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نتیجه جستجو - انرژی

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1 تمایز خونریزی حاصل از افزایش کنتراست با استفاده از CT طیف سنجی با لایه دوگانه در بیماران سکته حاد
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 4 - تعداد صفحات فایل doc فارسی: 8
بیماران سکته حاد به مراکز دارای ترومبکتومی(لخته برداری) (TCC) انتقال می یابند و پس از ورود به TCC ، تحت معاینه CT سر قرار می گیرند تا نمره ASPECTS و خونریزی داخل جمجمه آنها ارزیابی شود. تقویت پارانشیمی در بیمارانی که کنتراست یددار یا iodinated را قبل از انتقال دریافت کرده اند، در شبیه سازی خونریزی در این CT و پس از انتقال کمک می کند. دو مورد از کاربرد تصویربرداری طیفی CT به منظور تمایز بین افزایش کنتراست پارانشیمی و خونریزی در این مطالعه گزارش شده است. روش TCC، تصویربرداری با انرژی دوگانه یا لایه دوگانه (طیفی) را برای این گروه بیمار در نظر می گیرد.
کلمات کلیدی: سکته مغزی | تصویربرداری طیفی CT
مقاله ترجمه شده
2 بهبود تولید بیودیزل با کمک اولتراسونیک حاصل از ضایعات صنعت گوشت (چربی خوک) با استفاده از نانوکاتالیزور اکسید مس سبز: مقایسه سطح پاسخ و مدل سازی شبکه عصبی
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 11 - تعداد صفحات فایل doc فارسی: 25
سوخت زیستی سبز ، تمیز و پایدار تنها گزینه به منظور کاهش کابرد سوخت های فسیلی ، پاسخگویی به تقاضای زیاد انرژی و کاهش آلودگی هوا است. تولید بیودیزل زمانی ارزان می شود که از یک پیش ماده ارزان ، کاتالیزور سازگار با محیط زیست و فرآیند مناسب استفاده کنیم. پیه خوک از صنعت گوشت حاوی اسید چرب بالا است و به عنوان یک پیش ماده موثر برای تهیه بیودیزل کاربرد دارد. این مطالعه بیودیزل را از روغن پیه خوک از طریق فرآیند استری سازی دو مرحله ای با کمک اولتراسونیک و کاتالیزور تولید می کند. عصاره Cinnamomum tamala (C. tamala) برای تهیه نانوذرات CuO مورد استفاده قرار گرفت و با استفاده از طیف مادون قرمز ، پراش اشعه ایکس ، توزیع اندازه ذرات ، میکروسکوپ الکترونی روبشی و انتقال مشخص شد. تولید بیودیزل با استفاده از طرح Box-Behnken (BBD) و شبکه عصبی مصنوعی (ANN) ، در محدوده متغیرهای زمان اولتراسونیک (us )(20-40 min)، بارگیری نانوکاتالیزور 1-3) CuO درصد وزنی( ، و متانول به قبل از نسبت مولی PTO (10:1e30:1) مدلسازی شد. آنالیز آماری ثابت کرد که مدل سازی شبکه عصبی بهتر از BBD است. عملکرد بهینه 97.82٪ با استفاده از الگوریتم ژنتیک (GA) در زمان US: 35.36 دقیقه ، بار کاتالیزور CuO: 2.07 درصد وزنی و نسبت مولی: 29.87: 1 به دست آمد. مقایسه با مطالعات قبلی ثابت کرد که اولتراسونیک به میزان قابل توجهی موجب کاهش بار نانوکاتالیزور CuO می شود ، و نسبت مولی را افزایش می دهد و این فرایند را بهبود می بخشد.
کلمات کلیدی: چربی خوک | التراسونیک | اکسید مس | سنتز سبز | شبکه عصبی | سطح پاسخ
مقاله ترجمه شده
3 Industrial smart and micro grid systems e A systematic mapping study
سیستم های هوشمند و ریز شبکه صنعتی و یک مطالعه نقشه برداری منظم-2020
Energy efficiency and management is a fundamental aspect of industrial performance. Current research presents smart and micro grid systems as a next step for industrial facilities to operate and control their energy use. To gain a better understanding of these systems, a systematic mapping study was conducted to assess research trends, knowledge gaps and provide a comprehensive evaluation of the topic. Using carefully formulated research questions the primary advantages and barriers to implementation of these systems, where the majority of research is being conducted with analysis as to why and the relative maturity of this topic are all thoroughly evaluated and discussed. The literature shows that this topic is at an early stage but already the benefits are outweighing the barriers. Further incorporation of renewables and storage, securing a reliable energy supply and financial gains are presented as some of the major factors driving the implementation and success of this topic.
Keywords: Industrial smart grid | Industrial micro grid | Systematic mapping study | Strategic energy management | Industrial facility optimization | Renewable energy resources
مقاله انگلیسی
4 Intelligent energy management in off-grid smart buildings with energy interaction
مدیریت انرژی هوشمند در ساختمانهای هوشمند خارج از شبکه با تعامل انرژی-2020
The energy interaction between smart homes can be a solution for developing renewable energy systems in residential sections and optimal energy consumption in homes. The main objectives of such energy interactions are to increase consumer participation in energy management‘ boost economic efficiency‘ increase the user’s satisfaction by choosing between electricity sellers and buyers‘ and reduce the electricity purchased from the grid especially at peak hours. Thus, the innovations of this study includes defining an energy exchange method between smart buildings in an off-grid mode considering renewable energy systems, considering both thermal and electrical equilibrium and studying the lightning loads. it is assumed, here, that smart homes are off-grid‘ and the critical loads are supplied by the energy transfer between the homes using mixed integer linear programming. A compromise between the cost and time interval for using home appliances is considered to provide consumer’s comfort. An objective function is introduced considering programmable and non-programmable loads‘ thermal and electrical storages and lighting loads aiming to optimize the cost of energy between different smart buildings. Based on the method, which is tested in two different cases not only does the total cost of the smart buildings decrease but also the cost is reduced significantly when lightning loads are managed.
Keywords: Energy management | Smart homes | Smart microgrid | Energy storage system | Wind turbine
مقاله انگلیسی
5 Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty
تجزیه و تحلیل جایگزین قدرت تطبیقی مبتنی بر یادگیری تقویتی برای مدیریت انرژی سیستم های ذخیره سازی انرژی ترکیبی مستقل با توجه به عدم اطمینان-2020
Hybrid energy storage systems (HESS) involve synergies between multiple energy storage technologies with complementary operating features aimed at enhancing the reliability of intermittent renewable energy sources (RES). Nevertheless, coordinating HESS through optimized energy management strategies (EMS) introduces complexity. The latter has been previously addressed by the authors through a systems-level graphical EMS via Power Pinch Analysis (PoPA). Although of proven efficiency, accounting for uncertainty with PoPA has been an issue, due to the assumption of a perfect day ahead (DA) generation and load profiles forecast. This paper proposes three adaptive PoPA-based EMS, aimed at negating load demand and RES stochastic variability. Each method has its own merits such as; reduced computational complexity and improved accuracy depending on the probability density function of uncertainty. The first and simplest adaptive scheme is based on a receding horizon model predictive control framework. The second employs a Kalman filter, whereas the third is based on a machine learning algorithm. The three methods are assessed on a real isolated HESS microgrid built in Greece. In validating the proposed methods against the DA PoPA, the proposed methods all performed better with regards to violation of the energy storage operating constraints and plummeting carbon emission footprint.
Keywords: Hybrid energy storage systems | Energy management strategies | Model predictive control | Kalman filter | Reinforcement learning
مقاله انگلیسی
6 Influencing factors on energy management in industries
تأثیر عوامل مؤثر بر مدیریت انرژی در صنایع-2020
Energy management has been considered in the global agenda as a way to improve energy performance and greenhouse gas reduction in organizations. Industries account for a significant part of energy use worldwide and present opportunities for energy efficiency improvements. Within the industry, energy management is a complex task, regarding scenarios with variables related to the following perspectives: economics, contingency, technological change and behavioural. This paper aims at analyzing the influencing factors on energy management in industries from these perspectives. A survey with 40 variables was carried out with middle managers from different industrial sectors in Brazil. The variables were divided into three groups: drivers for investments in energy efficiency; organizational processes and actions in energy management; involvement of middle managers. Initially, an exploratory factor analysis technique was employed aiming at specifying the main factors influencing energy management. In the sequence, a confirmatory factor analysis was used to associate the variables to the main factors as well as to know how the factors relate to each other. The study showed a positive correlation among all the factors identified. Statistical tests suggested that the factors could not be explained separately. Hypotheses tests were applied to verify the influence of the factors among the groups surveyed. The final model comprised eight factors into the three groups: organizational (strategic, operational), involvement (motivation, support), drivers (production, economics, competitiveness, environment). The results and the main implications of the study are discussed in the paper.
Keywords: Energy management | ISO 50001 | Energy efficiency | Industries | Factor analysis
مقاله انگلیسی
7 Research on the policy route of China’s distributed photovoltaic power generation
تحقیق در مورد مسیر سیاست تولید انرژی فتوولتائیک و توزیع شده در چین-2020
The distributed photovoltaic power generation is an important way to make use of solar energy in cities. China issues a series of policies to support the development of distributed photovoltaics in law, electricity price, grid connection standard, project management, financial support and so on. However, there are still some defects in policies and market mechanism. China creates a competitive market with a significant number of projects of distributed photovoltaic power through the reform of the electricity market, yet substantial drawbacks of the corresponding investment subsidies prevent distributed photovoltaic power from rapidly developing. This paper summarizes the status quo of China’s distributed photovoltaic power development, given its long-term plan, presents excellences and shortcomings of the existing policy system, and looks into the supporting policies and implementation paths for China’s distributed photovoltaic power in different stages. Innovative business models and financial support models are conducive to the development of distributed photovoltaic power. Financial innovation methods such as crowd funding and asset securitization should be encouraged to develop a sound risk assessment mechanism for projects, involve insurance institutions, and establish a risk sharing mechanism. In the context of a series of supporting policies, the distributed photovoltaic power in China will move towards market-oriented standardization for a healthier and more stable development.
Keywords: Distributed photovoltaic power | Electricity price | Policy route | Development strategy
مقاله انگلیسی
8 Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle
استراتژی مدیریت انرژی مبتنی بر یادگیری تقویتی عمیق قانون برای خودروی الکتریکی هیبریدی تقسیم برق-2020
The optimization and training processes of deep reinforcement learning (DRL) based energy management strategy (EMS) can be very slow and resource-intensive. In this paper, an improved energy management framework that embeds expert knowledge into deep deterministic policy gradient (DDPG) is proposed. Incorporated with the battery characteristics and the optimal brake specific fuel consumption (BSFC) curve of hybrid electric vehicles (HEVs), we are committed to solving the optimization problem of multi-objective energy management with a large space of control variables. By incorporating this prior knowledge, the proposed framework not only accelerates the learning process, but also gets a better fuel economy, thus making the energy management system relatively stable. The experimental results show that the proposed EMS outperforms the one without prior knowledge and the other state-of-art deep reinforcement learning approaches. In addition, the proposed approach can be easily generalized to other types of HEV EMSs.
Keywords: Energy management strategy | Hybrid electric vehicle | Expert knowledge | Deep deterministic policy gradient | Continuous action space
مقاله انگلیسی
9 A robust co-state predictive model for energy management of plug-in hybrid electric bus
یک مدل پیش بینی شده مشترک قدرتمند برای مدیریت انرژی اتوبوس برقی هیبریدی پلاگین-2020
This paper proposes a robust co-state predictive model for Pontryagin’s Minimum Principle (PMP)-based energy management of plug-in hybrid electric bus (PHEB). The main innovation is that the robust costate predictive model is only expressed by a simplified formula. Moreover, it is exclusively designed by the Design For Six Sigma (DFSS) method in consideration of noises of driving cycles and stochastic vehicle mass. Because the DFSS strives to minimize the weighted sum of mean and standard deviation of fuel consumption, the proposed strategy can simultaneously improve the fuel economy of the PHEB and its robustness. The DFSS results show that the coefficients of the robust co-state predictive model can be found; the simulation results demonstrate that the proposed strategy has similar fuel economy to dynamic programming (DP); the hardware-in-loop (HIL) results demonstrate that the proposed strategy has good real-time control performance, and can averagely improve the fuel economy by 35.19% compared to a rule-based control strategy.
Keywords: Plug-in hybrid electric bus | Energy management | PMP | Co-state predictive model | Design for six sigma
مقاله انگلیسی
10 Deep reinforcement learning based energy management for a hybrid electric vehicle
مدیریت انرژی مبتنی بر یادگیری تقویت عمیق برای یک وسیله نقلیه الکتریکی هیبریدی-2020
This research proposes a reinforcement learning-based algorithm and a deep reinforcement learningbased algorithm for energy management of a series hybrid electric tracked vehicle. Firstly, the powertrain model of the series hybrid electric tracked vehicle (SHETV) is constructed, then the corresponding energy management formulation is established. Subsequently, a new variant of reinforcement learning (RL) method Dyna, namely Dyna-H, is developed by combining the heuristic planning step with the Dyna agent and is applied to energy management control for SHETV. Its rapidity and optimality are validated by comparing with DP and conventional Dyna method. Facing the problem of the “curse of dimensionality” in the reinforcement learning method, a novel deep reinforcement learning algorithm deep Qlearning (DQL) is designed for energy management control, which uses a new optimization method (AMSGrad) to update the weights of the neural network. Then the proposed deep reinforcement learning control system is trained and verified by the realistic driving condition with high-precision, and is compared with the benchmark method DP and the traditional DQL method. Results show that the proposed deep reinforcement learning method realizes faster training speed and lower fuel consumption than traditional DQL policy does, and its fuel economy quite approximates to global optimum. Furthermore, the adaptability of the proposed method is confirmed in another driving schedule.
Keywords: Hybrid electric tracked vehicle | Energy management | Dyna-H | Deep reinforcement learning | AMSGrad optimizer
مقاله انگلیسی
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