optimizing the oil recovery factor (ORF), reducing water cut, and solving other technical problems that arise during the development and operation of oil fields. GTM includes both mechanical and chemical methods, aimed at effective management of formation properties during exploitation. The use of new technologies not only helps increase the hydrocarbon recovery factor but also reduces the risks associated with the degradation of formation quality. The article examines various types and classifications of GTM, focusing on their impact on well performance and field operation. It also discusses problems that arise in complex geological and technical conditions and the methods required to address them. An important aspect is taking into account the unique characteristics of each field, as geological conditions can vary significantly and require specialized approaches. The article also considers the causes of permeability deterioration in the near-wellbore zone and methods for restoring permeability, including chemical and mechanical treatments. The effectiveness of these methods is evaluated based on the reservoir characteristics, and recommendations are provided for selecting the most suitable solutions. It should be emphasized that successful implementation of GTM requires highly qualified specialists and the use of advanced technologies, which contribute to improving economic indicators and reducing production costs. Optimization of GTM allows operators to ensure long-term productivity of fields, efficient hydrocarbon extraction, and minimal environmental impact. Furthermore, the successful integration of GTM significantly increases the service life of fields, which is important for the long-term stability of production. It is important to note that the effectiveness of each method depends on a comprehensive approach and continuous monitoring of changes in the reservoirs. Therefore, the development and implementation of GTM require constant data analysis and adaptation to changing field conditions. Effective coordination of all stages of geological and technical measures contributes not only to increased production but also to reducing operational risks.

Təqdim edilən məqalədə IV Sənaye inqilabının əmək bazarına təsirləri nəzərdən keçirilmişdir. Bilindiyi kimi, IV sənaye inqilabı səbəbindən bir çox sahələrdə rəqəmsallaşmanı görmək olar. Bundan dolayı əmək bazarında tələb edilən əsas bilik və bacarıqlarda, işçilər və işəgötürənlərin tələbələrində tranformasiyanın araşdırılması, yeni yaranan peşə və bacarıqların müəyyən edilməsi məqalənin əsas hədəflərindəndir. Suni intellekt, internet, böyük data, artırılmış reallıq, ağıllı fabriklər, bulud texnologiyası, üçölçülü çap cihazları IV Sənaye inqilabı nəticəsində ortaya çıxmış və əmək bazarına əsaslı təsir etmişdir. Bu texnologiyaların tətbiqi əvvəlki dövrlərdə avtomatlaşma səbəbindən işsizliyin yaranmasından fərqli xüsusiyyət daşıyır. İnsan resursları mütəxəsiləri bu texnologiyadan istifadə edə bilən kadrlar axtarır. Digər tərəfdən bu texnologiyaların tədbiqi nəticəsində bir çox insan işsiz qalır. Onların vəziyyətinin necə olacağı ilə bağlı bir çox fikirlər irəli sürülmüşdür. “Sosial dövlət” mexanizmi bunlardan ən geniş yayılanlardan biridir

This article explores a novel digital driving game that leverages advanced artificial intelligence (AI) techniques to create a realistic, adaptive, and immersive simulation environment. By integrating state-of-the-art machine learning algorithms with high-fidelity simulation frameworks, the system offers both a captivating entertainment experience and a robust platform for driver training. The game simulates dynamic traffic conditions, weather effects, and realistic non-player behaviors through adaptive neural networks and reinforcement learning strategies. Our comprehensive framework emphasizes experiential learning by providing immediate performance feedback, enabling players to refine their driving skills in a risk‐free virtual environment. Extended case studies—including adaptive difficulty scaling, behavioral prediction, and multimodal feedback systems—demonstrate the game’s dual role as an entertainment medium and an educational tool. The findings suggest that AI-driven digital driving games can enhance user engagement, promote safer driving practi

The increasing complexity of urban transportation systems and the growing volume of vehicles have made traffic congestion a persistent challenge in modern cities. Efficient traffic flow prediction is essential for mitigating congestion, improving road safety, optimizing traffic signal control, and enhancing overall transportation efficiency. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the field of traffic management, offering sophisticated algorithms capable of modeling, analyzing, and predicting complex traffic patterns with high accuracy. The application of AI in traffic flow prediction leverages vast amounts of real-time and historical data to generate precise forecasts, supporting data-driven decision-making by urban planners and traffic control authorities.
The prediction of traffic flow involves analyzing time-series data that exhibit nonlinear, dynamic, and often stochastic behavior. Traditional statistical models, such as autoregressive integrated moving average (ARIMA), have proven to be limited in handling the high dimensionality and variability inherent in traffic systems. In contrast, AI algorithms possess the capacity to learn and adapt from complex data inputs without the need for explicit programming, making them particularly suitable for traffic-related applications.
AI algorithms used in traffic flow prediction can be broadly categorized into machine learning (ML) and deep learning (DL) approaches. Machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), decision trees, and random forests have demonstrated effectiveness in short-term traffic prediction tasks. These algorithms are capable of identifying hidden patterns in traffic data and adjusting to changes in traffic behavior over time. Ensemble methods, which combine the strengths of multiple learning models, further enhance prediction accuracy and robustness.
Deep learning algorithms, a subfield of AI inspired by the human brain’s neural architecture, have shown exceptional performance in capturing spatial-temporal dependencies in traffic data. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks and gated recurrent units (GRUs), are widely used for their ability to process sequential data and retain information over extended time intervals. Convolutional neural networks (CNNs) are employed to extract spatial features from traffic sensor data or road network imagery. Hybrid models that integrate CNNs with RNNs have achieved high levels of predictive precision by simultaneously learning spatial and temporal correlations.
In addition to supervised learning methods, unsupervised and reinforcement learning techniques are also applied in traffic flow prediction. Clustering algorithms, such as k-means and DBSCAN, assist in identifying traffic patterns, while reinforcement learning models optimize adaptive traffic signal control systems by learning optimal actions through environmental interaction.
This study explores the different types of AI algorithms used in traffic flow prediction, examining their theoretical foundations, structural differences, and practical applications. It aims to evaluate the comparative advantages of various algorithms in addressing the challenges of real-time traffic prediction in increasingly complex transportation networks.

The article investigates the results of geophysical and exploration studies carried out in the Absheron Archipelago. It presents an analysis of prospecting and exploration drilling activities. The distribution zones and lithofacies characteristics of the Productive Series sediments have been determined. The variation in sand content across most reservoir units of the Productive Series has been examined. Moreover, the regional and local tectonic features of uplifts located in the Absheron Archipelago, as well as structural maps and regional and local geological profiles based on seismic and exploration data, have also been analyzed. Information has been provided on the lithology and facies–cyclic structure of the Productive Series. Most of the structures located in the northern and northwestern parts of the Absheron Peninsula are considered low-potential areas for gas field exploration. The prospective areas are located in the central and southwestern parts of the Absheron Peninsula. The prospectivity of Oligocene-Miocene deposits in these areas is substantiated by the increase in sand content in the southwestern direction, in areas adjacent to the Gobustan region of Western Absheron, and by the industrially significant oil and gas flows obtained from the Binagadi, Sulutepe, Karakheybeyt, Shubani, and Garadag fields, which are located in the boundaries of the Baku, Guzdek, and Chuvadag anticlinal structures, known as the main hydrocarbon generation zones. In the Absheron oil and gas region, the boundary of the distribution of the main oil and gas object, the MQ deposits, can be determined based on the thickness maps constructed for the MQ and its lower subdivision deposits.The Balakhani formation in the Absheron Archipelago is represented by interlayered clay, aleurolite, and sandstone. The analysis of the paleogeographic conditions for the formation of the Balakhani formation in the Absheron Archipelago reveals the complex geological history of the region. The formation of the Balakhani formation in the Absheron Archipelago occurred in a complex paleohydrological environment. During this period, alternating marine and continental sedimentation conditions, accompanied by active tectonic processes and volcanism, took place in the region. By studying the geological development characteristics of the structures in the northwestern part of the Absheron Archipelago, their oil and gas prospectivity is clarified. Since these structures are located in the transition zone between the young platform and the Alpine geosyncline, the prospectivity of both the MQ deposits existing in the geosynclinal regime and the Mesozoic deposits deposited in the platform regime is substantiated in their section. In the uplifts of the northwestern part of the Absheron Archipelago, the MQ deposits transgressively lie on the underlying Miocene deposits, and to the west, the layers in its foot gradually become truncated. In such truncated layers, the presence of stratigraphic and lithological non-anticlinal reservoirs is possible.

Oil, gas, and gas-condensate extracted from offshore fields play a crucial role in Azerbaijan’s economy, making the enhancement of oil and gas production efficiency highly important. Therefore, increasing oil and gas recovery from fields is one of the key issues in field development. This article focuses on the determination of the optimal regime for gas and gascondensate wells to effectively manage production.
First, the technological regime selection methods used in practice are analyzed, and based on the field characteristics, the most suitable approach is identified. The results of studies conducted on production wells in the field are refined using computational methods to propose optimal parameters. Based on the known well parameters, an optimal technological regime is selected for each well individually. Additionally, based on the average values of the parameters, a generalized technological regime is proposed. Wells with similar and interconnected characteristics are grouped together. A new optimal regime is then determined for each group.
The removal of liquid and solid particles from the lower zone of gas-condensate wells depends primarily on the upward gas flow rate within the wellbore and the production regime of the well. Additionally, the physical properties of gas, liquid, and solid particles, as well as the well’s structural characteristics and the lift diameter, are determining factors for these parameters.
To ensure the effective removal of liquid and solid particles from the lower zone of a gascondensate well, it is necessary to consider the minimum gas flow rates and the lowest production levels at which the lift ceases to function. Thus, obtaining prior information about the operational wells is crucial. To effectively remove liquid and solid particles from the wellbore, the required minimum production rate and gas flow must be adjusted to the well's determined bottom-hole pressure.

В статье рассматриваются мезокайнозойские отложения Шамахино-Гобустанского и Абшеронского нефтегазоносных районов на основе данных глубокого бурения, геологических и литературных данных с точки зрения перспектив нефтегазоносности. При этом обосновывается нефтегазоносность этих отложений на основе особенностей развития структур и изменения литолого-фациальных характеристик пород. Помимо изучения особенностей развития литофаций исследуемого региона, была предоставлена подробная информация об общем геологическом строении территории, ее литологостратиграфических и структурно-тектонических характеристиках и их связи с перспективами нефтегазоносности. В статье получены определенные результаты по изучению литолого-фациальной характеристики мезокайнозойских отложений, распространенных на всей территории: Глинистость осадочного чехла увеличивается от северной зоны Шамахинско-Гобустанской впадины на юг, а песчанистость, то есть количество и мощность песчаных отложений, увеличивается по направлению к ЮгоВосточному Гобустану; Одним из основных факторов оценки перспектив нефтегазоносности северной зоны Шамахино-Гобустанской впадины является приобретение вторично-производных коллекторских свойств за счет высокой степени трещиноватости карбонатных пород; Палеогеографические условия, в которых формировалась осадочная толща Абшеронской впадины, позволяют оценить литологофациальный состав отложений здесь как благоприятный в плане нефтегазоносности.

Azerbaijan’s tourism industry, rich with cultural heritage and breathtaking landscapes, is increasingly leveraging digital platforms to attract a global audience. Instagram, with its visually driven interface, has emerged as a powerful tool for destination branding and engaging potential travelers. This study investigates the role of Instagram as a pivotal digital marketing tool in promoting Azerbaijan’s tourism industry. Drawing from a survey with tourists visiting the country, the research explores how Instagram’s visual storytelling and influencer partnerships shape travel decisions and perceptions of Azerbaijan as a destination. Findings reveal that while the platform effectively highlights the country’s cultural heritage and natural beauty, gaps remain in leveraging user-generated content and targeting diverse demographics. By addressing these challenges, Azerbaijan can enhance its digital marketing strategies to attract and engage a broader global audience. This paper contributes to the understanding of social media’s impact on tourism and offers actionable insights for destination marketers.

This study focuses on the adsorption of Cu(II) ions using a novel magnetic adsorbent, which was synthesized by modifying a cellulose/Fe₃O₄/SiO₂ nanocomposite with 4-aminoantipyrine. The influence of pH, contact time and initial metal ion concentration on the adsorption capacity of the product was systematically investigated. Maximum adsorption efficiency was observed at pH 5. Equilibrium concentrations of Cu(II) ions in solution was set by using 1-phenyl-2- [2-hydroxy-3-sulfo-5-nitrophenylazo] 1,3-butadione (R) as reagent. During the study, several kinetic models parameters were evaluated. The equilibrium data were found to fit well with the pseudo-secondorder model, indicating that this model effectively describes the adsorption process. To analyze the adsorption behavior, several isotherm models including Langmuir, Freundlich, Temkin, and Redlich–Peterson were employed. Among them, the Langmuir model provided the best fit to the experimental data, indicating monolayer adsorption. The maximum adsorption capacity of the adsorbent for Cu²⁺ ions was calculated to be 138,9 mg/g. The present study also encompassed desorption experiments, revealing that a 0.5 mol·L-1 HCl solution exhibited the highest efficiency for desorbing Cu (II) ions.

In this scientific article,fluidized bad and its hydrodynamic characteristics were examined in the industry.When stays, the solid particles are dependent. These dependent forms are dynamic fluid properties..sols have a dynamic fluid properties,and the flow of particles in different directions and reminds the whole layer of boiling bed. Fluidized beds are currently used in many commercial processes. There are a number of advantages for particular applications. For example, fluidized beds provide uniform heat distribution avoiding hot spots and hence providing better control over reactions. This attribute is of great importance when a reaction has a narrow operating temperature. Fluidized beds also find applications in heat exchangers because of the high rate of heat transfer between particles, gas and immersed surfaces. Fluidized beds also have a large surface area between particle and gas,and unlike a packed bed, may be relatively well mixed. Fluidized beds also offer better interaction between the solid and fluid phases. In fluidized beds, the particulate phase is able to move which adds into the mixing of phases and hence fluidized beds can sometimes be regarded as well mixed. This mixing coupled with the large thermal capacity of the particle phase enhances thermal transport inside the system and gives good heat transfer between the bed and its container. These aspects also help to maintain a homogeneous temperature field inside a fluidized bed. In addition, the pressure drop across the bed cannot exceed the weight of particles per unit bed area, which can reduce pumping costs.I can say from my personal scientific research experience that a hot layer is used in the only sulfuric acid production plant in Azerbaijan, in the production of acid. In modern contact method,applied to hot layer reactors working with V2O5 catalysts. It is possible to get a stable temperature and high contact surface to transform with oxygen.İs possible to get this with a fluidized beds(hot layer) regime.Catalyst particles is located in this layer.Catalyst particles are kept in boilling in the gas flow,inrease the response surface.The flow regimes describe the action of the substance within the reactor.Why is fluidized beds selected because of the heat is equally distributed it becomes good mix between gas and solis phase.Fluidized bed it is known that many industrial processes are used.

Equations and models that describe the hydrodynamics of gas-liquid two-phase flows in porous media are becoming increasingly crucial for predicting their primary behaviors within porous structures. These models are essential in understanding the flow dynamics in various applications, such as enhanced oil recovery, soil treatment, and reactor design. The focus of this research was to examine the effects of capillary forces, viscous forces, inertial forces, and flow configurations on the hydrodynamic features of a gas-liquid two-phase flow within a glass micromodel. By conducting experiments, results were gathered and then compared with predictions made by three different established models. The two models, Fundamental Forces Balance and Fluid-Fluid Interface models, did not accurately capture the experimental behavior, despite the fact that the Fundamental Forces Balance model incorporates particular flow pattern characteristics. These models were not able to fully describe that real-word behavior of gas-liquid two-phase flow, indicating that improvements are necessary. On the other hand, semi-empirical models, such as the Relative Permeability model, provide a better representation of the physical flow characteristics. One key advantage of semi-empirical models is that they can be adjusted to account for additional effects, such as varying flow configurations and interfacial interactions, which are not initially included in the basic theoretical models. Traditionally, relative permeabilities have been almost exclusively linked to saturation conditions in porous media. However, this research concluded that liquid relative permeability is not solely dependent on saturation levels. Instead, is also depends on flow patterns and the Capillary number. These findings highlight the importance of incorporating multiple factors when developing more accurate and reliable models for gas-liquid two-phase flow in porous media.

The global energy landscape is undergoing a significant transformation driven by the pressing need to address climate change, environmental degradation, and the unsustainable reliance on fossil fuels. As concerns about the environmental impacts of traditional energy sources grow, the transition to cleaner, more sustainable energy solutions has become an imperative for governments, businesses, and individuals worldwide. Green technologies—ranging from renewable energy systems such as wind, solar, and hydroelectric power to energy-efficient technologies, energy storage systems, and carbon capture methods—are central to this transition. These technologies offer the potential to reduce greenhouse gas emissions, enhance energy security, and promote economic sustainability by lowering operating costs and minimizing environmental harm.
One of the key drivers behind the adoption of green technologies is the recognition of their economic benefits. While the initial capital investment in green technologies may be substantial, their long-term economic efficiency often outweighs the upfront costs. Technologies such as solar photovoltaic systems, wind turbines, and advanced energy storage solutions offer substantial savings in energy consumption and operational costs over time. Moreover, energy efficiency measures, such as the use of high-efficiency appliances and building insulation, reduce the need for additional energy production and distribution infrastructure, thereby lowering both energy costs and capital expenditures. These technologies contribute to economic efficiency by optimizing resource use, reducing waste, and enabling more sustainable growth patterns.
Furthermore, the integration of green technologies in the energy sector has significant implications for job creation and industrial growth. As the demand for renewable energy infrastructure increases, new industries emerge, stimulating innovation and creating a diverse range of employment opportunities. Green technologies not only foster the growth of renewable energy industries but also support the development of related sectors, such as energy storage, grid modernization, and sustainable construction. This shift toward a green economy presents a unique opportunity for economic revitalization, especially in regions that are transitioning from fossil fuel dependence.
In addition to the economic advantages, green technologies play a critical role in improving energy security and mitigating price volatility. By reducing dependence on imported fossil fuels and diversifying the energy mix, renewable energy sources provide a more resilient energy supply. This diversification mitigates the risks associated with global energy market fluctuations, ensuring more stable energy prices and reducing vulnerability to supply disruptions. Furthermore, the ability to generate energy locally, through decentralized systems like microgrids and rooftop solar panels, empowers communities and enhances energy resilience, particularly in remote or underserved areas. By examining the intersection of environmental sustainability and economic viability, it becomes clear that the transition to greener energy solutions not only addresses urgent climate challenges but also offers substantial long-term economic benefits. As countries continue to invest in and adopt these technologies, they pave the way for a more sustainable, efficient, and economically prosperous future.

In the perspective development of Azerbaijan's oil and gas industry, the formation conditions and distribution regularities of radioactive areas in depression zones, which are distinguished by geological-geophysical studies, the deep structure of the Earth's crust, and the relationship between oil and gas content and gamma activity, were clarified by conducting the first radiometric survey in the Kura Depression (in the structures of Kürovdağ, Mişovdağ, and Qalmaz). In subsequent studies, it was determined that all known oil and gas structures coincide with negative gamma anomalies. It was also established that they all have an elongated oval shape, and the level of radioactivity there is 10-15 nSv/h lower compared to the natural background around the structure. Later, most of the structures were checked with other geophysical methods, and the connection between these anomalies and oil and gas content was confirmed. Furthermore, detailed studies were conducted to clarify the reasons for the formation of gamma anomalies in oil and gas fields. Comparison of the obtained materials with seismic data showed that within the defined general contour, separate local zones with low radioactivity are observed. The results of these studies revealed that the formation of gamma anomalies over oil and gas structures occurs due to geochemical and physicochemical processes related to the presence of hydrocarbon deposits in the Earth's crust. Oil fields are characterized by low gamma activity values coinciding with the field's contour. Outside the contour of the oil field, a zone with high gamma activity, creating a ring-shaped effect, is noted. In the case of multilayer hydrocarbon deposits, the anomalous effect corresponds to the middle boundary of the field's contour.
The morphological manifestation of gamma activity anomalies is influenced by the lithological composition of the geological rocks. As a result of these studies, the high efficiency of radiometric methods in both the study of the deep structure of the Earth and the search for hydrocarbon deposits has been established. Thus, radiometric methods can be used as direct methods for identifying oil and gas fields. Given the increased scientific and technical level of radiometric research, it is considered useful to reinterpret previously collected materials and conduct new research at a new scientific and technical level using gamma spectrom