Dissertations / Theses on the topic 'Energy modeling and optimization'

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2004.
Includes bibliographical references (p. 217-223).
(cont.) Extensions to the core TOTEM model include a demand charge model, used for making daily optimal control decisions when the electric bill includes a charge based on the monthly maximum power draw. The problem of heating, ventilation, and air conditioning (HVAC) control is treated separately since it strongly violates TOTEM's linearity assumptions. Nonetheless, we describe a solution approach to the HVAC problem which operates in conjunction with TOTEM. We also provide an analysis of storage suitability in stochastic supply and demand networks. The node-based approach lends itself well to a software system that uses a drag- and-drop graphical network creation tool. We present a graphical user interface, the XML data representation, and the communication links to and from optimization software.
We develop an extensive yet tractable framework for analyzing and optimally controlling local energy networks. A local energy network is any set of generation, storage, and end-use devices existing to provide energy fulfillment to a building, a group of jointly operated buildings, or a village power system. The software developed is called TOTEM for Total Energy Management, and provides hourly (or sub-hourly) control over the flows in such energy networks. TOTEM manages multiple energy flows such as electricity, chilled water, heat, and steam together, since such energies are often coupled, particularly for networks containing cogeneration turbines (which produce electricity and steam) and absorption chillers (which use steam for driving refrigeration turbines). Due to the large number of interconnected devices in such networks, the model is kept as a linear mixed integer program, able to be solved rapidly with off-the-shelf mathematical optimization packages. Certain nonlinearities, for example input-output relationships for generators, are handled in this linear framework with piecewise linear approximations. Modeling flexibility is achieved by taking a node-centric approach. Each device in the network is represented as a node, and depending on each node's set membership, proper constraint and objective equations are written. Given the network, TOTEM uses hourly electricity and fuel pricing, weather, and demand projections to determine the optimal operating and scheduling strategy for the day, in both deterministic and stochastic settings. MIT's cogeneration plant is used as a case study, with other examples throughout the thesis demonstrate the use of TOTEM for assessing and controlling renewable resources, storage options, and
by David Craft.
Ph.D.

Over the past fifteen years, advances in Micro-Electro-Mechanical Systems (MEMS) technology have enabled rapid development of wireless sensor networks (WSNs). A WSN consists of a large number of sensor nodes that are typically powered by batteries. Each sensor node collects useful information from its environment, and forwards this data to a base station through wireless communications. Applications of WSNs include environmental monitoring, industrial monitoring, agriculture, smart home monitoring, military surveillance, to name a few. Due to battery constraint at each sensor node, a fundamental challenge for a WSN is its limited operational lifetime -- the amount of time that the network can remain operational before some or all of the sensor nodes run out of battery. To conserve energy and prolong the lifetime of a WSN, there have been active research efforts across all network layers. Although these efforts help conserve energy usage and prolong network lifetime to some extent, energy and lifetime remain fundamental bottlenecks and are the key factors that hinder the wide-scale deployment of WSNs. This dissertation addresses the energy problem of a WSN by exploiting a recent breakthrough in wireless energy transfer (WET) technology. This breakthrough WET technology is based on the so-called magnetic resonant coupling (MRC), which allows electric energy to be transferred from a source coil to a receive coil without any plugs or wires. The advantages of MRC are high energy transfer efficiency even under omni-direction, not requiring line-of-sight (LOS), and being robust against environmental conditions. Inspired by this enabling WET technology, this dissertation focuses on applying MRC to a WSN and on studying how to optimally use this technology to address lifetime problem for a WSN. The goal is to fundamentally remove lifetime bottleneck for a WSN. The main contributions of this dissertation are summarized as follows: 1. Single-node Charging for a Sparse WSN. We first investigate how MRC can be applied to a WSN so as to remove the lifetime performance bottleneck in a WSN, i.e., allowing a WSN to remain operational forever. We consider the scenario of a mobile wireless charging vehicle (WCV) periodically traveling inside the sensor network and charging each sensor node's battery wirelessly. We introduce the concept of renewable energy cycle and offer both necessary and sufficient conditions for a sensor node to maintain its renewable energy cycle. We study an optimization problem, with the objective of maximizing the ratio of the WCV's vacation time over the cycle time. For this problem, we prove that the optimal traveling path for the WCV is the shortest Hamiltonian cycle and uncover a number of important properties. Subsequently, we develop a near-optimal solution by a piecewise linear approximation technique and prove its performance guarantee. This first study shows that network lifetime bottleneck can be fundamentally resolved by WET. 2. Multi-node Charging for a Dense WSN. We next exploit recent advances in MRC that allows multiple sensor nodes to be charged at the same time, and show how MRC with multi-node charging capability can address the scalability problem associated with the single-node charging technology. We consider a WCV that periodically travels inside a WSN and can charge multiple sensor nodes simultaneously. Based on the charging range of the WCV, we propose a cellular structure that partitions the two-dimensional plane into adjacent hexagonal cells. We pursue a formal optimization framework by jointly optimizing the traveling path of the WCV, flow routing among the sensor nodes, and the charging time with each hexagonal cell. By employing discretization and a novel Reformulation-Linearization Technique (RLT), we develop a provably near-optimal solution for any desired level of accuracy. Through numerical results, we demonstrate that our solution can indeed address the scalability problem for WET in a dense WSN. 3. Bundling Mobile Base Station and Wireless Energy Transfer: The Pre-planned Path Case. Our aforementioned work is based on the assumption that the location of base station is fixed and known in the WSN. On the other hand, it has been recognized that a mobile base station (MBS) can offer significant advantages over a fixed one. But employing two separate vehicles, one for WET and one for MBS, could be expensive and hard to manage. So a natural question to ask is: can we bundle WET and MBS on the same vehicle? This is the focus of this study. Here, our goal is to minimize energy consumption of the entire system while ensuring that none of the sensor nodes runs out of energy. To simplify the problem, we assume that the path for the vehicle is given a priori. We develop a mathematical model for this problem. Instead of studying the general problem formulation (called CoP-t), which is time-dependent, we show that it is sufficient to study a special subproblem (called CoP-s), which only involves space-dependent variables. Subsequently, we develop a provable near-optimal solution to CoP-s with the development of several novel techniques including discretizing a continuous path into a finite number of segments and representing each segment with worst-case energy bounds. 4. Bundling Mobile Base Station and Wireless Energy Transfer: The Unconstrained Path Case. Based on our experience for the pre-planned path case, we further study the problem where the traveling path of the WCV (also carrying the MBS) can be unconstrained. That is, we study an optimization problem that jointly optimizes the traveling path, stopping points, charging schedule, and flow routing. For this problem, we propose a two-step solution. First, we study an idealized problem that assumes zero traveling time, and develop a provably near-optimal solution to this idealized problem. In the second step, we show how to develop a practical solution with non-zero traveling time and quantify the performance gap between this solution and the unknown optimal solution to the original problem. This dissertation offers the first systematic investigation on how WET (in particular, the MRC technology) can be exploited to address lifetime bottleneck of a WSN. It lays the foundation of exploring WET for WSNs and other energy-constrained wireless networks. On the mathematical side, we have developed or applied a number of powerful techniques such as piecewise linear approximation, RLT, time-space transformation, discretization, and logical point representation that may be applicable to address a broad class of optimization problems in wireless networks. We expect that this dissertation will open up new research directions on many interesting networking problems that can take advantage of the WET technology.
Ph. D.

Il existe différents types de codes correcteur d’erreurs (CCE), chacun offrant différents compromis entre la performance et la consommation d’énergie. Nous proposons de traiter ce problème pour les codes LDPC (Low-Density Parity Check). Dans ce travail, nous avons considéré les codes LDPC construits à partir de protographes avec un décodeur Min-Sum quantifié, pour leurs bonnes performances et leur implémentation matérielle efficace. Nous avons utilisé une méthode basée sur l’évolution de densité pour évaluer les performances à longueur finie du décodeur pour un protographe donné. Ensuite, nous avons introduit deux modèles pour estimer la consommation d’énergie du décodeur Min-Sum quantifié. A partir de ces modèles, nous avons développé une méthode d’optimisation afin de sélectionner des protographes qui minimisent la consommation d’énergie du décodeur tout en satisfaisant un critère de performance donné.Dans la seconde partie de la thèse, nous avons considéré un décodeur LDPC bruité, et nous avons supposé que le circuit introduit des défauts dans les unités de mémoire utilisées par le décodeur. Nous avons ensuite mis à jour le modèle d’énergie de la mémoire afin de prendre en compte le bruit dans le décodeur. Par conséquent, nous avons proposé une méthode alternative afin d’optimiser les paramètres du modèle et minimiser la consommation d’énergie du décodeur pour un protographe donné
There are different types of error correction codes (CCE), each of which gives different trade-offs interms of decoding performanceand energy consumption. We propose to deal with this problem for Low-Density Parity Check (LDPC) codes. In this work, we considered LDPC codes constructed from protographs together with a quantized Min-Sum decoder, for their good performance and efficient hardware implementation. We used a method based on Density Evolution to evaluate the finite-length performance of the decoder for a given protograph.Then, we introduced two models to estimate the energy consumption of the quantized Min-Sum decoder. From these models, we developed an optimization method in order to select protographs that minimize the decoder energy consumption while satisfying a given performance criterion. The proposed optimization method was based on a genetic algorithm called differential evolution. In the second part of the thesis, we considered a faulty LDPC decoder, and we assumed that the circuit introduces some faults in the memory units used by the decoder. We then updated the memory energy model so as to take into account the noise in the decoder. Therefore, we proposed an alternate method in order to optimize the model parameters so as to minimize the decoder energy consumption for a given protograph

Denna rapport är resultatet av ett examensarbete på Kungliga Tekniska Högskolan i samarbete med Skogforsk. Syftet är att analysera den trädkapningsprocessen hos en skördare, optimera dess energiförbrukning och föreslå modifieringar av systemet. En analys av skördarhuvudets energiförbrukning genomfördes baserat på testdata från Skogforsk. Denna undersökning gav en inblick i prestandan hos hydraulmotorn Parker F11-19 vid kapnng av träd med varierande diameter, samt en kvantifiering av mängden energi som används av skördarhuvudets olika komponenter. Hydrauliska och mekaniska modeller av skördarhuvudet skapades med hjälp av simuleringsverktygen Hopsan och Simulink. Dessa modeller användes för att verifiera optimeringsförslagen. Resultatet av denna studie är fyra optimeringslösningar för ett skördarhuvud. Det första förslaget är att använda en ackumulator för kinetisk energiåtervinning i matningsrullarna, vilket kommer att bidra med en minskning av energiförbrukningen med 6.85%. Det andra förslaget är att optimera sågcylinderns position, vilket kommer leda till en reduktion med 0%, dvs aktuell position bedöms vara optimal. Det tredje förslaget är förändring av kvistknivarnas utformning, vilket minskar energiförbrukningen med 2.72%. Det fjärde förslaget är att använda en alternativ motor som kräver mindre energi, vilket bidrar till en markant minskning av energiförbrukningen med 28.4%. Totalt kommer de föreslagna förändringarna att resultera i en reduktion av energiförbrukningen med 37.9%. Resultatet är teoretiskt och ytterligare fält- och riggprov är nödvändiga för att validera resultaten.
This report is the result of the Master of Science thesis project developed for KTH Royal Institute of Technology in collaboration with the Forestry Research Institute of Sweden (Skogforsk) for the Forestry Master Thesis School 2016. The purpose is to analyze the tree cutting process of a harvester machine, optimize the energy consumption and propose modifications to the system of components if applicable. A study on the energy usage of a harvester head was performed based on test data gathered by Skogforsk, providing insight about the performance of the hydraulic motor Parker F11-19 when cutting different tree diameters and quantifying the amount of energy used on each part of the harvester head. Hydraulic and mechanical models of the head were built using Hopsan and Simulink, respectively. These models were used for the verification of the optimizations proposed. The results from this research study are four optimization solutions for a harvester head. The first suggestion is to use an accumulator for kinetic energy recovery in the feeding rollers, which will contribute with a reduction in energy consumption of 6.85%. The second suggestion is to optimize the saw’s cylinder position, which did not provide any improvements. The third suggestion is a redesign of the delimbing knives, which will reduce the energy consumption with 2.72%. And the final suggestion is to use an alternative motor that requires less power, which will result in a significant decrease of energy consumption by 28.4%. In total, the changes suggested will result in a reduction of the energy consumption by 37.9%. The results are theoretical and further testing in practice is needed in order to assess the veracity of the results.

Traffic signal cycle lengths are traditionally optimized to minimize vehicle delay at intersections using the Webster formulation. This thesis includes two studies that develop new formulations to compute the optimum cycle length of isolated intersections, considering measures of effectiveness such as vehicle delay, fuel consumption and tailpipe emissions. Additionally, both studies validate the Webster model against simulated data. The microscopic simulation software, INTEGRATION, was used to simulate two-phase and four-phase isolated intersections over a range of cycle lengths, traffic demand levels, and signal timing lost times. Intersection delay, fuel consumption levels, and emissions of hydrocarbon (HC), carbon monoxide (CO), oxides of nitrogen (NOx), and carbon dioxide (CO2) were derived from the simulation software. The cycle lengths that minimized the various measures of effectiveness were then used to develop the proposed formulations. The first research effort entailed recalibrating the Webster model to the simulated data to develop a new delay, fuel consumption, and emissions formulation. However, an additional intercept was incorporated to the new formulations to enhance the Webster model. The second research effort entailed updating the proposed model against four study intersections. To account for the stochastic and random nature of traffic, the simulations were then run with twenty random seeds per scenario. Both efforts noted its estimated cycle lengths to minimize fuel consumption and emissions were longer than cycle lengths optimized for vehicle delay only. Secondly, the simulation results manifested an overestimation in optimum cycle lengths derived from the Webster model for high vehicle demands.
Master of Science

An unprecedented view into the nature of energy systems in the Temporary Entertainment Events Industry (TEEI) is developed. A characterization of the context of the latter and its energy systems state-of-the-art is performed. Allowing for the main development of the present study, the modeling of temporary energy systems in the TEEI, ultimately delivering a tool for the assessment of the performance of these systems at a level of analytical detail previously inexistent in the industry. In the absence of previews literature on the topic, state-of-the-art modeling techniques have been reviewed from the field of small-scale polygeneration system to develop a customized approach for the modeling of temporary energy systems. An Integrated Design, Synthesis and Operation Optimization (IDSOO) approach has been adapted in a Mixed Integer Linear Programing (MILP) model and contextualized for the TEEI. Additionally, a customized approach has been developed for the treatment and compression of the original energy demand curves in a separate optimization algorithm defined in respect to the constraints and priorities of the given context. The model has been developed to cater to the evaluation of the targets set by the Festival Vision 2025 pledge. Which is then applied in a case study event set in the United Kingdom (UK), with over 50 000 visitors over a period of 4 days. In addition to the evaluation of the pledge’s targets, two additional scenarios are created to better explore to full potential of the methodology presently developed. Namely, first an integrated system approach and its benefits has been evaluated to counterpose the established practice of isolating the event’s energy sub-systems. Secondly, the effects of the prevailing energy demand uncertainty in TEEI and its typical preference of overgenerous systems’ designs are analyzed in a hypothetical, yet representative, scenario. Finally, given the pioneering nature of the developed study, a listing has been made with the argued most relevant future study topics that were found to offer the highest benefits to the TEEI. In conclusion, it has been found that there seems to be more potential than previously thought for the improvement of the performance of the TEEI’s current energy systems. It is shown that even when optimized, isolated generator-based systems in some cases will inevitably incur into undesired operational conditions, thus demonstrating the limits of the current practices and technology selection. Nevertheless, it was found that, at least for the given case study, the Festival Vision 2025 medium term targets are achievable even when resorting solely to the optimization of the current diesel-based systems. Reinstating the need for better energy systems planning and designs. Ultimately, it was concluded that the developed model fulfills the objective of representing the TEEIs energy systems to a level of detail unprecedented and that it, or similar tools, could be used to quantify and substantiate the implications of the industry’s environmental goals for its energy systems.
En ny syn på energisystem för tillfällig underhållning (TEEI) är under utveckling. En karaktärisering av sammanhanget för det senare och dess energisystem har varit fokus i denna studie. Utgående från utvecklingen av den aktuella studien, modellering av tillfälliga energisystem i TEEI, levereras ett verktyg för bedömning av prestandan hos dessa system på en nivå av analytisk detaljering som tidigare inte fanns i branschen. I avsaknad av tidigare litteratur om ämnet har state-of-the-art modelleringstekniker från området för småskaligt polygenerationssystem använts för att utveckla ett anpassat tillvägagångssätt för modellering av tillfälliga energisystem. En integrerad strategi för design, syntes och driftoptimering (IDSOO) har anpassats i en MILPmodell (Mixed Integer Linear Programing) och kontextualiserats för TEEI. Dessutom har ett anpassat tillvägagångssätt utvecklats för behandling och komprimering av mätningar av faktiska energibehov i en separat optimeringsalgoritm definierad med avseende på begränsningarna och prioriteringarna i det givna sammanhanget. Modellen har utvecklats för att tillgodose utvärderingen av de mål som fastställts av Festival Vision 2025- pantsättningen. Som sedan tillämpas i en fallstudiehändelse i Storbritannien (Storbritannien), med över 50 000 besökare under en period av fyra dagar. Förutom utvärderingen av de uppsatta målen skapas ytterligare två scenarier för att bättre utforska till den fulla potentialen för den för närvarande utvecklade metodiken. Först har en integrerad systemansats och dess fördelar utvärderats för att motverka den etablerade praxisen att isolera evenemangets energi-delsystem. För det andra analyseras effekterna av den rådande osäkerheten om energibehov i TEEI och dess typiska preferens för systemdesign med alltör generösa säkerhetsmarginaler i ett hypotetiskt men ändå representativt scenario. Slutligen, med tanke på studiens banbrytande karaktär, har en lista gjorts med de mest relevanta framtida studieämnen som visat sig ge de främsta fördelarna för TEEI. Sammanfattningsvis har det visat sig att det verkar finnas mer potential än man tidigare trott för förbättring av prestandan i TEEI: s nuvarande energisystem. Det visas att även när optimerade och isolerade generatorbaserade system i vissa fall oundvikligen kommer att drabbas av oönskade driftsförhållanden och därmed demonstreras gränserna för den nuvarande praxisen och teknikvalet. Trots det konstaterades att, åtminstone för den givna fallstudien, Festival Vision 2025 målsättningarna på medellång sikt kan uppnås även om man endast använder sig av optimeringen av de nuvarande dieselbaserade systemen, vilket återinför behovet av bättre planering och design av energisystem. I slutändan drogs slutsatsen att den utvecklade modellen uppfyller målet att representera TEEI: s energisystem till en ny detaljnivå och att den, eller liknande verktyg, skulle kunna användas för att kvantifiera och underbygga konsekvenserna av branschens miljömål för dess energisystem.

This work is primarily motivated by the hope that Silicon (Si) can be utilized in Lithium (Li) ion batteries to enable an order of magnitude capacity increase if Li-Si systems can be better understood. In order to create a valuable tool that could be used to study a wide range of problem, pertinent physical models were implemented in an extended finite element method (XFEM) framework written in c++. One of the major contribution of this work goes to the battery modeling community, by generalizing several existing electrochemical-mechanical models which use a small deformation approximations so they can accommodate finite deformation. A general theory which can be used to guide the development of new finite element models is presented in detail. This work also contributes new finite element modeling tools with novel predictive capabilities to the battery modeling community, which will hopefully facilitate the design and optimization of next generation battery micro-structures. Studies within demonstrate that small deformation approximation models can produce incorrect predictions about the behavior of Li-Si systems, supporting the case for using finite deformation models. The developed tools are used to demonstrate that arbitrary geometries can easily be simulated on a the same fixed grid, facilitating automated geometry studies including parameter sweeping and topology optimization.

La récupération d'énergie dans l'environnement ambiant est une bonne solution d'alimentation durable et complémentaire dans certains produits électroniques grand public, réseaux de capteurs distribués sans fil, dispositifs portables ou implantables, systèmes "Internet of Things" avec beaucoup de nœuds, etc. par rapport aux batteries. Les mouvements et les vibrations sont des sources d’énergie les plus disponibles à cet effet. Les dispositifs collectant de l’énergie cinétique à petite échelle sont appelés récupérateurs d'énergie cinétique (RECs). Les RECs avec électrets (E-RECs) sont un type de RECs électrostatiques qui utilisent des électrets (diélectriques avec charges quasi permanentes) comme source de tension de polarisation, et qui peuvent générer de l'électricité grâce à l'effet d'induction électrostatique lorsque la la capacitance des E-RECs varie du fait des mouvements/vibrations. Cette thèse vise à étudier les caractéristiques de sortie transitoires des E-RECs à la fois par des simulations théoriques et des mesures expérimentales, et à optimiser l’efficacité et la puissance de sortie des E-RECs par charge triboélectrique et par d'autres méthodes adaptées à leurs caractéristiques de sortie, qui sont essentielles pour améliorer la performance des E-RECs par mouvements/vibrations.Tout d'abord, les caractéristiques de sortie à amplitude variable d'un E-REC en mode contact-séparation (CS) dans des cycles de travail transitoires sont examinées via les résultats de la simulation basés sur un modèle de circuit équivalent détaillé. Ces caractéristiques de sortie à amplitude variable sont attribuées au décalage du cycle de transfert de charge par rapport au cycle de mouvement d'excitation. Les influences de la condition initiale et de la résistance de charge sur la variation des pics de tension de sortie d'un tribo-électret REC (TE-REC) en mode CS réalisé avec un film électret en polytétrafluoroéthylène (PTFE) one été étudiées en détail et vérifiées à la fois par simulations et expériences.Deuxièmement, une méthode d'optimisation du temps de contact est utilisée pour améliorer la puissance de sortie et l'efficacité du TE-REC en mode CS avec une résistance de charge de 100 MΩ. L'énergie convertie théorique maximale par cycle de travail du TE-REC est analysée. Nous avons aussi étudié les influences de plusieurs facteurs défavorables qui généralement réduiraient la conversion d'énergie par cycle de travail du TE-REC. L’optimisation de l'intervalle d'air maximal et la méthode tribo-charge sont également utilisées pour améliorer la puissance moyenne sortie du TE- REC avec une surface de 4 cm × 4 cm, de ~ 150 μW à ~ 503 μW.Troisièmement, une méthode innovante et facile a été développée pour charger le film polymère électret en éthylène propylène fluoré (FEP) par pelage de ruban adhésif, sans utiliser de source de haute tension électrique. La distribution du potentiel de la surface du film de FEP est fortement modifiée après plusieurs pelages au ruban adhésif. Par conséquence, la tension et le courant de sortie des TE-REC fabriqués avec le film FEP traités sont beaucoup améliorés. Pour un TE-REC flexible d’une surface de 64 cm2 soufflé par du vent, une amélioration évidente d'environ 692% de la puissance de sortie, correspondant 2,5 μW à environ 19,8 μW, a été obtenue par cette méthode
Harvesting energy from the ambient environment is a good sustainable and complementary power supply solution in some consumer electronics, distributed wireless sensor networks, wearable or implantable devices, "Internet of Things" systems with lots of nodes, etc. in comparison with batteries. The ubiquitous kinetic energy in various motions and vibrations is one of the most available energy sources for such a purpose. The electret kinetic energy harvesters (E-KEHs) is one type of electrostatic kinetic energy harvesters using electrets (dielectrics with quasi-permanent charges) as the biasing voltage source, which can generate electricity based on the electrostatic induction effect when the capacitance of the E-KEHs is changed by the motions/vibrations. This thesis aims to investigate the transitory output characteristics of E-KEHs by both theoretical simulations and experimental measurements and to optimize the efficiency and output power of E-KEHs by tribo-charging and other methods adapted to their output characteristics, which are significant to improving the performance of E-KEHs.Firstly, the amplitude-variable output characteristics of a contact-separation (CS) mode E-KEH in transitory working cycles are investigated via the simulation results based on a detailed equivalent circuit model. These amplitude-variable output characteristics are attributed to the lag of the charge-transfer cycle behind the excitation motion cycle. The influences of both the initial condition and the load resistance on the variation in the output voltage peaks of a tribo-electret KEH (TE-KEH) are studied in detail and verified by both simulated and experimental data of a CS mode TE-KEH made with polytetrafluoroethylene (PTFE) electret film.Secondly, based on the analysis of the amplitude-variable output characteristics, a contact time optimization method is used to improve the output power and efficiency of the CS mode TE-KEH with a large load resistance of 100 MΩ. The theoretical maximum output energy per working cycle of the TE-KEH is analyzed. Several usually unfavorable factors that would reduce the practical output energy per working cycle of the TE-KEH are discussed. The maximum air gap optimization and the tribo-charging methods are also used together to further improve the average output power of the 4 cm × 4 cm sized TE-KEH from ~150 μW to ~503 μW.Thirdly, an innovative and facile tape-peeling tribo-charging method is developed to charge the fluorinated ethylene propylene (FEP) polymer film to make electrets without using any high voltage source. The surface potential distribution of the FEP film is apparently changed after several tape-peeling tribo-charging treatments. Consequently, the output voltage and current of TE-KEHs made with the FEP film are greatly improved. For a 64 cm2 sized flexible TE-KEH to harvest kinetic energy from wind, an apparent ~692% improvement in the output power from ~2.5 μW to ~19.8 μW was obtained by the tape-peeling charging method

As is now generally accepted, climate change and environmental degradation has largely been triggered by carbon emissions and energy modeling for policy analysis has therefore attained renewed urgency. It is important for governments to satisfy emission targets and timetables set down by international agreements without disregarding macroeconomic concerns and restrictions. In this study, we present a large-scale nonlinear optimization model that allows the analysis of macroeconomic and multi-sectoral energy policies in respect of technological and environmental options and scenarios. The model consists of a detailed representation of energy activities and disaggregates the rest of the economy into five main sectors. Economy-wide solutions are obtained by computing a utility maximizing aggregate consumption bundle on the part of a representative household. Intersectoral and foreign transaction balances are maintained using a modified accounting matrix. The model also computes the impact on macroeconomic variables of greenhouse gas (GHG) emission strategies and abatement schemes. As such the model is capable of producing solutions that can be used to benchmark regulatory instruments and policies. Several scenarios are presented for the case of Turkey in which the impact of a nuclear power programme and power generation coupled with carbon-capture-and-storage schemes are investigated as well as setting quotas on total and sectoral GHG emissions.

This study is going to investigate the energy performance of a temporary building on campus and analyze it thoroughly to identify the trends on energy consumption. Then, it is going to select the best strategy that can improve its performance in this region. Next, a prototype design of a high energy performance building is going to be proposed to the university authorities to be constructed in the permanent campus in the second phase and, identify a list of the best strategies that are more appropriate for the climate of the city. Finely, a comparative study is going to be conducted by using energy analyses software (eQUEST) to find out the annual saving of the proposed design over the existing building.

Impact waves are used in many different industries and are classified according to whether they cause plastic or elastic deformations. In the plastic deformation mode, these waves can be used to produce special electrical joints. In the elastic deformation mode, they can be used to detect leakage or to measure the thickness of pipes. Both modes have applications in offshore technology. In this thesis the application of impact waves in the plastic deformation mode and explosive welding are discussed. In the explosive welding (EXW) process a high velocity oblique impact produced by a carefully controlled explosion occurs between two or more metals. The high velocity impact causes the metals to behave like fluids temporarily and weld together. This process occurs in a short time with a high rate of energy.

EXW is a well known method for joining different metals together. It is a multidisciplinary research area and covers a wide range of science and technology areas including wave theory, fluid dynamics, materials science, manufacturing and modeling. Many of the important results in EXW research are obtained from experimentation.

This thesis is mainly based on experimental work. However, it begins with a review of the fundamental theory and mechanisms of explosive welding and the different steps of a successful welding operation. Many different EXW tests are done on horizontal and vertical surfaces with unequal surface areas, and on curved surfaces and pipes. The remainder of the thesis evaluates the results of these experiments, measures the main parameters, and shows the results of simulations to verify the experimental results. The thesis ends with a number of suggestions for improving and optimizing the EXW process. One of these improvements is a model for joining metallic plates with unequal surface areas. An Al-Cu joint based on this model is used in the ALMAHDI aluminum factory, a large company in southern Iran that produces more than 200,000 tons of aluminum per year. Improved methods are also suggested for joining curved surfaces. These methods may have extensive applications in pipelines in oil and gas industries, especially in underwater pipes.

Impact vågor används i många olika branscher och klassificeras enligt de deformationer de orsakat: elastiska och plastiska deformationer. I plastisk deformation mode, dessa vågor skulle kunna användas för att framställa särskild elektrisk lederna. I deformationen läge, de skulle kunna användas för att upptäcka läckage eller mäta tjockleken på rören. Båda har tillämpningar inom offshore-teknik. I denna avhandling tillämpningen av effekterna vågor i plastisk deformation mode och explosiva svetsning diskuteras. I den explosiva svetsning (EXW) process hög hastighet sned effekt som produceras av en noggrant kontrollerad explosion uppstår mellan två eller flera metaller. Den höga hastigheten effekt gör att metaller gå ihop samtidigt som beter sig som vätskor. Denna process sker i en kort tid med hög energi.

EXW är en känd metod för att gå med olika metaller tillsammans. Det är ett tvärvetenskapligt forskningsområde och omfattar ett brett spektrum av naturvetenskap och teknik, inklusive våg teori, vätskor dynamik, materialvetenskap, tillverkning och modellering. Många av de viktiga resultat i EXW forskning har erhållits från experiment.

Denna uppsats bygger främst på experimentella verk. Det kommer dock att börja med en genomgång av grundläggande teori och mekanism av explosiva svetsning och de olika stegen i en lyckad welding operation. Då många olika EXW tester göras på horisontella och vertikala ytor med icke lika ytor och på krökta ytor och ledningar. Utvärdering av resultaten, som mäter de viktigaste parametrarna, som utför vissa simuleringar för att verifiera experimentella resultat och några förslag för att förbättra och optimera EXW process utgör de andra delarna av uppsatsen. En av dessa förbättringar är en modell för att gå med metalliska plattor med icke-lika ytor. En Al-Cu gemensamt bygger på denna modell används i ALMAHDI aluminium fabrik, ett stort företag i södra Iran att produktionen är mer än 200000 ton per år. Dessutom en del andra förbättrade metoder föreslås för att gå med krökta ytor. Dessa metoder kan få omfattande tillämpningar inom olje-och gasindustrin som rörledningar, särskilt under rören.

Electric energy constitutes one of the most crucial elements to almost every aspect of life of people. The modern electric power systems face several challenges such as efficiency, economics, sustainability, and reliability. Increase in electrical energy demand, distributed generations, integration of uncertain renewable energy resources, and demand side management are among the main underlying reasons of such growing complexity. Additionally, the elements of power systems are often vulnerable to failures because of many reasons, such as system limits, weak conditions, unexpected events, hidden failures, human errors, terrorist attacks, and natural disasters. One common factor complicating the operation of electrical power systems is the underlying uncertainties from the demands, supplies and failures of system components. Stochastic programming provides a mathematical framework for decision making under uncertainty. It enables a decision maker to incorporate some knowledge of the intrinsic uncertainty into the decision making process. In this dissertation, we focus on application of two-stage and multistage stochastic programming approaches to electric energy systems modeling and optimization. Particularly, we develop models and algorithms addressing the sustainability and reliability issues in power systems. First, we consider how to improve the reliability of power systems under severe failures or contingencies prone to cascading blackouts by so called islanding operations. We present a two-stage stochastic mixed-integer model to find optimal islanding operations as a powerful preventive action against cascading failures in case of extreme contingencies. Further, we study the properties of this problem and propose efficient solution methods to solve this problem for large-scale power systems. We present the numerical results showing the effectiveness of the model and investigate the performance of the solution methods. Next, we address the sustainability issue considering the integration of renewable energy resources into production planning of energy-intensive manufacturing industries. Recently, a growing number of manufacturing companies are considering renewable energies to meet their energy requirements to move towards green manufacturing as well as decreasing their energy costs. However, the intermittent nature of renewable energies imposes several difficulties in long term planning of how to efficiently exploit renewables. In this study, we propose a scheme for manufacturing companies to use onsite and grid renewable energies provided by their own investments and energy utilities as well as conventional grid energy to satisfy their energy requirements. We propose a multistage stochastic programming model and study an efficient solution method to solve this problem. We examine the proposed framework on a test case simulated based on a real-world semiconductor company. Moreover, we evaluate long-term profitability of such scheme via so called value of multistage stochastic programming.

Heating, ventilating and air-conditioning (HVAC) system is complex non-linear system with multi-variables simultaneously contributing to the system process. It poses challenges for both system modeling and performance optimization. Traditional modeling methods based on statistical or mathematical functions limit the characteristics of system operation and management. Data-driven models have shown powerful strength in non-linear system modeling and complex pattern recognition. Sufficient successful applications of data mining have proved its capability in extracting models accurately describing the relation of inner system. The heuristic techniques such as neural networks, support vector machine, and boosting tree have largely expanded to the modeling process of HVAC system. Evolutionary computation has rapidly merged to the center stage of solving the multi-objective optimization problem. Inspired from the biology behavior, it has shown the tremendous power in finding the optimal solution of complex problem. Different applications of evolutionary computation can be found in business, marketing, medical and manufacturing domains. The focus of this thesis is to apply the evolutionary computation approach in optimizing the performance of HVAC system. The energy saving can be achieved by implementing the optimal control setpoints with IAQ maintained at an acceptable level. A trade-off between energy saving and indoor air quality maintenance is also investigated by assigning different weights to the corresponding objective function. The major contribution of this research is to provide the optimal settings for the existing system to improve its efficiency and different preference-based operation methods to optimally utilize the resources.

The lighting retrofit method is adopted as one of the solutions to reduce lighting energy consumption and improve lighting quality in existing buildings. Lighting controls and energy-efficient light sources are used to achieve the goals of the lighting retrofit. Nowadays, Light-Emitting Diodes (LEDs) are replacing traditional lighting technology owing to their high efficiency and longevity. One of the advantages of LEDs is the controllability function, which allows users to set the light level according to their preferences. This saves more energy and satisfies users’ lighting needs. However, over time, the performance of lighting retrofit projects deteriorates subject to failure of the retrofitted lights. Therefore, to maintain the performance of lighting retrofit projects, maintenance must be planned and performed. The impacts of the users’ lighting level requirements on LEDs’ life characteristics and lighting system performance are investigated by using lighting controls. Light and occupancy sensors adjust artificial light to the light level required by users and detect the presence of users in the zones, respectively. Light sensors measure the average illuminance in the zones. The measured illuminance is compared to the users’ set illuminance; if the measured illuminance is higher than the users’ set illuminance, lamps are dimmed to meet users’ lighting preference, when the measured illuminance is less than the users’ set illuminance, lamps in the zone are replaced by new ones. The dimming level in each zone at each sampling interval is used to estimate the operating junction temperature, thereafter the degradation rate and luminous flux are calculated. Light levels at workspace are modelled using the lumen method. This model helps to quantify energy savings and predict when lamps will fail to deliver the required light levels. In existing studies, users’ lighting level requirements are neglected when investigating the lifetime of the lighting system; however, users’ profile and driving schemes affect the operating conditions of a lighting system. From the simulation results, it is noted that lumen output degradation increases when the user’s set illuminance is above the illuminance required under normal operating conditions and decreases when the user’s set illuminance is below the illuminance required under normal operating conditions. Increased lumen output degradation shortens the lifetime of LEDs and reduces energy savings, while decreased lumen output degradation extends the lifetime and increases energy savings. Generally, lighting retrofit projects contain a large lighting population; investigating when each lamp will fail can be time-consuming and costly. In this research, a mathematical model is formulated to model LEDs’ failure by analysing the statistical properties of the lumen degradation rates. Based on the statistical properties of the degradation rates, the cumulative probability of failure distribution and the survival function are modelled. The formulated survival function is incorporated into the lighting maintenance optimization problem to balance energy savings and maintenance costs. A case study carried out shows that, in 10 years, the optimal lighting maintenance plan would save up to 59% of lighting energy consumption with acceptable maintenance costs. It is found that the proposed maintenance plan is more cost-effective than full maintenance. It is concluded that lumen degradation failure should be considered when investigating the performance of lighting retrofit projects, as this may not only affect energy savings but also reduce the level of illumination, which can cause visual discomfort. The initial investment costs of LEDs are still a barrier to the implementation of LED lighting systems in residential buildings. Energy-efficiency projects often face hurdles to access capital investments because decision-makers and funders do not have enough information about operational savings the project can provide and specific financial requirements applied to efficiency investment. In this research, an optimization model is formulated to give decision-makers and funders detailed information about the performance and operational savings that a LED lighting retrofit project can offer and its economic viability. The lumen degradation failure model developed is used to monitor and estimate the energy savings, and the optimal maintenance plan is scheduled to replace failed lamps. In the existing studies, the economic analysis of the lighting retrofit projects is assessed based on lighting population decay due to burnout failure while in this research economic analysis is assessed by considering the lumen degradation failure. The case study results show that the substitution of halogen light bulbs with LED light bulbs could save up to 291.4 GWh of energy consumption, and reduce 273:92 103 tons of CO2 emissions over 10-year period. The optimization model formulated is effective to help the decision-makers and funders to quantify the savings and assess the economic viability of the LED lighting retroïnˇA˛t project. This optimization model can help the decision-makers and funders to make an informed decision.
Thesis (PhD (Electrical Engineering))--University of Pretoria, 2020.
Electrical, Electronic and Computer Engineering
PhD (Electrical Engineering)
Unrestricted

Multi-parametric programming is a mathematical theory to address optimization problems involving varying parameters. This thesis is concerned with the development of model-based controllers via parametric programming and their application to the design, operation and control of process systems. In part I of this thesis two new algorithms are presented for solving parametric optimization problem of linear state space models via dynamic programming. The first algorithm solves the nominal case, while the second introduce the case of uncertainty in the system matrices. Moreover, an algorithm for robust Explicit model based controller for box-constrained linear systems. These algorithms constitute the basis for the development of model based controllers in the rest of the thesis. In part II of this thesis, dynamic mathematical models for the cases of metal hydride tank reactor, Proton Exchange Membrane (PEM) fuel cell unit and tunnel kiln process are presented. These mathematical models are used to derive reduced order linear models in order to design explicit/multi-parametric Model Predictive Controllers. Moreover, the extensive design of an experimental PEM fuel cell unit which includes the process and instrumentation diagram (PID), the complete list of materials and the three dimension design of the unit, is presented. Based on the experimental results provided by the manufacturer, a validated dynamic mathematical model of the PEM fuel cell unit is presented. Finally, mathematical modelling, dynamic optimization and design of PI controller for the firing process of a tunnel kiln is presented.

Ever-increasing energy consumption and consequent extensive greenhouse gas (GHG) emissions are two major urgent problems faced by all human beings in the 21st century. As a major contributor, the energy production section appears to be the most suitable field where further improvements could be explored to tackle these problems. Polygeneration is a typical type of next generation energy production technology with higher energy efficiency and lower/zero GHG emissions. However, methodologies guiding an efficient and stable transition from our existing energy systems to more advanced ones are still lacking. The purpose of this thesis is to provide a generic modelling and optimization framework to guide planning and design of energy systems. This framework of methodologies ad- dresses the following issues arising in the planning and designing of energy systems: a) decision making at both strategic planning level and process design level; b) selection of roadmaps, technologies, and types of equipment from many available options; c) planning or design according to both economic and environmental criteria; d) planning or design under inevitable and unpredictable future uncertainty. The thesis is organized as follows: first, a review of energy systems is presented, followed by methodologies of energy systems engineering and their applications. Then a section of polygeneration process modelling is provided, at both strategic planning and process design levels, comprising superstructure representations of polygeneration energy systems at different levels, implementations of the superstructure based modelling strategy using mixed-integer programming, multi-objective optimization for the optimal process design according to both economic and environmental criteria, and optimization under uncer- tainty to account the impacts of future uncertainties at the planning/design stage and to increase the flexibility and robustness of a process design. Finally, major achievements of this work are summarised and future research directions are recommended.

Portable devices, such as mobile phones, personal digital assistants, notebooks, etc., have become an indispensable part of our daily life. Mobility requires energy autonomy, which is achieved with the use of batteries. Due to stringent size and weight requirements, batteries do not last sufficiently long under high-power loads, counteracting many of the perceived benefits of mobile computing and communications. Consequently, energy efficiency has become one of the key design challenges. This dissertation studies two different approaches toward the design of energy-efficient portable electronic systems. The first approach abstracts a set of interdependent tasks, executed by the system, as a time-varying load on the battery. We assume that the system can operate at multiple supply voltages and clock frequencies. The goal is to develop algorithms for energy-aware task scheduling, exploiting the capability of changing the supply voltage and the clock frequency. Automated optimization of energy efficiency for battery-powered systems is inadequate without accurate predictions of the available energy, i.e. the lifetime of the battery, under a given load. Therefore, a key component of this effort involves the development of a robust model of battery behavior and efficient methods for predicting battery lifetime. The second part of this dissertation explores hardware-software cosynthesis for reconfigurable processors, with energy efficiency as the main objective. Hardware components in such processors can be reconfigured to respond to changes in user demands. Reconfigurability allows for system flexibility, thus reducing product volatility, time-to-market and cost of development and manufacturing. Even though hardware programmability offers a variety of benefits, the reconfiguration cost in terms of power dissipation and delay is the key factor limiting system performance. To address energy and delay issues associated with reconfiguration, we develop a method for binding user program blocks either to a software execution unit or to a reconfigurable hardware execution unit, accounting explicitly for energy and delay penalties due to both computations and configurations. In addition, we describe the organization of the reconfigurable hardware space that allows for simple dynamic placement and routing of hardware objects, and propose an efficient method for reducing the amount of reconfiguration during routing in such environment.

Many utility-scale solar farms use horizontal single axis tracking to follow the sun throughout the day and produce more energy. Solar farms on skewed topography produce complex shading patterns that require precise modeling techniques to determine the energy output. To accomplish this, MATLAB was used in conjunction with NREL weather predictions to predict shading shapes and energy outputs. The MATLAB models effectively predicted the sun’s position in the sky, panel tilt angle throughout the day, irradiance, cell temperature, and shading size. The Cal Poly Gold Tree Solar Farm was used to validate these models for various lengths of time. First, the models predicted the shading and power output for a single point in time. Four points of time measurements were taken; resulting in 6 to 32 percent difference in shade height, 5 to 60 percent difference for shade length, and 29 to 59 percent difference for power output. This shows the difficulty of predicting a point in time and suggests the sensitivity of numerous variables like solar position, torque tube position, panel tilt, and time itself. When predicting the power over an entire day, the power output curves for a single inverter matched almost exactly except for in the middle of the day due to possible inaccurate cell temperature modeling or the lack of considering degradation and soiling. Since the backtracking region of the power curve is modeled accurately, the optimization routine could be used to reduce interrow shading and maximize the energy output for a single zone of the solar field. By assuming every day is sunny, the optimization routine adjusted the onset of backtracking to improve the energy output by 117,695 kilowatt hours for the year or 8.14 percent compared to the nominal settings. The actual solar farm will likely never see this increase in energy due to cloudy days but should improve by a similar percentage. Further optimization of other zones can be analyzed to optimize the entire solar field.

HVAC (Heating Ventilating and Air-Conditioning) system is multivariate, nonlinear, and shares time-varying characteristics. It poses challenges for both system modeling and performance optimization. Traditional modeling approaches based on mathematical equations limit the nature of the optimization models and solution approaches. Computational intelligence is an emerging area of study which provides powerful tools for modeling and analyzing complex systems. Computational intelligence is concerned with discovery of structures in data and recognition of patterns. It encompasses techniques such as neural networks, fuzzy logic, and so on. These techniques derive rules, patterns, and develop complex mappings from the data. The recent advances in information technology have enabled collection of large volumes of data. Computational intelligence embraces biology-inspired paradigms like evolutionary computation and particle swarm intelligence in solving complex optimization problems. Successful applications of computational intelligence have been found in business, marketing, medical and manufacturing domains. The focus of this thesis is to apply computational intelligence approach in modeling and optimization of HVAC systems. In this research, four HVAC sub-systems are investigated: the AHU (Air Handling Unit), VAV (Variable Air Volume), ventilation system, and thermal zone. Various computational intelligence approaches are used to identify parameters or problem solving. Energy savings are accomplished by minimizing the cooling output, reheating output or fan running time as well as on-line monitoring. One contribution of the research reported in the thesis is the use of computational intelligence algorithms to establish nonlinear mappings among different parameters. Another major contribution is in using heuristics algorithms to solve multi-objective optimization problems.

Le programme environnemental des Nations Unies indique que les bâtiments utilisent près de 40% de l'énergie,25% de l'eau et 40% des ressources globales. Ils émettent approximativement un tiers des émissions de gaz à effetde serre. Ses chiffres sont en constante augmentation si l'on considère que de plus en plus de notre temps estpassé à l'intérieur des bâtiments, avec une recherche accrue vers davantage de confort dans les espaces de vie.Aujourd'hui, les technologies embarquées et ubiquitaires rendent possible cette amélioration du confort au sein demaisons intelligentes (Smart Homes).Les travaux décrits dans ce mémoire de thèse portent essentiellement sur les approches de gestion intelligente duconfort thermique dans les bâtiments, tout en réduisant également la consommation d'énergie. Le confort thermiqueest un des critères les plus importants à prendre en compte pour améliorer le confort global des utilisateurs.Dans un premier temps, notre démarche scientifique a consisté à percevoir chaque bâtiment comme un systèmecyber-physique, c'est-à-dire un système où des éléments informatiques collaborent pour le contrôle et la commanded'entités physiques. En vue d'améliorer le confort thermique dans les bâtiments résidentiels, nous avons toutd'abord analysé les avantages et les inconvénients de méthodes de contrôle local et de méthodes de contrôleoptimal de gestion du confort. Nous avons ensuite proposé, implémenté et validé une méthode de contrôle hybride.Celle-ci est divisée en deux modules : un module d'optimisation inverse PMV, et un module de contrôle de type PID.Le premier module est utilisé pour déterminer une température intérieure que le module PID devra atteindre en tantque température de consigne. Les expériences menées en simulation montrent que cette technique permetd'améliorer le confort thermique de manière satisfaisante et efficace par rapport à un panel de méthodes proposéesdans la littérature.Dans un second temps, notre attention s'est portée sur l'amélioration du confort et de l'efficacité énergétique au seind'un ensemble de bâtiments géographiquement proches et hétérogènes, c'est-à-dire comprenant des appareilsélectriques contrôlables. Pour modéliser ce large système, nous avons utilisé le concept d'holarchie, qui est unehiérarchie d'agents holoniques (ou holons), des agents pouvant être composés d'autres agents. Nous avons ensuiteproposé, implémenté et évalué une méta-heuristique basée sur l'optimisation particulaire coopérative pour optimiserle scheduling des appareils contrôlables dans l'ensemble des bâtiments. Les résultats des expériences desimulation montrent la pertinence de l'approche pour un système comportant deux niveaux dans la holarchie.Néanmoins une extension de notre approche à des holarchies comportant davantage de niveaux serait tout à faitenvisageable
With the development of human society, smart homes attract more and more attention from the research fields, since they can offer a great potential for improving indoor comfort and energy efficiency. In this work, firstly great effort is concentrated on the thermal comfort improvement, not only for its dominant influence on indoor comfort, but also the overriding energy consumption in residential buildings spent on it. Thus a cyber-physical system , which adopts a hybrid intelligent control method, to regulate the indoor temperature is designed and implemented. Based on this system, thermal comfort and the energy efficiency can be improved compared with other popular methods. Towards further improving energy efficiency of individual homes and the grid utility, a set of neighbouring smart homes, each comprising a number of controllable devices, and a smart grid adopting demand response are considered. Moreover, an organization centered multi-agent system is employed to model this large-scale complex system. For the reason that selfish and non-coordinated scheduling of the controllable devices for time-varying prices may lead to severe peak rebounds, hence a meta-heuristic algorithm based on cooperative particle swarm optimizationis proposed to optimize schedules of these devices. The experimental results have verified the proposed system and algorithm. In addition, the problems solved and the system designed in this dissertation belong to the two bottom layers of the smart grid holarchy. Based on these research results and the scalability of the holarchy, more complex problems from higher layers of the holarchy can be tackled in future

Thesis (Ph. D.)--Michigan State University. Dept. of Electrical and Computer Engineering, 2006.
Title from PDF t.p. (viewed on Nov. 17, 2008) Includes bibliographical references (p. 183-195). Also issued in print.

Depuis plus d’un siècle, la radiographie sur film est utilisée pour le contrôle non destructif (CND) de pièces industrielles. Avec l’introduction de méthodes numériques dans le domaine médical, la communauté du CND industriel a commencé à considérer également les techniques numériques alternatives au film. La radiographie numérique (en anglais Computed radiography -CR) utilisant les écrans photostimulables (en anglais imaging plate -IP) est une voie intéressante à la fois du point de vue coût et facilité d’implémentation. Le détecteur (IP) utilisé se rapproche du film car il est flexible et réutilisable. L’exposition de l’IP aux rayons X génère une image latente qui est ensuite lue et numérisée grâce à un système de balayage optique par laser. A basse énergie, les performances du système CR sont bonnes ce qui explique son utilisation importante dans le domaine médical. A haute énergie par contre, les performances du système CR se dégradent à la fois à cause de la mauvaise absorption de l’IP mais également de la présence de rayonnement diffusé par la pièce qui, étant d’énergie plus faible, est préférentiellement absorbée par l’IP. Les normes internationales préconisent l’utilisation d’écrans métalliques pour améliorer la réponse des systèmes CR à haute énergie. Néanmoins, la nature et l’épaisseur de ces écrans n’est pas clairement définie et la gamme des configurations possibles est large. La simulation est un outil utile pour prévoir les performances d’une expérience et déterminer les meilleures conditions opératoires. Les méthodes Monte Carlo sont communément admises comme étant les plus précises pour simuler les phénomènes de transport de rayonnement, et ainsi comprendre les phénomènes physiques en jeu. Cependant, le caractère probabiliste de ces méthodes implique des temps de calcul importants, voire prohibitifs pour des géométries complexes. Les méthodes déterministes au contraire, peuvent prendre en compte des géométries complexes avec des temps de calcul raisonnables, mais l’estimation du rayonnement diffusé est plus difficile. Dans ce travail de thèse, nous avons tout d’abord mené une étude de simulation Monte Carlo afin de comprendre le fonctionnement des IP avec écrans métalliques à haute énergie pour le contrôle de pièces de forte épaisseur. Nous avons notamment suivi le trajet des photons X mais également des électrons. Quelques comparaisons expérimentales ont pu être menées à l’ESRF (European Synchrotron Radiation Facility). Puis nous avons proposé une approche de simulation hybride, qui combine l'utilisation de codes déterministe et Monte Carlo pour simuler l'imagerie d'objets de forme complexe. Cette approche prend en compte la dégradation introduite par la diffusion des rayons X et la fluorescence dans l'IP ainsi que la diffusion des photons optiques dans l'IP. Les résultats de différentes configurations de simulation ont été comparés
For over a century, film-based radiography has been used as a nondestructive testing technique for industrial inspections. With the advent of digital techniques in the medical domain, the NDT community is also considering alternative digital techniques. Computed Radiography (CR) is a cost-efficient and easy-to-implement replacement technique because it uses equipment very similar to film radiography. This technology uses flexible and reusable imaging plates (IP) as a detector to generate a latent image during x-ray exposure. With an optical scanning system, the latent image can be readout and digitized resulting in a direct digital image. CR is widely used in the medical field since it provides good performance at low energies. For industrial inspection, CR application is limited by its poor response to high energy radiation and the presence of scattering phenomena. To completely replace film radiography by such a system, its performance still needs to be improved by either finding more appropriate IPs or by optimizing operating conditions. Guidelines have been addressed in international standards to ensure a good image quality supplied by CR system, where metallic screens are recommended for the case of using high energy sources. However, the type and thickness of such a screen are not clearly defined and a large panel of possible configurations does exist. Simulation is a very useful tool to predict experimental outcomes and determine the optimal operating conditions. The Monte Carlo (MC) methods are widely accepted as the most accurate method to simulate radiation transport problems. It can give insight about physical phenomena, but due to its random nature, a large amount of computational time is required, especially for simulations involving complex geometries. Deterministic methods, on the other hand, can handle easily complex geometry, and are quite efficient. However, the estimation of scattering effects is more difficult with deterministic methods. In this thesis work, we have started with a Monte Carlo simulation study in order to investigate the physical phenomena involved in IP and in metallic screens at high energies. In particular we have studied separately the behavior of X-ray photons and electrons. Some experimental comparisons have been carried out at the European Synchrotron Radiation Facility. Then, we have proposed a hybrid simulation approach, combining the use of deterministic and Monte Carlo code, for simulating the imaging of complex shapes objects. This approach takes into account degradation introduced by X-ray scattering and fluorescence inside IP, as well as optical photons scattering during readout process. Different simulation configurations have been compared

Energy issues in the building sector become more and more important nowadays. Although the technology improves, the energy consumption remains the same because of people’s way of living. To reduce the energy consumption, it is possible to improve the technical components that form the building envelope and to change people’s habits. This report aims at determining the best measurement methodology of the heating and hot water consumption of a building to insure real-time visualization and evaluating the energy savings that could be made by changing people habits. To do so, an existing measurement methodology is analyzed by making error calculations and computer-based modeling and simulations are carried out to determine the heating consumption of the building under different conditions. The program DesignBuilder is used to assess the energy consumption of the building. The study shows that a consequent reduction of the heating consumption is possible by only changing people’s habits. Real-time visualization would be really helpful but it needs very accurate measurements that are almost impossible if they are not integrated in the first stages of the building process.

La plupart des exercices d’analyse de politiques climatiques ou énergétiques font appelà des modèles dits "d’évaluation intégrée" (MEIs). Ces modèles économie-énergie-climat sont des modèles numériques pluridisciplinaires destinés à étudier lesquestions liées au changement climatique et à sa gestion. Socles d’une accumulationde connaissance, ils ont une visée prospective et aident à traduire les débatsqualitatifs des instances de décisions nationales et internationales en un ensemble dedonnées quantitatives, scientifiquement vérifiables. Leur faible capacité à prendre encompte les incertitudes inhérentes à tout exercice de prospective mais aussi leur tropgrande complexité expliquent pourquoi ces MEIs sont si souvent décriés et leurutilisation remise en question.Ce constat a guidé nos travaux dont l’objectif était de contribuer à améliorer larobustesse des MEIs, afin de renforcer la pertinence de leur utilisation pour l’analysede l’impact de politiques économiques sur le climat-énergie. Nous avons d’abordexaminé comment ces modèles participent aux débats sur le changement climatique etcomment améliorer leur utilisation. Nous avons retracé la genèse de ces modèles etleur évolution et analysé les principales critiques qui leur sont adressées. Dans unsecond temps, nous nous sommes focalisés sur l’un des principaux reproches faits auxMEIs : le traitement de l’incertitude. Sur la base de ces analyses, nous avons mis enoeuvre une approche récente de traitement des problèmes d’incertitude paramétrique:l’optimisation robuste, méthode encore très peu utilisée dans le cadre d’étudesprospectives
Energy-economy and energy-economy-environment models are widely used to assessenergy and climate policies. Developed during the last forty years, these models allowthe study of the interactions between the energy-transport system, the economy andthe climate system. These interactions are very complex as they involve linkages,feedback loops and delays that are not perfectly known and that take place over a longtime horizon.This complexity along with the large uncertainties weighing on the model parametersand main assumptions explain why the use of models in the policy debate, (where themodels address issues on climate change scenarios and on energy planning), is largelycriticized.Based on this observation, our work aimed primarily at increasing the robustness ofthese models, to reinforce the relevance of their use to evaluate economic policyimpacts. At first, we examine how these models should be used to contributeeffectively to the climate and energy policy analysis debate. We review the evolution ofthe modeling practice and question it, discussing its relevance. We then focus on theuncertainty treatment and on the basis of this review, we implement an alternativeway of considering parameter uncertainty when "modeling the future" using robustoptimization

The primary objective of this research is to model and optimize wastewater treatment process in a wastewater treatment plant (WWTP). As the treatment process is complex, its operations pose challenges. Traditional physics-based and mathematical- models have limitations in predicting the behavior of the wastewater process and optimization of its operations. Automated control and information technology enables continuous collection of data. The collected data contains process information allowing to predict and optimize the process. Although the data offered by the WWTP is plentiful, it has not been fully used to extract meaningful information to improve performance of the plant. A data-driven approach is promising in identifying useful patterns and models using algorithms versed in statistics and computational intelligence. Successful data-mining applications have been reported in business, manufacturing, science, and engineering. The focus of this research is to model and optimize the wastewater treatment process and ultimately improve efficiency of WWTPs. To maintain the effluent quality, the influent flow rate, the influent pollutants including the total suspended solids (TSS) and CBOD, are predicted in short-term and long-term to provide information to efficiently operate the treatment process. To reduce energy consumption and improve energy efficiency, the process of biogas production, activated sludge process and pumping station are modeled and optimized with evolutionary computation algorithms. Modeling and optimization of wastewater treatment processes faces three major challenges. The first one is related to the data. As wastewater treatment includes physical, chemical, and biological processes, and instruments collecting large volumes of data. Many variables in the dataset are strongly coupled. The data is noisy, uncertain, and incomplete. Therefore, several preprocessing algorithms should be used to preprocess the data, reduce its dimensionality, and determine import variables. The second challenge is in the temporal nature of the process. Different data-mining algorithms are used to obtain accurate models. The last challenge is the optimization of the process models. As the models are usually highly nonlinear and dynamic, novel evolutionary computational algorithms are used. This research addresses these three challenges. The major contribution of this research is in modeling and optimizing the wastewater treatment process with a data-driven approach. The process model built is then optimized with evolutionary computational algorithms to find the optimal solutions for improving process efficiency and reducing energy consumption.

Thesis (Ph.D.)--Boston University
Many-core systems, ranging from small-scale many-core processors to large-scale high performance computing (HPC) data centers, have become the main trend in computing system design owing to their potential to deliver higher throughput per watt. However, power densities and temperatures increase following the growth in the performance capacity, and bring major challenges in energy efficiency, cooling costs, and reliability. These challenges require a joint assessment of performance, power, and temperature tradeoffs as well as the design of runtime optimization techniques that monitor and manage the interplay among them. This thesis proposes novel modeling and runtime management techniques that evaluate and optimize the performance, energy, and reliability of many-core systems. We first address the energy and thermal challenges in 3D-stacked many-core processors. 3D processors with stacked DRAM have the potential to dramatically improve performance owing to lower memory access latency and higher bandwidth. However, the performance increase may cause 3D systems to exceed the power budgets or create thermal hot spots. In order to provide an accurate analysis and enable the design of efficient management policies, this thesis introduces a simulation framework to jointly analyze performance, power, and temperature for 3D systems. We then propose a runtime optimization policy that maximizes the system performance by characterizing the application behavior and predicting the operating points that satisfy the power and thermal constraints. Our policy reduces the energy-delay product (EDP) by up to 61.9% compared to existing strategies. Performance, cooling energy, and reliability are also critical aspects in HPC data centers. In addition to causing reliability degradation, high temperatures increase the required cooling energy. Communication cost, on the other hand, has a significant impact on system performance in HPC data centers. This thesis proposes a topology-aware technique that maximizes system reliability by selecting between workload clustering and balancing. Our policy improves the system reliability by up to 123.3% compared to existing temperature balancing approaches. We also introduce a job allocation methodology to simultaneously optimize the communication cost and the cooling energy in a data center. Our policy reduces the cooling cost by 40% compared to cooling-aware and performance-aware policies, while achieving comparable performance to performance-aware policy.

Electric power is one of the most critical parts of everyday life; from lighting, heating, and cooling homes to powering televisions and computers. The modern power grids face several challenges such as efficiency, sustainability, and reliability. Increase in electrical energy demand, distributed generations, integration of uncertain renewable energy resources, and demand side management are among the main underlying reasons of such growing complexity. Additionally, the elements of power systems are often vulnerable to failures because of many reasons, such as system limits, poor maintenance, human errors, terrorist/cyber attacks, and natural phenomena. One common factor complicating the operation of electrical power systems is the underlying uncertainties from the demands, supplies and failures of system components. Stochastic optimization approaches provide mathematical frameworks for decision making under uncertainty. It enables a decision maker to incorporate some knowledge of the uncertainty into the decision making process to find an optimal trade off between cost and risk. In this dissertation, we focus on application of three risk-averse approaches to power systems modeling and optimization. Particularly, we develop models and algorithms addressing the cost-effectiveness and reliability issues in power grids with integrations of renewable energy resources. First, we consider a unit commitment problem for centralized hydrothermal systems where we study improving reliability of such systems under water inflow uncertainty. We present a two-stage robust mixed-integer model to find optimal unit commitment and economic dispatch decisions against extreme weather conditions such as drought years. Further, we employ time series analysis (specifically vector autoregressive models) to construct physical based uncertainty sets for water inflow into the reservoirs. Since extensive formulation is impractical to solve for moderate size networks we develop an efficient Benders' decomposition algorithm to solve this problem. We present the numerical results on real-life case study showing the effectiveness of the model and the proposed solution method. Next, we address the cost effectiveness and reliability issues considering the integration of solar energy in distributed (decentralized) generation (DG) such as microgrids. In particular, we consider optimal placement and sizing of DG units as well as long term generation planning to efficiently balance electric power demand and supply. However, the intermittent nature of renewable energy resources such as solar irradiance imposes several difficulties in decision making process. We propose two-stage stochastic programming model with chance constraints to control the risk of load shedding (i.e., power shortage) in distributed generation. We take advantage of another time series modeling approach known as autoregressive integrated moving average (ARIMA) model to characterize the uncertain solar irradiance more accurately. Additionally, we develop a combined sample average approximation (SAA) and linearization techniques to solve the problem more efficiently. We examine the proposed framework with numerical tests on a radial network in Arizona. Lastly, we address the robustness of strategic networks including power grids and airports in general. One of the key robustness requirements is the connectivity between each pair of nodes through a sufficiently short path, which makes a network cluster more robust with respect to potential disruptions such as man-made or natural disasters. If one can reinforce the network components against future threats, the goal is to determine optimal reinforcements that would yield a cluster with minimum risk of disruptions. We propose a risk-averse model where clusters represents a R-robust 2-club, which by definition is a subgraph with at least R node/edge disjoint paths connecting each pair of nodes, where each path consists of at most 2 edges. And, develop a combinatorial branch-and-bound algorithm to compare with an equivalent mathematical programming approach on random and real-world networks.

Cette thèse traite de la modélisation, conception, optimisation ainsi que de la validation expérimentale d'une machine à reluctance variable à double alimentation (BDFRM-Brushless Doubly-Fed Reluctance Machine) destinée aux éoliennes. La BDFRM est notamment considérée comme une alternative viable à la Machine Asynchrone à Double Alimentation (MASDA) dans les systèmes éoliens à vitesse variable. Elle maintient les avantages de coût de la MASDA tout en permettant l'utilisation d'un convertisseur de puissance réduit ainsi que la diminution des coûts d'entretien en raison de son fonctionnement sans balais. Une revue de la littérature fait apparaitre un manque de recherches concernant la définition de procédures de conception pour rendre cette machine plus populaire en général, et dans l'éolien en particulier. L'objectif principal de cette thèse est de contribuer à la maîtrise du processus de conception optimale de la BDFRM en proposant une approche méthodologique basée sur différents niveaux de modélisation et sur l'optimisation. Elle examine comment l'optimisation pourrait être appliquée à toutes les étapes de développement avec des objectifs distincts à évaluer. Plus précisément, elle se focalise sur la définition du problème d'optimisation sous contraintes et sur sa solution itérative en utilisant un algorithme déterministe couplé à des modèles semi-analytiques de différents niveaux. Les activités effectuées au cours de cette thèse peuvent être divisées en cinq parties principales. La première se réfère à l'étude de la BDFRM et de son principe de fonctionnement dans le contexte de l'énergie éolienne. La seconde partie examine les aspects de modélisation électromagnétique de la BDFRM en utilisant différentes approches. Deux modèles orientés pour l'optimisation ont été développés: le Modèle Semi-Analytique (SAM-Semi Analytical Model ) et le modèle multistatique de réseau de reluctances (MSRN-Multi-Static Reluctance Network). La mise en œuvre des modèles axés sur l'optimisation déterministe et leurs vérifications par des simulations utilisant la méthode des éléments finis (MEF) constituent la troisième partie. Il est possible de conclure qu'à partir des résultats de simulation que le SAM a un niveau de précision limité et qu'il est alors recommandé de l'utiliser dans les étapes de prédimensionnement, où le concepteur est plus intéressé par l'acquisition de résultats avec des temps de calcul rapides que par l'obtention d'une plus grande précision. Le MSRN, au contraire, présente des résultats d'une précision remarquable par rapport à la MEF, ce qui donne un compromis très intéressant entre précision et temps de calcul. Cette thèse a permis aussi de spécifier et réaliser un prototype de BDFRM en utilisant une approche d'optimisation présenté en quatrième partie. Ensuite, les données expérimentales obtenues à partir du prototype ont été confrontées aux résultats de la simulation pour valider les modèles, mettant l'accent sur le processus de modulation de flux par le rotor à réluctance, en particulier l'inductance mutuelle entre les enroulements. Bien que les résultats soient dans un sens satisfaisant pour la validation des modèles, il y a des différences qui ont exigés un examen plus approfondie. Une discussion sur les hypothèses les plus probables a donc été effectuée, celle-ci a souligné le rôle important du processus de fabrication de la machine sur ses performances. La cinquième partie explore à travers une étude de cas l'utilisation de la procédure de conception de la BDFRM proposée dans cette thèse pour les éoliennes. En conclusion générale, on peut affirmer que la BDFRM est potentiellement une bonne candidate pour être utilisée dans les systèmes éoliens. Toutefois, les aspects techniques et économiques sur ce choix doivent être encore évalués, en analysant et en comparant la solution globale du système dans le même cadre de recherche avec d'autres solutions alternatives
This thesis addresses the modeling, design and optimization with experimental validation of the Brushless Doubly-Fed Reluctance Machine (BDFRM) for wind power systems. The BDFRM is being considered as a viable alternative to the Doubly Fed Induction Machine (DFIG) in variable speed wind energy conversion systems. It keeps the cost advantages of the DFIG by allowing the use of a fractionally rated power converter and it has the advantage of reduced maintenance costs due to its brushless operation. A literature review shows that there is still a lack of researches to define a design procedure to make this machine widely used in general and in such application in particular. The main goal of this thesis is to contribute on mastering the BDFRM optimized design by proposing a methodological approach based on different modeling levels and on optimization. It discusses how optimization could be applied in all development stages with distinct objectives to be assessed. More precisely, it draws its attention on setting the optimization problem and on the iterative solution of a constrained inputs/outputs problem by using a deterministic algorithm coupled to analytical-based modeling levels. The activities performed during this thesis can be divided in five main topics. The first refers to the study of the BDFRM and its operating principles in the context of wind power. The second discusses the BDFRM electromagnetic modeling aspects using different approaches. Two optimization-oriented models have been developed: the Semi-Analytical Model (SAM) and the Multi-Static Reluctance Network model (MSRN). The implementation of the models focusing on deterministic optimization and their verification through simulations using Finite Element Analysis (FEA) are considered the third topic. It can be concluded from the simulation results that the SAM has a limited accuracy level and it is recommended to be used in early design stages, where the designer is most interested in fast computation times to test many design variation than in obtaining the results with the highest possible accuracy. The MSRN, on the contrary, presents remarkably precise results when compared to FEA, yielding a very interesting trade-off among accuracy and computation time. This thesis has also allowed to specify and realize a BDFRM prototype using an optimization approach, presented in the fourth part. Then, the experimental data obtained from the prototype has been confronted to the simulation results to validate the models, focusing on the investigation of the flux modulation process by the reluctance rotor, especially the mutual inductance among the windings. Although the results were in a sense satisfactory to validate the models, there have been differences that demanded further investigation. A discussion on the most likely hypothesis for that has been performed, indicating the significant role of the manufacturing process on machine performance. The fifth topic explores through a case study the use of the proposed BDFRM design procedure for wind power applications. As a general conclusion, it can be stated that the BDFRM is potentially a good candidate to be used in wind power systems. However, the technical and economic aspects on this choice must be still assessed, analyzing and comparing the overall system solution of distinct topologies within the same framework
Esta tese aborda a modelagem, o projeto e a otimização, com validaçãoexperimental, de máquinas de relutância duplamente alimentadas sem escovas (BDFRM)para sistemas de geração de energia eólica.A BDFRM é considerada como uma alternativa viável para o gerador de indução duplamentealimentado (DFIG) em sistemas de geração de energia eólica com variação develocidade. Ela mantém as vantagens de custo da solução com o DFIG, permitindo autilização de um conversor de frequência de potência nominal reduzida, e tem a vantagemadicional de custos de manutenção mais baixos devido a sua operação sem escovas. Umarevisão da literatura evidencia que ainda há uma necessidade de pesquisas na área parade_nir um procedimento de projeto desta máquina para torná-la amplamente utilizada emaplicações em geral e, em particular, para geração eólica.O objetivo principal desta tese é de contribuir para o domínio de técnicas de projetootimizado para a BDFRM através da proposição de uma metodologia baseada em diferentesníveis de modelagem e em otimização. Discute-se como técnicas de otimização podem seraplicadas em todas as fases de desenvolvimento com objetivos distintos. Especi_camente,a metodologia proposta se concentra na de_nição e na solução iterativa de problemas deotimização com restrições nas saídas utilizando um algoritmo determinístico acoplado amodelos semi-analíticos de diferentes níveis.As atividades realizadas durante esta tese podem ser divididas em cinco tópicos principais.O primeiro refere-se ao estudo da BDFRM e seu princípio de funcionamento no contextode geração de energia eólica. O segundo trata dos aspectos de modelagem eletromagnética da BDFRM utilizando diferentes abordagens. Dois modelos orientados à otimizaçãoforam desenvolvidos: o modelo semi-analítico (SAM) e o modelo multi-estático de redes derelutâncias (MSRN). A implementação dos modelos com foco na otimização e a veri_caçãodeles através de simulações com o método de elementos _nitos (FEA) são consideradas aterceira parte. Pode-se concluir, a partir dos resultados de simulação, que o SAM tem umaprecisão limitada e é recomendado para ser utilizado em estágios iniciais de projeto, emque o projetista está mais interessado em cálculos rápidos para testar diversas variações deprojeto do que na obtenção de resultados com a maior precisão possível. O MSRN, ao contrário, apresenta resultados precisos quando comparado aos obtidos com o FEA, resultandonum interessante custo-benefício entre precisão e tempo de cálculo. Nesta tese, fabricou-setambém um protótipo da BDFRM, o qual foi especi_cado utilizando-se otimização e osdetalhes sobre ele são introduzidos na quarta parte. Os dados experimentais obtidos com oprotótipo foram confrontados com os resultados de simulação para validação dos modelos,focando-se na investigação do processo de modulação de _uxo pelo rotor relutância, especialmentea indutância mútua entre os enrolamentos. Embora os resultados obtidos sejamsatisfatórios para validar os modelos, encontraram-se diferenças que exigiram uma investigação mais detalhada. As hipóteses mais prováveis foram investigadas e as conclusõesindicam o papel determinante do processo de fabricação no desempenho da máquina. Oquinto tópico explora através de um estudo de caso a utilização do procedimento de projetoproposto da BDFRM para aplicações de geração de energia eólica.Como conclusão geral, pode-se a_rmar que a BDFRM é potencialmente uma boa candidatapara ser utilizada em sistemas de geração de energia eólica. Contudo, aspectostécnicos e econômicos sobre essa escolha devem ainda ser avaliados, comparando-se asdiferentes topologias existentes sob o mesmo enfoque metodológico

In the recent past, as more farm power is being demanded on farms, due to increased farm sizes and operating speeds, larger and heavier farm machines are deployed in various farming operations. Their cumulative negative effects have become more apparent with increased incidences of soil compaction problems. This has forced many farmers to practice deep tilling, using subsoilers to break up compacted subsoil layers. In some maize growing regions of South Africa, conventional subsoilers are used in a tandem configuration. The farmers believe that the use of subsoilers in this mode reduces the draft force per unit area tilled. This probably happens because the critical depth for the rear subsoiler is increased beyond its working depth of 600 mm. Operating in this mode necessitated this study, with the ultimate goal of testing an appropriate existing force model for a single tine in predicting the force requirements of the front subsoiler in a tandem configuration. Secondly, to develop an alternative model for the rear subsoiler based on the three-dimensional failed soil-profile and to determine the relative position of the front subsoiler at which energy utilization is optimized. To develop the proposed model, an analytical approach based on limit equilibrium analysis was used and a Matlab-based computer program was coded to solve it. Its verification was conducted through field experiments in sandy clay loam soil. The experiments consisted of a continuous measurement of the horizontal and vertical forces acting on each subsoiler by a two-dimensional force transducer system. At the same time, the three-dimensional and thus the cross-sectional areas of the disturbed soil-profiles at different sections were measured, as well as the soil characteristics. A manual method employing a pin-profile meter was used to measure the vertical cross-sectional areas of the failed soil-profiles at 100 mm intervals. Further more, a technique using an automatic penetrometer and a computer program was developed to identify and map the three-dimensional failed soil-profiles. This technique indicated that the subsoiler failed the soil beyond its maximum operating depth and width. The results also indicated that the soil-failure pattern at close spacing is in phase at both subsoilers, leading to reduced total draft force requirements. At a wider spacing, the soil-failure pattern was out of phase, thus resulting in increased total draft force requirements. At the same time, the cross-sectional area tilled per unit draft force increased with increased spacing. This was because the failed maximum cross-sectional area increased in size faster than the total draft force as the spacing was increased. The proposed model verification results show that the predicted and recorded forces at the rear subsoiler correlated reasonably well at a wider spacing. When the front subsoiler was shallow working and close to the rear subsoiler, the model under- predicted the measured forces on the rear subsoiler, whilst the Swick-Perumpral model over predicted the applied forces to the front subsoiler and this was generally the case at wider spacings. Furthermore the efficiency of the subsoilers was maximized when the longitudinal spacing was such that it allowed the soil failed by the front subsoiler to stabilize before the rear subsoiler reached it. The maximum cross-sectional area failed per unit draft force was recorded when the depth of the front subsoiler was equal to about 80% of the rear subsoiler-operating depth. The knowledge contributed by this research will not only facilitate qualitative field operations and optimize energy use, but also promote better management decisions.
Thesis (PhD (Engineering))--University of Pretoria, 2004.
Civil Engineering
unrestricted

This work explores the development of a home energy management system (HEMS) that uses weather and market forecasts to optimize the usage of home appliances and to manage battery usage and solar power production. A Moving Horizon Estimation (MHE) application is used to find the unknown home model parameters. These parameters are then updated in a Model Predictive Controller (MPC) which optimizes and balances competing comfort and economic objectives. Combining MHE and MPC applications alleviates model complexity commonly seen in HEMS by using a lumped parameter model that is adapted to fit a high-fidelity model. HVAC on/off behaviors are simulated by using Mathematical Program with Complementary Constraints (MPCCs) and solved in near real-time with a nonlinear solver. Removing HVAC on/off as a discrete variable decreases potential solutions and consequently reduces solve time and increases the probability of reaching a more optimal solution. The results of this work indicate that energy management optimization significantly decreases energy costs and balances energy usage more effectively throughout the day compared to a home with regular temperature control. A case study for Phoenix, Arizona shows an energy reduction of 21% and a cost reduction of 40%. Homes using this home energy optimization will contribute less to the grid peak load and therefore, improve grid stability and reduce the amplitude of load following cycles for utilities. This case study combines renewable energy, energy storage, forecasts, cooling system, variable rate electricity plan and a multi-objective function allowing for a complete home energy optimization assessment. There remain several challenges, including improved forecast models, improved computational performance to allow the algorithms to run in real-time, and mixed empirical / first principles machine learning methods to guide the model structure.

The Swedish energy system is changing and two major events that are taking place are the phase out of nuclear power and the increase of wind power. The associated changes affect the electricity market and the electricity producers, including combined heat and power plants. This thesis evaluates the Swedish energy system of 2025 with focus on electricity spot prices. It also investigates how a combined heat and power plant might perform in the future, given certain changes in the electricity price. Six different scenarios are developed where the electricity price is modified according to findings with regards to the influence of wind- and nuclear power. A model of a combined heat and power plant and a district heating network is created in BoFiT. The scenarios are applied to the model and results are analyzed in terms of heat production, choice of operational mode, merit order and economical performance. Major findings show a more volatile electricity price in 2025. Low price hours (<100SEK/MWh) occur throughout the year, while high price hours (>640SEK/MWh) take place mostly during winter - the season during which the heat demand is at its peak. Results show that the developed electricity prices require much more regulation from the modelled power plant and that the power plant is more adapted to handling high price hours than low price hours. The district heating network is also affected by the volatile electricity prices, and more frequent and greater variations are observed in the merit order. This suggests that in the future, the electricity prices will need to be followed more actively, and that a strategy will need to be developed, allowing for quick adaptation to the prices - communication and cooperation between the different actors in the network will be needed.
Sveriges energisystem är i förändring där avvecklingen av kärnkraft och ökad implementering av vindkraft är i fokus. Konsekvenserna av dessa förändringar kommer påverka elmarknaden och därmed elproducenterna, bland dem kraftvärmeverk. Detta examensarbete utvärderar energisystemet i Sverige 2025 med fokus på elmarknaden. Arbetet undersöker också hur ett kraftvärmeverk kan prestera i framtiden baserat på förändringar i elpriset. Sex olika scenarios har utvecklats där elpriset har modifierats baserat på analysen av vind- och kärnkraftsutvecklingen i Sverige och dess påverkan på elpriset. Ytterligare skapas en modell av ett kraftvärmeverk och ett fjärrvärmenät i BoFiT. Scenarierna implementeras i modellen och resultat extraheras och analyseras baserat på värmeproduktion, val av driftläge, körordning i systemet samt ekonomisk prestanda. Resultaten visar främst att volatiliteten i elpriset ökar till 2025. Låga elpristimmar (<100SEK/MWh) visar sig inträffa under hela året medan höga elpristimmar(>640 SEK/MWh) dominerar under vintern - säsongen där efterfrågan på värme är som högst. Resultaten visar att det förväntade elpriset kräver högre reglering av det modellerade kraftvärmeverket och att anläggningen idag är anpassad för att hantera framförallt höga elpriser men inte låga elpriser. Även fjärrvärmenätet i sig påverkas av volatilitet i elpriserna och mer frekventa och större variationer observeras i körordningen. Detta antyder att elpriserna i framtiden måste följas mer aktivt och att en strategi, som möjliggör snabb reglering för anpassning av elpriserna, måste utvecklas. Kommunikation och samarbete mellan parterna i fjärrvärmesystemet kommer därmed vara av hög betydelse.

The industrial sector uses about one third of the energy end-use in the world. Since energy use in many cases highly affects both the local and global environment negatively, it is of common interest to increase energy efficiency within industries. Furthermore, seen from the industrial perspective, it is also important to reduce dependency on energy resources with unstable prices in order to obtain economic predictability. In this thesis, the energy-saving potential within the chemical pulp and paper sector is analyzed. One market pulp mill and one integrated pulp and paper mill were studied as cases. Energy system changes at the mills were analyzed through cost minimization. The thesis focuses on principal energy issues such as finding the most promising alternatives for use of industrial excess heat, possible investments in electricity generation and choice of fuel. In order to find synergies, the same system was optimized first from the perspective of different operators respectively, and then from a joint regional perspective. Also, the prerequisites for a regional heat market in the region were analyzed. This thesis reveals that the use of excess heat from pulp and paper mills for district heating does not generally conflict with process integration measures. This is partly because of the great availability of industrial excess heat and partly because the different purposes require different temperatures and thereby do not compete. Rather, the results show that they strengthen each other since steam and hot water of higher temperatures are made available for district heating when hot water of lower temperature is used for process integration. However, there are cases when the conditions are complicated by preexisting technical solutions within a system. In these cases, a combination of measures could be necessary. Furthermore, it is concluded that energy cooperation in terms of a heat market between municipalities and industries in the studied region gives opportunity for positive synergies. Switching from expensive fuels such as oil to less expensive biofuel in the region proved to be particularly beneficial. Expanding the capacity for combined heat and power generation is also beneficial for the region as well as increased use of industrial excess heat for district heating. The most financially beneficial scenarios also have the greatest potential for CO2 emission reduction; the emissions would be reduced by about 700 thousand tonnes CO2/year for the region in those scenarios.
Den industriella energianvändningen utgör en tredjedel av världens totala energianvändning. Eftersom energianvändning i många fall har negativ miljöpåverkan både lokalt och globalt är det av allmänt intresse att öka industriernas energieffektivitet. Sett ur industriernas perspektiv är det dessutom viktigt att minska beroendet av bränslen med osäkra priser för att uppnå ekonomisk förutsägbarhet. I den här avhandlingen analyseras energibesparingspotentialen inom massa- och pappersindustrin. Ett fristående kemiskt massabruk och ett integrerat kemiskt massa- och pappersbruk har studerats. Förändringar i energisystemen på bruken analyserades genom kostnadsminimeringar. Avhandlingen fokuserar på principiella energifrågor, som att utvärdera olika sätt att använda industriellt spillvärme, investeringar i elgenerering och val av bränsle. För att hitta synergier optimerades samma system ur olika aktörers perspektiv och sedan ur ett regionalt perspektiv. Även förutsättningarna för en regional värmemarknad analyserades. Avhandlingen visar att användandet av överskottsvärme från massa- och pappersindustrin till fjärrvärme generellt sett inte står i konflikt med processintegreringsåtgärder inom bruken. Detta beror delvis på att stora mängder överskottsvärme finns tillgängliga och delvis på att det är olika temperaturnivåer som behövs till de olika syftena som därför inte konkurrerar. Resultaten visar snarare att de två åtgärderna stärker varandra eftersom processintegrering gör att större mängder varmvatten av högre temperatur blir tillgängliga för fjärrvärme. Det finns dock fall då förutsättningarna kompliceras av redan befintliga tekniska lösningar inom ett system. I dessa fall kan det vara nödvändigt med en kombination av åtgärder. Vidare dras slutsatsen att energisamarbete mellan kommuner och industrier i form av en värmemarknad ger möjlighet till positiva synergier i den studerade regionen. Särskilt lönsamt visade det sig vara att byta från dyra bränslen såsom olja till billigare bränslen som biobränslen. Att utöka kraftvärmekapaciteten inom värmemarknaden är också lönsamt liksom utökat användande av industriell spillvärme till fjärrvärme. De fall som var mest ekonomiskt lönsamma har även störst möjlighet till minskning av CO2-utsläpp; utsläppen från regionen skulle kunna minskas med cirka 700 000 ton CO2/år i dessa fall.

The objective of this thesis is to include strategic and tactical level decisions into the biorefinery supply chain design for a specific region while comparing multiple conversion technologies and biomass feedstocks. The allocation of biomass feedstocks, products, and the respective supply chain configuration locations are determined while ensuring the regions monthly biomass availability and product market demand constraints are met. This research considers all actions required to bring the bio-based products to market from harvesting, storing, and processing the biomass to market distribution. Two different conversion technologies are chosen for comparison: one advanced conversion technology and one conventional technology. Potential investors and policy makers will be able to use this region specific tool by maximizing annual profitability to evaluate potential lignocellulosic biomass feedstocks and conversion technologies for the production of energy, fuels, and chemicals. The tool utilizes ILOG OPL software for optimization while interfacing with Microsoft Excel for parameter inputs and results output. From the sensitivity analysis, further insight is gained to what key drivers greatly influence the performance of each supply chain. The results demonstrate the practicality of this tool, which then can be further analyzed through other models such as discrete event simulation.

In this work, a thermoelectric energy harvester system aimed at harvesting energy for locally powering sensor nodes in nuclear power plant coolant loops has been designed, fabricated and tested. Different mathematical modeling methods have been validated by comparing with experimental results. The model developed by this work has the best accuracy in low temperature range and can be adapted and used with any heat sink, heat pipe, or thermoelectric system, and have proven to provide results closely matching experimental data. Using the models, an optimization of the thermoelectric energy harvesting system has been performed which is applicable to any energy harvester of this variety. With experimental validation, the system is capable of generating sufficient energy to power all the sensors and electronical circuits designed for this application. The effect of gamma radiation on this thermoelectric harvester has also been proved to be small enough through radiation experiment.
Master of Science

Les réseaux de capteurs sans fil (RCSFs), sont composés d'un ensemble de nœuds avec des capteurs, transmetteur/récepteur, d'un système de traitement et d'une réserve d'énergie. Au regard d'applications, de travaux de recherche sont développés sur l'utilisation de ce réseau leur performance, fiabilité ou durée de vie. La durée de vie RCSFs correspond à la période à travers laquelle le RCSF fonctionne parfaitement. Cette durée de vie est très affectée par de nombreux facteurs comme la quantité d'énergie disponible, la probabilité de défaillance et les dégradations des composants. L'énergie disponible devient le facteur prépondérant dans les cas d'applications avec des composants difficilement rechargeables ou non renouvelables. Différents algorithmes, stratégies et techniques d'optimisation ont été élaborées et mises en œuvre à cet effet sur la possibilité d'activer un sous-ensemble de capteurs qui satisfont à la contrainte de surveillance et de garder les autres capteurs en mode veille pour pouvoir être mis en œuvre ultérieurement. Ainsi, c'est un problème de type NP complet de maximisation qui peut être résolu en considérant des Ensembles Disjoints de capteurs de Couverture (EDC). Mais la solution obtenue à l'aide des EDCs ne conduit pas toujours à une extension significative de la durée de vie des RCSFs. Le présent travail vise à rechercher une meilleure solution basée sur des capteurs regroupés dans des ensembles nondisjoints de couverture (ECND). Cette approche permet à un capteur de participer à une ou plusieurs ensembles de capteurs de couvertures. Nous avons alors étudié un modèle de représentation binaire des ECNDs pour déterminer un ordonnancement optimum permettant de maximiser la vie d'un RCSF. De plus, nous avons développé une heuristique basée sur un algorithme génétique, pour trouver une solution proche de l'optimal dans un délai raisonnable. Ainsi, pour un ensemble de m capteurs utilisés pour surveiller un ensemble de n cibles, cette heuristique permet construire un nombre maximum q d'ensembles ECNDs. Des efforts supplémentaires sont donc nécessaires pour trouver le meilleur ordonnancement pour la mise en œuvre des ECNDs, qui maximise la durée de vie globale du RCSF, compte tenu de l'énergie initialement disponible dans chaque capteur. Ce problème est formulé à l'aide d'un modèle mathématique de programmation linéaire en nombres entiers (PLE). La fonction objective de ce problème est la somme de toutes les périodes de surveillance pour les q ECNDs programmés, et la contrainte est la consommation d'énergie de tous les capteurs constituant les ECNDs. La possibilité de trouver la solution à ce problème par PLE dans une période de temps donnée dépend de la complexité du modèle et des instances utilisées. Pour trouver la solution dans un délai raisonnable, nous avons développé un algorithme génétique (AG) basé sur les ECNDs. Les solutions potentielles sont représentées dans des chromosomes composés d'un certain nombre de gènes correspondant aux ECNDs, et chaque gène est caractérisé par la période de surveillance d'un ECND. Nous avons ensuite développé un AG qui combine quatre opérateurs de croisement et quatre opérateurs de mutation. La méthode basée cet AG a été codée dans le langage de programmation C pour obtenir une solution satisfaisante et le logiciel Cplex a été utilisé de déterminer la solution exacte correspondant. Une comparaison des solutions obtenues sur de petites instances en utilisant la PLE par rapport aux solutions obtenues par notre AG montre que la méthode basée sur les AG peut trouver une solution proche de l'optimale dans un délai raisonnable. Ensuite, en comparant les solutions en utilisant l'AG ECNDs à l'AG EDCs de la littérature, nous montrons que l'AG avec ECND peut prolonger la durée de vie des RCSFs plus que les AG avec EDCs pour les mêmes instances. Notre approche combine ainsi les principes d'ordonnancement et les techniques d'optimisation pour maximiser la durée de vie des RCSFs
Wireless sensor networks (WSNs), as a collection of sensing nodes with limited processing, limited energy reserve and radio communication capabilities, are widely implemented in many areas of applications such as industry, environment, healthcare, etc. Regarding this large range of applications, many research issues are introduced including the applications, performance, reliability, lifetime, etc. The WSNs lifetime considered in this work is the period of time through which theWSN is perfectly completing its function. This lifetime is affected by many factors including the amount of energy available, failure probability and components degradation. The amount of energy available become the most important factor in case of non renewable components applications. Different algorithms, strategies and optimization techniques were developed and implemented for this purpose based on the possibility of activating a subset of sensors that satisfied the monitoring constraint, while keeping the others in sleep mode to be implemented later. This is an NP complete maximization problem that can be solved using disjoint set covers (DSCs). But the solution obtained using DSCs does not extend always significantly the WSNs lifetime. So, the present work aims to search for a better solution using non-disjoint set covers (NDSCs). This approach gives the opportunity for a sensor to be implemented in one or more subset covers. For that purpose, we studied a binary representation based model to maximize the number of NDSCs. Also, we developed a genetic algorithm based heuristic based on this model to find out the maximum number of NDSCs in a reasonable time. Thus, for a set of m sensors used to monitor a set of n targets or a field, this heuristic allows to construct a maximum number q of NDSCs. Additional effort is required to find the best scheduling for implementing the NDSCs so as to maximize the lifetime of the sensors involved in the WSNs, considering their limited available energy. This problem is formulated using integer linear programming (ILP) mathematical model. The objective function of this problem is the sum of all monitoring seasons on which all q NDSCs scheduled, and the constraint is the energy consumption in all sensors included in all NDSCs. Solving this problem using ILP in a period of time depends on the complexity of the model and the instances used. To find the solution in reasonable time, we have developed a NDSCs based genetic algorithm (NDSC-GA). The candidate solutions are represented in chromosomes composed of a number of genes equal to the number q of NDSCs, and each gene is the number of monitoring seasons on which a NDSC is scheduled. We have then developed a GA that combines the four crossover operators and four mutation operators. The GA based methods are coded in C programming language to obtain a satisfying solution and the Cplex software was used to obtain the corresponding exact solution. Comparing the optimal solution obtained using the ILP on small instances, to the solutions obtained using our GA based method explained that our methods can find a solution near the optimal in reasonable time. Then, comparing the solution obtained using our NDSCs GA based methods, to the DSCs GA based method in the literature, we showed that the NDSCs GA can prolong the WSNs lifetime better than DSCs GA for the same instances. Our approach combines together the scheduling principles and the optimization techniques to maximizing the WSNs lifetime

This dissertation presents several novel accomplishments in the research area of Wireless Body Area Networks (WBANs), including in vivo channel characterization, optimization and game theoretical approaches for energy efficiency in WBANs. First, we performed the in vivo path loss simulations with HFSS human body model, built a phenomenological model for the distance and frequency dependent path loss, and also investigated angle dependent path loss of the in vivo wireless channel. Simulation data is produced in the range of 0.4−6 GHz for frequency, a wide range of distance and different angles. Based on the measurements, we produce mathematical models for in body, on body and out of body regions. The results show that our proposed models fit well with the simulated data. Based on our research, a comparison of in vivo and ex vivo channels is summarized. Next, we proposed two algorithms for energy efficiency optimization in WBANs and evaluated their performance. In the next generation wireless networks, where devices and sensors are heterogeneous and coexist in the same geographical area creating possible collisions and interference to each other, the battery power needs to be efficiently used. The first algorithm, Cross-Layer Optimization for Energy Efficiency (CLOEE), enables us to carry out a cross-layer resource allocation that addresses the rate and reliability trade-off in the PHY, as well as the frame size optimization and transmission efficiency for the MAC layer. The second algorithm, Energy Efficiency Optimization of Channel Access Probabilities (EECAP), studies the case where the nodes access the medium in a probabilistic manner and jointly determines the optimal access probability and payload frame size for each node. These two algorithms address the problem from an optimization perspective and they are both computationally efficient and extensible to 5G/IoT networks. Finally, in order to switch from a centralized method to a distributed optimization method, we study the energy efficiency optimization problem from a game theoretical point of view. We created a game theoretical model for energy efficiency in WBANs and investigated its best response and Nash Equilibrium of the single stage, non-cooperative game. Our results show that cooperation is necessary for efficiency of the entire system. Then we used two approaches, Correlated Equilibrium and Repeated Game, to improve the overall efficiency and enable some level of cooperation in the game.

The increasing trend of world-wide energy consumption emphasizes the importance of ongoing optimization of new and existing technologies. In this dissertation, two energy–intensive systems are simulated and optimized. Advanced estimation, optimization, and control techniques such as a moving horizon estimator and a model predictive controller are developed to enhance the profitability, product quality, and reliability of the systems. An enabling development is presented for the solution of complex dynamic optimization problems. The strategy involves an initialization approach to large–scale system models that both enhance the computational performance as well as the ability of the solver to converge to an optimal solution. One particular application of this approach is the modeling and optimization of a batch distillation column. For estimation of unknown parameters, an L1-norm method is utilized that is less sensitive to outliers than a squared error objective. The results obtained from the simple model match the experimental data and model prediction for a more rigorous model. A nonlinear statistical analysis and a sensitivity analysis are also implemented to verify the reliability of the estimated parameters. The reduced–order model developed for the batch distillation column is computationally fast and reasonably accurate and is applicable for real time control and online optimization purposes. Similar to estimation, an L1-norm objective function is applied for optimization of the column operation. Application of an L1-norm permits explicit prioritization of the multi–objective problems and adds only linear terms to the problem. Dynamic optimization of the column results in a 14% increase in the methanol product obtained from the column with 99% purity. In a second application of the methodology, the results obtained from optimization of the hybrid system of a cryogenic carbon capture (CCC) and power generation units are presented. Cryogenic carbon capture is a novel technology for CO2 removal from power generation units and has superior features such as low energy consumption, large–scale energy storage, and fast response to fluctuations in electricity demand. Grid–level energy storage of the CCC process enables 100% utilization of renewable power sources while 99% of the CO2 produced from fossil–fueled power plants is captured. In addition, energy demand of the CCC process is effectively managed by deploying the energy storage capability of this process. By exploiting time–of–day pricing, the profit obtained from dynamic optimization of this hybrid energy system offsets a significant fraction of the cost of construction of the cryogenic carbon capture plant.