https://jgt.irangi.org/ Journal of Gas Technology en daily 1 Mon, 22 Dec 2025 00:00:00 +0330 Mon, 22 Dec 2025 00:00:00 +0330 https://jgt.irangi.org/article_737827.html The dividing wall column (DWC) represents a form of process intensification that offers the potential to lower energy usage and initial investment costs in distillation practices. This research undertakes a systematic optimization and economic comparison between DWC and a traditional distillation column process for the separation of a three-component mixture of N-Pentane, N-Hexane, and N-Heptane. The analysis delves into the exergy flows and economic aspects of these two processes in detail. In this study the sequential quadratic programming (SQP) optimization method was used to minimize the total reboiler and condenser duties and the Peng Robinson model is applied to predict the vapor-liquid equilibrium (VLE) of the systems under study. In this study, DWC is evaluated against a conventional two-column distillation setup in terms of total annual cost (TAC) and energy consumption under the same feed ratio conditions. The results show that implementing DWC leads to a 37% decrease in TAC and a 38% reduction in energy requirements. This research proposes the use of dividing wall columns as an alternative to traditional distillation columns, providing a new method for separating hydrocarbons that emphasizes economic efficiency and encourages the adoption of innovative practices for energy conservation and environmental protection. https://jgt.irangi.org/article_737845.html Iran's industrial transition toward carbon emission reduction technologies, particularly Carbon Capture, Utilization, and Storage (CCUS) technology systems, has been considered in recent years a strategic axis for promoting environmental sustainability and enhancing the efficiency of energy-intensive industries. This study analyzed potential deployment patterns of CCUS technologies by examining three development approaches: gradual, accelerated, and Hybrid Scenarios (HS). The gradual scenario focuses on phased development and the reduction of initial risks; the accelerated scenario seeks the rapid deployment of CCUS infrastructure through extensive investment and policy support; and the HS attempts to provide a sustainable and manageable industrial process by balancing costs, risks, and reliability. In addition, this study examines the technical, economic, and policy challenges associated with CCUS implementation and proposes implementation strategies, including the formulation of supportive policies, the development of CO2 transport and storage infrastructure, technological capacity building, and the strengthening of public–private partnerships. To evaluate the different pathways, an AHP-based hierarchical model was used to analyze the performance of CCUS applications in power plants, industrial facilities, and refineries using six main indicators: cost, technological attractiveness, technological capability, cultural index, passive defense, and environmental criteria. The results show that the CCUS option achieves the highest scores across all three applications and represents the most efficient option for emission reduction and economic value creation. These findings reinforce the importance of the planned development of CCUS in the country’s energy landscape and highlight the need to establish support mechanisms for its commercialization and the expansion of industrial applications. https://jgt.irangi.org/article_737830.html Electrical Submersible Pumps (ESPs) play a critical role in enhancing production rates and operational efficiency in oil wells. However, their complex operating environments make them highly vulnerable to mechanical and electrical failures. Early detection of abnormal conditions and accurate prediction of pump performance are therefore essential for reducing downtime, minimizing operational costs, and extending pump lifespan. This study investigates the application of machine learning techniques for identifying unstable operating conditions, detecting anomalies, and predicting failures in ESP systems. Using simulated monitoring data including motor temperature, intake temperature, current, and vibration several preprocessing and analytical methods were employed. Boxplot analysis was used to identify outliers, while Z-score analysis provided statistical detection of abnormal data points. K-means clustering and Principal Component Analysis (PCA) were implemented to reduce dimensionality, enhance visualization, and classify pump operating states into stable, unstable, anomalous, and failure modes. Furthermore, a decision tree classifier was trained to determine the relative contribution of each parameter to pump failure. Model performance was evaluated using confusion matrices before and after applying PCA and clustering. Results show that incorporating these preprocessing techniques significantly improved classification accuracy from 0.9835 to 0.9967 for stable conditions and from 0.700 to 0.800 for failure conditions. The findings demonstrate the strong potential of machine learning methods for predictive maintenance of ESP systems and emphasize their value as a cost-effective strategy for enhancing reliability in the oil and gas industry. https://jgt.irangi.org/article_737843.html This paper examines the role of advanced nanomaterials —specifically quantum dots, metal nanoparticles, and graphene—in enhancing the efficiency and overall performance of modern photovoltaic systems. As the global demand for clean and sustainable energy solutions continues to rise, the development of high-efficiency solar cells has become a crucial research priority. Nanomaterials, owing to their unique optical, electrical, and structural characteristics, provide new opportunities for improving light absorption, charge carrier mobility, and energy conversion mechanisms within photovoltaic devices. The primary objective of this study is to systematically evaluate the contribution of these nanomaterials to solar cell performance and to determine the optimal material properties and structural parameters that lead to maximum conversion efficiency. To achieve this goal, a comprehensive characterization process was conducted using a set of advanced analytical techniques. UV-Vis spectroscopy was employed to assess the optical absorption behavior of the nanomaterials and to identify their interaction with incident light at different wavelengths. Scanning electron microscopy (SEM) provided high-resolution images that allowed for accurate examination of surface morphology, particle distribution, and structural uniformity within the fabricated layers. Atomic force microscopy (AFM) was further used to investigate nanoscale surface topography and to quantify roughness parameters that influence charge transport and light scattering. Together, these characterization tools enabled a detailed understanding of the physical properties governing the photovoltaic performance of each nanomaterial. Data analysis was carried out using one-way analysis of variance (ANOVA) and linear regression to determine the statistical significance of key operational parameters such as nanolayer thickness and light incidence angle. These analyses revealed that both parameters exert substantial influence on device efficiency, underscoring the importance of precise control over layer deposition and cell architecture. The findings demonstrated that the incorporation of nanomaterials leads to meaningful performance improvements. Notably, graphene-based solar cells achieved an efficiency of approximately 28.3 ± 0.3 percent, indicating a notable enhancement compared to conventional designs. Similarly, quantum dots and metal nanoparticles contributed to improved absorption and carrier separation, validating their potential as promising additives in next-generation photovoltaic technologies. Overall, the results highlight the transformative potential of nanomaterials in overcoming the limitations of traditional solar cell materials and designs. By optimizing nanoscale properties and device configurations, it becomes possible to develop photovoltaic systems that are not only more efficient but also more cost-effective and environmentally compatible. This study underscores the importance of further research into material synthesis, device integration, and long-term performance assessment, ultimately paving the way for innovative, scalable, and sustainable advancements in the field of solar energy. https://jgt.irangi.org/article_699814.html In Iran, the buildings built around the gas transmission pipelines must observe two points: first, the density of the buildings, and second, the distance from the axis of the pipeline. These values ​​are determined by standard tables IGS-C-SF-015. However, by only the two mentioned points cannot be calculated the level of risk caused by the threat of pipeline explosion. The best way to calculate the level of risk that threatens buildings around pipelines is to use computer calculations such as PHAST software to estimate the consequences of accidents and analyze the results based on actual accidents. Nevertheless, it should be noted that the PHAST software also cannot calculate the effects of soil in the explosion for burial pipes. Therefore, the simulation by PHAST for an actual explosion-exposed gas pipeline can be a basis for other evaluations. After determining the correct software model, the effectiveness of using equipment that can reduce the explosion's consequences is also investigated. In this paper, after logical modeling for the actual explosion, the effectiveness of a standard protective device in gas pipelines called Line Break Valve (LBV) for reducing the consequences of an explosion is measured. First, is calculated the probability of performance LBV occurred in the explosion; then, the diagrams are compared the consequences of the explosion for two states; correct operation and non-operation of LBVs.Finally, for the simulated mode, it is observed that the correct operation of the LBV system could reduce the accident consequences by more than 60%. https://jgt.irangi.org/article_737844.html Drilling wastewater generated during oil and gas operations presents a complex environmental challenge due to its highly variable composition, large volumes, and the presence of hazardous organic and inorganic contaminants. Conventional treatment approaches, although widely implemented, often fail to achieve consistent performance under dynamic operating conditions and increasingly stringent environmental regulations. This review provides a comprehensive and forward-looking analysis of drilling wastewater management by integrating treatment technologies with emerging concepts of circular economy, data-driven intelligence, and uncertainty quantification. The characteristics and sources of drilling wastewater are examined to highlight their inherent variability and associated treatment challenges. Conventional, advanced, and hybrid treatment technologies are critically evaluated in terms of efficiency, limitations, and applicability for water reuse and resource recovery. Circular economy strategies, including water recycling, material recovery, and zero liquid discharge (ZLD), are also assessed from a sustainability perspective. In addition, the role of machine learning in performance prediction, process optimization, and real-time monitoring is discussed, alongside uncertainty quantification approaches such as probabilistic modeling and Bayesian inference for risk-informed decision-making. From an industrial perspective, the integration of intelligent modeling and uncertainty-aware frameworks enables more reliable, adaptive, and efficient treatment systems. Despite these advancements, challenges remain in data availability, model transferability, system integration, and economic feasibility. This review establishes a unified framework that bridges environmental engineering, data science, and sustainability, providing a pathway toward intelligent, circular, and resilient drilling wastewater treatment systems. https://jgt.irangi.org/article_707201.html In some countries, decline in energy resources including fuel gas happenes in cold months of the year. In the other hand, the capacity of pipelines, which transfer the natural gas to the costumers, exhibits some limitations. Gas storage located near the consumption area could be considered as a practical solution to overcome to this limitation during the cold months.Determining the reservoir storage, injectivity capacities and the impact of pertinant petrophysical properties on the gas storage process is a key factor that should be performed prior to the field planning and operation.In this work, a full-field study is conducted on Underground Gas Storage (UGS) in one of the Iranian formations to identify the effect of controlling parameters on the process of gas storage. For this purpose, a commercial numerical reservoir simulator is used and the results and predictions are produced to demonstrate how the formation would react towards the gas injection process in an underground geological formation. https://jgt.irangi.org/article_707202.html In all gas plants,off-spec, useless and contaminated gases are sent to be burned in flare system through the flaring header. Khangiran Gas Treating Plant was designed by American Davy PowerGas Inc. Until 2011,all flashing gases wassent to flare system that was guessed to be 1 percent of its NG production. Environmental and economical concerns led to plan a conceptual design project on its recovery. A comprehensive design was raised to achieve a near zero-flaring condition. This plan consisted of three main steps.These steps will cause to achieve 50/25 and 25 percent of the final recovery goal, respectively and to reduce 167000 tons/ year CO2 emission.By implementing FGR project following results will be obtained: Recovery of significant amount of valuable gas and consume it as fuel gas Recover and increase sulphur production Reduction of CO2 and SO2 emissionRecovered gases at the first phase of this project were used in 4 SRU's incinerators. These incinerators use 7760 Sm3/Hr that would be the equivalent of 12200 Sm3/Hr recovered gas from the first phase of the project. By further sweetening the gases by phase two,these gases areused as second fuel in two boilers in our utility units.The field results show that by the full implementation of the FGR Project, green house gas reduction are about 4,000 metric tons of sulphur dioxide per year and saves about 20 percent of the refinery fuel gas. The statistical result shows that fuel gas reduction (also CO2 and SO2) emission will be decreased to 167200 tons/yearr and 4585 tons/year respectively.