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1 |
Evaluating the urban metabolism sustainability of municipal solid waste management system: An extended exergy accounting and indexing perspective
ارزیابی متابولیسم شهری پایداری سیستم مدیریت ضایعات جامد شهری: حسابداری اگزرژی گسترده و دیدگاه نمایه سازی-2021 In this study, Extended Exergy Accounting was adopted to develop an accounting model to evaluate the performance of a Municipal Solid Waste Management System. Furthermore, urban metabolism sustainability index
for waste was also proposed to represent the unified society-economy-environment impacts of the MSWMS under
the framework of a comprehensive sustainability evaluation. A detailed analysis of wood and horticultural waste
treatment scenarios in Singapore was done as a case study. It was found that the gasification scenario theoretically performs significantly better than the incineration scenario, in terms of energy carrier consumption,
emissions, thermodynamic efficiency and sustainability. Analysis results show that, if extrapolated to Singapore’s
total wood and horticultural waste, gasification technology has potential to reduce energy consumption and
increase electricity output. An uncertainty analysis was carried out and it was found that the main extended
exergetic parameters of the two scenarios considered were in the range of 3–8%, thus confirming the reliability of the accounting results. A sensitivity analysis of the urban metabolism sustainability index for waste was conducted for the gasification scenario to identify key influencing factors and seek potential improvements; this was
done by considering changes in four variables: transportation distance, electrical efficiency, working hour
increment and gross capital cost per ton waste treated. It was found that, to ensure the feasibility and sustainability of gasification scenario, the following are required: keeping the electricity production efficiency greater
than 21.33%; the transportation distance between the gasification power plant and source of wood and horticultural waste should be kept within 17.08 km; employment of per kton annual treatment capacity should be less
than 0.14 workers; wood and horticultural waste source should control the waste collection frequency of no more
than 3 times per day and the number of workers participating in the collection each time is less than 4 persons,
totaling to 12 workers per day.
keywords: تجزیه و تحلیل Exergy را گسترش دهید | زباله جامد شهری | ارزیابی پایداری | متابولیسم شهری | اگزرژی کار | Exergy اصلاح محیط زیست | Extend exergy analysis | Municipal solid waste | Sustainability assessment | Urban metabolism | Labor exergy | Environmental remediation exergy |
مقاله انگلیسی |
2 |
Accounting for uncertainties due to high-impact low-probability events in power system development
محاسبه عدم قطعیت های ناشی از رویدادهای با احتمال کم تاثیر زیاد در توسعه سیستم قدرت-2021 In the long-term development of the electric power system, system operators should consider the socio-economic
balance between grid investment costs and security of supply, including the risk of power supply interruptions.
Cost-benefit analyses conducted for this purpose are associated with many uncertainties but have traditionally
focused on the expected value of the net socio-economic benefits of risk-reducing measures. This article focuses
on the large uncertainties that are associated with the possible occurrence of high-impact low-probability
interruption events (HILP events). The objective is to quantify and visualize the implications of uncertainties due
to HILP events in the context of power system development. More specifically, this article describes a method-
ology accounting for uncertainties in socio-economic cost-benefit analysis of measures for reducing the risk of
HILP events. The methodology accounts for the contributions of both aleatory and epistemic uncertainties and
comprises a hybrid probabilistic-possibilistic uncertainty analysis method. Applying the methodology to a real
case involving a grid investment decision, it is demonstrated how it provides additional insight compared to
conventional cost-benefit analyses considering expected values where uncertainties are not accounted for
explicitly. It is furthermore discussed how these results can help to better inform grid development decisions. keywords: برنامه ریزی سیستم قدرت | تحلیل ریسک | آسیب پذیری | قابلیت اطمینان سیستم قدرت | رویدادهای فوق العاده | Power system planning | Risk analysis | Vulnerability | Power system reliability | Extraordinary events |
مقاله انگلیسی |
3 |
Deriving reservoir operation rule based on Bayesian deep learning method considering multiple uncertainties
استخراج قانون انباره بر اساس روش یادگیری عمیق بیزی با توجه به عدم قطعیت های متعدد-2019 Reservoir operation rules play a key role in real-time reservoir operation; the main factors affecting operation
decisions are the current reservoir status and the future inflows. However, the future reservoir inflows are
stochastic and always contain uncertainty. To study the influence of inflow uncertainty on reservoir operation
rules, this paper proposes a Bayesian Deep learning method that considers both model parameter uncertainty
and inflow uncertainty. In the model, the Monte Carlo integration is used to convert the complex integrals of
inflow probability into a summation form. Variational inference is employed to obtain the posterior distribution
of model parameters. The proposed method is applied to a real-world application at Three Gorges Project on the
Yangtze River. The uncertainty estimation results show that the influence of inflow uncertainty on reservoir
operation rule is greater than model parameter uncertainty, and the decision of reservoir operation is more
sensitive to the reservoir inflow during dry season than other seasons. The experimental results demonstrate that
the proposed Bayesian deep learning performs better than the comparison method in term of hydropower
generation and the root mean square errors. Moreover, the proposed method is more robust than the comparison
method when considering the inflow uncertainty. Keywords: Reservoir operation | Operation rule | Uncertainty analysis | Deep learning | Variational inference | Bayesian neural networks |
مقاله انگلیسی |
4 |
Machine learning aided stochastic structural free vibration analysis for functionally graded bar-type structures
تحلیل ارتعاش آزاد ساختاری تصادفی کمکی یادگیری ماشین برای ساختارهای نوع-نواری هدفمند-2019 This paper presents a machine learning aided stochastic free vibration analysis for functionally graded (FG) bartype
structures through finite element method (FEM). The considered system uncertainties including the constituent
material properties, the dimensions of structural members, and the degree of the gradation of the FGM
are incorporated. A novel kernel-based machine learning technique, namely the extended support vector regression
(X-SVR), is presented to estimate the governing relationship between the uncertain system parameters
and the structural natural frequencies. Subsequently, by applying the Monte-Carlo Simulation (MCS) through the
established regression model, various types of statistical characteristics (i.e., mean, standard deviation, probability
density function or PDF, and cumulative distribution function or CDF) of structural natural frequencies
can be effectively established. Four numerical examples including test functions and practically stimulated
engineering structures are thoroughly investigated herein to demonstrate the accuracy, applicability, and
computational efficiency of the proposed approach. Keywords: Functionally graded material | Stochastic free vibration analysis | Machine learning | Uncertainty analysis |
مقاله انگلیسی |
5 |
Localized damage identification in plate-like structures using selfpowered sensor data: A pattern recognition strategy
شناسایی آسیب محلی در ساختارهای صفحه مانند با استفاده از داده های سنسور خود قدرت: یک استراتژی تشخیص الگو-2019 This study presents a novel strategy for localized damage identification in plate-like structures based on
the binary data provided by self-powered wireless sensors that use a pulse switching communication
architecture. The energy-aware pulse switching communication architecture uses single pulses instead
of multi-bit packets for information delivery resulting in discrete binary data. A system employing such
an energy-efficient technology requires dealing with power budgets for sensing and communication that
leads to unique time delay constraints. This paper presents a new paradigm for localized damage detection
using time-delayed and sometimes limited binary data. The binary data from the sensor nodes
resembles an image, or pattern. Data analysis using pattern recognition (PR) framework incorporating
PR methods and a conditional probability chain is thus presented. Numerous features extracted from
cumulative acceleration on dynamically loaded plates are used to determine damage indication parameters.
Performance of the proposed damage detection strategy was evaluated through finite element simulations
for the case of a simply supported aluminum plate under distributed harmonic loading. Different
damage states were considered to calibrate the damage detection algorithm, and an uncertainty analysis
was performed by superposing random noise to the input vectors. Results show that the proposed PR
framework is capable of identifying damage even in the presence of high noise levels. The findings
demonstrate satisfactory performance of the localized damage detection strategy, and the applicability
of PR to detect and localize damage in plate-like structures from the time-delayed binary data generated
by self-powered sensors. Keywords: Structural health monitoring | Damage identification | Pattern recognition | Self-powered sensors | Binary data |
مقاله انگلیسی |
6 |
Uncertainty quantification in erosion predictions using data mining methods
اندازه گیری عدم قطعیت در پیش بینی های فرسایش با استفاده از روش های داده کاوی-2018 The transport of solids in multiphase flows is common practice in energy industries due to the unavoidable
extraction of solids from oil and gas bearing reservoirs. The persistent collision of solids to the pipeline can lead
to erosion, i.e., the removal of internal surface of the pipeline. Reliable estimates of erosion rates are essential for
designing and safely operating pipelines that transport solids. Prediction of erosion rates in multiphase flow is a
complex problem due to the lack of accurate models for predicting particle movements in the flow and their
impact velocities to the wall. The erosion-rate calculations also depend on the accuracy of the flow regime
predictions in the pipeline. The comparisons of existing model predictions to experimental data revealed that the
predictions might differ by several orders of magnitude for some operating conditions. The goal of this paper is
to introduce a computational framework that estimates the model-prediction uncertainty of erosion-rate models.
The inputs are a model predicting erosion rates and a database containing erosion-rate measurements at various
operating conditions. The framework utilizes a non-parametric regression analysis, Gaussian Process Modeling
(GPM), for estimating the model-prediction uncertainty. We compare two approaches for clustering the data
prior to training GPMs: (1) a flow regime based clustering, and (2) a new clustering approach introduced in this
paper. The results reveal that the new data clustering approach significantly shrinks the confidence intervals of
the uncertainty estimates.
Keywords: Uncertainty analysis ، Data mining ، Gaussian process modeling ، Multiphase flow ، Erosion prediction |
مقاله انگلیسی |
7 |
Emergy-based comparative analysis of urban metabolic efficiency and sustainability in the case of big and data scarce medium-sized cities: A case study for Jing-Jin-Ji Region (China)
تجزیه و تحلیل تطبیقی مبتنی بر امیدهای رفاه و پایداری متابولیسم شهری در مورد شهرهای بزرگ و کم اهمیت متوسط: مطالعات موردی برای منطقه جینگ جین جی (چین)-2018 Emergy-based Urban Sustainability Assessment Framework (EmUSAF) is an effective and widely
advocated method to evaluate the general condition at city-scale. However, applying this framework to mediumsized cities has encountered obstacles owing to the data scarcity and inconsistency problem. One the one hand, a
range of data such as detailed import-export goods data are only compiled at provincial level therefore for
prefectural cities the lack of data is very common. On the other hand, the original environmental data provided by
local government is usually not uniform and well-formatted, meaning that the data inventory disclosed by the
environmental data compilation administration in different cities are not identical, leading to the difficulty of
comparison. Meanwhile, for a given year, not all elements needed in the emergy synthesis inventory are available
due to the difference compilation and disclosure timelines of different statistics reports. These facts hinder both
city-specific research at local scale and regional research. To address this lingering issue, we developed a
methodology for deriving substitute data from extant available data of upper-level administration division and forintegrating them into the EUSAF. With a case study on Jing-Jin-Ji region, covering 11 medium-sized prefecture
cities, we conducted an emergy analysis of the whole region and examined the methodology discussing its
uncertainties and limitations.
This study contributes to emergy synthesis in four aspects: (1) Provide a solution to the enduring data scarcity and
inconsistency problem of emergy-based urban sustainability assessment; (2) Establish the deduction method which
can deduce data at larger scale to smaller scale when lacking physical data at smaller scale instead of using
monetary estimates, and also the accuracy of the deduced data can be identified by using uncertainty analysis; (3)
Investigate the strategically essential Jing-Jin-Ji region as a case, applying the new approach to get the data for
prefectural cities and carrying out an emergy analysis. We found that the indicators, reflecting the specific
development traits of each city, are well confirmed by the status quo in the region. This fact proves that our new
method framework is affordable and could be extended in the future study of medium-sized cities suffering from
data scarcity. (4) Demonstrate the significant gap between using money value and physical amount to calculate
emergy of imported goods, proving the importance of physical amount data in the Emergy-based comparative
analysis of urban metabolic efficiency and sustainability.
Keywords: Data scarcity, Uncertainty, urban emergy inventory, Jing-Jin-Ji region |
مقاله انگلیسی |
8 |
Towards a scenario-based solution for extreme metocean event simulation applying urgent computing
به سوی یک راه حل مبتنی بر سناریو برای شبیه سازی رویداد متروک افراطی با استفاده از محاسبات فوری-2017 Today, metocean investigations, combined with forecasts and analysis of extreme events,
require new design and development approaches because of their complexity. Extreme
metocean events forecasting and prevention is an urgent computing task from decision
making and for reaction point of view. In this case, urgent computing scenario is an
essential part that should be included in the hazard simulation and prevention system.
However, existed urgent computing technological concepts does not perfectly fit all tasks
in a frame of extreme metocean events simulation. Many of these tasks should be
executed during the overall lifecycle of hazard prevention system that includes not only
urgent scenario but research part, as well. In this paper, we decompose all tasks in three
groups by most significant computational aspects (taking into consideration different
criteria of data processing and high-performance contributions) and suggest a new
solution that is adaptable for both research in normal (non-urgent) and urgent computing
modes, where potential tasks can be structured in the form of scenarios. Suggested
solution implements CLAVIRE platform core and extends its with advanced features
(regarding simulation frequency, computational performance, and data-driven
computing). As an example, in the metocean subject area, a complex application for
Baltic Sea simulations is presented. The case studies describe three scenarios with
proposed infrastructure features that are the most interesting for highlighting relevant
problems of metocean simulations within the Baltic Sea. These features are:
computational optimization possibilities for real-time forecast system calibration; data
replacement capabilities within retrospective ensemble extreme values analysis; and hard
deadline features within uncertainty analysis of an urgent scenario for complex floods.
Keywords: Computational platform | Scenario | Extreme metocean events | Flood prevention | Urgent Computing |
مقاله انگلیسی |
9 |
Application of Valuation-Based Systems for the availability assessment of systems under uncertainty
استفاده از سیستم های مبتنی بر ارزیابی برای ارزیابی در دسترس بودن سیستم ها تحت عدم قطعیت-2017 The aim of the paper is twofold. First, it proposes an original application of the Valuation-Based System (VBS) for
the availability assessment of systems under uncertainty in a time-varying fashion. Uncertainties related to failure
data of components (data uncertainty) and the system structure (model uncertainty) are analysed in the proposed
model. Second, it proposes the application of the VBS for the availability assessment of the European Rail Traffic
Management System (ERTMS) Level 2 under uncertainty according to the railway dependability standards. The
originality of this work lies in the application of the VBS for the availability assessment of systems under data and
model uncertainties, and the proposition of a temporal VBS to evaluate the instantaneous system availability.
Keywords: Valuation-Based System | Belief functions theory | Availability assessment | Uncertainty analysis | ERTMS Level 2 |
مقاله انگلیسی |
10 |
SFCOMPO-2:0: An OECD NEA database of spent nuclear fuel isotopic assays, reactor design specifications, and operating data
SFCOMPO-2: 0: پایگاه داده OECD NEA آزمایشات ایزوتوپ سوخت هسته ای، مشخصات طراحی راکتور و داده های عملیاتی-2017 SFCOMPO-2.0 is the new release of the Organisation for Economic Co-operation and Development (OECD)
Nuclear Energy Agency (NEA) database of experimental assay measurements. These measurements are
isotopic concentrations from destructive radiochemical analyses of spent nuclear fuel (SNF) samples.
The measurements are supplemented with design information for the fuel assembly and fuel rod from
which each sample was taken, as well as with relevant information on operating conditions and charac
teristics of the host reactors. These data are necessary for modeling and simulation of the isotopic evo
lution of the fuel during irradiation. SFCOMPO-2.0 has been developed and is maintained by the OECD
NEA under the guidance of the Expert Group on Assay Data of Spent Nuclear Fuel (EGADSNF), which is
part of the NEA Working Party on Nuclear Criticality Safety (WPNCS). Significant efforts aimed at estab
lishing a thorough, reliable, publicly available resource for code validation and safety applications have
led to the capture and standardization of experimental data from 750 SNF samples from more than 40
reactors. These efforts have resulted in the creation of the SFCOMPO-2.0 database, which is publicly avail
able from the NEA Data Bank. This paper describes the new database, and applications of SFCOMPO-2.0
for computer code validation, integral nuclear data benchmarking, and uncertainty analysis in nuclear
waste package analysis are briefly illustrated.
Keywords: SFCOMPO | Spent nuclear fuel database | Radiochemical assay data | Experimental isotopic compositions| Nuclear fuel depletion | Nuclear fuel evolution | Code validation | Integral benchmarks |
مقاله انگلیسی |