Search Results (7)

The dehydrogenation of n-butane to butenes is a crucial process for producing valuable petrochemical intermediates. This study explores the role of oxyphilic metal promoters (Sn, Zn, and Ga) in enhancing the performance and stability of Pt@MFI catalysts for n-butane dehydrogenation. The presence of Sn in the catalyst inhibits the agglomeration of Pt clusters, maintaining their subnanometric particle size. PtSn@MFI exhibits superior stability and selectivity for butenes while suppressing cracking reactions, with selectivity for C1–C3 products as low as 2.1% at 550 °C compared to over 30.5% for Pt@MFI. Using a combination of high-angle annular dark-field scanning transmission electron microscopy, X-ray photoelectron spectroscopy, thermogravimetric analysis, and Raman spectroscopy, we examined the structural and electronic properties of the catalysts. Our findings reveal that Zn tends to consume hydroxyl groups and substitute framework sites, and Ga induces more defective sites in the zeolite structure. In contrast, the interaction between SnO_x and the zeolite framework does not depend on reactions with hydroxyl groups. The incorporation of Sn significantly prevents Pt particle agglomeration, maintaining smaller Pt particle sizes and reducing coke formation compared to Zn and Ga promoters. Theoretical calculations showed that Sn increases the positive charge on Pt clusters, enhancing their interaction with the zeolite framework and reducing sintering, albeit with a slight increase in the energy barrier for C-H activation. These results underscore the dual benefits of Sn as a promoter, offering enhanced structural stability and reduced coke formation, thus paving the way for the rational design of more effective and durable catalysts for alkane dehydrogenation and other high-value chemical processes. Full article

Petrochemical clusters are forms of industrialization that use compounds and polymers derived directly or indirectly from gas or crude oil for chemical applications. They pose a variety of short- and long-term risks to the environment and the people who live nearby. The aim of this study is to determine whether there is a correlation between the degree of perceived technological risk and the emotional value generated by the contemplation of the petrochemical industry landscape in order to try to establish strategic lines of action to mitigate the perception of risk and improve the emotional well-being of the population. This study uses manipulated pictures and a Public Participation Geographic Information System (PPGIS) survey to assess changes in perception and emotional response in residents in Teheran (Iran). Key findings show an insignificant relationship between technological risk and landscape value perception in both original and manipulated pictures. However, taking into account that, in general, in manipulated pictures, there is a more significant relationship, designing the landscape could help to mitigate the technological risk perception. This study contributes to the broader discussion about industrialization and its environmental and social consequences. It emphasizes the importance of considering public perception when planning and developing industrial areas, so as to balance industrial functionality and environmental and aesthetic considerations for long-term urban development. Full article

The intensity of the odor in food-grade paraffin waxes is a pivotal quality characteristic, with odor panel ratings currently serving as the primary criterion for its assessment. This study presents an innovative method for assessing odor intensity in food-grade paraffin waxes, employing headspace gas chromatography with mass spectrometry (HS/GC-MS) and integrating total ion spectra with advanced machine learning (ML) algorithms for enhanced detection and quantification. Optimization was conducted using Box–Behnken design and response surface methodology, ensuring precision with coefficients of variance below 9%. Analytical techniques, including hierarchical cluster analysis (HCA) and principal component analysis (PCA), efficiently categorized samples by odor intensity. The Gaussian support vector machine (SVM), random forest, partial least squares regression, and support vector regression (SVR) algorithms were evaluated for their efficacy in odor grade classification and quantification. Gaussian SVM emerged as superior in classification tasks, achieving 100% accuracy, while Gaussian SVR excelled in quantifying odor levels, with a coefficient of determination (R²) of 0.9667 and a root mean square error (RMSE) of 6.789. This approach offers a fast, reliable, robust, objective, and reproducible alternative to the current ASTM sensory panel assessments, leveraging the analytical capabilities of HS-GC/MS and the predictive power of ML for quality control in the petrochemical sector’s food-grade paraffin waxes. Full article

To detect low concentration of CH₄ is indeed meaningful in industrial manufacturing, such as the petrochemical industry and natural gas catalysis, but it is not easy to detect low concentration of CH₄ due to its high symmetrical and stable structure. In this work, defect-rich ZnO_1−x nanoflowers (NFs) were synthesized by a two-step route so as to obtain defect-enhanced gas-sensing performance, namely hydrothermal synthesis followed by H₂ treatment. In order to achieve low-concentration detection of CH₄, the ultra-thin Pd clusters’ (Cs, diameter about 1–2 nm) sensitizer was synthesized and decorated onto the surface of ZnO_1−x NFs. It is found that Pd Cs-2/ZnO_1−x gas sensors show enhanced gas-sensing properties to CH₄, even at ppm concentration level. At its optimal working temperature of 260 °C, the gas response to 50 ppm CH₄ can reach 5.0 with good gas selectivity; the response and recovery time is only 16.2 and 13.8 s, respectively. In the Results, we discussed the CH₄ gas-sensing mechanism deeply. Overall, it is very necessary to detect low-concentration methane safely. It is possible for further safe detection of low-concentration methane gas in the future. Full article

The problem of staffing the gas and petrochemical cluster is acutely raised in all oil-producing states. This article’s purpose is to study program-targeted and problem-oriented approaches to forming infrastructure provision for personnel needs in Iran’s gas and petrochemical cluster. Their peculiarity is that they belong to natural monopolies characterized by a high level of capital concentration. In this study, two approaches were identified to form infrastructure provision for the needs of personnel in the cluster. The first approach, program-targeted, relies on developing programs to overcome the lack of qualified specialists. The second approach, problem-oriented, considers the causes of the problem itself and the ways to prevent it. Based on the results of the study, several conclusions can be drawn. First, the traditional understanding of human resources infrastructure is insufficient to develop Iran’s gas and petrochemical cluster (GPC). Secondly, for the successful development of social production, it is necessary to adequately develop infrastructure, the technical and economic justification of all processes, and to focus the entire industry on endogenous factors of scientific, technical, and socio-economic progress. Finally, the most critical issue in the system is the issue of staffing each stage with employees of mass professions, engineering, and scientific personnel, specialists in the field of economics, organization, and management, and executives of various levels. Full article

The existence of external two-fold pressure regarding competitiveness and sustainable development in a capital-intensive industry supports the need for sustainable performance. However, endeavors to create a sustainable framework to measure the performance of the oil and gas (O&G) industry are mostly devoted to the production and supply chain of petrochemical products and rarely focus on a maintenance perspective. Motivated by such scarcity, the goal of this research was to discuss and articulate the performance assessment framework by integrating concepts of maintenance and sustainability in the O&G industry. This study proposed the use of a range of performance measures for assessing sustainability on offshore production and drilling platforms. The conceptual framework consists of four aspects of sustainability categorized into technical, environmental, social, and economic dimensions. Each measure was assigned according to its relevance at the strategic, tactical, and functional levels of maintenance decision making. The conceptual framework resulted in hierarchical clusters of twelve strategic indicators. These indicators consist of conventional measures as well as new ones relating to the safety and reliability on offshore platforms. The potential contribution of the present study is found in its intention to empower a better understanding of sustainable maintenance and encourage those making decisions about practical implementation within the O&G industry. This paper culminates with directions for future studies. Full article

Heavy industrial burning contributes significantly to the greenhouse gas (GHG) emissions. It is responsible for almost one-quarter of the global energy-related CO₂ emissions and its share continues to grow. Mostly, those industrial emissions are accompanied by a great deal of high-temperature heat emissions from the combustion of carbon-based fuels by steel, petrochemical, or cement plants. Fortunately, these industrial heat emission sources treated as thermal anomalies can be detected by satellite-borne sensors in a quantitive way. However, most of the dominant remote sensing-based fire detection methods barely work well for heavy industrial heat source discernment. Although the object-oriented approach, especially the data clustering-based approach, has guided a novel method of detection, it is still limited by the costly computation and storage resources. Furthermore, when scaling to a national, or even global, long time-series detection, it is greatly challenged by the tremendous computation introduced by the incredible large-scale data clustering of tens of millions of high-dimensional fire data points. Therefore, we proposed an improved parallel identification method with geocoded, task-tree-based, large-scale clustering for the spatial-temporal distribution analysis of industrial heat emitters across the United States from long time-series active Visible Infrared Imaging Radiometer Suite (VIIRS) data. A recursive k-means clustering method is introduced to gradually segment and cluster industrial heat objects. Furthermore, in order to avoid the blindness caused by random cluster center initialization, the time series VIIRS hotspots data are spatially pre-grouped into GeoSOT-encoded grid tasks which are also treated as initial clustering objects. In addition, some grouped parallel clustering strategy together with geocoding-aware task tree scheduling is adopted to sufficiently exploit parallelism and performance optimization. Then, the spatial-temporal distribution pattern and its changing trend of industrial heat emitters across the United States are analyzed with the identified industrial heat sources. Eventually, the performance experiment also demonstrated the efficiency and encouraging scalability of this approach. Full article