Fadhil Y. Al-Aboosi, M. El‐Halwagi, M. Moore
et al.
Decarbonization of the maritime transportation fuels is a primary objective of the shipping industry. Ammonia is an attractive option because of its relatively low greenhouse gas (GHG) emissions, high energy density, competitive cost, and ubiquitous infrastructure for manufacturing, storage, and distribution. The environmental benefits are enhanced when the manufacture of ammonia is assisted by renewable energy and feedstocks. This paper evaluates the potential usage of ammonia, especially renewable ammonia, as a maritime transportation fuel. The assessment covers manufacturing approaches, energy and feedstock sources, economics, and environmental impact based on well-to-tank and tank-to-propeller bases. Comparison is also made with conventional fuels that are currently used in the shipping industry. Finally, the paper discusses future directions and impact of technological advances on the potential use of renewable ammonia as a transportation fuel.
Accelerating digital and intelligent transformation is a crucial measure for oil and gas enterprises to advance industrial transformation and upgrading and foster new productive forces. Sinopec’s upstream sector in China has thoroughly implemented the “Digital and Intelligent Sinopec” initiative, focusing on supporting corporate reform and management. By closely aligning with the development trends of digital and intelligent technologies and the demands of exploration and production operations, the digital and intelligent transformation has been steadily advanced. A group-level Exploration and Development Data Center (EPDC) has been established, aggregating 17.2 PB of various types of exploration and development data, which has enabled centralized data management and shared applications. An Internet of Things network covering oil and gas production sites has been nearly completed, with digital coverage rates for oil, gas, and water wells, and station facilities reaching 94.90% and 92.30%, respectively. This has fundamentally transformed the traditional manual management model of stationing personnel at wells and stations, effectively supporting the reform of production operation modes and labor organization under digital and intelligent conditions. The construction and deepened application of unified systems have been advanced coordinately, continuously improving the digital coverage across all exploration and development business operations. Sinopec has also actively promoted the construction of artificial intelligence (AI) scenarios and their pilot applications, achieving notable results in scenarios such as intelligent seismic processing and interpretation, intelligent rock thin-section identification and analysis, intelligent reservoir numerical simulation, intelligent drilling, intelligent fracturing, and intelligent well condition diagnosis. Looking ahead to the “15th Five-Year Plan”, Sinopec’s upstream sector in China aims to build intelligent oil and gas fields, accelerate the integration of data flow, business flow, value flow, and supervision flow (“four flows in one”), and promote the construction and application of high-value AI scenarios across the entire business chain. These efforts will support the deeper and more substantive integration of digitalization and intellectualization, enhancing the operational efficiency, economic benefits, and management capability of oil and gas exploration, development, and production.
Petroleum refining. Petroleum products, Gas industry
Carmine Cancro, Mauro Atrigna, Aniello Borriello
et al.
The work involved in this paper has been developed as part of project NEST “Network for Energy Sustainable Technologies" framed in the PE program "Green Energies for the Future". It aims to develop technologies for the conversion and use of renewable sources that should be sustainable, both from an environmental and a social point of view. In particular, the paper shows the design of a process that employs the high temperature heat produced by a solar furnace, realized at Research Centre of ENEA in Portici, to supply a system for the hydrogen production based on the steam reforming natural gas process. In the direction of the decarbonization of the chemical industry, the solar concentration-based plant appears well compatible with the endothermic stage of a reforming based plant, in which the selected hydrocarbon reacts with steam to produce a hydrogen-rich syngas (to be refined in the further purification stages). [1]
With improving economic conditions, consumer demand for fresh-cut produce is rising. The development of the fresh-cut industry has been hindered by pathogenic contamination and quality deterioration. Scientific communities have developed novel preservation technologies for fresh-cut produce. As an innovative non-thermal processing method, cold plasma effectively preserves the nutritional value and inactivates pathogens in fresh-cut produce. This review delineates the principles of cold-plasma generation and concludes with the primary factors influencing its efficacy. These factors include the specifications and parameters of the equipment utilized, the properties of the conductive gas utilized, the method of treatment, and the intrinsic properties of a sample subjected to treatment. Furthermore, this review delineates various scenarios for cold-plasma applications. This review focuses on its effects on enzymatic activities (including peroxidase, polyphenol oxidase, and pectin methylesterase), pathogenic microorganisms, and nutritional value. This review concludes with the potential application of cold-plasma technology in the processing of fresh-cut products. This study proposes advancing plasma technology in fresh-cut produce processing by (1) optimizing cold-plasma parameters for diverse fruit and vegetable varieties and (2) scaling up to facilitate industrial application.
Alex Ayedun Avwunuketa, Michael Quaicoe, Francis Ayomide Adedeji
Much research has been undertaken on the sustainable aviation fuels as the alternative to jet A-1 fuels due to magnitude demands of having carbon-free alternative fuel in the aviation industry. This study gives information on the combustion dynamics and emission performance of synthetic fuel blends of a small PTD 500 turbofan engine using a kinetically shaped reactivity model that is dependent on chemical kinetics and thermodynamics. Several blends of fuels containing different volumes of SAF content were created and tested on their conformance with ASTM D7566 and ASTM D4054 requirements. The experimental test of the engine measured thrust, fuel consumption and emissions over a range of operating conditions, reactivity model included activation energy and combustion kinetics to measure the dependence of fuel mass flow on exhaust gas forming Combustion behaviours was correlated to thrust output and CO and emissions by use of α 1 reactivity coefficient. In terms of the Take-off operation, the thrust contribution of engines ranged between 80%-90% but in idle operation, recorded 10%-15.As evident in the results, addition of SAF has insignificant impact on engine thrust gain, with significant impacts on combustion paths and routes related to the incomplete combustion formation. This was proved empirically as there was no difference in the consistency of the fuel blends and these differences in combustion chemistry were related to the change in the composition and calorific value of the hydrocarbons. The present research has established the compatibility of SAF with the existing engine systems and also the need to have a detailed kinetic model to optimize the use of fuel and reduce its environmental effects. The reactivity model is expected to be extended in future to include long-term engine operating conditions under typical SAF applications, which can be used to support the propulsion aviation sector transition to green propulsion.
Kurmet G. Satenov, Sultan M. Tkenbayev, Zholaman A. Tashenov
et al.
In oil and gas production, treatment and transportation technologies, gas hydrates cause serious problems associated with disruption of these technological processes. The traditional and most common method of combating gas hydrates in the oil and gas industry is the use of methanol as a hydrate inhibitor. The specific consumption indicators of methanol consumption as an inhibitor of the formation of gas hydrates directly depend on the composition of the extracted products, as well as on the technology for preparing the extracted products for transportation. Gas hydrates represent one of the major economic and safety problems in the oil and gas industry in the exploration, production, processing and transportation of gas and hydrocarbons. This article analyzes modern methods for methanol regeneration at oil and gas industry enterprises, and describes in detail the methods and parameters of processing plants that are used for the regeneration of water-methanol solutions. The advantages and disadvantages of advanced methods of water-methanol solutions regeneration are described. As a result of the review of existing technologies, the distillation method was determined to be the most preferable, as the most proven and widely used method today.
L. Behrooznia, M. Khojastehpour, H. Hosseinzadeh-Bandbafha
IntroductionPomegranate has gained global popularity due to its high vitamin content and antioxidant properties, attracting fans worldwide. The processing of pomegranate into various products, including pomegranate juice, has become a thriving industry. However, this processing requires significant energy and chemicals—most of which are derived from fossil fuels. The combustion of these fuels releases harmful gases, contributing to global warming, environmental damage, and health risks. The costs tied to these environmental burdens are often overlooked, neglecting the principles of environmental sustainability. Therefore, it is vital to assess the monetary value of the environmental impacts throughout the entire life cycle of pomegranate juice production. This research aims to investigate the costs imposed on society, including the social costs of carbon emissions, damage costs from air pollution, and costs associated with environmental prevention measures related to processing pomegranate juice. Feel free to ask for further changes or adjustments.Materials and MethodsThis study focuses on assessing the environmental impact and associated costs generated during the processing of pomegranate juice in Mashhad, Iran, from 2022 to 2023. The research examines the case study of Saman Bazar Razavi Co. to conduct an environmental impact cost assessment. The study begins by evaluating the environmental impacts associated with the pomegranate juice production process using a life cycle assessment (LCA) approach. The costs related to these impacts are then estimated by multiplying the impact amounts with predetermined monetary coefficients. The study adopts a system boundary that extends from the arrival of the fruit at the factory to the departure of the packaged juice, defining a 160g pack of pomegranate juice as the functional unit (FU). SimaPro software, version 9, is utilized for analyzing the environmental impacts. The evaluation of environmental impact costs encompasses three categories: social costs of carbon emissions, damage costs from air pollution, and costs for environmental prevention measures. Carbon dioxide emissions are considered to assess social costs, while five other gases—nitrogen oxides, particulate matter, sulfur dioxide, volatile organic compounds, and ammonia vapor—are included in investigating air pollution damage costs. Furthermore, the calculation of environmental prevention costs takes into account seven impact categories: global warming, photochemical oxidation, respiratory inorganic effects, human toxicity, ecotoxicity, eutrophication, and acidification.Results and DiscussionHere’s the edited text with corrections marked: The investigation reveals that the production of pomegranate juice emits approximately 0.12 kg CO2 eq of carbon, with a social cost of $0.0062 per functional unit. The primary contributors to carbon emissions are natural gas and electricity. Furthermore, the evaluation of air-polluting gases indicates a total cost of $0.021 for air pollution damage. Among the five considered gases, ammonia vapor, sulfur dioxide, and nitrogen oxides incur the highest damage costs. The assessment of environmental prevention costs demonstrates a total calculated cost of $0.026, with the impact categories of global warming and acidification making the most substantial contributions of 59% and 28%, respectively. This finding suggests that the majority of costs for preventing damage in pomegranate juice production should be focused on mitigating the effects of global warming. The consumption of natural gas and electricity during the pomegranate juice production process is the main source of carbon dioxide emissions and global warming. Additionally, in terms of acidification, the contributions of pomegranate, electricity, apple, natural gas, and sugar are noteworthy. Based on these findings, it is evident that the resources used in pomegranate juice processing, derived from fossil fuels, have the most significant impact on environmental damage. Therefore, one practical method to prevent the creation of these pollutants is the utilization of alternative bioproducts produced from biomass. Considering the substantial amount of pomegranate waste generated after juice processing,which is often not utilized; these wastes can be effectively employed to produce bioenergy, such as biogas. This approach not only prevents waste disposal but also offers economic and environmental benefits.ConclusionThis article provides an overview of the environmental impacts and associated costs of pomegranate juice production in Mashhad. Using the life cycle assessment approach, the study calculates the environmental impacts per functional unit (a 160g juice pack) and estimates the corresponding costs. The results indicate that the social cost of carbon emissions, the total damage costs of air pollution, and the total environmental prevention costs per functional unit are $0.0062, $0.021, and $0.026, respectively. These costs should be allocated to mitigating the environmental damage caused by pomegranate juice production in the region.AcknowledgmentsThe authors express their gratitude to Ferdowsi University of Mashhad for funding this research (Grant No. 54189).
In the context of carbon neutrality and emission reduction goals, energy consumption optimization in the oil and gas industry is crucial for reducing carbon emissions and improving energy efficiency. As a key component in drilling operations, optimizing the energy consumption of drilling pumps has significant potential for energy savings. However, due to the complex and variable geological conditions, diverse operational parameters, and inherent nonlinear relationships in the drilling process, accurately predicting energy consumption presents considerable challenges. This study proposes a novel Long Short-Term Memory Attention model for precise prediction of drilling pump energy consumption. By integrating Long Short-Term Memory (LSTM) networks with the Attention mechanism, the model effectively captures complex nonlinear relationships and long-term dependencies in energy consumption data. Comparative experiments with traditional LSTM and Convolutional Neural Network (CNN) models demonstrate that the LSTM-Attention model outperforms these models across multiple evaluation metrics, significantly reducing prediction errors and enhancing robustness and adaptability. The proposed model achieved Mean Absolute Error (MAE) values ranging from 5.19 to 10.20 and R<sup>2</sup> values close to one (0.95 to 0.98) in four test scenarios, demonstrating excellent predictive performance under complex conditions. The high-precision prediction of drilling pump energy consumption based on this method can support energy optimization and provide guidance for field operations.
Md Bokhtiar Al Zami, Shaba Shaon, Vu Khanh Quy
et al.
Industrial networks are undergoing rapid transformation driven by the convergence of emerging technologies that are revolutionizing conventional workflows, enhancing operational efficiency, and fundamentally redefining the industrial landscape across diverse sectors. Amidst this revolution, Digital Twin (DT) emerges as a transformative innovation that seamlessly integrates real-world systems with their virtual counterparts, bridging the physical and digital realms. In this article, we present a comprehensive survey of the emerging DT-enabled services and applications across industries, beginning with an overview of DT fundamentals and its components to a discussion of key enabling technologies for DT. Different from literature works, we investigate and analyze the capabilities of DT across a wide range of industrial services, including data sharing, data offloading, integrated sensing and communication, content caching, resource allocation, wireless networking, and metaverse. In particular, we present an in-depth technical discussion of the roles of DT in industrial applications across various domains, including manufacturing, healthcare, transportation, energy, agriculture, space, oil and gas, as well as robotics. Throughout the technical analysis, we delve into real-time data communications between physical and virtual platforms to enable industrial DT networking. Subsequently, we extensively explore and analyze a wide range of major privacy and security issues in DT-based industry. Taxonomy tables and the key research findings from the survey are also given, emphasizing important insights into the significance of DT in industries. Finally, we point out future research directions to spur further research in this promising area.
Bruno Santos, Rogério Luís C. Costa, Leonel Santos
Unlocking the potential of Industry 5.0 hinges on robust cybersecurity measures. This new Industrial Revolution prioritises human-centric values while addressing pressing societal issues such as resource conservation, climate change, and social stability. Recognising the heightened risk of cyberattacks due to the new enabling technologies in Industry 5.0, this paper analyses potential threats and corresponding countermeasures. Furthermore, it evaluates the existing industrial implementation frameworks, which reveals their inadequacy in ensuring a secure transition from Industry 4.0 to Industry 5.0. Consequently, the paper underscores the necessity of developing a new framework centred on cybersecurity to facilitate organisations' secure adoption of Industry 5.0 principles. The creation of such a framework is emphasised as a necessity for organisations.
This paper investigates the interplay between cross-culture, organisational culture, path-goal leadership, and team effectiveness in Nigerian oil and gas projects. Employing a quantitative research approach with a philosophical assumption between positivism and relativism, the study examines path-goal leadership and organisational culture as mediating variables. A survey instrument was administered to 230 participants using judgmental recruitment, with a response of 91.3%. A partial least square structural equation modelling approach was implemented for data analysis. The findings reveal that high achievement and directive leadership styles in the Nigerian oil and gas industry lead workers to adopt a long-term orientation cross-culture to effectively adapt to the project working environments. Additionally, the dimensions of organizational culture exert a dominant influence on defining project environments in the industry. To enhance ownership and shared leadership, the study recommended the need to strike a balance between achievement-oriented and shared leadership throughout the project duration. Moreover, proactive occupational health measures can help manage the possible health effect of adaptive work behaviour. Furthermore, industry-wide project audits based on the study's variables can enhance leadership policies and promote a people-oriented leadership approach. The research presented in this study offers both theoretical and practical implications in the Nigerian oil and gas industry.
History of scholarship and learning. The humanities, Social sciences (General)
Caroline Taylor, Justin Maroccia, Margaret Masterson
et al.
High fructose corn syrup (HFCS) 55 (HFCS55) is a sweetener made from corn composed of 55% fructose and a mix of glucose and minor amounts of short chain oligosaccharides. It is widely used in food applications as a sweetener, preservative, flavor enhancer, moisture retainer, and to provide texture. The objective of this study was to assess the cradle-to-gate environmental impacts of average U.S. production of HFCS55. Our assessment was based on confidential primary data supplied by 13 participating facilities, including material and energy inputs and emissions for milling, refining and coproduct drying. We estimated life cycle impacts using the Tool for the Reduction and Assessment of Chemical and Environmental Impacts (TRACI 2.1). Additional impact categories were included for land use (midpoint H), Global Warming Potential (100 years) (GWP), and Cumulative Energy Demand. Environmental hotspots identified in the production of HFCS55 indicated that direct emissions from the conversion process contribute over 50% of the global warming potential, as well as the overwhelming bulk of energy and fuels used in the system, 35%–40% of acidification and ecotoxicity impact potentials and potential respiratory effects caused by particulate matter. Coal and natural gas extraction and combustion accounted for 70%–90% of the global warming impacts for process heat and power, and their extraction also contribute significantly to acidification and ecotoxicity. About 40% of the global warming potential for HFCS55 was generated by fuel used in processing for the aggregated industry average HFCS55. Nearly half of the HFCS55 was produced in facilities that utilize coal in cogeneration of steam and electricity, and that grouping had almost 20% higher impact than the U.S. aggregated. Replacing coal in the industry average with natural gas could lower impact about 20%; for the coal-using cogeneration group, that benefit would be larger. Replacing all coal and natural gas with biogas from process residues potentially could more than halve the global warming potential depending on which grouping a facility was in, although this scenario needs further evaluation. A sensitivity analysis of energy replacement options for HFCS55 showed that transition from coal to natural gas or other energy sources would lower the GWP and other impacts. This assessment was the first study of its kind; further work is needed to explicitly define areas for industry improvement. This study focuses primarily on High Fructose Corn Syrup (HFCS), specifically HFCS55, production. This work addresses that crucial gap by providing the most up-to-date, industry-relevant life cycle assessment (LCA) and associated life cycle inventory available for corn wet milling and HFCS55 and other products. It also provides a life cycle inventory for the production of U.S. corn used for wet milling and refining. Prior to this work, there are no published analyses of the life cycle environmental impacts of HFCS 55 (or associated sweeteners) reflecting industry practice from primary data at industry scale. Indeed, there are very few assessments of the environmental impact of HFCS as a product, industry-scale or otherwise.
Maksim Papenkov, Chris Meredith, Claire Noel
et al.
Accurate industry classification is a critical tool for many asset management applications. While the current industry gold-standard GICS (Global Industry Classification Standard) has proven to be reliable and robust in many settings, it has limitations that cannot be ignored. Fundamentally, GICS is a single-industry model, in which every firm is assigned to exactly one group - regardless of how diversified that firm may be. This approach breaks down for large conglomerates like Amazon, which have risk exposure spread out across multiple sectors. We attempt to overcome these limitations by developing MIS (Multi-Industry Simplex), a probabilistic model that can flexibly assign a firm to as many industries as can be supported by the data. In particular, we utilize topic modeling, an natural language processing approach that utilizes business descriptions to extract and identify corresponding industries. Each identified industry comes with a relevance probability, allowing for high interpretability and easy auditing, circumventing the black-box nature of alternative machine learning approaches. We describe this model in detail and provide two use-cases that are relevant to asset management - thematic portfolios and nearest neighbor identification. While our approach has limitations of its own, we demonstrate the viability of probabilistic industry classification and hope to inspire future research in this field.
Fatai Anifowose, Mokhles Mezghani, Saleh Badawood
et al.
The current utility of mud gas data is typically limited to geological and petrophysical correlation, formation evaluation, and fluid typing. A critical and comprehensive review of the literature on mud gas data revealed that the mud gas data is abundantly acquired during drilling but not sufficiently utilized in real time. There is the need to leverage the current advances in machine learning technology and the race towards the digital transformation of the petroleum industry to create new opportunities for more extensive utility of mud gas data. Now that data is the new “oil” or “gold”, the utility of the rich and abundant mud gas data could be explored for real-time applications. Such new possibilities are capable of adding more value to the reservoir characterization workflow ahead of geophysical logging, geological core data analysis, and well testing. Achieving this will facilitate early decision-making, improve safety, reduce nonproductive time, and ultimately accelerate the attainment of the digital transformation objective of the petroleum industry. We conclude with identifying possible future directions for the ultimate attainment of maximizing the utility of mud gas data through real-time and more advanced applications.
Massive data of bending strain along pipelines can be detected by the Inertial Measurement Unit(IMU), but there is no effective method available for the quantitative evaluation of pipelines with the IMU data so far. To address this issue, an identification method was proposed for thaw settlement segments of pipelines in permafrost zones based on IMU strain data. The method mainly includes the pre-processing method of IMU strain data, i.e., the data alignment method, the identification method of large strain characteristics of the non-thaw settlement pipeline segments, such as bends, dents and tees, and the quick positioning method of geological disaster pipeline segments determined by presetting the identification threshold and the abnormal pipeline segments to be inspected. Thus, the IMU detection data of the First Mohe-Daqing Pipeline in the permafrost region during 2013 to 2018 were identified and analyzed, and the identification results were in good agreement with the geometric inspection data. On that basis, a statistical analysis was made to study the distribution of abnormal pipeline segments in various areas of this permafrost region. The results indicate that the seasonal frozen region has more geological change points than the permafrost region, but the change is small. Generally, quantitative data are provided through this research for the integrity assessment of pipelines in geological disaster areas, which could be used to solve the technical bottleneck of failure to determine the external load of pipelines at present. Hence, it is a significant new direction for the development of pipeline integrity assessment technology.
Rapidly growing social networks and other graph data have created a high demand for graph technologies in the market. A plethora of graph databases, systems, and solutions have emerged, as a result. On the other hand, graph has long been a well studied area in the database research community. Despite the numerous surveys on various graph research topics, there is a lack of survey on graph technologies from an industry perspective. The purpose of this paper is to provide the research community with an industrial perspective on the graph database landscape, so that graph researcher can better understand the industry trend and the challenges that the industry is facing, and work on solutions to help address these problems.
Society is inextricably dependent on the Internet and other globally interconnected infrastructures used in the provisioning of information services. The growth of information technology (IT) and information systems (IS) over the past decades has created an unprecedented demand for access to information. The implication of wireless mobility are great, and the commercial possibilities of new and innovative wireless flexibility are just beginning to be realized through the emergence of the Internet of Things (IoT). This article takes a look the history of hacking and professionalization of the hacker industry. As the hacker industry becomes more fully professionalized, it is becoming much more adaptive and flexible, making it harder for intelligence and law enforcement to confront. Furthermore, the hacker industry is blurring the distinction between motivated crime and traditional computer security threats - including the disruption of critical infrastructures or the penetration of networks.
The fourth industrial revolution and the digital transformation, commonly known as Industry 4.0, is exponentially progressing in recent years. Connected computers, devices, and intelligent machines communicate with each other and interact with the environment to require only a minimum of human intervention. An important issue in Industry 4.0 is the evaluation of the quality of the process in terms of KPIs. Monte Carlo simulations can play an important role to improve the estimations. However, there is still a lack of clear workflow to conduct the Monte Carlo simulations for selecting different Monte Carlo methods. This paper, therefore, proposes a simulation flow for conducting Monte Carlo methods comparison in Industry 4.0 applications. Based on the simulation flow, we compare Cumulative Monte Carlo and Markov Chain Monte Carlo methods. The experimental results show the way to use the Monte Carlo methods in Industry 4.0 and possible limitations of the two simulation methods.