Global greenhouse gas (GHG) emissions can be traced to five economic sectors: energy, industry, buildings, transport and AFOLU (agriculture, forestry and other land uses). In this topical review, we synthesise the literature to explain recent trends in global and regional emissions in each of these sectors. To contextualise our review, we present estimates of GHG emissions trends by sector from 1990 to 2018, describing the major sources of emissions growth, stability and decline across ten global regions. Overall, the literature and data emphasise that progress towards reducing GHG emissions has been limited. The prominent global pattern is a continuation of underlying drivers with few signs of emerging limits to demand, nor of a deep shift towards the delivery of low and zero carbon services across sectors. We observe a moderate decarbonisation of energy systems in Europe and North America, driven by fuel switching and the increasing penetration of renewables. By contrast, in rapidly industrialising regions, fossil-based energy systems have continuously expanded, only very recently slowing down in their growth. Strong demand for materials, floor area, energy services and travel have driven emissions growth in the industry, buildings and transport sectors, particularly in Eastern Asia, Southern Asia and South-East Asia. An expansion of agriculture into carbon-dense tropical forest areas has driven recent increases in AFOLU emissions in Latin America, South-East Asia and Africa. Identifying, understanding, and tackling the most persistent and climate-damaging trends across sectors is a fundamental concern for research and policy as humanity treads deeper into the Anthropocene.
A. Hassanpouryouzband, E. Joonaki, Mehrdad Vasheghani Farahani
et al.
Gas hydrates have received considerable attention due to their important role in flow assurance for the oil and gas industry, their extensive natural occurrence on Earth and extraterrestrial planets, and their significant applications in sustainable technologies including but not limited to gas and energy storage, gas separation, and water desalination. Given not only their inherent structural flexibility depending on the type of guest gas molecules and formation conditions, but also the synthetic effects of a wide range of chemical additives on their properties, these variabilities could be exploited to optimise the role of gas hydrates. This includes increasing their industrial applications, understanding and utilising their role in Nature, identifying potential methods for safely extracting natural gases stored in naturally occurring hydrates within the Earth, and for developing green technologies. This review summarizes the different properties of gas hydrates as well as their formation and dissociation kinetics and then reviews the fast-growing literature reporting their role and applications in the aforementioned fields, mainly concentrating on advances during the last decade. Challenges, limitations, and future perspectives of each field are briefly discussed. The overall objective of this review is to provide readers with an extensive overview of gas hydrates that we hope will stimulate further work on this riveting field.
As a multiple-energy carrier, hydrogen can facilitate the transition to a low-carbon future, and coupling renewable energy sources with hydrogen-power generation systems (e.g., gas turbines) can markedly enhance gas turbine combined cycles (GTCCs) power generation regarding cleanliness and flexibility. Conventional gas turbines fuel the natural gas–hydrogen mixture and encounter issues like unstable combustion and elevated nitrogen oxide (NO<sub>x</sub>) emissions. Initially, the alterations in combustion characteristics resulting from the fuel transition are analyzed, and the principal technical challenges of hydrogen-mixed combustion are summarized. It is found that hydrogen exhibits a laminar flame speed approximately 7–10 times higher than that of methane, and a hydrogen blending ratio beyond 30% significantly increases the risk of flashback and thermoacoustic oscillations. The existing technical proficiencies of advanced hydrogen combustion strategies are delineated to offer decision-making assistance for the industry. For instance, micromix combustors can achieve NOx emissions below 20 ppm even with 100% hydrogen, while axial staging technology expands the stable operating range to 25–106% load. Additionally, current research on hydrogen-fueled gas turbines primarily focuses on enhancing traditional combustor designs. Conversely, the focus on the overall alteration of gas turbines has been relatively restricted. It further examines component failure issues arising from elevated temperatures and material hydrogen embrittlement, highlighting that X80 pipeline steel experiences a 17-fold increase in hydrogen embrittlement index when the hydrogen blending ratio rises from 1% to 20%, as well as safety concerns related to fuel transitions from conventional gas turbines to hydrogen gas turbines, offering technical references for the comprehensive optimization of hydrogen-fueled gas turbines.
Oil and gas construction projects are critical for meeting global demand for fossil fuels, but they also present unique risks and challenges that require innovative construction approaches. Artificial Intelligence (AI) has emerged as a promising technology for tackling these challenges, and this study examines its applications for sustainable development in the oil and gas industry. Using a systematic literature review (SLR), this research evaluates research trends from 2011 to 2022. It provides a detailed analysis of how AI suits oil and gas construction. A total of 115 research articles were reviewed to identify original contributions, and the findings indicate a positive trend in AI research related to oil and gas construction projects, especially after 2016. The originality of this study lies in its comprehensive analysis of the latest research on AI applications in the oil and gas industry and its contribution to developing recommendations for improving the sustainability of oil and gas projects. This research’s originality is in providing insight into the most promising AI applications and methodologies that can help drive sustainable development in the oil and gas industry.
ObjectiveUtilizing existing product oil pipelines for additional methanol transport can enhance hydrogen and methanol delivery while addressing the issue of insufficient throughput in product oil pipelines. However, China has not yet developed a comprehensive technological system for this purpose. MethodsThe basic properties of methanol and relevant specifications and standards were reviewed to discuss the batch design for incorporating methanol transport in product oil pipelines. Precautions for the adjacent transportation of methanol with diesel and gasoline were analyzed. The impact of additional methanol transport on mixed oil volumes was examined, and key aspects of isolation fluid selection and application were identified. With a focus on quality management for additional methanol transport in product oil pipelines, process technologies such as batch interface monitoring and tracking, methanol/gasoline and methanol/diesel mixed oil cutting, and mixed oil treatment were elaborated on. ResultsThe adjacent batch transportation of methanol with diesel and gasoline respectively has its own advantages. A feasible batch design for additional methanol transport in product oil pipelines can be established by comparing pipeline oil transportation, oil consumption, and pipeline characteristics, while giving special focus on the control of oil mixing. If isolation fluid is employed, its selection should be based on a comprehensive analysis of the feasibility of the isolation process, oil quality before and after treatment, cost, and reliability. Online density meters, optical interface meters, and online batch interface tracking technologies can effectively monitor and track the interface in the adjacent batch transportation of methanol with diesel and gasoline. Boiling range and flash point were recommended as criteria for methanol/gasoline and methanol/diesel mixed oil cutting. To maintain oil quality and simplify mixed oil treatment, the three types of mixed oil (methanol/gasoline, methanol/diesel, and gasoline/diesel) should be received and stored separately for each batch. ConclusionTransporting additional methanol through product oil pipelines is technically feasible. However, future research is required on the flow characteristics of the mixed oil segment between methanol and diesel, the diffusion characteristics of the mixed oil, the mixed oil characteristics at the batch interface between water-containing methanol and gasoline/diesel, and the calculation model for mixed oil during the batch transportation of methanol with gasoline and diesel. This research aims to provide technical support for the operation and management of additional methanol transport through product oil pipelines.
The petrochemical industry faces significant safety challenges, necessitating stringent protocols and advanced monitoring systems. Traditional methods rely on manual inspections and fixed sensors, often reacting to hazards only after they occur. Multimodal AI, integrating visual, sensor, and textual data, offers a transformative solution for real-time, proactive safety management. This paper evaluates AI models—Gemini 1.5 Pro, OPENAI GPT-4, and Copilot—in detecting workplace hazards, ensuring compliance with Process Safety Management (PSM) and DuPont safety frameworks. The study highlights the models’ potential in improving safety outcomes, reducing human error, and supporting continuous, data-driven risk management in petrochemical plants. This paper is the first of its kind to use the latest multimodal tech to identify the safety hazard; a similar model could be deployed in other manufacturing industries, especially the oil and gas (both upstream and downstream) industry, fertilizer industries, and production facilities.
Alkali residue is a byproduct of the ammonia-soda process used to produce soda, characterized by high production volume, low utilization efficiency, high moisture content, high porosity, and fine particle size. The primary disposal methods for alkali residue include surface stacking (e.g., constructing tailing dams) and direct discharge into water bodies such as rivers or seas. The hazards associated with surface stacking include land resource occupation, reduced agricultural yield, groundwater and air contamination, soil pollution, adverse effects on vegetation growth, ecological imbalance, and the formation of saline-alkali land. Additionally, the discharge of alkali residue into rivers or seas can lead to water pollution, threatening the sustainability of aquatic ecosystems. Sedimentation may also occur, potentially blocking river channels, reducing flow cross-sections, and significantly impairing the river’s flood discharge capacity. These challenges have resulted in a large-scale accumulation of alkali residue, severely constraining the development of the soda industry. Therefore, there is an urgent need to accelerate its large-scale application. This study provides a comprehensive review of the latest research findings on the fundamental properties and engineering applications of alkali residue. The results indicate that similar to soil, alkali residue exhibits a three-phase system, where the solid phase, composed of various mineral components, forms a skeletal structure. In contrast, the liquid and gas phases fill the pores, creating a porous medium. The properties of alkali residue can be characterized using soil indicators. Its mineral composition includes CaCO3, CaSO4, CaCl2, and NaCl, where CaCO3 and CaSO4 are insoluble salts, while CaCl2 and NaCl readily dissolve in water. The CaCO3 content ranges from 32.52% to 64.00%, while the chemical composition is dominated by CaO, accounting for 32.25% to 74.20%. Alkali residue solutions are slightly alkaline, with pH values typically ranging from 8 to 12. As a general industrial solid waste, alkali residue contains heavy metals such as copper, zinc, cadmium, lead, total chromium, and chromium, all of which meet environmental standards. Alkali residue has been explored for various engineering applications, including the remediation of bioleached heavy metal-laden sediment, sludge, clay, expansive soil, contaminated soil, shield tunneling slag, weathered mudstone, and coal gangue, as well as for backfill materials and the preparation of alkali residue-based soil and composite cementitious materials. However, current application methods suffer from low alkali residue utilization efficiency, limited application scenarios, challenges in Cl- solidification, potential steel reinforcement corrosion, and risks of secondary pollution. To address these challenges, the author systematically investigates the fundamental properties of alkali residue from Lianyungang and proposes a method for producing alkali residue-based lightweight soil (A-LS) by combining alkali residue, cement, and granulated blast furnace slag (GGBS). A-LS was utilized as a roadbed filler in the Lianyungang—Suqian expressway, demonstrating compressive strength, California bearing ratio (CBR), rebound modulus, and deflection that met design and specification requirements. The material exhibited strong road performance, high resistance to wet-dry cycling, freeze-thaw cycling, and sulfate corrosion, with a durability coefficient ranging from 0.71 to 1.51. Furthermore, A-LS offers several advantages, including high strength, low density, a simple production process, high efficiency, and low cost. With a 28-day compressive strength ranging from 0.96 to 4.27 MPa, A-LS is suitable as a subgrade filling material. The alkali residue content in A-LS ranges from 87.01 to 164.35 kg·m−3, facilitating large-scale disposal and high-value utilization of alkali residue.
This paper presents a novel approach to e-commerce payment fraud detection by integrating reinforcement learning (RL) with Large Language Models (LLMs). By framing transaction risk as a multi-step Markov Decision Process (MDP), RL optimizes risk detection across multiple payment stages. Crafting effective reward functions, essential for RL model success, typically requires significant human expertise due to the complexity and variability in design. LLMs, with their advanced reasoning and coding capabilities, are well-suited to refine these functions, offering improvements over traditional methods. Our approach leverages LLMs to iteratively enhance reward functions, achieving better fraud detection accuracy and demonstrating zero-shot capability. Experiments with real-world data confirm the effectiveness, robustness, and resilience of our LLM-enhanced RL framework through long-term evaluations, underscoring the potential of LLMs in advancing industrial RL applications.
Large language models have shown impressive performance in various domains, including code generation across diverse open-source domains. However, their applicability in proprietary industrial settings, where domain-specific constraints and code interdependencies are prevalent, remains largely unexplored. We present a case study conducted in collaboration with the leveling department at ASML to investigate the performance of LLMs in generating functional, maintainable code within a closed, highly specialized software environment. We developed an evaluation framework tailored to ASML's proprietary codebase and introduced a new benchmark. Additionally, we proposed a new evaluation metric, build@k, to assess whether LLM-generated code successfully compiles and integrates within real industrial repositories. We investigate various prompting techniques, compare the performance of generic and code-specific LLMs, and examine the impact of model size on code generation capabilities, using both match-based and execution-based metrics. The findings reveal that prompting techniques and model size have a significant impact on output quality, with few-shot and chain-of-thought prompting yielding the highest build success rates. The difference in performance between the code-specific LLMs and generic LLMs was less pronounced and varied substantially across different model families.
The Da'anzhai Member of the Lower Jurassic on the eastern slope of the Western Sichuan Depression in the Sichuan Basin is a main target for the development of tight oil and gas. Fractures are essential for achieving high production in the Da'anzhai Member reservoirs. Due to their complex lithology, traditional methods for fracture prediction and evaluation have limited applicability and low prediction accuracy. Based on core fracture investigations and thin-section identification data, combined with geological statistics and numerical simulations, the lithological characteristics of various reservoirs were clarified, and the fracture development characteristics in the Da'anzhai Member were revealed. Considering both the fracture foundation and external fracturing forces, three fracture evaluation factors were proposed and constructed to predict and evaluate the planar distribution of fractures in each sub-member by distinguishing lithology and sub-members. The results showed that: (1) The Da'anzhai Member reservoirs had complex lithologies with interbedded (shell) limestone, sandstone, and shale. (2) Fully filled fractures were mainly developed, and (shell) limestone exhibited mainly structural fractures, while sandstone and shale mainly showed interlayer fractures with dissolution features on the fracture surfaces, which was beneficial for enhancing oil and gas flow. (3) Three fracture evaluation factors were proposed and constructed, which included lithologic thickness, tectonic deformation intensity, and fracture rupture intensity. A quantitative model for comprehensive prediction and evaluation of fractures was established to predict and evaluate the planar distribution of fractures in each sub-member comprehensively. The predicted fracture density aligned well with the fracture development index identified in individual wells, indicating the reliability of the prediction results. This method for fracture planar distribution prediction and evaluation offers a reference for similar oil and gas reservoirs.
Abstract Purification of ethylene (C2H4) as the most extensive and output chemical, from complex multi-components is of great significance but highly challenging. Herein we demonstrate that precise pore structure tuning by controlling the network hydrogen bonds in two highly-related porous coordination networks can shift the efficient C2H4 separation function from C2H2/C2H4/C2H6 ternary mixture to CO2/C2H2/C2H4/C2H6 quaternary mixture system. Single-crystal X-ray diffraction revealed that the different amino groups on the triazolate ligands resulted in the change of the hydrogen bonding in the host network, which led to changes in the pore shape and pore chemistry. Gas adsorption isotherms, adsorption kinetics and gas-loaded crystal structure analysis indicated that the coordination network Zn-fa-atz (2) weakened the affinity for three C2 hydrocarbons synchronously including C2H4 but enhanced the CO2 adsorption due to the optimized CO2-host interaction and the faster CO2 diffusion, leading to effective C2H4 production from the CO2/C2H2/C2H4/C2H6 mixture in one step based on the experimental and simulated breakthrough data. Moreover, it can be shaped into spherical pellets with maintained porosity and separation performance.
Najmul Alam, M. A. Rahman, Md. Rashidul Islam
et al.
Abstract The exponential rise of electric vehicles (EVs) is transforming the global automobile industry, driving a shift towards greater cleanliness and environmental sustainability. EV charging stations (EVCSs) play a pivotal role in this massive transition towards EVs, where accurate forecasting of EVCS demand is crucial for seamlessly integrating EVs into existing power grids. Most of the existing research mainly concentrates on univariate forecasting, neglecting the multiple factors influencing EVCS demand. Hence, this study offers a comparative analysis of different algorithms for univariate forecasting and multivariate forecasting, where the multivariate scheme incorporates metadata such as charging time, greenhouse gas savings, and gasoline savings. The experimental results indicate the superiority of the multivariate scheme over the univariate forecasting. For multivariate forecasting, the gated recurrent unit (GRU) has outperformed other models such as categorical boosting (Catboost), recurrent neural network (RNN), long short‐term memory (LSTM), extreme gradient boosting (XGBoost), random forest, convolutional neural network (CNN), CNN + LSTM, and LSTM + LSTM. The results of this study emphasize the significance of using the GRU model for multivariate forecasting with metadata during normal and noisy scenarios to yield more reliable and accurate predictions. This approach enhances decision‐making, policy development, and efficient grid integration in the growing EV sector.
Hydrogen, as an efficient energy carrier and clean fuel, has a demand for large-scale storage. At present, the underground hydrogen storage (UHS), in types of salt caverns, depleted oil and gas reservoirs and aquifers, is the most feasible solution balancing the storage security and economy. Specifically, UHS in the depleted oil and gas reservoirs is most promising among the three types. Herein, the particularity of UHS was discussed by comparing the difference of physical properties among H2, CH4 and CO2. In particular, the technical challenges and coping strategies for UHS in depleted oil and gas reservoirs were overviewed: (1) For the gas leakage caused by unstable displacement, seepage and diffusion, efforts should be made to control the gas injection rate, optimize the scheme of injection-production and cushion gas arrangement, and study the caprock breakthrough pressure, the surface interface characteristics and the flow and mass transfer mechanism. (2) For the hydrogen-consuming geochemical reaction and microbial catalysis, the strata with highly hydrogen-sensitive minerals, ions and microorganisms should be excluded from UHS potential sites to prevent unacceptable H2 consumption. (3) To cope with the integrity failures of traps and artificial materials, the risk of formation damage and gas leakage should be evaluated considering the macroscopic and microscopic deformation and fracture evolution characteristics of the formation. Besides, appropriate materials should be selected to enhance the resistance of artificial facilities to corrosion and hydrogen embrittlement. Finally, the directions of research on UHS technology were pointed out, including the research on H2 loss mechanism under multi-scale and multi-field coupling, the numerical simulation study on-site scale of UHS, and the gas migration and leakage monitoring technology in UHS. Generally, this research could provide a reference for promoting the engineering practice of UHS.
Abdullah Musa Ali, Mohammed Yerima Kwaya, Abubakar Mijinyawa
et al.
This study uses simulations to explore the energy distributions involved in the adsorption of methane gas in shales. Molecular mechanics calculations were carried out using the Forcite module in BIOVIA material studio software. The critical challenge in molecular-scale simulations remains the need to improve the description of the gas adsorption prior to up-scaling to a realistic scenario. Resolving this challenge requires the implementation of multi-scale techniques that employ atomistic/molecular-level results as input. Thus, it is pertinent that the appropriate molecular data on CH4 gas interaction with shale is obtained. This study provides empirical data on CH4 sorption/adsorption in shale at the molecular level to confirm the CH4 storage potential of shales. The effect of pressure on the CH4 sorption/adsorption was also investigated. A vital aspect of this study is elucidating the energy distribution and dominant energy that controls CH4 sorption/adsorption to serve as a basis for the exploitation of CH4 in productive shales. Following the intensive simulation exercise, the average total energy of CH4 sorption varied from approximately −30 to −120 kcal/mol with increase in pressure from 500 to 2500 psi, suggesting increasing thermodynamic stability. The results indicated that van der Waals energy is the major sorption energy with values ranging from 60 to −250 kcal/mol as the sorption pressure increased, while electrostatic energy recorded the least contribution. The total adsorption energy increased from −5 to −16 kcal/mol for reservoir pressure range of 1–15 MPa. This energy distribution data confirmed the possibility of CH4 adsorption on shale under reservoir pressure conditions.
Carine Menezes Rebello, Johannes Jäschkea, Idelfonso B. R. Nogueira
The concept of creating a virtual copy of a complete Cyber-Physical System opens up numerous possibilities, including real-time assessments of the physical environment and continuous learning from the system to provide reliable and precise information. This process, known as the twinning process or the development of a digital twin (DT), has been widely adopted across various industries. However, challenges arise when considering the computational demands of implementing AI models, such as those employed in digital twins, in real-time information exchange scenarios. This work proposes a digital twin framework for optimal and autonomous decision-making applied to a gas-lift process in the oil and gas industry, focusing on enhancing the robustness and adaptability of the DT. The framework combines Bayesian inference, Monte Carlo simulations, transfer learning, online learning, and novel strategies to confer cognition to the DT, including model hyperdimensional reduction and cognitive tack. Consequently, creating a framework for efficient, reliable, and trustworthy DT identification was possible. The proposed approach addresses the current gap in the literature regarding integrating various learning techniques and uncertainty management in digital twin strategies. This digital twin framework aims to provide a reliable and efficient system capable of adapting to changing environments and incorporating prediction uncertainty, thus enhancing the overall decision-making process in complex, real-world scenarios. Additionally, this work lays the foundation for further developments in digital twins for process systems engineering, potentially fostering new advancements and applications across various industrial sectors.