55 research outputs found
Policy Model of Waste Management - Modelling of Shanghai Municipal Solid Waste Management Regulations
Waste management systems have always been considered complex. Scholars have studied waste management mostly from a macro perspective for a long time, considering waste management policies as a fraction of this complex system. This study presents the causal variables and feedback relationships related to waste management from the inside of the policy, using the macroscopic ideas of Environment-Based Design (EBD) and the system dynamics pictorial representation. The policy model developed in this thesis provides a graphical representation of the abstract policy language. The policy system model constructed based on the policy model further clarifies the linkage between policy and waste management systems. The way the policy really works is also clear from the analysis of the results, i.e., the policy controls the entire waste management system by controlling a subset of variables that affect other variables but are not affected by other variables. These variables can be divided into three categories: user-related variables, policy-related variables, and resource-related variables.
Along with the analysis of policy statements and the search for policy variables, this thesis investigates the process of policy generation and evolution. The general structure of the policy is linked to the responsibilities and work requirements of the various stakeholders within the policy based on three aspects: master plan, management hierarchy, and legal penalties. These structural maps will work together with the policy model to help policymakers further enhance the comprehension and improve the content of the policy in the future
Short-term crash risk prediction considering proactive, reactive, and driver behavior factors
Providing a safe and efficient transportation system is the primary goal of transportation engineering and planning. Highway crashes are among the most significant challenges to achieving this goal. They result in significant societal toll reflected in numerous fatalities, personal injuries, property damage, and traffic congestion. To that end, much attention has been given to predictive models of crash occurrence and severity. Most of these models are reactive: they use the data about crashes that have occurred in the past to identify the significant crash factors, crash hot-spots and crash-prone roadway locations, analyze and select the most effective countermeasures for reducing the number and severity of crashes. More recently, the advancements have been made in developing proactive crash risk models to assess short-term crash risks in near-real time. Such models could be applied as part of traffic management strategies to prevent and mitigate the crashes. The driver behavior is found to be the leading cause of highway crashes. Nevertheless, due to data unavailability, limited studies have explored and quantified the role of driver behavior in crashes. The Strategic Highway Research Program Naturalistic Driving Study (SHRP 2 NDS) offers an unprecedented opportunity to perform an in-depth analysis of the impacts of driver behavior on crashes events.
The research presented in this dissertation is divided into three parts, corresponding to the research objectives. The first part investigates the application of advanced data modeling methods for proactive crash risk analysis. Several proactive models for segment level crash risk and severity assessment are developed and tested, considering the proactive data available to most transportation agencies in real time at a regional network scale. The data include roadway geometry characteristics, traffic flow characteristics, and weather condition data. The analysis methods include Random-effect Bayesian Logistics Regression, Random Forest, Gradient Boosting Machine, K-Nearest Neighbor, Gaussian Naive Bayes (GNB), and Multi-layer Feedforward Deep Neural Network (MLFDNN). The random oversampling technique is applied to deal with the problem of data imbalance associated with the injury severity analysis. The model training and testing are completed using a dataset containing records of 10,155 crashes that occurred on two interstate highways in New Jersey over a period of two years. The second part of the study analyzes the potential improvement in the prediction abilities of the proposed models by adding reactive data (such as vehicle characteristics and driver characteristics) to the analysis. Commonly, the reactive data is only available (known) after the crash occurs. In the proposed research, the crash analysis is performed by classifying crashes in multiple groupings (instead of a single group), constructed based on the age of drivers and vehicles to account for the impact of reactive data on driver injury severity outcomes. The results of the second part of the study show that while the simultaneous use of reactive and proactive data can improve the prediction performance of the models, the absolute crash probability values must be further improved for operational crash risk prediction. To this end, in the third part of the study, the Naturalistic Driving Study data is used to calibrate the crash risk models, including the driver behavior risk factors. The findings show significant improvement in crash prediction accuracy with the inclusion of driver behavior risk factors, which confirms the driver behavior to be the most critical risk factor affecting the crash likelihood and the associated injury severity
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Innovations towards Climate-Induced Disaster Risk Assessment and Response
A changing climate may portend increasing disaster risk across many countries and business enterprises. While many aspects of the hazards, exposure and vulnerability that constitute disaster risk have been well studied, several challenges remain. A critical aspect that needs to be addressed is the rapid response and recovery from a climate-induced disaster. Often, governments need to allocate funds or design financial instruments that can be activated rapidly to mobilize response and recovery. The proposed research addresses this general problem, focusing on a few selected issues. First, there is the question of how to rapidly detect and index a climate hazard, such as a flood, given proxy remote sensing data on attributes that may be closely related to the hazard. The second is the need to robustly estimate the return periods of extreme climate hazards, and the temporal changes in their projected frequency of occurrence using multi-century climate proxies. The third is the need to assess the potential losses from the event, including the disruption of services, and cascading failure of interlinked infrastructure elements. The fourth is the impact on global and regional supply chains that are induced by the event, and the associated financial impact. For each of these cases, it is useful to ground an analysis and the development of an approach around real world examples, which can then collectively inform a strategy for emergency response. Here, this will be pursued through an analysis of flooding in the Philippines, livestock mortality induced by drought and freezing winter in Mongolia, Hurricane Sandy impacts in New York, supply chain impacts in Thailand, and an end to end analysis of the potential process using data from Thailand and Bangladesh. Collectively, these analyses are expected to inform climate hazard planning and securitization processes with broad applicability at a regional to national level
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Innovations towards Climate-Induced Disaster Risk Assessment and Response
A changing climate may portend increasing disaster risk across many countries and business enterprises. While many aspects of the hazards, exposure and vulnerability that constitute disaster risk have been well studied, several challenges remain. A critical aspect that needs to be addressed is the rapid response and recovery from a climate-induced disaster. Often, governments need to allocate funds or design financial instruments that can be activated rapidly to mobilize response and recovery. The proposed research addresses this general problem, focusing on a few selected issues. First, there is the question of how to rapidly detect and index a climate hazard, such as a flood, given proxy remote sensing data on attributes that may be closely related to the hazard. The second is the need to robustly estimate the return periods of extreme climate hazards, and the temporal changes in their projected frequency of occurrence using multi-century climate proxies. The third is the need to assess the potential losses from the event, including the disruption of services, and cascading failure of interlinked infrastructure elements. The fourth is the impact on global and regional supply chains that are induced by the event, and the associated financial impact. For each of these cases, it is useful to ground an analysis and the development of an approach around real world examples, which can then collectively inform a strategy for emergency response. Here, this will be pursued through an analysis of flooding in the Philippines, livestock mortality induced by drought and freezing winter in Mongolia, Hurricane Sandy impacts in New York, supply chain impacts in Thailand, and an end to end analysis of the potential process using data from Thailand and Bangladesh. Collectively, these analyses are expected to inform climate hazard planning and securitization processes with broad applicability at a regional to national level
Robust Optimization Framework to Operating Room Planning and Scheduling in Stochastic Environment
Arrangement of surgical activities can be classified as a three-level process that directly impacts the overall performance of a healthcare system. The goal of this dissertation is to study hierarchical planning and scheduling problems of operating room (OR) departments that arise in a publicly funded hospital. Uncertainty in surgery durations and patient arrivals, the existence of multiple resources and competing performance measures are among the important aspect of OR problems in practice. While planning can be viewed as the compromise of supply and demand within the strategic and tactical stages, scheduling is referred to the development of a detailed timetable that determines operational daily assignment of individual cases. Therefore, it is worthwhile to put effort in optimization of OR planning and surgical scheduling. We have considered several extensions of previous models and described several real-world applications. Firstly, we have developed a novel transformation framework for the robust optimization (RO) method to be used as a generalized approach to overcome the drawback of conventional RO approach owing to its difficulty in obtaining information regarding numerous control variable terms as well as added extra variables and constraints into the model in transforming deterministic models into the robust form. We have determined an optimal case mix planning for a given set of specialties for a single operating room department using the proposed standard RO framework. In this case-mix planning problem, demands for elective and emergency surgery are considered to be random variables realized over a set of probabilistic scenarios. A deterministic and a two-stage stochastic recourse programming model is also developed for the uncertain surgery case mix planning to demonstrate the applicability of the proposed RO models. The objective is to minimize the expected total loss incurred due to postponed and unmet demand as well as the underutilization costs. We have shown that the optimum solution can be found in polynomial time. Secondly, the tactical and operational level decision of OR block scheduling and advance scheduling problems are considered simultaneously to overcome the drawback of current literature in addressing these problems in isolation. We have focused on a hybrid master surgery scheduling (MSS) and surgical case assignment (SCA) problem under the assumption that both surgery durations and emergency arrivals follow probability distributions defined over a discrete set of scenarios. We have developed an integrated robust MSS and SCA model using the proposed standard transformation framework and determined the allocation of surgical specialties to the ORs as well as the assignment of surgeries within each specialty to the corresponding ORs in a coordinated way to minimize the costs associated with patients waiting time and hospital resource utilization. To demonstrate the usefulness and applicability of the two proposed models, a simulation study is carried utilizing data provided by Windsor Regional Hospital (WRH). The simulation results demonstrate that the two proposed models can mitigate the existing variability in parameter uncertainty. This provides a more reliable decision tool for the OR managers while limiting the negative impact of waiting time to the patients as well as welfare loss to the hospital
Sequential assimilation of crowdsourced social media data into a simplified flood inundation model
Flooding is the most common natural hazard worldwide. Severe floods can cause significant
damage and sometimes loss of life. During a flood event, hydraulic models play an important
role in forecasting and identifying potential inundated areas, where emergency responses
should be deployed. Nevertheless, hydraulic models are not able to capture all of the
processes in flood propagation because flood behaviour is highly dynamic and complex.
Thus, there are always uncertainties associated with model simulations. As a result, near-real
time observations are required to incorporate with hydraulic models to improve model
forecasting skills. Crowdsourced (CS) social media data presents an opportunity for
supporting urban flood management as it can provide insightful information collected by
individuals in near real-time.
In this thesis, approachesto maximise the impact of CS social media data (Twitter) to reduce
uncertainty in flood inundation modelling (LISFLOOD-FP) through data assimilation were
investigated. The developed methodologies were tested and evaluated using a real flooding
case study of Phetchaburi city, Thailand. Firstly, two approaches (binary logistic regression
and fuzzy logic) were developed based on Twitter metadata and spatiotemporal analysis to
assess the quality of CS social media data. Both methods produced good results, but the
binary logistic model was preferred as it involved less subjectivity. Next, the generalized
likelihood uncertainty estimation methodology was applied to estimate model uncertainty
and identify behavioural parameter ranges. Particle swarm optimisation was also carried out
to calibrate for an optimum model parameter set. Following this, an ensemble Kalman filter
was applied to assimilate the flood depth information extracted from the CS data into the
LISFLOOD-FP simulations using various updating strategies. The findings show that the
global state update suffers from inconsistency of predicted water levels due to overestimating
the impact of the CS data, whereas a topography based local state update provides
encouraging results as the uncertainty in model forecasts narrows, albeit for a short time
period. To extend the improvement time span, a combination of state and boundary updating
was further investigated to correct both water levels and model inputs, and was found to
produce longer lasting improvements in terms of uncertainty reduction. Overall, the results
indicate the feasibility of applying CS social media data to reduce model uncertainty in flood
forecasting
Integration of energy and urban planning dynamics for cities' climate-neutrality
145 p.La sociedad se enfrenta a la emergencia climática, donde las ciudades juegan un papel crucial como concentradoras de población, productoras de PIB, consumidoras de energía, generadoras de residuos y emisoras de GEI. Con la premisa de mejorar una respuesta eficaz de las administraciones locales a esta crisis, este doctorado se centra en dar soporte a aquellas ciudades que están dispuestas a iniciar su viaje hacia la neutralidad climática (cero emisiones netas de CO2) y en cómo orquestar esta transición desde un enfoque local. En términos de planificación del proceso hacia una meta tan ambiciosa, esta investigación ha identificado la falta de integración entre las dinámicas de planificación energética y urbana como una barrera clave en esta transición. En consecuencia, el objetivo de esta investigación es mejorar ese nivel de integración por diferentes medios. Primero, a través de un liderazgo integrado de procesos estratégicos municipales; en segundo lugar, mediante el uso de herramientas y procedimientos de gestión de datos adecuados que permitan la integración de elementos energéticos y espaciales en la toma de decisiones; y finalmente, a través de una involucración adecuada de los agentes locales en el proceso de planificación hacia la neutralidad climática. Todas estas hipótesis son analizadas por 4 estudios de investigación conectados entre sí, y validados posteriormente a través de su aplicación en el proceso de coordinación del plan de neutralidad climática de Vitoria-Gasteiz
Protection concepts in distribution networks with decentralised energy resources
Die stetig steigende Anbindung von dezentralen Energieerzeugern (DER) an Mittel- (MS) und Niederspannungsnetze (NS) fordert eine Analyse der bestehenden Netzschutzkonzepte. Die Beeinflussung der Netzschutzkonzepte ist abhängig davon, wie die DER an das Mittelspannungsnetz angebunden sind. Die vorliegende Arbeit konzentriert sich auf die Analyse von Beeinflussungen durch kleine DER, die an das Mittelspannungsnetz über einen Umrichter angebunden sind. Das erste Problem, das in dieser Arbeit untersucht ist, ist die Beeinflussung der unterschiedlichen Schutzalgorithmen durch hohe Anteile von Harmonischen. Diese werden verursacht durch die steigende Zahl elektrischer Geräte, sowohl auf der Verbraucherseite als auch auf der Seite der Energieerzeuger. Die Beeinflussung, entsprechend der Norm IEC 61000-3–2, wurde an unterschiedlichen Typen von Netzschutzsystemen untersucht. Die getesteten Distanzschutzalgorithmen basierten auf konventionellen Methoden zu Berechnung der Impedanz wie: SinusAlgorithmen, Algorithmen basierend auf der Leitungs-Differentialgleichung erster oder zweiter Ordnung, Filteralgorithmen für Berechnung komplexer Zeiger, und Algorithmen, die auf künstliche Intelligenz basieren, wie harmonisch aktivierte neuronale Netze. Die unterschiedlichen Typen von Netzschutzprinzipien, die untersucht wurden sind: Überstrom, Distanz und Differenzial. Einige Untersuchungen wurden auch im Netzschutzlabor der Universität durchgeführt. Bei beiden Tests konnte nachgewiesen werden, dass die heutigen state-of-the-art Netzschutzsysteme durch Harmonische entsprechend IEC 61000-3–2, praktisch nicht beeinflusst werden. Der zweite Problemkreis der in dieser Arbeit diskutiert wird sind die Anforderungen, welche die Anbindung von DER an das Netz, an moderne Netzschutzsysteme stellen. Einige Beispiele illustrieren die Lage der Energieversorgung der Zukunft und zeigen Selektivitätsprobleme auf, sollten nur konventionelle Netzschutzsysteme benutzt werden. In dieser Arbeit wird ein neues Schutzkonzept für Mittelspannungsnetze mit hohem Anteil an DER vorgestellt und analysiert. Das Konzept beruht auf der neuen Norm für „Substation Automatisation System - IEC 61850“ und einem Netzschutz-Managementsystem. Die Methode der zusätzlichen Signal-Einspeisung wurde ebenfalls vorgestellt. Die Basis eines effizienten Netzschutz-Managementsystems ist das Wissen vom Verhalten des Systems in normalen Betrieb und unter Fehlerbedingungen. Die Computer- und Internettechnologie, die moderne Kommunikation, der interdisziplinäre Datenaustausch stellen ganz neue Anforderungen an die Wissensbasis energietechnischer Ingenieure. Mit dem Ziel neue Medien in der Ingenieurausbildung einzusetzen ist, im Rahmen dieser Arbeit ein E-learning Kurs entwickelt worden. Dabei ermöglicht das Internet neue Methoden zur Wissensvermittlung zu entwickeln. Die Unabhängigkeit von Zeit und Ort, die große Anzahl von Lehrmöglichkeiten und die Online-Diskussionen sind nur einige zu nennende Vorteile. In dieser Arbeit ist die Idee zur Realisierung sowie Ergebnisse des E-learning Kurses im Bereich digitaler Netzschutztechnik, als Erweiterung der konventionellen Lehrveranstaltung präsentiert worden. Dieser Kurs wird den Studenten der Universität in einem speziell gestalteten Multimedialabor angeboten. Es besteht via Internet die Möglichkeit den Kurses z.B. zu Hause zur Wiederholung und Prüfungsvorbereitung nochmals zu bearbeiten.
The continuously rising implementation of DER in the distribution network requests analyses of the present network protection concepts. Depending on the type of connection to the network, the influences of the DER on the network protection systems vary. This dissertation concentrates on the analyses of the influence of implementation of small DER, which are connected to the network via an inverter. The first problem discussed in this dissertation is the influence of high level of harmonics on the protection devices. The rising implementation of power electronic devices into the network, both on the side of the energy generation and energy consumption, leads to a high level of injected harmonics into the network. The influence of a high amount of harmonics, according to the Standard IEC 61000-3–2, on different types of algorithms implemented in different types of protection devices was investigated using a test network. The tested algorithms implemented in the distance protection devices were based on conventional methods such as steady state algorithms, algorithms using the differential equation of first or second order written for the protected line, algorithms based on the filter approach, and on the “new” methods using artificial intelligence i.e.: parametrical estimation and harmonic activated neuronal networks. The different types of protection devices that were investigated were based on the principle of over-current (definite-current and inverse time), distance and differential. Some of the tests were conducted in the protection technique laboratory at the university. From both tests (simulation and practical) it is concluded that the state-of-the-art protection devices are insensitive to harmonics according to the allowed level by the standard IEC 61000-3–2. The tendency of today’s protection technology engineers lies in searching for ways to shorten of the calculation time of the algorithms. The second problem discussed is the challenge set to the network protection systems in the distribution networks with implemented DER. A few examples illustrate the situation of the energy supply of the future illustrate the problems of lack of protection with the present protection concepts. In this sense, this work presents and analyses a protectionconcept in distribution networks with DER, using the substation automation system and the protection management system based on the new standard IEC 61850 for communication networks in substations. The method of using an additional signal injection as additional criteria for the presented network protection concept is also discussed. The basis for efficient protection system management is the knowledge of power system performance under fault and normal operation (service) conditions as well as the switchgear interfaces. This requires a proper knowledge of power system engineering. With a changeable power system infrastructure, the protection system management becomes a real challenge to the network protection experts. Computer- and internet technology, modern serial communications, sharing of data with other disciplines and a trend towards system engineering require a broader knowledge and close co-operation with others, beside the protection system engineers. With the goal of spreading the knowledge of network protection systems, in the frames of this work a special e-learning course was realised. The internet provides new possibilities for gaining and spreading knowledge. The time and place independence, the high amount of possibilities for knowledge sources and on line discussions are just a few of the possibilities. In this work, the idea, the realisation and the implementation of this new way of teaching and studying digital network protection alongside the conventional way are presented as well. An importance is also given to the feed back of the user of the e-learning course. This course is offered to the students at the university in a specially realised multimedia laboratory and used for gaining knowledge in the area of network protection technique. The possibility of using the course at home for re-capitulation of the taught material and for self-test is also possible, by simply logging on to the e-learning course. This course could also be used by engineers who want to refresh their knowledge in the form of a fast (self) training.
 
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