"December, 2004." / Bibliography: leaves 229-251. / xvi, 265 leaves : ill. (some col.) ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, School of Agriculture and Wine, Disciplines of Animal Science and Biometrics SA, 2005?

This study presents an optimization approach to heat exchanger network synthesis (HENS). HENs are widely used in industry and bring several fluid streams into their desired temperatures by using available heat in the process for efficient usage of energy. Our aim is to provide a network design coupled with a detailed equipment design for heat exchangers. The suggested approach involves discretization of temperatures based on heat load equalities and reformulation as a shortest-path problem, rather than dealing with a nonlinear model and a previously structured HEN, which are common methods in the literature. We generate a shortest path network whose every node corresponds to a heat exchanger alternative and each path represents a HEN design alternative. A mixed-integer nonlinear programming model is solved to design each exchanger alternative in detail, considering all thermo-physical and transport properties of streams at their temperatures and pressures. Our approach has modeling flexibility and successfully finds the required number of heat exchangers and their connections. In addition, one can control the solution quality by deciding on the heat load steps between stream inlet and outlets. Several HEN examples from the literature are solved to assess the performance of our approach and comparable results are obtained.

Although an increasing number of research studies in mathematics education have begun focusing their efforts on mathematical modeling as a need for change to convey mathematical ideas beyond schools, there is not enough information about the nature of the teacher knowledge for effective use of modeling in mathematics teaching and how this knowledge evolves. The goal of this study is to investigate teachers&rsquo / evolving knowledge when they engage in professional development activities based on lesson study cycle from modeling perspective. Professional development program of this study included a cyclical process. Lasting a month, each cycle consisted of meeting before the implementation of the model eliciting activity, implementation of the activity and meeting after the implementation. The study took five months and was conducted in two public schools. The participants were four in-service mathematics teachers where two teachers were selected from each school by purposive sampling. The study was designed as case study. Data analyses were conducted during and after data collection and with two approaches as with-in case and cross-case analysis. As the professional development activities created learning environments for the teachers to develop their models for teaching mathematics from a modeling perspective, the results of this study showed that the professional development program used in the study had a positive effect on teachers&rsquo / evolving pedagogical content knowledge and pedagogical knowledge based on the theoretical and empirical backgrounds in the literature. Besides, implications, suggestions for professional development, for teachers and for further research are provided.

Molten carbonate fuel cells (MCFCs) have become an attractive emerging technology for stationary co-generation of heat and power. From a technical perspective, dynamic operation has a significant effect on the fuel cell life cycle and, hence, economic viability of the device. The scope of this thesis is to present an improved understanding of the system behaviour at transient operation that can be used to design a more robust control system in order to overcome the cost and the operating lifetime issues. Hence, a comprehensive multi-component multidimensional transient mathematical model is developed based on the conservation laws of mass, momentum, species, energy and electric charges coupled through the reaction kinetics. In essence, this model is a set of partial differential equations that are discretized and solved using the finite-volume based commercial software, ANSYS FLUENT 12.0.1. The model is validated with two sets of experimental results, available in open literature, and good agreements are obtained. The validated model is further engaged in an extensive study. First, the MCFC behaviour at high current densities or oxidant utilization, when the mass transfer becomes dominant, is investigated using peroxide and superoxide reaction mechanisms. In brief, both mechanisms predicted the linear region of the polarization curve accurately. However, none of these mechanisms showed a downward bent in the polarization curve. A positive exponent for the carbon-dioxide mole fraction is probably essential in obtaining the downward bent (“knee”) at high current densities which is in contrast to what has been reported in the literature to date. Next, a sinusoidal impedance approach is used to examine the dynamic response of the unit cell to inlet perturbations at various impedance frequencies. This analysis is further used to determine the phase shifts and time scales of the major dynamic processes within the fuel cell. Furthermore, numerical simulation is utilized in order to investigate the underlying electrochemical and transport phenomena without performing costly experiments. Results showed that the electrochemical reactions and the charge transport process occur under a millisecond. The mass transport process showed a comparatively larger time scale. The energy transport process is the slowest process in the cell and takes about an hour to reach its steady state condition. Furthermore, the developed mathematical model is utilized as a predictive tool to provide a three-dimensional demonstration of the transient physical and chemical processes at system startiv up. The local distribution of field variables and quantities are presented. The results show that increasing the electrode thickness provides a higher reaction rate, but may lead to larger ohmic loss which is not desirable. The reversible heat generation and consumption mechanisms of the cathode and anode are dominant in the first 10 s while the heat conduction from the solid materials to the gas phase is not considerable. The activation and ohmic heating have the same impact within the anode and cathode because of their similar electric conductivity and voltage loss. Increasing the thermal conductivity of the cathode material will facilitate the process of heat transport throughout the cell. This can also be accomplished by lowering the effects of heat conduction by means of a cathode material with a smaller thickness. In addition, a thermodynamic model is utilized to examine energy efficiency, exergy efficiency and entropy generation of a MCFC. By changing the operating temperature from 883 K to 963 K, the energy efficiency of the unit cell varies from 42.8 % to 50.5 % while the exergy efficiency remains in the range of 26.8% to 36.3%. Both efficiencies initially rise at lower current densities up to the point that they attain their maximum values and ultimately decrease with the increase of current density. With the increase of pressure, both energy and exergy efficiencies of the cell increase. An increase in this anode/cathode flow ratio lessens the energy and exergy efficiencies of the unit cell. Higher operating pressure and temperature decrease the unit cell entropy generation. / UOIT

Controlling fates of stem and progenitor cells is one of the central goals of regenerative medicine. However, conventional cell enumeration methods are unable to distinguish between the effects of cell death, proliferation, and differentiation through molecular interventions on the output of specific cell types. We have devised a strategy to simultaneously obtain proliferation and death rates in cultures of highly purified erythroid progenitors. The approach is based on combining cell-surface marker analysis, cell-division tracking and 7-amino-actinomycin-D staining to monitor cell death. A compartment model of cell proliferation was developed to evaluate cell generation-specific length of cell-division, rates of entry into division, and cell death, from the experimental cell-division tracking data obtained following stimulation with erythropoietin (EPO) and Stem cell factor (SCF). The results indicated that EPO and SCF, either as single factor or in combination, differentially affect the rates of differentiation, length of cell-division and rates of death.

Controlling fates of stem and progenitor cells is one of the central goals of regenerative medicine. However, conventional cell enumeration methods are unable to distinguish between the effects of cell death, proliferation, and differentiation through molecular interventions on the output of specific cell types. We have devised a strategy to simultaneously obtain proliferation and death rates in cultures of highly purified erythroid progenitors. The approach is based on combining cell-surface marker analysis, cell-division tracking and 7-amino-actinomycin-D staining to monitor cell death. A compartment model of cell proliferation was developed to evaluate cell generation-specific length of cell-division, rates of entry into division, and cell death, from the experimental cell-division tracking data obtained following stimulation with erythropoietin (EPO) and Stem cell factor (SCF). The results indicated that EPO and SCF, either as single factor or in combination, differentially affect the rates of differentiation, length of cell-division and rates of death.

The extent to which economic evaluations of public health programs in Ontario are conducted and used by decision makers is currently very limited. This thesis supports public health decision-making through applied and methodological work. The applied work demonstrates different methods to evaluate the cost-effectiveness of public health interventions using the examples of seasonal and pandemic influenza immunization programs. The methodological component explores whether time horizon choice, one methodological consideration in economic evaluations, introduces bias. The economic evaluation of Ontario’s universal influenza immunization program (UIIP) uses primarily provincial health administrative databases to assess the impact of UIIP on health outcomes (quality-adjusted life years (QALYs), mortality), health care resource use (physician office visits, emergency department visits, and hospitalizations), and costs due to seasonal influenza. Ontario’s UIIP was found to be cost-effective compared to a targeted program. The economic evaluation of Ontario’s H1N1 (2009) mass immunization program uses a mathematical modeling approach to describe the pandemic as observed in Ontario. By removing immunization from the simulation, the impact of the program was evaluated. Outcome measures include health outcomes (attack rate, deaths, QALYs), resource use, and cost (physician office visits, emergency department visits, hospitalizations). The analysis found Ontario’s mass immunization program to be highly cost-effective despite high program cost. The methodological component investigates whether time horizon choice, a major methodological choice, introduces bias to economic evaluations. The existence, magnitude and direction of time horizon bias are demonstrated using a formal model. This work supports current guidelines in recommending a lifetime time horizon and provides a framework to discuss bias in economic evaluations. This thesis demonstrates different approaches to evaluate the cost-effectiveness of public health interventions, informs decision-making, and establishes the groundwork to guide future economic evaluations of public health interventions.

The extent to which economic evaluations of public health programs in Ontario are conducted and used by decision makers is currently very limited. This thesis supports public health decision-making through applied and methodological work. The applied work demonstrates different methods to evaluate the cost-effectiveness of public health interventions using the examples of seasonal and pandemic influenza immunization programs. The methodological component explores whether time horizon choice, one methodological consideration in economic evaluations, introduces bias. The economic evaluation of Ontario’s universal influenza immunization program (UIIP) uses primarily provincial health administrative databases to assess the impact of UIIP on health outcomes (quality-adjusted life years (QALYs), mortality), health care resource use (physician office visits, emergency department visits, and hospitalizations), and costs due to seasonal influenza. Ontario’s UIIP was found to be cost-effective compared to a targeted program. The economic evaluation of Ontario’s H1N1 (2009) mass immunization program uses a mathematical modeling approach to describe the pandemic as observed in Ontario. By removing immunization from the simulation, the impact of the program was evaluated. Outcome measures include health outcomes (attack rate, deaths, QALYs), resource use, and cost (physician office visits, emergency department visits, hospitalizations). The analysis found Ontario’s mass immunization program to be highly cost-effective despite high program cost. The methodological component investigates whether time horizon choice, a major methodological choice, introduces bias to economic evaluations. The existence, magnitude and direction of time horizon bias are demonstrated using a formal model. This work supports current guidelines in recommending a lifetime time horizon and provides a framework to discuss bias in economic evaluations. This thesis demonstrates different approaches to evaluate the cost-effectiveness of public health interventions, informs decision-making, and establishes the groundwork to guide future economic evaluations of public health interventions.

With the rapid growth of energy demand and consequently growth in supply, increasing energy costs, and environmental concerns, there is a critical need to find new ways to make better use of existing energy systems and resources and decelerate the demand growth towards a sustainable energy system. All of these facts are leading to the proposal of novel approaches to optimize the utilization of energy in different sectors to reduce the customer's total energy costs, demand and greenhouse gas (GHG) emissions while taking into account the end-user preferences. Utilities have implemented Demand Side Management (DSM) and Demand Response (DR) programs to better manage their network, offer better services to their customers, handle the increase in electricity demand, and at the same time increase system reliability and reduce environmental impacts. Smart Grid developments such as information technology, communication infrastructure and smart meters improve the effectiveness and capability of Energy Management Systems (EMSs) and facilitate the development of automated operational decision-making structures for energy systems, thus assisting DSM and DR programs to reach their full potential. The literature review indicates that whereas significant work has been done in DSM and DR in utilities, these works have mostly focused on direct load control of particular loads, and there is a lack of a general framework to consider all types of energy hubs in an integrated Energy Hub Management System (EHMS). In this context, mathematical modeling of energy systems for EMSs, which is the main concern of the present work, plays a critical role. This research proposes mathematical optimization models of energy hubs which can be readily incorporated into EHMS in the context of Smart Grids. The energy hub could be a single or multi-carrier energy system in residential, commercial, agricultural and/or industrial sectors. Therefore, mathematical models for energy hubs in residential, commercial, and agricultural sectors have been developed and are presented and discussed in this thesis. In the residential sector, this research presents mathematical optimization models of residential energy hubs which can be readily incorporated into automated decision making technologies in Smart Grids, and can be solved efficiently in a real-time frame to optimally control all major residential energy loads, storage and production components while properly considering the customer preferences and comfort levels. Mathematical models for major household demand, i.e., fridge, freezer, dishwasher, washer and dryer, stove, water heater, hot tub, and pool pumps, are formulated. Also, mathematical models of other components of a residential energy system including lighting, heating, and air-conditioning are developed, and generic models for solar PV panels and energy storage/generation devices are proposed. The developed mathematical models result in a Mixed Integer Linear Programming (MILP) optimization problem, whose objective is to minimize demand, total costs of electricity and gas, emissions and peak load over the scheduling horizon while considering end-user preferences. The application of this model to a real household are shown to result in savings of up to 20% on energy costs and 50% on peak demand, while maintaining the household owner's desired comfort levels. In the commercial sector, mathematical optimization models of produce storage facilities to optimize the operation of their energy systems are proposed. In the storage facilities, climate control of the storage rooms consumes considerable energy; thus, a mathematical model of storage facilities appropriate for their optimal operation is developed, so that it can be implemented as a supervisory control in existing climate controllers. The proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing climate control systems in storage facilities. The objective is to minimize total energy costs and demand charges while considering important parameters of storage facilities; in particular, inside temperature and humidity should be kept within acceptable ranges. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied via Monte-Carlo simulations. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints. In the agricultural sector, this work presents mathematical optimization models of greenhouses to optimize the operation of their energy systems. In greenhouses, artificial lighting, CO2 production, and climate control consume considerable energy; thus, a mathematical model of greenhouses appropriate for their optimal operation is developed, so that it can be implemented as a supervisory control in existing greenhouse controllers. The proposed model incorporates weather forecasts, electricity price information, and the end-user preferences to optimally operate existing control systems in greenhouses. The objective is to minimize total energy costs and demand charges while considering important parameters of greenhouses; in particular, inside temperature and humidity, CO2 concentration, and lighting levels should be kept within acceptable ranges. Effects of uncertainty in electricity price and weather forecast on optimal operation of the storage facilities are studied via Monte-Carlo simulations and robust optimization approach. The presented simulation results show the effectiveness of the proposed model to reduce total energy costs while maintaining required operational constraints.

Polymer electrolyte membrane (PEM) fuel cells have increasingly become promising green energy sources for automobile and stationary cogeneration applications but its success in commercialization depends on performance optimization and manufacturing cost. The activation losses, expensive platinum catalyst, and water flooding phenomenon are the key factors currently hindering commercialization of PEM fuel cells. These factors are associated with the cathode catalyst layer (CCL), which is about ten micrometers thick. Given the small scale of this layer, it is extremely difficult to study transport phenomena inside the catalyst layer experimentally, either intrusively or non-intrusively. Therefore, mathematical and numerical models become the only means to provide insight on the physical phenomena occurring inside the CCL and to optimize the CCL designs before building a prototype for engineering application. In this thesis research, a comprehensive two-phase mathematical model for the CCL has been derived from the fundamental conservation equations using a volume-averaging method. The model also considers several water transport and physical processes that are involved in the CCL. The processes are: (a) electro-osmotic transport from the membrane to the CCL, (b) back-diffusion of water from the CCL to the membrane, (c) condensation and evaporation of water, and (d) removal of liquid water to the gas flow channel through the gas diffusion layer (GDL). A simple analytical model for the activation overpotential in the CCL has also been developed and an optimization study has been carried out using the analytical activation overpotential formulation. Further, the mathematical model has been simplified for the CCL and an analytical approach has been provided for the liquid water transport in the catalyst layer. The volume-averaged mathematical model of the CCL is finally implemented numerically along with an investigation how the physical structure of a catalyst layer affects fuel cell performance. Since the numerical model requires various effective transport properties, a set of mathematical expressions has been developed for estimating the effective transport properties in the CCL and GDL of a PEM fuel cell. The two-dimensional (2D) numerical model has been compared with the analytical model to validate the numerical results. Subsequently, using this validated model, 2D numerical studies have been carried out to investigate the effect of various physical and wetting properties of CCL and GDL on the performance of a PEM fuel cell. It has been observed that the wetting properties of a CCL control the flooding behavior, and hydrophilic characteristics of the CCL play a significant role on the cell performance. To investigate the effect of concentration variation in the flow channel, a three-dimensional numerical simulation is also presented.