Hasil untuk "Gas industry"

Andreas Mershin, Nikolas Stefanou, Adan Rotteveel et al.

Machine olfaction is rapidly emerging as a transformative capability, with applications spanning non-invasive medical diagnostics, industrial monitoring, agriculture, and security and defense. Recent advances in stabilizing mammalian olfactory receptors and integrating them into biophotonic and bioelectronic systems have enabled detection at near single-molecule resolution thus placing machines on par with trained detection dogs. As this technology converges with multimodal AI and distributed sensor networks imbued with embedded AI, it introduces a new, biochemical layer to a sensing ecosystem currently dominated by machine vision and audition. This review and industry roadmap surveys the scientific foundations, technological frontiers, and strategic applications of machine olfaction making the case that we are currently witnessing the rise of a new industry that brings with it a global chemosensory infrastructure. We cover exemplary industrial, military and consumer applications and address some of the ethical and legal concerns arising. We find that machine olfaction is poised to bring forth a planet-wide molecular awareness tech layer with the potential of spawning vast emerging markets in health, security, and environmental sensing via scent.

Xiaoyu Li, Xiao Li, Li Gao et al.

The evolution of Large Language Models (LLMs) has significantly advanced multi-turn conversation systems, emphasizing the need for proactive guidance to enhance users' interactions. However, these systems face challenges in dynamically adapting to shifts in users' goals and maintaining low latency for real-time interactions. In the Baidu Search AI assistant, an industrial-scale multi-turn search system, we propose a novel two-phase framework to provide proactive guidance. The first phase, Goal-adaptive Supervised Fine-Tuning (G-SFT), employs a goal adaptation agent that dynamically adapts to user goal shifts and provides goal-relevant contextual information. G-SFT also incorporates scalable knowledge transfer to distill insights from LLMs into a lightweight model for real-time interaction. The second phase, Click-oriented Reinforcement Learning (C-RL), adopts a generate-rank paradigm, systematically constructs preference pairs from user click signals, and proactively improves click-through rates through more engaging guidance. This dual-phase architecture achieves complementary objectives: G-SFT ensures accurate goal tracking, while C-RL optimizes interaction quality through click signal-driven reinforcement learning. Extensive experiments demonstrate that our framework achieves 86.10% accuracy in offline evaluation (+23.95% over baseline) and 25.28% CTR in online deployment (149.06% relative improvement), while reducing inference latency by 69.55% through scalable knowledge distillation.

Jiasong CHEN, Xuefeng BAI, Guijiu WANG et al.

ObjectiveGiven that most salt rock strata in China consist of thin-layered salt formations, conventional single-well and single-cavity construction technologies are no longer adequate for the efficient construction of large-size salt cavities. In this context, the application of horizontal multi-step cavity-building technology for salt cavern gas storage can enhance the construction of salt cavities with expanded volumes in salt rock strata of limited thickness. MethodsThis study explored the influence of key parameters on the final shapes of cavities created through the horizontal multi-step cavity-building approach and analyzed both the cavity shape and the construction process from an engineering perspective, thus presenting recommended values for these key parameters. A physical simulation experimental setup was designed to examine cavity expansion patterns during horizontal multi-step cavity construction. Subsequent experiments incorporated various cavity-building parameters to generate horizontal cavities of different shapes. Finally, 3D scanning technology was employed to create complete 3D cavity models based on the cavities obtained from the experiments through mirroring operations. ResultsThe following results were derived from analyzing these 3D cavity models corresponding to various cavity-building parameters. For cavities with equal volumes, variations in water injection flow rates had a significant influence on their height, length, and maximum width. Tubing withdrawal distances had a major impact on the shape of the cavity roofs, while their effect on the overall size of the cavities was relatively minor. Additionally, the air cushion used during cavity construction to protect the roofs resulted in “flat top” shapes, which not only affected the stability of the cavities but also increased the economic costs for cavity construction. ConclusionWater injection rates ranging from 160 m3/h to 240 m3/h are considered rational for horizontal multi-step cavity building. It is recommended to use small tubing withdrawal distances. Additionally, continuous injection of dissolution inhibitors during construction for cavity roof protection is not advised. The research results offer valuable references and guidance for shape design and process parameter optimization of cavities using the horizontal multi-step construction approach for salt cavern gas storage.

Mohamed Imam, Alesandros Glaros, Cheney Chen et al.

This paper explores the potential of Building-Integrated Agriculture (BIA) as a strategy to align urban agriculture systems with building lifecycle sustainability goals. BIA systems such as indoor vertical farms, rooftop greenhouses, and soil-based urban farms promise to bolster urban food security and resource circularity. However, their environmental impacts can be further optimized via integration with building resources and strategic design, which requires a standardized framework for evaluating life-cycle metrics. This study develops a cross-industry Life Cycle Assessment (LCA) framework that harmonizes agricultural and building performance indicators, using carbon as a unifying metric to evaluate operational and embodied impacts. The research combines a meta-analysis of existing LCA studies, detailed case study evaluations, and novel paired metrics to quantify energy use, water use, and greenhouse gas emissions within a case study. Key findings identify operational carbon hotspots, infrastructure inefficiencies, and embodied carbon challenges while highlighting opportunities for integrating resource recovery strategies, such as greywater reuse and waste heat recovery. The results reveal trade-offs between productivity and environmental impact, with vertical farms demonstrating high yields but significant energy intensity, while soil-based systems excel in resource efficiency but exhibit lower output. This work introduces a structured methodology for cross-industry data integration and offers actionable insights for designers, growers and developers. By redefining system boundaries and incorporating reciprocal benefits between BIA and host buildings, this framework provides a pathway toward more sustainable urban agricultural practices and resilient urban ecosystems.

Paula Fraga-Lamas, Tiago M Fernandez-Carames, Oscar Blanco-Novoa et al.

Shipbuilding companies are upgrading their inner workings in order to create Shipyards 4.0, where the principles of Industry 4.0 are paving the way to further digitalized and optimized processes in an integrated network. Among the different Industry 4.0 technologies, this article focuses on Augmented Reality, whose application in the industrial field has led to the concept of Industrial Augmented Reality (IAR). This article first describes the basics of IAR and then carries out a thorough analysis of the latest IAR systems for industrial and shipbuilding applications. Then, in order to build a practical IAR system for shipyard workers, the main hardware and software solutions are compared. Finally, as a conclusion after reviewing all the aspects related to IAR for shipbuilding, it is proposed an IAR system architecture that combines Cloudlets and Fog Computing, which reduce latency response and accelerate rendering tasks while offloading compute intensive tasks from the Cloud.

Idoia Berges, Víctor Julio Ramírez-Durán, Arantza Illarramendi

The growing trends in automation, Internet of Things, big data and cloud computing technologies have led to the fourth industrial revolution (Industry 4.0), where it is possible to visualize and identify patterns and insights, which results in a better understanding of the data and can improve the manufacturing process. However, many times, the task of data exploration results difficult for manufacturing experts because they might be interested in analyzing also data that does not appear in pre-designed visualizations and therefore they must be assisted by Information Technology experts. In this paper, we present a proposal materialized in a semantic-based visual query system developed for a real Industry 4.0 scenario that allows domain experts to explore and visualize data in a friendly way. The main novelty of the system is the combined use that it makes of captured data that are semantically annotated first, and a 2D customized digital representation of a machine that is also linked with semantic descriptions. Those descriptions are expressed using terms of an ontology, where, among others, the sensors that are used to capture indicators about the performance of a machine that belongs to a Industry 4.0 scenario have been modeled. Moreover, this semantic description allows to: formulate queries at a higher level of abstraction, provide customized graphical visualizations of the results based on the format and nature of the data, and download enriched data enabling further types of analysis.

Víctor Julio Ramírez-Durán, Idoia Berges, Arantza Illarramendi

Many different worldwide initiatives are promoting the transformation from machine dominant manufacturing to digital manufacturing. Thus, to achieve a successful transformation to Industry 4.0 standard, manufacturing enterprises are required to implement a clear roadmap. However, Small and Medium Manufacturing Enterprises (SMEs) encounter many barriers and difficulties (economical, technical, cultural, etc.) in the implementation of Industry 4.0. Although several works deal with the incorporation of Industry 4.0 technologies in the area of the product and supply chain life cycles, which SMEs could use as reference, this is not the case for the customer life cycle. Thus, we present two contributions that can help the software engineers of those SMEs to incorporate Industry 4.0 technologies in the context of the customer life cycle. The first contribution is a methodology that can help those software engineers in the task of creating new software services, aligned with Industry 4.0, that allow to change how customers interact with enterprises and the experiences they have while interacting with them. The methodology details a set of stages that are divided into phases which in turn are made up of activities. It places special emphasis on the incorporation of semantics descriptions and 3D visualization in the implementation of those new services. The second contribution is a system developed for a real manufacturing scenario, using the proposed methodology, which allows to observe the possibilities that this kind of systems can offer to SMEs in two phases of the customer life cycle: Discover & Shop, and Use & Service.

K. Das, N. Gonzalez Szwacki, K. Gas et al.

Accurate modelling of magnetism is pivotal for elucidating the microscopic origins of magnetic phenomena in functional materials. However, for a specified class of materials, such as random dilute ferromagnets or alloys, the reliance on simplifying assumptions, such as single-ion anisotropy, limits the accuracy of existing spin models. In such systems, there is a significant probability of the formation of nearest-neighbor magnetic ion pairs or higher order clusters, whose presence breaks the local symmetry of otherwise isolated magnetic species. Here, we introduce the concept of pair-induced uniaxial anisotropy and demonstrate how nearby atoms influence each other's anisotropic behavior. This effect is investigated in the dilute magnetic semiconductor Ga$_{1-x}$Mn$_x$N, by means of density functional theory calculations. The inclusion of pair anisotropy in the atomistic spin simulations significantly improves the agreement between simulated and experimental magnetization curves, in contrast to models that consider only single-ion anisotropy.

XUE Gang,GUO Tao,ZHANG Ye,XU Xiangyang,WANG Wei,HAN Kening,GUO Dongxin,JIN Xiaobo

The exploration and development of coalbed methane（CBM）in the Permian Longtan Formation in south Chongqing is in the initial stage. In order to reveal the general geological conditions of coalbed methane in the coal seam C25, by the experimental and geological data obtained from the coal mines and coalbed methane drilling, the geological characteristics such as coal rock and coal quality, coal pore penetration and gas content have been analyzed. The results show that the C25 coalbed methane of Longtan Formation of Permian in the study area is characterized by “stable development, relatively large thickness, low pore permeability, high metamorphism and high gas content”. The development of the coal seam C25 is stable throughout the whole area, showing the trend of “thick in the north and thin in the south”, and the thickness in the northern part of the coalbed is generally larger than 1.5 m. The coal quality belongs to semi-bright coal. The content of vitrinite in the organic component is 51.7%~72.2%; the vitrinite reflectance ranges from 1.8%~2.2%, and the metamorphism degree is high-over maturity. The porosity and permeability of the coal rock are relatively low, with the porosity ranging from 3.46%~8.46% and the permeability of mostly lower than 0.01×10-3 μm2. The gas content of the coal bed is high, generally more than 10.0 m3/t; meanwhile, the top and bottom plates of the coal bed are good sealing layers. Based on the production of Q1 and Y2, it is believed that the coal seam C25 of Permian Longtan Formation in south Chongqing area has good geological conditions for CBM exploration and development.

Dong Hongchuan, Gu Ye, Zhang Jihong

Grid emission factor has become the most critical variable in carbon market quota allocation and carbon footprint accounting of international trade products. Different grid emission factors will lead to great differences in carbon emissions of enterprises or products. It is found that the temporal resolution, spatial resolution and energy distribution of cogeneration have great influence on the emission factors of power grid. This paper uses the data of China’s power industry to carry out empirical analysis. For each dimension, we set different choices and explore their specific impacts. It is found that the different choices of the three dimensions will cause 5.01%, 20.59%, 4.38% differences in the calculation results of grid emission factors. Finally, based on the research results, this paper puts forward suggestions from three aspects: authoritative guidance, data accuracy and international communication, in order to provide reference for Scope 2 greenhouse gas emissions accounting and reporting.

Wei Xia, Zhijie Zhou, Liangzheng Sheng et al.

Abstract The separation of fluorinated propane/propylene mixtures remains a major challenge in the electronics industry. Inspired by biological ion channels with negatively charged inner walls that allow selective transport of cations, we presented a series of formic acid-based metal-organic frameworks (MFA) featuring biomimetic multi-hydrogen confined cavities. These MFA materials, especially the cobalt formate (CoFA), exhibit specific recognition of hexafluoropropylene (C3F6) while facilitating size exclusion of perfluoropropane (C3F8). The dual-functional adsorbent offers multiple binding sites to realize intelligent selective recognition of C3F6, as supported by theoretical calculations and in situ spectroscopic experiments. Mixed-gas breakthrough experiments validate the capability of CoFA to produce high-purity (>5 N) C3F8 in a single step. Importantly, the stability and cost-effective scalable synthesis of CoFA underscore its extraordinary potential for industrial C3F6/C3F8 separations. This bioinspired molecular recognition approach opens new avenues for the efficient purification of fluorinated electronic specialty gases.

Sonia Dell’Aversano, Carlo Villante, Katia Gallucci et al.

E-fuels represent a crucial technology for transitioning to fossil-free energy systems, driven by the need to eliminate dependence on fossil fuels, which are major environmental pollutants. This study investigates the production of carbon-neutral synthetic fuels, focusing on e-hydrogen (e-H₂) generated from water electrolysis using renewable electricity and carbon dioxide (CO₂) captured from industrial sites or the air (CCUS, DAC). E-H₂ can be converted into various e-fuels (e-methane, e-methanol, e-DME/OME, e-diesel/kerosene/gasoline) or combined with nitrogen to produce e-ammonia. These e-fuels serve as efficient energy carriers that can be stored, transported, and utilized across different energy sectors, including transportation and industry. The first objective is to establish a clear framework encompassing the required feedstocks and production technologies, such as water electrolysis, carbon capture, and nitrogen production techniques, followed by an analysis of e-fuel synthesis technologies. The second objective is to evaluate these technologies’ technological maturity and sustainability, comparing energy conversion efficiency and greenhouse gas emissions with their electric counterparts. The sustainability of e-fuels hinges on using renewable electricity. Challenges and future prospects of an energy system based on e-fuels are discussed, aiming to inform the debate on e-fuels’ role in reducing fossil fuel dependency.

Farad Kamyabi, Arif Mammad-Zade

Enhancing oil recovery from clayey reservoirs is a significant challenge in petroleum industry due to complex interactions between fluids and rock surfaces, particularly clay swelling. This study presents the first empirical analysis of magnetic fields' impact on fluid flow in clayey porous media. Our core findings indicate that magnetic treatment of water increases oil recovery by an average of 15–30% in clayey media, with limited effectiveness in pure quartz media. Detailed experiments unraveled that improved recovery factor by magnetic treatment stem from both mitigated swelling and altered magnetic properties at clay surface; introducing 30% clay to porous medium decreased the recovery by 32% compared to pure quartz sand. Heating the clay to around 1000 °C to reduce its swelling property improved the recovery by only 16%, suggesting magnetic treatment is not solely attributed to clay swelling mitigation. Treating ferromagnetic films at clay surface with HCl to produce non-magnetic FeCl3 resulted in a high recovery factor, similar to the clay-free medium. Moreover, it was determined that a magnetic field intensity of 43760–51740 A/m is optimal for fluid displacement in clayey media. Notably, the intensity of 47760 A/m increased recovery to 84.5% in a 30% clay medium, compared to 49.7% without treatment. Interestingly, it was observed that the maximum flow rate was associated with zero potential difference across the medium, providing a faster method to determine the optimum magnetic field intensity. Lastly, the concept of ‘Magnetic memory’ was investigated, referring to the persistence of magnetic field's influence after its removal. Our findings indicated that pressure build-up time stability lasted 10 days post-treatment, after which water behavior reverts, and clay swelling resumes. This insight into the temporal dynamics of magnetic field application provides a deeper understanding of its long-term impacts on fluid flow in clayey reservoirs.

Yuanyuan Tian, Yuanyuan Tian, Qing Chen et al.

As fluid flow paths in fractural-cavity carbonate reservoirs, fractures have a significant impact on the production performance of carbonate reservoirs. In particular, well production depends on the apertures of the fractures, which vary with the effective stress acting on the fractures. Thus, predicting the fracture closure pressure is crucial for carbonate reservoir development. In our research, fracture closure pressures are derived using the Zienkiewicz–Pande failure criterion, which defines the pressure at which most asperities come into contact. The results reveal that fracture closure is influenced by the geo-stress field, rock mechanics, and spatial location of the fracture. Ultimately, the fracture closure pressure of typical wells located in different tectonic zones in the Shunbei Oilfield is calculated, and the results indicate that the fracture closure pressure in the Shunbei Oilfield is significantly affected by the dip of fractures and the angle between the fracture strike and maximum principal stress. To demonstrate the accuracy of the estimated fracture closure pressure, production performance corresponding to fracture closure was evaluated. It reveals that the flowing bottom pressure decreases rapidly and the recoverable oil reserves reduce when the pressure approaches the fracture closure pressure. This observation verifies that the fracture closure pressure determined using our formula is a feasible predictor of the production performance of fractural-cavity carbonate reservoirs.

Ge TANG, Xinyu ZHAO, Yuxiang WANG et al.

As an important branch of computed tomography (CT), industrial CT is used widely in many fields, such as aerospace, military industry, and geological analysis fields, because of its advantages of high resolution, repeatability, and wide detection range. On the basis of thorough investigation and study, this paper summarizes three typical industrial CT technologies (i.e., seismic wave CT, resistivity CT, and electromagnetic wave CT) as well as the comprehensive geophysical exploration methods used in the geosciences. The current applications of industrial CT in pore structure studies, gas hydrate studies, digital core construction, and geological utilization and storage of carbon dioxide are introduced. The development trend of industrial CT in the geosciences is also discussed.

Alireza Dehlaghi-Ghadim, Ali Balador, Mahshid Helali Moghadam et al.

With the advent of smart industry, Industrial Control Systems (ICS) are increasingly using Cloud, IoT, and other services to meet Industry 4.0 targets. The connectivity inherent in these services exposes such systems to increased cybersecurity risks. To protect ICSs against cyberattacks, intrusion detection systems and intrusion prevention systems empowered by machine learning are used to detect abnormal behavior of the systems. Operational ICSs are not safe environments to research intrusion detection systems due to the possibility of catastrophic risks. Therefore, realistic ICS testbeds enable researchers to analyze and validate their intrusion detection algorithms in a controlled environment. Although various ICS testbeds have been developed, researchers' access to a low-cost, adaptable, and customizable testbed that can accurately simulate industrial control systems and suits security research is still an important issue. In this paper, we present ICSSIM, a framework for building customized virtual ICS security testbeds, in which various types of cyber threats and attacks can be effectively and efficiently investigated. This framework contains base classes to simulate control system components and communications. ICSSIM aims to produce extendable, versatile, reproducible, low-cost, and comprehensive ICS testbeds with realistic details and high fidelity. ICSSIM is built on top of the Docker container technology, which provides realistic network emulation and runs ICS components on isolated private operating system kernels. ICSSIM reduces the time for developing ICS components and offers physical process modelling using software and hardware in the loop simulation. We demonstrated ICSSIM by creating a testbed and validating its functionality by showing how different cyberattacks can be applied.

Abubakar Sadiq Mohammed, Philipp Reinecke, Pete Burnap et al.

The offshore oil and gas industry has recently been going through a digitalisation drive, with use of `smart' equipment using technologies like the Industrial Internet of Things (IIoT) and Industrial Cyber-Physical Systems (ICPS). There has also been a corresponding increase in cyber attacks targeted at oil and gas companies. Oil production offshore is usually in remote locations, requiring remote access and control. This is achieved by integrating ICPS, Supervisory, Control and Data Acquisition (SCADA) systems, and IIoT technologies. A successful cyber attack against an oil and gas offshore asset could have a devastating impact on the environment, marine ecosystem and safety of personnel. Any disruption to the world's supply of oil and gas (O\&G) can also have an effect on oil prices and in turn, the global economy. This makes it important to secure the industry against cyber threats. We describe the potential cyberattack surface within the oil and gas industry, discussing emerging trends in the offshore sub-sector, and provide a timeline of known cyberattacks. We also present a case study of a subsea control system architecture typically used in offshore oil and gas operations and highlight potential vulnerabilities affecting the components of the system. This study is the first to provide a detailed analysis on the attack vectors in a subsea control system and is crucial to understanding key vulnerabilities, primarily to implement efficient mitigation methods that safeguard the safety of personnel and the environment when using such systems.