با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
ردیف | عنوان | نوع |
---|---|---|
1 |
Bi-objective optimal design of hydrogen and methane supply chains based on Power-to-Gas systems
طراحی بهینه دو هدفه زنجیره های تأمین هیدروژن و متان بر اساس سیستم های نیرو به گاز-2021 This paper presents a methodological design framework for Hydrogen and Methane Supply Chains (HMSC) based on Power-to-Gas (PtG) systems. The novelty of the work is twofold, first considering a specific demand for hydrogen for electromobility in addition to the hydrogen demand required as a feedstock to produce synthetic methane from the methanation process. and performing a bi-objective optimization of the HMSC to provide effective support for the study of deployment scenarios. The approach is based on a Mixed Integer Linear Programming (MILP) approach with augmented epsilon-constraint implemented in the GAMS environment according to a multi-period approach (2035-2050) with several available energy sources (wind, PV, hydro, national network) for hydrogen production. Carbon dioxide sources stem mainly from mechanization and gasification processes. The objectives to be minimized simultaneously are the Total Annual Cost and the greenhouse gas emissions related to the whole HMSC over the entire period studied. KEYWORDS: Power-to-Gas | Methanation | Hydrogen | MILP | Augmented epsilon constraint | GAMS | optimization approach |
مقاله انگلیسی |
2 |
A methodological design framework for hydrogen and methane supply chain with special focus on Power-to-Gas systems: application to Occitania region, France
یک چارچوب طراحی روش برای زنجیره تأمین هیدروژن و متان با تمرکز ویژه بر روی سیستم های نیرو به گاز: کاربرد در منطقه اوکسیتانیا ، فرانسه-2021 This work presents a methodological design framework for Hydrogen and Methane Supply Chains (HMSC). An innovative approach is to focus on Power-to-Hydrogen (PtH) and Power-to-Methane (PtM) concepts, and their interactions with other technologies, and energy carriers (i.e., Steam Methane Reforming – SMR, and natural gas). The overall objective of this work is to perform single objective and multi-objective optimizations for HMSC design to provide effective support for deployment scenarios. The methodological framework developed is based on a Mixed Integer Linear Programming (MILP) approach with augmented ε-constraint implemented in the GAMS environment according to a multi-period approach (2035-2050). Several available energy sources (wind, PV, hydro, national power grid, and natural gas) for hydrogen production through electrolysis and SMR are included. Carbon dioxide sources stem mainly from methanization and gasification processes, which are used to produce methane through methanation. The objective to be minimised in the single optimization approach is the total annual cost considering the externality of greenhouse gas emissions through the carbon price for the whole HMSC over the entire period studied. The multi-objective optimization includes as objectives the total annual cost, greenhouse gas emissions, and the total methane production from methanation. The Levelized Cost of Energy (LCOE), and the greenhouse gas emissions for each energy carrier are also computed. The results show that renewable hydrogen from PtG can be competitive with SMR through the implementation of carbon prices below 0.27 €/kgCO2. In the case of synthetic methane, the available resources can meet the demand through PtG, and even if synthetic methane for natural gas network injection is thus far from competitive with natural gas, power-to-gas technologies have the potential to decarbonize the fossil economy and achieve a circular economy through CO2 recovery. KEYWORDS: Power-to-Gas | Methanation | Hydrogen | MILP | supply chain | optimization |
مقاله انگلیسی |
3 |
Hydrogen supply chain and challenges in large-scale LH2 storage and transportation
زنجیره تامین هیدروژن و چالش هایی در ذخیره سازی و حمل و نقل LH2 در مقیاس بزرگ-2021 Hydrogen is considered to be one of the fuels of future and liquid hydrogen (LH2) technology
has great potential to become energy commodity beyond LNG. However, for commercial
widespread use and feasibility of hydrogen technology, it is of utmost importance to develop
cost-effective and safe technologies for storage and transportation of LH2 for use in stationary applications as well as offshore transportation. This paper reviews various aspects of
global hydrogen supply chain starting from several ways of production to storage and delivery to utilization. While each these aspects contribute to the overall success and efficiency
of the global supply chain, storage and delivery/transport are the key enablers for establishing global hydrogen technology, especially while current infrastructure and technology
are being under development. In addition, while all storage options have their own advantages/disadvantages, the LH2 storage has unique advantages due to the familiarity
with well-established LNG technology and existing hydrogen technology in space programs.
However, because of extremely low temperature constraints, commercialization of LH2
technology for large-scale storage and transportation faces many challenges, which are
discussed in this paper along with the current status and key gaps in the existing technology.
© 2021 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Keywords: Hydrogen supply chain | Hydrogen production | Liquefaction | Hydrogen storage | Insulation strategy | Thermal modeling |
مقاله انگلیسی |
4 |
Techno-economic and life cycle greenhouse gas emissions assessment of liquefied natural gas supply chain in China
ارزیابی فنی و اقتصادی و چرخه زندگی انتشار گازهای گلخانه ای از زنجیره تامین گاز طبیعی مایع در چین-2021 This study assessed the techno-economic performance and life cycle greenhouse gas (GHG) emissions for various liquefied natural gas (LNG) supply chains in China in order to find the most efficient way to supply and use LNG. This study improves current literature by adding supply chain optimization options (cold energy recovery and hydrogen production) and by analyzing the entire supply chain of four different LNG end-users (power generation, industrial heating, residential heating, and truck usage). This resulted in 33 LNG pathways for which the energy efficiency, life cycle GHG emissions, and life cycle costs were determined by process-based material and energy flow analysis, life cycle assessment, and pro- duction cost calculation, respectively. The LNG and hydrogen supply chains were compared with a reference chain (coal or diesel) to determine avoided GHG emissions and GHG avoidance costs. Results show that NG with full cryogenic carbon dioxide capture (FCCC) is most beneficial pathway for both avoided GHG emissions and GHG avoidance costs (70.5e112.4 g CO2-e/MJLNG and 66.0e95.9 $/t CO2-e). The best case was obtained when NG with FCCC replaces coal-fired power plants. Results also indicate that hydrogen pathways requires maturation of new technology options and significant capital cost reductions to become attractive.© 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/4.0/). Keywords: Liquefied natural gas | Techno-economic assessment | Life cycle greenhouse gas emission | Cold recovery | Blue hydrogen |
مقاله انگلیسی |
5 |
Analyses of hydrogen energy system as a grid management tool for the Hawaiian Isles
تجزیه و تحلیل سیستم انرژی هیدروژن به عنوان ابزاری برای مدیریت شبکه برای جزایر هاوایی-2020 One of the objectives of the research project at Hawaii Natural Energy Institute (HNEI) is to
demonstrate long-term durability of the electrolyzer when operated under cyclic operation
for frequency regulation on an Island grid system. In this paper, a Hydrogen Energy System
with an electrolyzer is analyzed as a potential grid management tool. A simulation tool
developed with a validated model of the hydrogen energy system and Island of Hawaii grid
model is presented and employed for this investigation. The simulation study uses realistic
measured solar and wind power profiles to understand what optimal electrolyzer size
would be required to achieve the maximum level of grid frequency stabilization. The
simulation results give insight into critical information when designing a hydrogen energy
system for grid management applications and the economic impact it has when operated
as a pure grid management scheme or as a limitless hydrogen production system. Keywords: Hydrogen | Hydrogen energy system | Electrolyzer | Grid management | Renewable energy |
مقاله انگلیسی |
6 |
Development of short chain fatty acid-based artificial neuron network tools applied to biohydrogen production
توسعه ابزارهای شبکه عصبی مصنوعی مبتنی بر اسیدهای چرب با زنجیره کوتاه استفاده شده برای تولید بیوهیدروژن-2020 The biological production of biohydrogen through dark fermentation is a very complex
system where the use of an artificial neuron network (ANN) for prediction, controlling and
monitoring has a great potential. In this study three ANN models based on volatile fatty
acids (VFA) production and speciation were evaluated for their capacity to predict (i)
accumulated H2 production, (ii) hydrogen production rate and (iii) H2 yield. Lab-scale biohydrogen
and VFA production kinetics from a previous study were used for training and
validation of the models. The input parameters studied were: time and acetate and butyrate
concentrations (model 1), time and lactate, acetate, propionate and butyrate concentrations
(model 2), time and the sum of all VFA (model 3) and time and butyrate/acetate
(model 4). All models could predict biohydrogen accumulated production, hydrogen production
rate and H2 yield with high accuracy (R2 > 0.987). VFAT is the input parameter
indicated for processes using pure cultures, while for complex/mixed cultures a model
based on acetate and butyrate is recommended. Keywords: Volatile fatty acids | Artificial intelligence | Biofuel | Dark fermentation |
مقاله انگلیسی |
7 |
Second law analysis of CuCl2 hydrolysis reaction in the CueCl thermochemical cycle of hydrogen production
تجزیه و تحلیل قانون دوم واکنش هیدرولیز CuCl2 در چرخه گرمایشی CueCl تولید هیدروژن-2020 This article presents an exergy study of the hydrolysis reaction in the copper-chlorine thermochemical
cycle for hydrogen production. It examines the reactor performance through a detailed analysis of
thermal and chemical irreversibilities in the two-phase gas-solid reacting flow. A thermodynamic
analysis is developed based on the flow availability and availability transfer in the spray reaction process
to determine the ireversibilities of the reaction at different operating conditions. The reactor operating
temperature and steam to copper chloride molar ratio, along with changes in the reaction kinetics, are
considered as the critical parameters which affect the exergy efficiency of the hydrolysis reactor. The
entropy generation associated with adding extra steam into the reactor and changing the reaction
temperature is evaluated to assess the effectiveness of the decomposition process and the lost availability
from the hydrolysis reaction. The effects from the side reaction and unreacted CuCl2 in the simulation
reveal the exergy loss and resulting value of the exergy efficiency of 2.8% compared to the stoichiometry
condition with a relatively high exergy efficiency of more than 75%. Such a large difference indicates the
importance of analyzing real reaction conditions to provide a more reliable insight in identifying the
sources of exergy losses. Keywords: Hydrogen production | Thermochemical cycle | Hydrolysis reaction | Energy | Exergy | Efficiency |
مقاله انگلیسی |
8 |
Data mining in photocatalytic water splitting over perovskites literature for higher hydrogen production
داده کاوی در تقسیم آب فوتوکاتالیستی بر ادبیات پروسکایت برای تولید هیدروژن بالاتر-2019 A database containing 540 cases from 151 published papers on photocatalytic water splitting over perovskites
was constructed and analyzed using data mining tools; the factors leading high hydrogen production were
identified by association rule mining while some useful heuristics for the future studies were developed by
decision tree analysis. Additionally, the predictive models were developed using random forest regression.
In about half of the works, the perovskites were doped by A-site, B-site or both; however, only some portion of
doped catalysts had better activity than plain perovskites while doping also improved stability in some cases.
The effect of co-catalyst on activity seems to be also irregular; no definitive conclusion could be drawn. The
effects of preparation methods on surface area, band gap and crystal structure were noticeable. This is also
observed in visible light activity; for example the materials prepared by hydrothermal synthesis method appeared
to perform better under visible light. Methanol and other sacrificial agents were used in both UV and
visible light tests while inorganic additives have been commonly utilized under visible light. The band gap was
found to be highly predictable but it could not be directly linked to the hydrogen production. As the result,
although there has been significant progress in the field, the improvement in hydrogen production appeared to
be always limited; the sound solutions like ion doping to modify the band gap, use of co-catalyst for charge
separation or use of additives as sacrificial agents did not to help as much as desired. Keywords: Photocatalytic water splitting | Perovskite semiconductor | Band gap modification | Machine-learning | Data mining |
مقاله انگلیسی |
9 |
Hydrogen production via biomass gasification, and modeling by supervised machine learning algorithms
تولید هیدروژن از طریق گاز زدایی زیست توده و الگوریتم های یادگیری ماشین نظارت شده-2019 Prediction of clean hydrogen production via biomass gasification by supervised machine
learning algorithms was studied. Lab-scale gasification studies were performed in a steel
fixed bed updraft gasifier having a cyclone separator. Pure oxygen, and dried air with
varying flow rates (0.05e0.3 L/min) were applied to produce syngas (H2, CH4, CO). Gas
compositions were monitored via on-line gas analyzer. Various regression models were
created by using different Machine Learning (ML) algorithms which are Linear Regression
(LR), K Nearest Neighbors (KNN) Regression, Support Vector Machine Regression (SVMR)
and Decision Tree Regression (DTR) algorithms to predict the value of H2 concentration
based on the other parameters that are time, temperature, CO, CO2, CH4, O2 and heating
value. The highest hydrogen value in syngas was found around 35% vol. after gasification
experiments with higher heating value (HHV) of approximately 3400 kcal/m30.05 L/min and
0.015 L/min were the optimum flow rates for dried air and pure oxygen, respectively. In
modeling section, it was observed that H2 concentrations were being reflected effectively
by the concentrations estimated through the proposed model structures, and by having r2
values of 0.99 which were ascertained between actual and model results. Keywords: Machine learning algorithms | Gasification | Biomass | Hydrogen |
مقاله انگلیسی |