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A conventional radial-field FSPM machine was designed and studied. The research focussed on the effectiveness of using a parametric study to obtain an optimized solution compared to using a computerized optimizer; as well as an in-depth core loss analysis. The designing process started with an analytical design that was used for initial design purposes, and this was followed by numerical simulations to get an optimized solution. Within the numerical simulations, the parametric analysis and optimization were performed. The final optimized design was designed to be manufactured and compared to both the analytical and numerical results for validation. The analytical and numerical results were obtained using MathWorks MATLAB 2019a and Ansys Maxwell 19.1 respectively. The results show that an optimizer is more effective in finding an optimized solution in the design space, however, the parametric analyses are still useful in order to determine the design regions for the optimizer and how sensitive certain parameters are towards the FSPM machine's performance. In the end, these analyses are used to speed up the design process by minimizing computational time, and also provides an understanding to the designer of parameter changes on the FSPM machine's performance.

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The use of titanium and titanium alloys, particularly titanium-6-aluminium-4-vanadium (Ti6Al4V), manufactured by 3D printing, also known as additive manufacturing, is fast evolving in several industries including the aerospace industry, medical industry, and the automotive industry. At present, titanium parts fabricated by additive manufacturing techniques such as the selective laser melting practice are built at conventional laser scan speeds typically between 0.1 m/s and 1 m/s. This project is focused on investigating the influence of high laser scan speeds on the microstructure and mechanical properties of Ti6Al4V fabricated by the selective laser melting technique. Building material at much higher scan speeds allows for a higher rate of productivity and efficiency as more parts can be built in a shorter space of time. The aim of this project is to ensure that the integrity and mechanical behaviour of the Ti6Al4V parts at the high scan speeds is still maintained by investigating whether acceptable mechanical properties are still achieved when material is built at higher scan speeds. The material tested in this project was built by the selective laser melting (SLM) technique at four different high scan speeds namely 5.75 m/s, 6.0 m/s, 6.25 m/s and 6.5 m/s. The material for mechanical testing consisted of tensile specimens and compact-tension (CT) specimens for fracture toughness (FT) testing and fatigue crack growth rate (FCGR) testing respectively. The same material was used for microstructure analysis. Furthermore, the tensile specimens were fabricated in different build orientations namely X-TA, Y-TA, and Z-TA to investigate the effect of build orientation on tensile properties. During the SLM process, considerable thermal related stresses develop in the material being built. After fabrication, a heat treatment protocol was therefore applied to the material for stress relief. The material was heated at 600°C for two hours then cooled in air. Tensile testing was performed on the SLM Ti6Al4V built tensile bars according to the ASTM E8/E8M standard on the Zwick machine at a strain rate of 10-3 /s in conjunction with a video extensometer for more accurate results. Specimens were loaded in tension with force of approximately 20 000N. The results indicated that there is no significant influence of high scan speed on the tensile properties of the material tested as there was no difference observed in tensile properties of the material built at the four different high scan speeds. The same phenomenon was observed with build orientation. The tensile properties of the specimens built in the horizontal direction (X-TA and Y-TA) and the specimens built in the vertical direction (Z-TA) were within the same range. The FCGR and FT tests were performed on the ESH servo hydraulic fatigue machine at room temperature according to the ASTM E647-15 and E399 standards, respectively. The FCGR tests were conducted at a load range of 1.3kN and stress ratio of 0.1. The results indicated that there is no difference in FCGR behaviour with respect to scan speed between the 5.75 m/s and 6.0 m/s specimens as one set and no difference between the 6.25 m/s and 6.5 m/s specimens as the other set, but a difference is observed between the two sets of speeds. All specimens however, displayed reasonable resistance to crack growth. The FT tests were performed at a crosshead speed of 1 mm/min. The results indicated that high scan speed has no significant influence on the fracture toughness of SLM Ti6Al4V material as there was no difference observed in fracture toughness of the materials with increase in scan speed. The techniques used for microstructure analysis were light microscopy and scanning electron microscopy (SEM). Light microscopy was performed to reveal the microstructure and surface topography of the material built at the four different high scan speeds and three different build orientations. The results indicated that high scan speed has no significant influence on the microstructure of the material investigated. The results also indicated that build orientation influences the microstructure of the material tested. A difference in microstructure, particularly the orientation of β-grains, was observed with build orientation. The X-TA and Y-TA specimens have β-grains aligned more perpendicular to the tensile axis whilst the Z-TA specimens have the β-grains aligned more parallel to the tensile axis. However, this difference in β-grain orientation did not influence the tensile properties of these specimens to a greater extent. SEM was performed to obtain quantitative information on microstructure, particularly porosity, and for β-grain reconstruction by electron backscatter diffraction (EBSD) for the material at a much higher magnification and depth of field. The results indicated that high scan speed has no influence on porosity of the material tested as no difference was observed in the number and size of pores amongst the samples built at the four high scan speeds. However, average relative density of 97-99% was reported for these specimens which is lower in comparison with average relative density of >99% reported for the majority of the specimens built at conventional scan speeds. EBSD analysis shows that there is no difference in the size and morphology of reconstructed β-grains across the material built at the high scan speed. It is concluded that there is no significant influence of high scan speed on the microstructure and mechanical properties of SLM Ti6Al4V within the scan speed range of 5.75 m/s to 6.5 m/s investigated in this project. The specimens built at the specified scan speeds have similar energy density input which attributes to the similar microstructure and mechanical behaviour of the specimens observed.

High-rate anaerobic biological wastewater treatment using the upflow anaerobic sludge bed (UASB) reactor technology offers the potential to reform wastewater treatment. However, the lack of clarity regarding the mechanisms responsible for self-immobilisation of the microbial consortia involved, known as granulation, presents an obstacle to the wide-spread use of this technology. In this study, two laboratory-scale UASB reactors were commissioned for the purpose of generating datasets for model development. A sucrose-based feed was used for the experiments, which were conducted at 37°C. Deterioration of the sludge granules used as inoculum into undesirable bulking-type sludge resulted in refocusing the study to investigate the granulation process. After consulting the literature on granulation, an experimental investigation into the effect of providing additional hydraulic mixing by recycling reactor effluent on granulation was conducted. It was hypothesised that the additional hydraulic mixing would result in the formation of more settleable granules. However, it was found that inclusion of the additional hydraulic mixing resulted in a less dense sludge bed which contained more visual signs (presence of both more loosely-bound exogenous polymeric substance and long filaments presumed to be Methanosaeta Spp.) of bulking-type sludge. In hindsight it was found that application of too low a sludge loading rate in the experimental investigations was the cause of the granulation issues, but that this was exacerbated by the additional hydraulic mixing. Apart from granulation issues, a low effluent pH of 6.5 was obtained from the reactors during the experimental investigations in spite of a high feed pH of 8.0. It was hypothesised that the production of VFA and consumption of NH3 were the primary causes of the acidity generation. A fixed-conversion model of the digester pH was developed to investigate the conversions of the relevant weak acid and base species present and the effects of these conversions on the digester pH. It was found that the dissolution of CO2 to satisfy the vapour-liquid equilibrium between the headspace CO2 partial pressure and dissolved carbonic acid concentration was predominantly responsible for the decrease in pH across the reactors. It is on the basis of these findings that both hypotheses were refuted.

There has been a shift towards energy sustainability in recent years, and this shift should continue. The steady growth of energy demand because of population growth, as well as heightened worries about the number of anthropogenic gases released into the atmosphere and deployment of advanced grid technologies, has spurred the penetration of renewable energy resources (RERs) at different locations and scales in the power grid. As a result, the energy system is moving away from the centralized paradigm of large, controllable power plants and toward a decentralized network based on renewables. Microgrids, either grid-connected or islanded, provide a key solution for integrating RERs, load demand flexibility, and energy storage systems within this framework. Nonetheless, renewable energy resources, such as solar and wind energy, can be extremely stochastic as they are weather dependent. These resources coupled with load demand uncertainties lead to random variations on both the generation and load sides, thus challenging optimal energy management. This thesis develops an optimal energy management system (EMS) for a grid-tied solar PV-battery microgrid. The goal of the EMS is to obtain the minimum operational costs (cost of power exchange with the utility and battery wear cost) while still considering network constraints, which ensure grid violations are avoided. A reinforcement learning (RL) approach is proposed to minimize the operational cost of the microgrid under this stochastic setting. RL is a reward-motivated optimization technique derived from how animals learn to optimize their behaviour in new environments. Unlike other conventional model-based optimization approaches, RL doesn't need an explicit model of the optimization system to get optimal solutions. The EMS is modelled as a Markov Decision Process (MDP) to achieve optimality considering the state, action, and reward function. The feasibility of two RL algorithms, namely, conventional Q-learning algorithm and deep Q network algorithm, are developed, and their efficacy in performing optimal energy management for the designed system is evaluated in this thesis. First, the energy management problem is expressed as a sequential decision-making process, after which two algorithms, trading, and non-trading algorithm, are developed. In the trading algorithm case, excess microgrid's energy can be sold back to the utility to increase revenue, while in the latter case constraining rules are embedded in the designed EMS to ensure that no excess energy is sold back to the utility. Then a Q-learning algorithm is developed to minimize the operational cost of the microgrid under unknown future information. Finally, to evaluate the performance of the proposed EMS, a comparison study between a trading case EMS model and a non-trading case is performed using a typical commercial load curve and PV generation profile over a 24- hour horizon. Numerical simulation results indicated that the algorithm learned to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility based on the time-varying tariff and battery wear cost) in both summer and winter case studies. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one decreased cost by 4.033% in the summer season and 2.199% in the winter season. Secondly, a deep Q network (DQN) method that uses recent learning algorithm enhancements, including experience replay and target network, is developed to learn the system uncertainties, including load demand, grid prices and volatile power supply from the renewables solve the optimal energy management problem. Unlike the Q-learning method, which updates the Q-function using a lookup table (which limits its scalability and overall performance in stochastic optimization), the DQN method uses a deep neural network that approximates the Q- function via statistical regression. The performance of the proposed method is evaluated with differently fluctuating load profiles, i.e., slow, medium, and fast. Simulation results substantiated the efficacy of the proposed method as the algorithm was established to learn from experience to raise the battery state of charge and optimally shift loads from a one-time instance, thus supporting the utility grid in reducing aggregate peak load. Furthermore, the performance of the proposed DQN approach was compared to the conventional Q-learning algorithm in terms of achieving a minimum global cost. Simulation results showed that the DQN algorithm outperformed the conventional Q-learning approach, reducing system operational costs by 15%, 24%, and 26% for the slow, medium, and fast fluctuating load profiles in the studied cases.