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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

Optimization of reverse osmosis membrane networks

Maskan, Fazilet, Chemical Engineering & Industrial Chemistry, UNSW January 2000 (has links)
The optimization of a reverse osmosis (RO) system includes optimization of the design of the individual membrane modules, the system structure and the operating conditions of the system. Most previous studies considered either the optimal design of individual modules only or optimization of system structure and operating conditions for fixed module dimensions. This thesis developed a method to simultaneously optimize the module dimensions, system structure and operating conditions. The method comprised rules for generating a general superstructure for an RO system given the number of modules along with rules for generating technically and mathematically feasible sub-structures. The superstructure was based on maximum connectivity between unit operations. A connectivity matrix was used to represent the superstructure. The matrix was useful for checking sub-structure's feasibility and deriving a model for the sub-structure's optimization, comprising the minimum number of variables and constraints which minimized computational time and increased accuracy. For optimization, a nonlinear objective function of the annualized profit of the RO system was formulated, consisting of the revenue obtained from permeate sales, capital costs of the unit operations and operating costs for the system. It was found that RO system optimization is a nonconvex optimization problem. The most effective optimization procedure involved a combination of evolutionary computation, which was good for locating the global optimum, and a gradient-based method, which was superior in finding the exact optimum. Small population size, adaptive mutation rate and steady state replacement were the most efficient parameter settings for the evolutionary computation. Optimal design of two-stage RO systems with and without energy recovery, bypass and recycle streams was studied. Dimensions of predicted optimal modules approached those of current commercial modules but with much shorter feed channels. The mathematical optimum also had higher operating pressures. The optimum system structure was a series arrangement with different module dimensions in each stage. A sensitivity analysis showed that trends in the optimal design were similar when unit costs changed. An investigation of the scalability of the method for a three-stage RO system revealed several weaknesses. These are probably surmountable with the addition of more RO system specific knowledge.

A Comparative Study on Optimization of Constrained Layer Damping for Vibration Control of Beams

Pau, G.S.H., Zheng, H., Liu, Guirong 01 1900 (has links)
This paper presents a comparison of optimization algorithms for constrained damping (CLD) patches’ layout to minimize the maximum vibration response of the odd modes, which constitutes the dominant acoustic radiation, of a simply-supported beam excited by a harmonic transverse force. An analytical model based on Euler-Bernoulli beam assumptions is derived first to relate the displacement response of the beam with bonded CLD patches and their layout. Four different nonlinear optimization methods/algorithms are then respectively used to optimize the CLD patches’ locations and lengths with aim of minimum displacement amplitude at middle of the beam. The considered methods include subproblem approximation method, the first-order method, sequential quadratic programming (SQP) and genetic algorithm (GA). The efficiency of each considered optimization method is evaluated and also compared in terms of obtained optimal beam displacement. The results show that GA is most efficient in obtaining the best optimum for this optimization problem in spite of highest computation efforts required to improve its stability. / Singapore-MIT Alliance (SMA)

Optimization of Passive Constrained Layer Damping Treatments for Vibration Control of Cylindrical Shells

Zheng, H., Pau, G.S.H., Liu, Guirong 01 1900 (has links)
This paper presents the layout optimization of passive constrained layer damping (PCLD) treatment for vibration control of cylindrical shells under a broadband force excitation. The equations governing the vibration responses are derived using the energy approach and assumed-mode method. These equations provided relationship between the integrated displacement response over the whole structural volume, i.e. the structural volume displacement (SVD), of a cylindrical shell to structural parameters of base structure and multiple PCLD patches, Genetic algorithms (GAs) based penalty function method is employed to find the optimal layout of rectangular PCLD patches with minimize the maximum displacement response of PCLD-treated cylindrical shells. Optimization solutions of PCLD patches’ locations and shape are obtained under the constraint of total amount of PCLD in terms of percentage added weight to the base structure. Examination of the optimal layouts reveals that the patches tend to increase their coverage in the axial direction and distribute over the whole surface of the cylindrical shell for optimal control of the structural volume displacement. / Singapore-MIT Alliance (SMA)

Nonlinear estimation of water network demands form limited measurement information

Rabie, Ahmed Ibrahim El Said 15 May 2009 (has links)
Access to clean drinking water is very important to the health and well-being of the population. Mathematical modeling, optimization, and online estimation are needed to solve challenging problems in water network applications such as the requirement to meet the new dynamic regulations in the Safe Drinking Water Act and the Clean Water Act. This includes providing sufficient capacity to satisfy uncertain and changing water demands, maintaining consistent water quality, and identifying and responding to abnormal events. In most of these applications, reliable knowledge of the water flow velocity is necessary. However, in practice, few measurements are usually available. This work uses a nonlinear optimization framework to estimate the unknown water demands and velocities from limited measurements. The problem is formulated as a constrained nonlinear least squares estimation problem. The constraints represent the basic governing mass and energy conservation laws as well as some operational constraints. Given the limited number of flow measurements, the estimation problem is ill-posed. Non-unique solutions may exist in which many demand profiles can match the limited number of measurements. Offline estimates of the demand patterns based on historical data are used to regularize the problem and force a unique solution. In the first phase of this project, a hydraulic model was developed for water distribution systems. This model showed very good agreement when it was validated against the simulator EPANET using 3 case studies. In the second phase, the estimation formulation was tested using the same 3 case studies with different sensor configurations. In each of the case studies, estimation results are reasonable with fewer sensors than the available degrees of freedom.

An effective dimensional inspection method based on zone fitting

Pendse, Nachiket Vishwas 12 April 2006 (has links)
Coordinate measuring machines are widely used to generate data points from an actual surface. The generated measurement data must be analyzed to yield critical geometric deviations of the measured part according to the requirements specified by the designer. However, ANSI standards do not specify the methods that should be used to evaluate the tolerances. The coordinate measuring machines employ different verification algorithms which may yield different results. Functional requirements or assembly conditions on a manufactured part are normally translated into geometric constraints to which the part must conform. Minimum zone evaluation technique is used when the measured data is regarded as an exact copy of the actual surface and the tolerance zone is represented as geometric constraints on the data. In the present study, a new zone-fitting algorithm is proposed. The algorithm evaluates the minimum zone that encompasses the set of measured points from the actual surface. The search for the rigid body transformation that places the set of points in the zone is modeled as a nonlinear optimization problem. The algorithm is employed to find the form tolerance of 2-D (line, circle) as well as 3-D geometries (cylinder). It is also used to propose an inspection methodology for turbine blades. By constraining the transformation parameters, the proposed methodology determines whether the points measured at the 2-D cross-sections fit in the corresponding tolerance zones simultaneously.

A Unified Approach for Analysis of Cable and Tensegrity Structures Using Memoryless Quasi-Newton Minimization of Total Potential Energy

Branam, Nathan J. January 2017 (has links)
No description available.

Performance Improvement of Switched Reluctance Motor (SRM) Drives Through Online Optimization Based Reference Current Identification and Digital Sliding-Mode Control

Dhale, Sumedh January 2021 (has links)
This thesis presents a torque control mechanism for switched reluctance machine (SRM) drives. The proposed mechanism is capable of maintaining ripple free torque control while minimizing the copper loss or mode-0 radial force or both at a fixed switching frequency. In the proposed approach, the torque control problem is addressed by splitting it into two parts. The first part consists of identification of optimum phase current references while the second part incorporates the design of an efficient current controller. For the identification of optimum phase current references, three algorithms are presented in the form of a developmental process. The nature of the online optimization problem is demonstrated using a simple 2-dimensional gradient descent method. Subsequently, a parametric form gradient descent algorithm is presented which transforms the original optimization problem into two 1-dimensional problems, viz. torque error minimization and identification of optimum search direction. This method yields improved computational efficiency and accuracy. The third algorithm incorporates projection using equality constraint on the phase torque contributions to achieve a 1-dimensional solution process. Although this algorithm takes more iteration as compared to the parametric form gradient descent algorithm, it demonstrates greater accuracy and computational efficiency. A comparative analysis of these algorithms is performed in at different operating conditions in terms of the torque ripple magnitude and computational effort. The thesis also presents a comprehensive analysis of well known control techniques for application in SRM current control in discrete-time domain. This analysis also presents a comparative evaluation of these control techniques under different operating conditions. On account of this analysis, several recommendations pertaining to the performance improvement are presented. Finally, a digital sliding-mode based model-free current controller suitable for fixed switching frequency operation is presented. The proposed controller is capable of providing a consistent dynamic response over wide operating range without utilizing any model information. The reference current tracking performance of this controller is verified through simulation studies in MATLAB/Simulink® environment and over a 1.2kW, 100V, 2500RPM, 12/8 experimental SRM drive. / Thesis / Doctor of Philosophy (PhD)

Unified Nonlinear Optimization-Based Sensorless Control for Switched Reluctance Machine Drives

Rotilli Filho, Silvio January 2022 (has links)
Rotor position estimation of switched reluctance machines (SRMs) is the main focus of this work. Rotor position sensors are a crucial component of optimal motor controls. Fail-safe operation and system cost reduction have been extensively researched and implemented in industry and academia. Position sensorless control on switched reluctance machines introduces a new challenge due to high nonlinearity under different operating conditions. A comprehensive review of SRM analytical modeling is presented, detailing each technique's main advantages and drawbacks. A least square-based analytical model (LSA) is proposed, which provides a simpler implementation and improved performance when compared to the methods commonly used in the literature. A literature review of rotor position sensor technology, position sensor fail modes, and position sensorless control is presented, providing a good roadmap of potential development and current limitations of the current technology. A wide speed range sensorless control is usually required when considering fail-safe techniques, fail detection methods, and low-cost applications. A unified nonlinear optimization-based sensorless control is proposed in this thesis, where a single method is used for startup, low and high speeds, with reduced memory allocation where a look-up table is not required, optimal transient response due to the elimination of a phase-locked-loop (PLL), and robustness against parameter variation. The method is validated at a wide speed range and torque conditions, thus showing the performance against conventional methods. / Thesis / Doctor of Philosophy (PhD)

Combinatorial and price efficient optimization of the underlying assets in basket options / Kombinatorisk och priseffektiv optimering av antalet underliggande tillgångar i aktiekorgar

Alexis, Sara January 2017 (has links)
The purpose of this thesis is to develop an optimization model that chooses the optimal and price efficient combination of underlying assets for a equally weighted basket option. To obtain a price efficient combination of underlying assets a function that calculates the basket option price is needed, for further use in an optimization model. The closed-form basket option pricing is a great challenge, due to the lack of a distribution describing the augmented stochastic price process. Many types of approaches to price an basket option has been made. In this thesis, an analytical approximation of the basket option price has been used, where the analytical approximation aims to develop a method to describe the augmented price process. The approximation is done by moment matching, i.e. matching the first two moments of the real distribution of the basket option with an lognormal distribution. The obtained price function is adjusted and used as the objective function in the optimization model. Furthermore, since the goal is to obtain en equally weighted basket option, the appropriate class of optimization models to use are binary optimization problems. This kind of optimization model is in general hard to solve - especially for increasing dimensions. Three different continuous relaxations of the binary problem has been applied in order to obtain continuous problems, that are easier to solve. The results shows that the purpose of this thesis is fulfilled when formulating and solving the optimization problem - both as an binary and continuous nonlinear optimization model. Moreover, the results from a Monte Carlo simulation for correlated stochastic processes shows that the moment matching technique with a lognormal distribution is a good approximation for pricing a basket option. / Syftet med detta examensarbete är att utveckla ett optimeringsverktyg som väljer den optimala och priseffektiva kombinationen av underliggande tillgångar för en likaviktad aktiekorg. För att kunna hitta en priseffektiv kombination av underliggande tillgångar behöver man finna en passande funktion som bestämmer priset på en likaviktad aktiekorg. Prissättningen av dessa typer av optioner är en stor utmaning. Detta är på grund av bristen av en sannolikhetsfördelning som kan beskriva den utökade och korrelerade stokastiska prisprocess som uppstår för en aktiekorg. Många typer av prissättningar har undersökts och tillämpats. I detta arbete har en analytisk approximation använts för att kunna beskriva den underliggande pris processen approximativt. Uppskattningen görs genom att matcha de tvåförsta momenten av den verkliga fördelningen med motsvarande moment för en lognormal fördelning. Den erhållna prisfunktionen justeras och används som målfunktionen i optimeringsmodellen. Binära ickelinjära optimeringsproblem är i allmänhet svåra att lösa - särskilt för ökande dimensioner av variabler. Tre olika kontinuerliga omformuleringar av det binära optimeringsproblemet har gjorts för att erhålla kontinuerliga problem som är lättare att lösa. Resultaten visar att en optimal och priseffektiv kombination av underliggande aktier är möjlig att hitta genom att formulera ett optimeringsproblem - både som en binär och kontinuerlig ickelinjär optimeringsmodell. Dessutom visar resultaten från en Monte Carlo-simulering, i detta fall för korrelerade stokastiska processer, att moment matching metoden utförd med en lognormal fördelning är en god approximation för prissättningen av aktiekorgar.

Supervised Descent Method

Xiong, Xuehan 01 September 2015 (has links)
In this dissertation, we focus on solving Nonlinear Least Squares problems using a supervised approach. In particular, we developed a Supervised Descent Method (SDM), performed thorough theoretical analysis, and demonstrated its effectiveness on optimizing analytic functions, and four other real-world applications: Inverse Kinematics, Rigid Tracking, Face Alignment (frontal and multi-view), and 3D Object Pose Estimation. In Rigid Tracking, SDM was able to take advantage of more robust features, such as, HoG and SIFT. Those non-differentiable image features were out of consideration of previous work because they relied on gradient-based methods for optimization. In Inverse Kinematics where we minimize a non-convex function, SDM achieved significantly better convergence than gradient-based approaches. In Face Alignment, SDM achieved state-of-the-arts results. Moreover, it was extremely computationally efficient, which makes it applicable for many mobile applications. In addition, we provided a unified view of several popular methods including SDM on sequential prediction, and reformulated them as a sequence of function compositions. Finally, we suggested some future research directions on SDM and sequential prediction.

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