Hasil untuk "Gas industry"

Junling Feng, Ye Zhang, Ying He et al.

Due to the complex geological conditions of coal-rock, the cutting head of coal mine excavation machines experiences severe fluctuations in loads, making it difficult for existing macroscopic controls to accurately capture the microscopic loads on the cutting pick. Therefore, a dynamic simulation and intelligent control model for the cutting head load of an adaptive roadheader based on multi-scale coupled simulation is developed. The study first modifies the classical load model through finite element method to accurately simulate the microscopic interaction between the cutting pick and the rock mass. The non-dominated sorting genetic algorithm II in elite strategy is used to construct a multi-objective optimization model to determine the optimal parameters for cutting head speed and swing speed. Finally, load dynamic control is achieved by combining radial basis function proportional-integral-derivative controller, and multi-body dynamics-discrete element method and proximal policy optimization are introduced to improve the adaptability to complex working conditions. Test results from different operation scenarios showed that the path planning error of the model met high-precision excavation requirements in regular roadways. During the long-term stable operation phase, the energy consumption ratio and energy utilization efficiency were significantly improved compared to traditional solutions. Faced with slight changes in coal-rock hardness, this model provided early warnings effectively. Under single-point fracture failure, load stability was quickly restored. In the constant operating condition performance test, the model demonstrated significant steady-state control accuracy with minimal mean square error and zero overshoot. Furthermore, a pilot engineering application in a high-gas coal mine roadway demonstrated that the relative error between the simulated and measured loads was controlled within 6.5%, validating the practical feasibility of the proposed system. This study can effectively reduce pick failure, improve excavation efficiency, provide core technical support for the "less manpower, unmanned" operation of coal mines, and assist in the safe and efficient upgrading of the coal industry.

Hui SUN, Haibin WANG, Lei YANG et al.

ObjectiveNatural gas safety remains a critical issue for China’s energy security. A scientific assessment of China’s natural gas safety is essential for effective energy strategy development and supply security. However, a comprehensive evaluation system that integrates Chinese characteristics, industry consensus, and practical operability has yet to be established. MethodsUtilizing the CNKI (China National Knowledge Infrastructure) database as the sole data source, searches for “natural gas safety” and “natural gas risk” were conducted. Through bibliometric and content analysis, the methods for constructing China’s natural gas safety evaluation indicator system were systematically reviewed across five aspects: indicator selection, screening methods, assignment techniques, weight determination, and safety rating approaches. Current research gaps were identified, and future research directions were outlined. Results(1) China’s natural gas safety evaluation should encompass six dimensions: domestic production, resource imports, market demand, price level, infrastructure, and the economic-social environment. The first three dimensions are central to the current evaluation system, with 10 of the 14 most frequently used indicators (over 10.0％ usage) belonging to these dimensions, accounting for 71.4％. The three most widely used indicators are external dependence, natural gas consumption intensity, and reserve-production ratio, with frequencies of 60.3％, 46.6％, and 46.6％, respectively. (2) The current indicators selected for China’s natural gas safety evaluation system are generally indirect or secondary indicators. Among the 14 most frequently used indicators (over 10.0％ usage), all but per capita GDP and transport distance are indirect indicators, meaning they must be derived from other data. (3) There is a shift from subjective judgment to mathematical methods/models in indicator screening and weight determination. The entropy weight method (44.8％) and principal component analysis (24.1％) are the most frequently used. Despite progress, a widely accepted natural gas safety evaluation indicator system has not yet been established in China. ConclusionA natural gas safety evaluation system tailored to China’s national context, aligned with domestic policy, and balancing scientific rigor with operability should be developed. Dynamic monitoring and iterative adjustments are vital to support national energy security decision-making and the industry’s sustainable development.

Qi Zhang, Meng Su, Danli Song et al.

Ascites syndrome (AS), a prevalent nutritional and metabolic disorder in broilers, causes substantial economic losses to the poultry industry. However, its comprehensive molecular mechanisms, particularly the interplay between epigenetic regulation and transcriptional dynamics, remain incompletely elucidated. This study compared AS broilers and normal broilers by routine hematological examinations and blood gas indicators. These analyses revealed significant abnormalities in AS broilers. Histological examination of myocardial tissue using HE staining revealed cardiac tissue damage in AS broilers. To investigate the underlying mechanisms, an integrated multi-omics analysis was performed using whole genome bisulfite sequencing and transcriptome data. This approach identified 891 genes in heart tissue and 1,424 genes in lung tissue with expression levels negatively correlated with promoter methylation. Among these, 191 genes were shared between the two tissues and were functionally associated with metabolic disturbances (glycolysis/gluconeogenesis, ether lipid metabolism), dysregulated signaling pathways (TGF-β, Notch, and calcium signaling), increased vascular permeability, and persistent inflammation in AS broilers. Validation via qPCR and bisulfite sequencing confirmed a significant negative correlation between the expression levels of HEMK1 and PMEPA1 and the methylation levels of their promoter CpG islands, supporting their roles as candidate genes in AS pathogenesis. Collectively, this study identifies key pathways and genes associated with AS development, providing valuable insights into its underlying mechanisms and potential preventive strategies.

Yihong Tang, Kehai Chen, Liang Yue et al.

With the rise of large language models (LLMs), LLM agents capable of autonomous reasoning, planning, and executing complex tasks have become a frontier in artificial intelligence. However, how to translate the research on general agents into productivity that drives industry transformations remains a significant challenge. To address this, this paper systematically reviews the technologies, applications, and evaluation methods of industry agents based on LLMs. Using an industry agent capability maturity framework, it outlines the evolution of agents in industry applications, from "process execution systems" to "adaptive social systems." First, we examine the three key technological pillars that support the advancement of agent capabilities: Memory, Planning, and Tool Use. We discuss how these technologies evolve from supporting simple tasks in their early forms to enabling complex autonomous systems and collective intelligence in more advanced forms. Then, we provide an overview of the application of industry agents in real-world domains such as digital engineering, scientific discovery, embodied intelligence, collaborative business execution, and complex system simulation. Additionally, this paper reviews the evaluation benchmarks and methods for both fundamental and specialized capabilities, identifying the challenges existing evaluation systems face regarding authenticity, safety, and industry specificity. Finally, we focus on the practical challenges faced by industry agents, exploring their capability boundaries, developmental potential, and governance issues in various scenarios, while providing insights into future directions. By combining technological evolution with industry practices, this review aims to clarify the current state and offer a clear roadmap and theoretical foundation for understanding and building the next generation of industry agents.

H. Alves, Cicero Bley Júnior, Rafael Rick Niklevicz et al.

Liuguo Shao, Xiao Yu, Chao Feng

Because the industrial process always results in pollutant emissions, pollution treatment has become necessary for the sustainable development of industry. This paper aims to evaluate the eco-efficiency of China's industrial sectors between 2007 and 2015 by using the directional distance function (DDF) of network data envelopment analysis (DEA), which contains a two-stage structure that divides industrial processes into three linked subprocesses, i.e., the production, wastewater and waste gas treatment processes. The results show that (1) from 2007 to 2015, the eco-efficiency and all process efficiencies of China's industries achieved considerable improvement. This finding indicates that China's industries have already achieved a win-win situation in the development of environmental protection and economic growth over the past few years. (2) During the sampling period, the highest performance was observed during the waste gas treatment process, followed by the wastewater treatment and production processes. During the same period, the performance of wastewater treatment exhibited the fastest growth, followed by the performance of production and waste gas treatment. (3) The eco-efficiencies of China's industrial subsectors exhibited significant industrial differences: the eco-efficiency of the mining industry was the lowest due to a decline in its performance during the waste gas treatment process, while due to the excellent performance during the production wastewater treatment process, the eco-efficiencies of the electricity, gas production and supply industries were the highest.

Xiao Sun, H. Atiyeh, R. Huhnke et al.

Abstract Syngas is produced by thermochemical conversion, e.g., pyrolysis and gasification, of biomass, animal waste, coal, municipal solid waste and other carbonaceous materials, or directly from CO-rich off-gases from industry, e.g. steel mills. Syngas components (mainly CO, H2 and CO2) are converted to alcohols and other chemicals by acetogenic bacteria through the Wood-Ljungdahl pathway or its derivatives. Syngas fermentation is affected by the acetogen(s), type of reactor, gas composition, medium components, operating parameters, gas-liquid mass transfer and fermentation strategies. These factors affect product distribution, titer, yield, productivity and process feasibility. In this article, syngas fermentation process development with focus on microorganisms, gas composition, medium design, gas-liquid mass transfer fermentation strategies, techno-economic analysis and commercialization efforts are critically reviewed. This review provides new insights in syngas fermentation, which can guide future research towards commercialization of renewable and sustainable biofuels and chemicals.

Nesreen Mufid

Nowadays, electric robots play big role in many fields as they can replace humans and/or decrease the amount of load on humans. There are several types of robots that are present in the daily life, some of them are fully controlled by humans while others are programmed to be self-controlled. In addition there are self-control robots with partial human control. Robots can be classified into three major kinds: industry robots, autonomous robots and mobile robots. Industry robots are used in industries and factories to perform mankind tasks in the easier and faster way which will help in developing products. Typically industrial robots perform difficult and dangerous tasks, as they lift heavy objects, handle chemicals, paint and assembly work and so on. They are working all the time hour after hour, day by day with the same precision and they do not get tired which means that they do not make errors due to fatigue. Indeed, they are ideally suited to complete repetitive tasks.

Xiaoran Ye, Tao Yu, Zhaoxin Liang

Equation of state (EOS) for a pure two-dimensional (2D) Bose gas exhibits a logarithmic dependence on the s-wave scattering length [L. Salasnich, Phys. Rev. Lett. 118, 130402 (2017)]. The pronounced disparity between the EOS of a 2D Bose gas and its 3D counterpart underscores the significance of exploring the dimensional crossover between these two distinct dimensions. In this work, we are motivated to deduce nonuniversal corrections to EOS for an optically trapped Bose gas along the dimensional crossover from 3D to 2D, incorporating the finite-range effects of the interatomic potential. Employing the framework of effective field theory, we derive the analytical expressions for both the ground state energy and quantum depletion. The introduction of the lattice induces a transition from a 3D to a quasi-2D regime. In particular, we systematically analyze the asymptotic behaviors of both the 2D and 3D aspects of the model system, with a specific focus on the nonuniversal effects on the EOS arising from finite-range interactions. The nonuniversal effects proposed in this study along the dimensional crossover represent a significant stride toward unraveling the intricate interplay between dimensionality and quantum fluctuations.

Hemanth Vikram P․R, Gunjan Kumar, Rajashree Deka et al.

Nitrosamine impurities identified in several pharmaceuticals during recent times has raised concerns leading to product recalls worldwide and necessitating sensitive liquid and gas chromatographic methods for trace level detection of nitrosamine impurities. This study developed and validated a ultra-fast liquid chromatography-tandem mass spectrometry (UFLC-MS/MS) method for the quantification of NDMA in Levosulpuride drug substance and tablet formulation. Current method utilizes a triple quadrupole analyzer, atmospheric pressure chemical ionization (APCI) ionization source and multiple reaction monitoring (MRM) scan mode for the analysis. Chromatographic separation was achieved on a Gemini NX-C18 column (150 × 4.6 mm, 3 µm) maintained at 40 °C. The mobile phase consisted of a binary gradient of solvent A (0.1 % formic acid in water) and solvent B (methanol), with a total run time of 18 minutes. Current method achieved excellent linearity, recovery, precision, and sensitivity. Greenness of the developed method was evaluated using the GAPI, AGREE, and AES metrics. Current method is sensitive and selective for NDMA in levosulpuride drug substance and tablet formulations and can be employed for routine quality control analysis in pharmaceutical industry.

Weirong Li, Tianyang Zhang, Xinju Liu et al.

Abstract In the process of developing tight oil and gas reservoirs, multistage fractured horizontal wells (NFHWs) can greatly increase the production rate, and the optimal design of its fracturing parameters is also an important means to further increase the production rate. Accurate production prediction is essential for the formulation of effective development strategies and development plans before and during project execution. In this study, a novel workflow incorporating machine learning (ML) and particle swarm optimization algorithms (PSO) is proposed to predict the production rate of multi-stage fractured horizontal wells in tight reservoirs and optimize the fracturing parameters. The researchers conducted 10,000 numerical simulation experiments to build a complete training and validation dataset, based on which five machine learning production prediction models were developed. As input variables for yield prediction, eight key factors affecting yield were selected. The results of the study show that among the five models, the random forest (RF) model best establishes the mapping relationship between feature variables and yield. After verifying the validity of the Random Forest-based yield prediction model, the researchers combined it with the particle swarm optimization algorithm to determine the optimal combination of fracturing parameters under the condition of maximizing the net present value. A hybrid model, called ML-PSO, is proposed to overcome the limitations of current production forecasting studies, which are difficult to maximize economic returns and optimize the fracturing scheme based on operator preferences (e.g., target NPV). The designed workflow can not only accurately and efficiently predict the production of multi-stage fractured horizontal wells in real-time, but also be used as a parameter selection tool to optimize the fracture design. This study promotes data-driven decision-making for oil and gas development, and its tight reservoir production forecasts provide the basis for accurate forecasting models for the oil and gas industry.

Ying GAO, Yanan TU, Weidong WANG et al.

With the overconsumption of fossil carbon resources, resulting in a large amount of CO2 and other greenhouse gas emissions, caused by global climate change has become one of the major challenges facing all mankind. At the same time, the development and utilization of coal resources will produce a large amount of coal-based solid wastes containing calcium and magnesium, such as fly ash, desulfurization gypsum and other solid wastes. How to realize efficient CO2 capture and sequestration is a very challenging issue nowadays, as well as the large-scale comprehensive utilization of fly ash, coal gasification slag, and other large-scale heavy polluting industrial solid wastes is still in need of a breakthrough. Under the goal of “double carbon”, CO2 mineralization from coal-based solid waste is a potential strategy to effectively address global warming, and mineral carbonation of coal-based solid wastes containing calcium and magnesium has great prospects for carbon dioxide capture and sequestration (CCS) as well as for the resource-based disposal of solid waste. However, the industrial application bottleneck of CO2 mineralization in coal-based solid waste is still unable to break through. This paper reviews the current development of CO2 mineralization from fly ash (FA), desulfurization gypsum (FGDG), and coal gasification slag (CGS), with the aim of exploring the reasons for their technical limitations. Firstly, this paper briefly elaborates on the pathways for CO2 mineralization of coal based solid waste, revealing the reaction process and principles of CO2 mineralization of coal based solid waste. Secondly, the mineralization potential and process of typical coal-based solid waste were summarized and compared to clarify the mechanism of the regulation of the process parameters on the product properties during CO2 mineralization process. Finally, the performance of CO2 mineralization products from coal-based solid waste was summarized, and the feasibility of the mineralization process and its environmental impact were elucidated using a Life Cycle Assessment (LCA). This paper will provide optimization suggestions on the process technology of coal-based solid waste CO2 mineralization to promote the strategic goal of low-carbon transformation in the coal industry.

Tsai-Chi Kuo, Hsiang-Yue Chen, Billy Chong et al.

Biogas is a kind of renewable energy resource and can be burned to produce electricity and heat. If methane is released directly into the air, it would be a very serious source of GHG emissions. Therefore, capturing and recycling methane for power generation would not only significantly reduce industrial greenhouse gas emissions, but also reduce the need to purchase electricity.In addition, with the Net Zero in 2050 initiative and circular economy, more academic and industry research investigate the biogas energy. However, the cost and carbon footprint of biogas energy is varied with the technologies. In this research, the As-Is (biomass waste treatment) and To-Be (biomass waste to energy) model is compared. It shows it is worth to do the investment of bioenergy system for the farm to reduce the GHG emissions if the carbon tax is added. However, the cost of bioenergy facilities will influence the income. A case study is also illustrated to provide the biogas energy production for the government policy.

M. Kalyuzhnaya, Aaron W. Puri, M. Lidstrom

Sara P. Cuéllar-Bermúdez, J. Garcia-Perez, B. Rittmann et al.

One of the most important industrial activities related to the greenhouse gases emissions is the cement manufacturing process, which produces large amounts of carbon dioxide (CO2). Only in 2010, 8% of CO2 global emissions were due to cement industry. In this work, the use of CO2 released by the cement sector is described as potential gas for microalgae culture since their biofixation efficiency is higher than terrestrial plants. Therefore, transformation of polluting gas fluxes into new and valuable products is feasible. In addition, bulk applications such as wastewater treatment and biofuels production can be coupled. Finally, microalgae biomass can be also used for the production of valuable compounds such as pigments, food supplements for both humans and animals, and fertilizers. In this review, flue gas emissions coupled to microalgae cultures are described. In addition, since microalgae can produce energy, the biorefinery concept is also reviewed.

K. Bjørlykke

Simerjot Kaur, Andrea Stefanucci, Sameena Shah

Determining industry and product/service codes for a company is an important real-world task and is typically very expensive as it involves manual curation of data about the companies. Building an AI agent that can predict these codes automatically can significantly help reduce costs, and eliminate human biases and errors. However, unavailability of labeled datasets as well as the need for high precision results within the financial domain makes this a challenging problem. In this work, we propose a hierarchical multi-class industry code classifier with a targeted multi-label product/service code classifier leveraging advances in unsupervised representation learning techniques. We demonstrate how a high quality industry and product/service code classification system can be built using extremely limited labeled dataset. We evaluate our approach on a dataset of more than 20,000 companies and achieved a classification accuracy of more than 92\%. Additionally, we also compared our approach with a dataset of 350 manually labeled product/service codes provided by Subject Matter Experts (SMEs) and obtained an accuracy of more than 96\% resulting in real-life adoption within the financial domain.

Jia-ming LI, Ming-zhi JIAO, Chen QIAN

With the development of the industry of semiconductor integrated circuits, microelectromechanical system (MEMS) products have made rapid progress. The development of MEMS and the combination of sensor technology have yielded compact sensors with increased functions and intelligence levels. MEMS-based microhotplate (MHP)-type metal oxide methane sensors have the advantages of low power consumption and fast response and have been widely used in methane detection applications. In particular, ZnO methane-sensitive materials have attracted significant attention due to their high sensitivity, small poisoning effect, and low operating temperature. Notably, the response performance of sensors prepared from these sensitive materials is still significantly affected by the heating temperature and thermal distribution of the MEMS-based MHP. The purpose of our experiment is to optimize the heat generation of the heating electrodes of MHP, optimize the thermal distribution of MHP, and further reduce the power consumption of MHP sensors. The heating electrodes of MHP are made of platinum materials that have high thermal conductivity and stable performance. In this study, we use the Multiphysics module in the finite element analysis software COMSOL to simulate and analyze the temperature in the physical field for the two structures of serpentine platinum heating electrodes of MHP. By comparison, the structure of the heating electrodes affects the temperature distribution under the same working conditions. The structure with a larger width in the middle of the heating plate electrode and gradually narrowing to both sides generates more heat than that with the same width. When the heating plate reaches 300 ℃, it needs about 75 mW of power. Next, ZnO thin film methane sensors were constructed by sputtering ZnO methane-sensitive materials on the interdigital electrode of a commercial MHP, and the response of the gas sensor was tested using the HIS9010 of Hefei Micro-Nano Company. The static measurement method was used to inject methane gas into a 1-L gas chamber. In order to verify the superior response of our sensor, it has been compared that performance of commercial methane sensors and ZnO methane sensors made by. The response linearity in the interval is relatively good, and the response value for 10000×10−6 methane reaches 30. The response of our fabricated sensor is higher than those of existing domestic and foreign commercial methane sensors, showing significant potential in related applications.

Fatouma Mohamed Abdoul-Latif, Mohammed Ejjabraoui, Ayoub Ainane et al.

The use of natural biopesticides, specifically essential oils, is being explored as an alternative solution to protect stored foodstuffs. This study focuses on a formulation of phosphogypsum–<i>Pinus halepensis</i> essential oil as a pesticidal product. First, the essential oil chemical composition was determined using gas chromatography with mass spectrometry (GC-MS), while the phosphogypsum (waste from the phosphate mining industry) was analyzed using scanning electron microscopy, X-ray fluorescence, X-ray diffraction, Fourier-transform infrared spectroscopy, and thermogravimetric–differential thermal analysis; thus, physico-chemical properties and heavy metal contents were determined. In a second step, the preparation of the formulation consists in grafting the essential oil on the phosphogypsum (adsorption) in a cylindrical geometric shape adapted to the models applied in the bioprocesses of storage. The study of essential oil transfers in the material in the case of desorption along the axis (<i>Oz</i>) was carried out using analytical and numerical models of the Fickian diffusion process to understand the behavior of the oil and determine physicochemical parameters such as diffusivity (<i>D</i>) and evaporation flux (<i>F</i>). By using statistical methods such as experimental design and principal component analysis, these parameters can help explain the mechanisms involved in the insecticidal activities against the primary pest of lentils (<i>Bruchus signaticornis</i>) and in the parameters of lentil seed germination.

J. Giesekam, J. Barrett, P. Taylor