<p>In this thesis structural optimization of castings and thermomechanical analysis of castings are studied.</p><p>In paper I an optimization algorithm is created by using Matlab. The algorithm is linked to the commercial FE solver Abaqus by using Python script. The optimization algorithm uses the successive response surfaces methodology (SRSM) to create global response surfaces. It is shown that including residual stresses in structural optimization of castings yields an optimal shape that differs significantly from the one obtained when residual stresses are excluded.</p><p>In paper II the optimization algorithm is expanded to using neural networks. It is tested on some typical bench mark problems and shows very promising results. Combining paper I and II the response surfaces can be either analytical functions, both linear and non-linear, or neural networks. The optimization is then performed by using sequential linear programming or by using a zero-order method called Complex. This is all gathered in a package called StuG-OPT.</p><p>In paper III and IV focus is on the thermomechanical problem when residual stresses are calculated. In paper III a literature review is performed and some numerical simulations are performed to see where numerical simulations can be used in the industry today. In paper IV simulations are compared to real tests. Several stress lattices are casted and the residual stresses are measured. Simulations are performed by using Magmasoft and Abaqus. In Magmasoft a J2-plasticity model is used and in Abaqus two simulations are performed using either J2-plasticity or the ”Cast Iron Plasticity” available in Abaqus that takes into account the different behavior in tension and compression for grey cast iron.</p> / Report code: LIU-TEK-LIC-2007:34.

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With the growth of wireless networks and the increase in personal internet use for a wide diversity of applications, the importance of the quality of service (QoS) delivered to clients has become of great importance. In order to evaluate QoS, this study explores the application of the linear response surface analysis (LRSA) technique as an evaluation tool for QoS factors such as Throughput and Delay. An 802.11n prototype wireless network is constructed in order to capture QoS data that is then used to construct LRSA models in order to evaluate the QoS factors. The LRSA models are maximised and minimised while constraining specific measured QoS factors and the subsequent results are analysed. Based on this analysis, recommendations for the improvement of wireless networks are made as well as the use of the LRSA technique to evaluate QoS within a wireless network. / Thesis (MSc (Computer Science))--North-West University, Potchefstroom Campus, 2013

With the growth of wireless networks and the increase in personal internet use for a wide diversity of applications, the importance of the quality of service (QoS) delivered to clients has become of great importance. In order to evaluate QoS, this study explores the application of the linear response surface analysis (LRSA) technique as an evaluation tool for QoS factors such as Throughput and Delay. An 802.11n prototype wireless network is constructed in order to capture QoS data that is then used to construct LRSA models in order to evaluate the QoS factors. The LRSA models are maximised and minimised while constraining specific measured QoS factors and the subsequent results are analysed. Based on this analysis, recommendations for the improvement of wireless networks are made as well as the use of the LRSA technique to evaluate QoS within a wireless network. / Thesis (MSc (Computer Science))--North-West University, Potchefstroom Campus, 2013

Current prices and demand for petroleum hydrocabons have generated an increase of oil spills around the country and the world. Health and environmental impacts associated to these organic pollutants represent a huge concern for the general public, leading the public and private sector to develop new technologies and methods to minimize or eliminate those risks. Ex-Situ bioremediation through biopiles, as a main remediation technique to treat a wide range of hydrocarbons, has been a topic of considerable research interest over the last years. It provides an economical and environmental solution to restore the environment to background levels. Nevertheless, successful bioremediation under cold climate conditions is of considerable concern in countries like Canada, as low temperatures can delay the rate of bioremediation of oil hydrocarbons, thus limiting the operation of soil treatment facilities to certain times of the year. Recent research has found out that bioremediation could be conducted even at low or cold temperatures with larger periods of times. And even more, the addition of petroleum degrading microorganisms (bioaugmentation) and nutrients or biosurfactants (biostimulation) could enhance the process in some cases. In the present study, a comprehensive assessment of bioaugmentation and biostimulation strategies for ex-situ bioremediation of petroleum contaminated soil under cold climate conditions is proposed. Field scale biopiles were constructed and subjected to different concentrations of commercial microbial consortia and mature compost, as bioaugmentation and biostimulation strategies, in a soil treatment facility at Moose Creek, Ontario over a period of 94 days (November 2012 to February 2013). Assessment and comparison of the biodegradation rates of total petroleum hydrocarbons (TPH) and their fractions were investigated. Furthermore, a response surface methodology (RSM) based on a factorial design to investigate and optimize the effects of the microbial consortia application rate and amount of compost on the TPH removal was also assessed. Results showed that biopiles inoculated with microbial consortia and amended with 10:1 soil to compost ratio under aerobic conditions performed the best, degrading 82% of total petroleum hydrocarbons (TPHs) with a first-order kinetic degradation rate of 0.016 d_1, under cold temperature conditions. The average removal efficiencies for TPHs after 94 days for control biopiles, with no amendments or with microbial consortia or compost only treatments were 48%, 55%, and 52%, respectively. Statistical analyses indicated a significant difference (p < 0.05) within and between the final measurements for TPHs and a significant difference between the treatment with combined effect, and the control biopiles. On the other hand, the modeling and optimization statistical analysis of the results showed that the microbial consortia application rate, compost amendment and their interactions have a significant effect on TPHs removal with a coefficient of determination (R2) of 0.88, indicating a high correlation between the observed and the predicted values for the model obtained. The optimum concentrations predicted via RSM were 4.1 ml m-3 for microbial consortia application rate, and 7% for compost amendment to obtain a maximum TPH removal of 90.7%. This research contributes to provide valuable knowledge to practitioners about cost-effective and existing strategies for ex-situ bioremediation under cold weather conditions.

The control of pest populations by using insect pathogens has been an attractive alternative to the application of chemical pesticides employed for the same purpose. As these chemicals not only damage the environment, but also trigger development of resistance by the pests and can harm other organisms together with the target pest, biological control is preferable and Bacillus thuringiensis (Bt) subspecies have been the most widely used bioinsecticides in forestry, agriculture and mosquito/ black fly control. The most important property of Bt subspecies is the synthesis of protoxins named as delta-endotoxins (crystal proteins). In this study, response surface optimization of Bt subsp. israelensis HD500 batch fermentation for high level production of its toxin proteins Cry4Ba and Cry11Aa was performed. As the interaction of the medium components as well as cultivation conditions are expected to influence the production of the toxin proteins, an experimental chart was prepared by accepting the previously reported optimal values for the most important parameters as zero points: [Mn], 10-6 M / [K2HPO4], 50 mM / C:N ratio, 20:1 and incubation temperature / 30&deg / C. When the combinations of these variables at different levels were studied at 30 batch cultures and analysed for the optimum toxin protein concentrations, temperature: 28.3&amp / #61616 / C, [Mn]: 3.3x10-7M, C:N ratio: 22.2 and [K2HPO4]: 66.1mM yielded the highest concentrations of both Cry4Ba and Cry11Aa toxin proteins.

In this thesis, it is aimed to design lithium extraction from boron clays using statistical design of experiments and robust design methodologies. There are several factors affecting extraction of lithium from clays. The most important of these factors have been limited to a number of six which have been gypsum to clay ratio, roasting temperature, roasting time, leaching solid to liquid ratio, leaching time and limestone to clay ratio. For every factor, three levels have been chosen and an experiment has been designed. After performing three replications for each of the experimental run, signal to noise ratio transformation, ANOVA, regression analysis and response surface methodology have been applied on the results of the experiments. Optimization and confirmation experiments have been made sequentially to find factor settings that maximize lithium extraction with minimal variation. The mean of the maximum extraction has been observed as 83.81% with a standard deviation of 4.89 and the 95% prediction interval for the mean extraction is (73.729, 94.730). This result is in agreement with the studies that have been made in the literature. However / this study is unique in the sense that lithium is extracted from boron clays by using limestone directly from the nature, and gypsum as a waste product of boric acid production. Since these two materials add about 20% cost to the extraction process, the results of this study become important. Moreover, in this study it has been shown that statistical design of experiments help mining industry to reduce the need for standardization.

The main objective of this study was to optimize processing conditions during halogen lamp-microwave combination baking of cake by using Response Surface Methodology. It was also aimed to compare quality of products baked in microwave-halogen lamp combination oven, halogen lamp oven, microwave oven and conventional oven. In the first part of the study, as independent variables, baking time for conventional oven / microwave power and baking time for microwave oven / halogen lamp power and baking time for halogen lamp oven and microwave power, halogen lamp power and baking time for halogen lamp-microwave combination oven were used. Weight loss, specific volume, firmness and color of the cakes were measured during the study. Cakes baked in conventional oven at 175&deg / C for 24 minutes were determined as the control cakes. Weight loss of cakes increased with increasing independent variables for all oven types. Specific volume and firmness of cakes increased with increasing microwave power, but decreased with upper halogen lamp power. Color formation was achieved in the combination baking but not as much as in the conventional baking. Response Surface Methodology was used to optimize the baking conditions in the second part of the study. Upper and lower halogen lamp powers, microwave power and baking time were used as independent variables. Optimum processing conditions were found as 60% for upper halogen lamp power, 70% for lower halogen lamp power, 30% for microwave power and 5 minutes for baking time. Cakes baked at optimum baking conditions had comparable quality with conventionally baked ones, except color. In short, by the usage of halogen lamp-microwave combination oven it was possible to obtain high quality cakes by reducing of conventional baking time about 79%.

Grape pomace is a by-product of wineries. It is one of the most potent antioxidant sources due to its high phenolic content. In this thesis study, ultrasound assisted extraction of phenolic compounds from Merlot grape pomace has been studied. The effects of sonication time, subsequent extraction time in shaking water bath at 45&deg / C and composition of the solvent on extraction efficiency and recovery of phenolics were studied by response surface methodology. Folin-Ciocalteu colorimetric method was used to analyze effects of process parameters on the total phenolic content of the extracts. The best recovery (47.2 mg gallic acid equivalents of total phenolics per g of dried grape pomace) was obtained using 30 % aqueous ethanol and applying 6 minutes of sonication followed by 12 minutes of shaking in water bath at 45&deg / C.

This thesis develops a methodology for designing resilient system-of-systems (SoS) networks. This methodology includes a capability-based resilience assessment framework, used to quantify SoS resilience. A complex networks approach is used to generate potential SoS network designs, focusing on scale-free and random network topologies, degree-based and random rewiring adaptation, and targeted and random node removal threats. Statistical design methods, specifically response surface methodology, are used to evaluate SoS networks and provide an understanding of the advantages and disadvantages of potential designs. Linear regression is used to model a continuous representation of the network design space, and determine optimally resilient networks for particular threat types. The methodology is applied to an information exchange (IE) network model (i.e., a message passing network model) and military command and control (C2) model. Results show that optimally resilient IE network topologies are random for networks with adaptation, regardless of the threat type. However, the optimally resilient adaptation method sharply transitions from being fully random to fully degree-based as threat randomness increases. These findings suggest that intermediately defined networks should not be considered when designing for resilience. Cost-benefit analysis of C2 networks suggests that resilient C2 networks are more cost-effective than robust ones, as long as the cost of rewiring network links is less than three-fourths the cost of creating new links. This result identifies a threshold for which a resilient network design approach is more cost-effective than a robust one.This thesis develops a methodology for designing resilient system-of-systems (SoS) networks. This methodology includes a capability-based resilience assessment framework, used to quantify SoS resilience. A complex networks approach is used to generate potential SoS network designs, focusing on scale-free and random network topologies, degree-based and random rewiring adaptation, and targeted and random node removal threats. Statistical design methods, specifically response surface methodology, are used to evaluate SoS networks and provide an understanding of the advantages and disadvantages of potential designs. Linear regression is used to model a continuous representation of the network design space, and determine optimally resilient networks for particular threat types. The methodology is applied to an information exchange (IE) network model (i.e., a message passing network model) and military command and control (C2) model. Results show that optimally resilient IE network topologies are random for networks with adaptation, regardless of the threat type. However, the optimally resilient adaptation method sharply transitions from being fully random to fully degree-based as threat randomness increases. These findings suggest that intermediately defined networks should not be considered when designing for resilience. Cost-benefit analysis of C2 networks suggests that resilient C2 networks are more cost-effective than robust ones, as long as the cost of rewiring network links is less than three-fourths the cost of creating new links. This result identifies a threshold for which a resilient network design approach is more cost-effective than a robust one.