scholarly journals Systematic Integration of Energy-Optimal Buildings With District Networks

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2945 ◽  
Author(s):  
Raluca Suciu ◽  
Paul Stadler ◽  
Ivan Kantor ◽  
Luc Girardin ◽  
François Maréchal
Keyword(s):  
Low Temperature ◽  
Operating Cost ◽  
Urban Systems ◽  
Mixed Integer ◽  
Fourth Generation ◽  
Low Carbon ◽  
Energy Networks ◽  
First Case ◽  
Investment Cost

The residential sector accounts for a large share of worldwide energy consumption, yet is difficult to characterise, since consumption profiles depend on several factors from geographical location to individual building occupant behaviour. Given this difficulty, the fact that energy used in this sector is primarily derived from fossil fuels and the latest energy policies around the world (e.g., Europe 20-20-20), a method able to systematically integrate multi-energy networks and low carbon resources in urban systems is clearly required. This work proposes such a method, which uses process integration techniques and mixed integer linear programming to optimise energy systems at both the individual building and district levels. Parametric optimisation is applied as a systematic way to generate interesting solutions for all budgets (i.e., investment cost limits) and two approaches to temporal data treatment are evaluated: monthly average and hourly typical day resolution. The city center of Geneva is used as a first case study to compare the time resolutions and results highlight that implicit peak shaving occurs when data are reduced to monthly averages. Consequently, solutions reveal lower operating costs and higher self-sufficiency scenarios compared to using a finer resolution but with similar relative cost contributions. Therefore, monthly resolution is used for the second case study, the whole canton of Geneva, in the interest of reducing the data processing and computation time as a primary objective of the study is to discover the main cost contributors. The canton is used as a case study to analyse the penetration of low temperature, CO2-based, advanced fourth generation district energy networks with population density. The results reveal that only areas with a piping cost lower than 21.5 k/100 m2ERA connect to the low-temperature network in the intermediate scenarios, while all areas must connect to achieve the minimum operating cost result. Parallel coordinates are employed to better visualise the key performance indicators at canton and commune level together with the breakdown of energy (electricity and natural gas) imports/exports and investment cost to highlight the main contributors.

Analysis of cumulative climate risks and associated impact cascades in Switzerland

2021 ◽  
Author(s):  
Raphael Neukom ◽  
Nadine Salzmann ◽  
Christian Huggel ◽  
Veruska Muccione ◽  
Sabine Kleppek ◽  
...  
Keyword(s):  
Case Studies ◽  
National Level ◽  
Swiss Alps ◽  
Urban Systems ◽  
Knowledge Gaps ◽  
Bottom Up ◽  
Protective Function ◽  
Climate Risks ◽  
First Case

<p>A recent study on ‘climate-related risks and opportunities’ of the Swiss Federal Office for the Environment (FOEN) identified knowledge gaps and related missing planning tools for risks with low probability of occurrence but potentially very severe impacts for society and/or the environment. Such risks refer in particular to risks triggered by cumulating meteorological/climatic extremes events, which (i) exacerbate through process cascades or (ii) return within shorter time intervals than expected.</p><p>To respond to these knowledge gaps and ‘blind spots’ in climate risks, a collaborative effort including academic and government institutions at different administrative levels is undertaken in order to explore and analyse the potential of such large cumulative, complex risks and to suggest actions needed to manage them in Switzerland. The project is based on two case studies, which are developed in consultation with stakeholders from science, policy and practice at the national and sub-national level.</p><p>The case studies analyse risks triggered by meteorological events based on projected and recently published Swiss Climate Scenarios CH2018, considering rare but plausible scenarios where such triggering events cumulate and/or occur in combinations.</p><p>The first case study focuses on mountain systems in the southern Swiss Alps, with a potential reduction of the protective capacity of forests caused by extreme drought and heat, and subsequent increase of risks due to multiple natural hazards (fires, snow avalanches, landslides). A semi-quantitative analysis based on expert surveys allows us to estimate the probability of different levels of loss of the protective function caused by the given meteorological trigger event. In a parallel bottom-up approach we perform the analysis with an impacts-perspective and estimate the ecological and climatological thresholds that lead to a partial or complete loss of protective function. Results from the two methods are qualitatively compatible, but the bottom-up approach tends to show a higher risk of damage compared to the more ‘classical’ top-down analysis for similar meteorological events.</p><p>The second case study focuses on cascading impacts in relation with recurrent large-scale drought and heat events on urban systems and their vulnerable elements. We draw potential process cascades across various socio-economic systems for the urban area of Basel based on a systematic analysis of potentially relevant precedent information from selected past cases worldwide.</p><p>Our study is expected to provide important information concerning highly vulnerable systems and elements, their protection, and tipping points towards severe risk amplification. Moreover, we point to feasible risk management approaches and suggest transformative adaptation measures.</p>

scholarly journals A Risk-Averse Shelter Location and Evacuation Routing Assignment Problem in an Uncertain Environment

2019 ◽  
Vol 16 (20) ◽  
pp. 4007
Author(s):  
Bian Liang ◽  
Dapeng Yang ◽  
Xinghong Qin ◽  
Teresa Tinta
Keyword(s):  
Value At Risk ◽  
Computational Study ◽  
Mixed Integer ◽  
Conditional Value At Risk ◽  
Chance Constraint ◽  
Risk Averse ◽  
Evacuation Time ◽  
First Case ◽  
Non Linear

Disasters such as hurricanes, earthquakes and floods continue to have devastating socioeconomic impacts and endanger millions of lives. Shelters are safe zones that protect victims from possible damage, and evacuation routes are the paths from disaster zones toward shelter areas. To enable the timely evacuation of disaster zones, decisions regarding shelter location and routing assignment (i.e., traffic assignment) should be considered simultaneously. In this work, we propose a risk-averse stochastic programming model with a chance constraint that takes into account the uncertainty in the demand of disaster sites while minimizing the total evacuation time. The total evacuation time reflects the efficacy of emergency management from a system optimal (SO) perspective. A conditional value-at-risk (CVaR) is incorporated into the objective function to account for risk measures in the presence of uncertain post-disaster demand. We resolve the non-linear travel time function of traffic flow by employing a second-order cone programming (SOCP) approach and linearizing the non-linear chance constraints into a new mixed-integer linear programming (MILP) reformulation so that the problem can be directly solved by state-of-the-art optimization solvers. We illustrate the application of our model using two case studies. The first case study is used to demonstrate the difference between a risk-neutral model and our proposed model. An extensive computational study provides practical insight into the proposed modeling approach using another case study concerning the Black Saturday bushfire in Australia.

scholarly journals Sustainable reverse logistic network design for End-of-Life Use-Case Study

2019 ◽  
Author(s):  
SUNA CINAR
Keyword(s):  
End Of Life ◽  
Carbon Emissions ◽  
Reverse Logistics ◽  
Practical Implication ◽  
Operating Cost ◽  
Mixed Integer ◽  
Logistics System ◽  
Reverse Logistic ◽  
The Impact

Due to the increased interests in environmental issues along with stringent environmental legislation and regulations, companies start taking a fresh look at the impact on their reverse logistic activties on the environment. This paper is an example of the recovery of valuable material that can be recycled/recovered or remanufactured at the end of product useful life by designing an effective reverse logistics network. In this study, a mixed integer linear programming (MILP) model is proposed to determine a long-term strategy for end-of-life (EOL). The mathematical model not only takes into account the minimization of system operating costs, but also considered minimization of carbon emissions related to the transportation and processing of used products. Therefore, the objective in this model was to minimize the transportation and operating cost as well as minimizing environmental effects these activities. The results of this study show the trade-off between the costs and carbon emissions, and cost effectiveness for improving environmental performance, all of which have great practical implication on decision-making of network configurations a reverse logistics system. The proposed model is validated by examining a case study from wind turbine (WT) sector.

Determinants and barriers of PV self-consumption in Spain from the perception of the installers for the promotion of distributed energy systems

2020 ◽  
pp. 153-169
Author(s):  
José Ángel Gimeno ◽  
Eva Llera Sastresa ◽  
Sabina Scarpellini
Keyword(s):  
European Union ◽  
Energy Transition ◽  
Legal Framework ◽  
Low Carbon ◽  
The European Union ◽  
Distributed Energy ◽  
Sustainable Solutions ◽  
Highly Sensitive ◽  
Transition Scenario

Currently, self-consumption and distributed energy facilities are considered as viable and sustainable solutions in the energy transition scenario within the European Union. In a low carbon society, the exploitation of renewables for self-consumption is closely tied to the energy market at the territorial level, in search of a compromise between competitiveness and the sustainable exploitation of resources. Investments in these facilities are highly sensitive to the existence of favourable conditions at the territorial level, and the energy policies adopted in the European Union have contributed positively to the distributed renewables development and the reduction of their costs in the last decade. However, the number of the installed facilities is uneven in the European Countries and those factors that are more determinant for the investments in self-consumption are still under investigation. In this scenario, this paper presents the main results obtained through the analysis of the determinants in self-consumption investments from a case study in Spain, where the penetration of this type of facilities is being less relevant than in other countries. As a novelty of this study, the main influential drivers and barriers in self-consumption are classified and analysed from the installers' perspective. On the basis of the information obtained from the installers involved in the installation of these facilities, incentives and barriers are analysed within the existing legal framework and the potential specific lines of the promotion for the effective deployment of self-consumption in an energy transition scenario.

NICLOY 5

Alloy Digest ◽  
1960 ◽  
Vol 9 (3) ◽  
Keyword(s):  
Fracture Toughness ◽  
Corrosion Resistance ◽  
High Temperature ◽  
Low Temperature ◽  
Physical Properties ◽  
Tensile Properties ◽  
Ferritic Steel ◽  
Heat Treating ◽  
Low Carbon ◽  
Temperature Performance

Abstract NICLOY 5 is a low carbon, nickel ferritic steel reecommended for low temperature service. This datasheet provides information on composition, physical properties, hardness, elasticity, and tensile properties as well as fracture toughness. It also includes information on low and high temperature performance, and corrosion resistance as well as forming, heat treating, machining, and joining. Filing Code: SA-96. Producer or source: Babcock & Wilcox Company.

Scales and Units

2018 ◽  
Author(s):  
Kathryn M. de Luna
Keyword(s):  
Language Change ◽  
Historical Linguistics ◽  
Bantu Languages ◽  
The North ◽  
First Case ◽  
Linguistic Evidence ◽  
River Region ◽  
History Of ◽  
Alternative Approaches

This chapter uses two case studies to explore how historians study language movement and change through comparative historical linguistics. The first case study stands as a short chapter in the larger history of the expansion of Bantu languages across eastern, central, and southern Africa. It focuses on the expansion of proto-Kafue, ca. 950–1250, from a linguistic homeland in the middle Kafue River region to lands beyond the Lukanga swamps to the north and the Zambezi River to the south. This expansion was made possible by a dramatic reconfiguration of ties of kinship. The second case study explores linguistic evidence for ridicule along the Lozi-Botatwe frontier in the mid- to late 19th century. Significantly, the units and scales of language movement and change in precolonial periods rendered visible through comparative historical linguistics bring to our attention alternative approaches to language change and movement in contemporary Africa.

Applications of random graphs

2017 ◽  
Author(s):  
A.C.C. Coolen ◽  
A. Annibale ◽  
E.S. Roberts
Keyword(s):  
Social Networks ◽  
Random Graphs ◽  
Interaction Network ◽  
Power Grids ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
First Case ◽  
The Stability ◽  
Protein Protein Interaction Network

This chapter reviews graph generation techniques in the context of applications. The first case study is power grids, where proposed strategies to prevent blackouts have been tested on tailored random graphs. The second case study is in social networks. Applications of random graphs to social networks are extremely wide ranging – the particular aspect looked at here is modelling the spread of disease on a social network – and how a particular construction based on projecting from a bipartite graph successfully captures some of the clustering observed in real social networks. The third case study is on null models of food webs, discussing the specific constraints relevant to this application, and the topological features which may contribute to the stability of an ecosystem. The final case study is taken from molecular biology, discussing the importance of unbiased graph sampling when considering if motifs are over-represented in a protein–protein interaction network.

Investigating the potential of redundant manipulators in narrow channels

2020 ◽  
pp. 095440622096451
Author(s):  
Ashish Singla ◽  
Jyotindra Narayan ◽  
Himanshu Arora
Keyword(s):  
Case Studies ◽  
Fuzzy Inference ◽  
Redundant Manipulators ◽  
Redundancy Resolution ◽  
Inference System ◽  
Narrow Channels ◽  
First Case ◽  
Task Priority ◽  
Patient Side

In this paper, an attempt has been made to investigate the potential of redundant manipulators, while tracking trajectories in narrow channels. The behavior of redundant manipulators is important in many challenging applications like under-water welding in narrow tanks, checking the blockage in sewerage pipes, performing a laparoscopy operation etc. To demonstrate this snake-like behavior, redundancy resolution scheme is utilized using two different approaches. The first approach is based on the concept of task priority, where a given task is split and prioritize into several subtasks like singularity avoidance, obstacle avoidance, torque minimization, and position preference over orientation etc. The second approach is based on Adaptive Neuro Fuzzy Inference System (ANFIS), where the training is provided through given datasets and the results are back-propagated using augmentation of neural networks with fuzzy logics. Three case studies are considered in this work to demonstrate the redundancy resolution of serial manipulators. The first case study of 3-link manipulator is attempted with both the approaches, where the objective is to track the desired trajectory while avoiding multiple obstacles. The second case study of 7-link manipulator, tracking trajectory in a narrow channel, is investigated using the concept of task priority. The realistic application of minimum-invasive surgery (MIS) based trajectory tracking is considered as the third case study, which is attempted using ANFIS approach. The 5-link spatial redundant manipulator, also known as a patient-side manipulator being developed at CSIR-CSIO, Chandigarh is used to track the desired surgical cuts. Through the three case studies, it is well demonstrated that both the approaches are giving satisfactory results.

scholarly journals Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Markus J. Ankenbrand ◽  
Liliia Shainberg ◽  
Michael Hock ◽  
David Lohr ◽  
Laura M. Schreiber
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Cardiac Mri ◽  
Magnetic Resonance Images ◽  
Model Interpretation ◽  
First Case ◽  
Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

scholarly journals Optimal placement design for picking and packing in distribution centers

2021 ◽  
Vol 13 (4) ◽  
pp. 168781402110106
Author(s):  
John Rios ◽  
Rodrigo Linfati ◽  
Daniel Morillo-Torres ◽  
Iván Derpich ◽  
Gustavo Gatica
Keyword(s):  
Optimal Allocation ◽  
Programming Model ◽  
Operating Cost ◽  
Nonlinear Integer Programming ◽  
Operating Costs ◽  
Optimal Placement ◽  
Distribution Centers ◽  
Suitable Space ◽  
Efficient Distribution

An efficient distribution center (DC) is one that receives, stores, picks and packs products into new logistics units and then dispatches them to points of sale at the minimal operating cost. The picking and packing processes represent the highest operating cost of a DC, and both require a suitable space for their operation. An effective coordination between these zones prevents bottlenecks and has a direct impact on the DC’s operational results. In the existing literature, there are no studies that optimize the distribution of the picking and packing areas simultaneously while also reducing operating costs. This article proposes an integer nonlinear integer programming model that minimizes order preparation costs. It does so by predicting customer demand based on historical data and defining the ideal area for picking and packing activities. The model is validated through a real case study of seven clients and fifteen products. It achieves a [Formula: see text] reduction in operating costs when the optimal allocation of the picking and packing areas is made.

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