Traditional single stage design optimality procedures can be used to efficiently generate data for an assumed model y = f(x^(m),b) + ε. The model assumptions include the form of f, the set of regressors, x^(m) , and the distribution of ε. The nature of the response, y, often provides information about the model form (f) and the error distribution. It is more difficult to know, apriori, the specific set of regressors which will best explain the relationship between the response and a set of design (control) variables x. Misspecification of x^(m) will result in a design which is efficient, but for the wrong model. A Bayesian two stage design approach makes it possible to efficiently design experiments when initial knowledge of x^(m) is poor. This is accomplished by using a Bayesian optimality criterion in the first stage which is robust to model uncertainty. Bayesian analysis of first stage data reduces uncertainty associated with x^(m), enabling the remaining design points (second stage design) to be chosen with greater efficiency. The second stage design is then generated from an optimality procedure which incorporates the improved model knowledge. Using this approach, numerous two stage design procedures have been developed for the normal linear model. Extending this concept, a Bayesian design augmentation procedure has been developed for the purpose of efficiently obtaining data for variance modeling, when initial knowledge of the variance model is poor. / Ph. D.

Proper engine selection for Reusable Launch Vehicles (RLVs) is a key factor in the design of low cost reusable launch systems for routine access to space. RLVs typically use combinations of different types of engines used in sequence over the duration of the flight. Also, in order to properly choose which engines are best for an RLV design concept and mission, the optimal trajectory that maximizes or minimizes the mission objective must be found for that engine configuration. Typically this is done by the designer iteratively choosing engine combinations based on his/her judgment and running each individual combination through a full trajectory optimization to find out how well the engine configuration performed on board the desired RLV design. This thesis presents a new method to reliably predict the optimal engine configuration and optimal trajectory for a fixed design of a conceptual RLV in an automated manner. This method is accomplished using the original code Steele-Flight. This code uses a combination of a Genetic Algorithm (GA) and a Non-Linear Programming (NLP) based trajectory optimizer known as GPOPS II to simultaneously find the optimal engine configuration from a user provided selection pool of engine models and the matching optimal trajectory. This method allows the user to explore a broad range of possible engine configurations that they wouldn't have time to consider and do so in less time than if they attempted to manually select and analyze each possible engine combination. This method was validated in two separate ways. The codes ability to optimize trajectories was compared to the German trajectory optimizer suite known as ASTOS where only minimal differences in the output trajectory were noticed. Afterwards another test was performed to verify the method used by Steele-Flight for engine selection. In this test, Steele-Flight was provided a vehicle model based on the German Saenger TSTO RLV concept and models of turbofans, turbojets, ramjets, scramjets and rockets. Steele-Flight explored the design space through the use of a Genetic Algorithm to find the optimal engine combination to maximize payload. The results output by Steele-Flight were verified by a study in which the designer manually chose the engine combinations one at a time, running each through the trajectory optimization routine to determine the best engine combination. For the most part, these methods yielded the same optimal engine configurations with only minor variation. The code itself provides RLV researchers with a new tool to perform conceptual level engine selection from a gathering of user provided conceptual engine data models and RLV structural designs and trajectory optimization for fixed RLV designs and fixed mission requirement. / Master of Science

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<p>In this thesis deposit and loan rate-setting equations for chartered banks are derived on the premise that these rates are set so as to maximize the banking industry's profits. Because of the oligopolistic nature of the Canadian banking industry and because explicit collusion is illegal an optimizing model of chartered bank ratesetting behaviour was integrated into the institutional framework of the Canadian banking industry.</p> <p>To do this a two-stage model of the Canadian banking industry is proposed. At the first stage, the prime rate on loans and the rate on non-chequing personal savings deposits are set so as to maximize the collective profits of the industry. To circumvent the illegality of explicit collusion a price leadership model is developed. In this model it is not one of the individual banks which is a price leader, but rather changes in the bank rate act as a signal for all of the individual banks to change their rates. The formulation proposed was tested and the hypothesis accepted for both rates. The second stage of the two-stage model is concerned with asset and liability management and is not developed in this thesis.</p> <p>A second contribution of this thesis is to take into account chartered bank rate-setting behaviour when estimating demand equations for both business loans and nonchequing personal savings deposits. When the estimation procedure used reflects these problems it is found that there are large changes in the values of the estimated coefficients in the demand functions for loans and deposits, compared to the simple O.L.S. estimates of the parameter values.</p> / Doctor of Philosophy (PhD)

Yes / Reverse osmosis (RO) has become a common method for treating wastewater and removing several harmful organic compounds because of its relative ease of use and reduced costs. Chlorophenol is a toxic compound for humans and can readily be found in the wastewater of a wide range of industries. Previous research in this area of work has already provided promising results in respect of the performance of an individual spiral wound RO process for removing chlorophenol from wastewater, but the associated removal rates have stayed stubbornly low. The literature has so far confirmed that the efficiency of eliminating chlorophenol from wastewater using a pilot-scale of an individual spiral wound RO process is around 83 %, compared to 97 % for dimethylphenol. This paper explores the potential of an alternative configuration of two-stage/two-pass RO process for improving such low chlorophenol rejection rates via simulation and optimisation. The operational optimisation carried out is enhanced by constraining the total recovery rate to a realistic value by varying the system operating parameters according to the allowable limits of the process. The results indicate that the proposed configuration has the potential to increase the rejection of chlorophenol by 12.4 % while achieving 40 % total water recovery at an energy consumption of 1.949 kWh/m³.