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نتیجه جستجو - Oil industries

تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 Application of green supply chain management in the oil Industries: Modeling and performance analysis
کاربرد مدیریت زنجیره تامین سبز در صنایع نفت: مدل سازی و تحلیل عملکرد-2021
Environmental concerns relating to production affairs have made various organizations use green practices in different processes of supply chain, because the green supply chain management (GSCM) is considered as an important organizational philosophy to decrease environmental risks and as a preventive approach in order to increase environmental performance and achievement of competitive advantages for organizations. The purpose of the present article is to design an interactive model for the practices of GSCM and its application to clustering oil industries for analyzing their green performance. Therefore, the literature was studied and a total of fifteen practices were obtained using experts’ opinions in academic and oil industry professionals. In next, the fuzzy interpretative structural modeling (FISM) approach was utilized so as to determine the relationship between the practices through considering the linguistic ambiguities of judgments and designing the structural model. The existing relationships within the structural model were studied and tested by means of structural equation modeling (SEM). After that, the relative importance of each practice was calculated by applying fuzzy analysis network process (FANP). In the next, the oil industries were categorized in two clusters using the K-means algorithm aggregated to the particle swarm optimization algorithm. Results of the present study showed that ‘‘legal requirements and regulations”, ‘‘intra-organizational environmental management”, ‘‘green design” and ‘‘green technology” are of root and influential practices with relatively more importance than others; in addition, it was cleared that the first cluster industries have high performance whereas the second ones have medium performance from the viewpoint of considering the practices of GSCM. Finally, the discriminant function designed to forecasting environment performance of the oil industries and member- ship to clusters for each of them.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Web International Conference on Accelerating Innovations in Material Science – 2020.
Keywords: Green Supply Chain Management | Oil industries | Fuzzy interpretative structural modeling | Kmeans | Particle swarm optimization | Clustering | Discriminant analysis
مقاله انگلیسی
2 Actor–Critic Reinforcement Learning and Application in Developing Computer-Vision-Based Interface Tracking
یادگیری و کاربرد تقویت کننده منتقد در توسعه ردیابی رابط مبتنی بر بینایی ماشین-2021
This paper synchronizes control theory with computer vision by formalizing object tracking as a sequential decision-making process. A reinforcement learning (RL) agent successfully tracks an interface between two liquids, which is often a critical variable to track in many chemical, petrochemical, metallurgical, and oil industries. This method utilizes less than 100 images for creating an environment, from which the agent generates its own data without the need for expert knowledge. Unlike supervised learning (SL) methods that rely on a huge number of parameters, this approach requires far fewer parameters, which naturally reduces its maintenance cost. Besides its frugal nature, the agent is robust to environmental uncertainties such as occlusion, intensity changes, and excessive noise. From a closed-loop control context, an interface location-based deviation is chosen as the optimization goal during training. The methodology showcases RL for real-time object-tracking applications in the oil sands industry. Along with a presentation of the interface tracking problem, this paper provides a detailed review of one of the most effective RL methodologies: actor–critic policy.
Keywords: Interface tracking | Object tracking | Occlusion | Reinforcement learning | Uniform manifold approximation and projection
مقاله انگلیسی
3 A three year study of metal levels in skin biopsies of whales in the Gulf of Mexico after the Deepwater Horizon oil crisis
سه سال مطالعه سطح فلز در بیوپسی پوست نهنگ در خلیج مکزیک پس از بحران نفت افق Deepwater-2018
In response to the explosion of the Deepwater Horizon and the massive release of oil that followed, we conducted three annual research voyages to investigate how the oil spill would impact the marine offshore environment. Most investigations into the ecological and toxicological impacts of the Deepwater Horizon Oil crisis have mainly focused on the fate of the oil and dispersants, but few have considered the release of metals into the environ ment. From studies of previous oil spills, other marine oil industries, and analyses of oil compositions, it is evident that metals are frequently encountered. Several metals have been reported in the MC252 oil from the Deepwater Horizon oil spill, including the nonessential metals aluminum, arsenic, chromium, nickel, and lead; genotoxic metals, such as these are able to damage DNA and can bioaccumulate in organisms resulting in persistent exposure. In the Gulf of Mexico, whales are the apex species; hence we collected skin biopsies from sperm whales (Physeter macrocephalus), short-finned pilot whales (Globicephala macrorhynchus), and Brydes whales (Balaenoptera edeni). The results from our three-year study of monitoring metal levels in whale skin show (1) genotoxic metals at concentrations higher than global averages previously reported and (2) patterns for MC252-relevant metal concentrations decreasing with time from the oil spill.
Keywords: Chromium ، Deepwater Horizon ، Gulf of Mexico ، Metals ، Nickel ، Oil spill ، Whales
مقاله انگلیسی
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