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Biological sulphate reduction (BSR) has been identified as a promising alternative technology for the treatment of acid mine drainage. BSR is a process that uses sulphate reducing bacteria to reduce sulphate to sulphide using substrates as nutrients under anaerobic conditions. The performance of BSR is dependent on several factors including substrate, pH, temperature and hydraulic retention time (HRT). In a quest to find a cost effective technology, Mintek conducted bench-scale tests on BSR that led to the commissioning of a pilot plant at a coal mine in Mpumalanga province, South Africa. This current study forms part of the ongoing tests that are conducted to improve Mintek’s process. The purpose of this study was to investigate the robustness of Mintek’s process and to develop a tool that can be used to predict the process’ performance with varying pH, temperature and HRT. Design Expert version 11.1.2.0 was used to design the experiments using the Box-Behnken design. In the design, pH ranged from 4 to 6, temperature from 10 °C to 30 °C and HRT from 2 d to 7 d with sulphate reduction efficiency, sulphate reduction rate and sulphide production as response variables. Experiments were carried out in water jacketed packed bed reactors that were operated in a down-flow mode. The reactors were packed with woodchips, wood shaving, hay, lucerne straw and cow manure as support for sulphate reducing bacteria (SRB) biofilm. Cow manure and lucerne pellets were used as the main substrates and they were replenished once a week. These reactors mimicked the pilot plant. The data obtained were statistically analysed using response surface methodology. The results showed that pH did not have a significant impact on the responses (p>0.05). Temperature and HRT, on the other hand, greatly impacted the process (p<0.05) and the interaction between these two factors was found to be strong. Sulphate reduction efficiency and sulphate reduction rate decreased by over 60 % with a decrease in temperature 30 °C to 10 °C. Generally, a decrease in sulphide production was observed with a decrease in temperature. Overall, a decrease in HRT resulted in a decline of sulphate reduction efficiency and sulphide production but favoured sulphate reduction rate. This study demonstrated that Mintek’s process can be operated at pH as low as 4 without any significant impact on the performance. This decreases the lime requirements and sludge production during the pre-neutralisation stage by close to 50 %. There was, however, a strong interaction between temperature and HRT which can be used to improve the performance especially during the winter season. / Dissertation (MEng)--University of Pretoria, 2020. / Chemical Engineering / MEng / Unrestricted

In today's competitive manufacturing industries, companies strive to reduce manufacturing development costs and lead times in hopes of reducing costs and capturing more market share from early release of their new or redesigned products. Tooling lead time constraints are some of the more significant challenges facing product development of advanced free-form components. This is especially true for complex designs in which large dies, molds or other large forming tools are required. The lead time for tooling, in general, consists of three main components; material acquisition, tool design and engineering, and tool manufacturing. Lead times for material acquisition and tool manufacture are normally a function of vendor/outsourcing constraints, manufacturing techniques and complexity of tooling being produced. The tool design and engineering component is a function of available manpower, engineering expertise, type of design problem (initial design or redesign of tooling), and complexity of the design problem. To reduce the tool design/engineering lead time, many engineering groups have implemented Computer-Aided Design, Engineering, and Manufacturing (CAD/CAE/CAM or CAx) tools as their standard practice for the design and analysis of their products. Although the predictive capabilities are efficient, using CAx tools to expedite advanced die design is time consuming due to the free-form nature and complexity of the desired part geometry. Design iterations can consume large quantities of time and money, thus driving profit margins down or even being infeasible from a cost and schedule standpoint. Any savings based on a reduction in time are desired so long as quality is not sacrificed. This thesis presents an automated tool design methodology that integrates state-of-the-art numerical surface fitting methods with commercially available CAD/CAE/CAM technologies and optimization software. The intent is to virtually create tooling wherein work-piece geometries have been optimized producing products that capture accurate design intent. Results show a significant reduction in design/engineering tool development time. This is due to the integration and automation of associative tooling surfaces automatically derived from the known final design intent geometry. Because this approach extends commercially available CAx tools, this thesis can be used as a blueprint for any automotive or aerospace tooling need to eliminate significant time and costs from the manufacture of complex free-form components.

Impaired reproduction studies with Poisson responses are among a growing class of toxicity studies in the biological and medical realm. In recent years, little effort has been focused on the development of efficient experimental designs for impaired reproduction studies. This research concentrates on two areas: 1) the use of Bayesian techniques to make single regressor designs robust to parameter misspecification and 2) the extension of design optimality methods to the k-regressor model. The standard Poisson model with log link is used. Bayesian designs with priors on the parameters are explored using both the D and F-optimality criteria for the single regressor Poisson exponential model. Since these designs are found via numeric optimization techniques, Bayesian equivalence theory functions are derived to verify the optimality of these designs. Efficient Bayesian designs which provide for lack-of-fit testing are discussed. Characterizations of D, D_s, and interaction optimal designs which are factorial in nature are demonstrated for models involving interaction through k factors. The optimality of these designs is verified using equivalence theory. In addition, augmentations of these designs that result in desirable lack of fit properties are discussed. Also, a structure for fractional factorials is given in which specific points are added one at a time to the main effect design in order to gain estimability of the desired interactions. Robustness properties are addressed as well. Finally, this entire line of research is extended to industrial exponential models where different regressors work to increase and/or decrease a count data response produced by a process. / Ph. D.

Yes / This paper investigates the coupled effect of functionalized multiwall carbon nanotubes (MWCNTs-COOH), nanomontmorillonite (NM), and sodium dodecyl benzene sulfonate (SDBS) anionic surfactant on compressive and flexural strengths of cement paste. The response surface methodology (RSM) was used to optimize the content of the two nanomaterials and surfactant, and to analyze the effect of their interactions on mechanical properties and microstructural characteristics of the paste. Test results indicate that the simultaneous use of NM and MWCNT can lead to 30% gain in compressive strength and 40% increase in flexural strength. Using analysis of variance, it was possible to predict the optimal weight percentage of nanomaterials. Atomic Force Microscope observations showed that the use of NM and MWCNT can reduce the surface roughness of cement paste and refine porosity, thus reducing the risk of cracking at the cement matrix and improving the homogeneity of the microstructure.

Crystallisation is gaining a lot of interest in pharmaceutical industry to help designing active ingredients with tailored physicochemical properties. Many factors have been found to affect the crystallisation process, including process parameters and material attributes. Several studies in the literature have discussed the role of these parameters in the crystallisation process. A comprehensive study is still missing in this field where all the significant terms are taken into consideration, including the square effect and the interaction terms between different parameters. In this study, a thorough investigation into the main factors affecting crystallisation of a polymeric system, processed via injection moulding, was presented and a sample of response optimisation was introduced which can be mimicked to suite a specific need. Three grades of pure polyethylene oxide; 20K, 200K and 2M, were first characterised using differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), powder X-ray diffraction (PXRD) and shear rheometry. The onset of degradation and the rate varied according to molecular weight of polyethylene oxide (PEO). The peak melting temperature and the difference in enthalpy between melting and crystallisation were both in a direct proportion with PEO molecular weight. PEO200K and PEO2M struggle to recrystallise to the same extent of the original state at the tested cooling rates, while PEO20K can retain up to a similar crystallinity degree when cooled at 1 °C/min. Onset of crystallisation temperature (Tc1) was high for PEO2M and the difference between the 20K and 200K were pronounced at low cooling rate (20K is higher than 200K). The rheometer study showed that PEO2M has a solid-like structure around melting point which explains the difficulty in processing this grade at a low temperature via IM. PEO20K was almost stable within the strain values studied (Newtonian behaviour). For higher grades, PEO showed a shear thinning behaviour. The complex viscosity for PEO2M is characterised by a steeper slope compared to PEO200K, which indicates higher shear thinning sensitivity due to higher entanglement of the longer chains. For binary blends of PEO, the enthalpy of crystallisation studied by DSC was in direct proportion to the lowest molecular weight PEO content (PEOL %) in PEO20K/200K and PEO20K/2M blends. The effect of PEOL% on Tc1 became slightly pronounced for PEO20K-2M blends where Tc1 exhibited slight inverse proportionality to PEOL% and it became more significant for PEO200K-2M blends. It was interesting to find that Tc1 for the blends did not necessarily lie between the values of the homopolymers. In all binary blends, Tc1 was inversely proportional to cooling rate for the set of cooling rates tested. Thermal analysis using hot stage polarised light microscopy yields different behaviours of various PEO grades against the first detection of crystals especially where the lowest grade showed highest detection temperature. Visual observation of PEO binary blends caplets processed at various conditions via injection moulding (IM) showed the low-quality caplets processed at mould temperature above Tc1 of the sample. The factors affecting crystallisation of injection moulded caplets were studied using response surface methodology for two responses; peak melting temperature (Tm) and relative change in crystallinity (∆Xc%) compared to an unprocessed sample. Mould temperature (Tmould) was the most significant factor in all binary blend models. The relationship between Tmould and the two responses was positive non-linear at the Tmould ˂ Tc1. Injection speed was also a significant factor for both responses in PEO20K-200K blends. For Tm, the injection speed had a positive linear relationship while the opposite trend was found for ∆Xc%. The interaction term found in the RSM study for all models was only between the injection speed and the PEOL % which shows the couple effect between these two factors. Molecular effect was considered a significant factor in all ∆Xc% models across the three binary blends. The order of ∆Xc% sensitivity to the change in PEOL% was 3, 5 and 7 % for 20K-200K, 200K-2M and 20K-2M.

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Tsunami and storm surge are two of the main destructive and costly natural hazards faced by coastal communities around the world. To enhance coastal resilience and to develop effective risk management strategies, accurate and efficient tsunami and storm surge prediction models are needed. However, existing physics-based numerical models have the disadvantage of being difficult to satisfy both accuracy and efficiency at the same time. In this dissertation, several surrogate models are developed using statistical and machine learning techniques that can rapidly predict a tsunami and storm surge without substantial loss of accuracy, with respect to high-fidelity physics-based models. First, a tsunami run-up response function (TRRF) model is developed that can rapidly predict a tsunami run-up distribution from earthquake fault parameters. This new surrogate modeling approach reduces the number of simulations required to build a surrogate model by separately modeling the leading order contribution and the residual part of the tsunami run-up distribution. Secondly, a TRRF-based inversion (TRRF-INV) model is developed that can infer a tsunami source and its impact from tsunami run-up records. Since this new tsunami inversion model is based on the TRRF model, it can perform a large number of tsunami forward simulations in tsunami inversion modeling, which is impossible with physics-based models. And lastly, a one-dimensional convolutional neural network combined with principal component analysis and k-means clustering (C1PKNet) model is developed that can rapidly predict the peak storm surge from tropical cyclone track time series. Because the C1PKNet model uses the tropical cyclone track time series, it has the advantage of being able to predict more diverse tropical cyclone scenarios than the existing surrogate models that rely on a tropical cyclone condition at one moment (usually at or near landfall). The surrogate models developed in this dissertation have the potential to save lives, mitigate coastal hazard damage, and promote resilient coastal communities. / Doctor of Philosophy / Tsunami and storm surge can cause extensive damage to coastal communities; to reduce this damage, accurate and fast computer models are needed that can predict the water level change caused by these coastal hazards. The problem is that existing physics-based computer models are either accurate but slow or less accurate but fast. In this dissertation, three new computer models are developed using statistical and machine learning techniques that can rapidly predict a tsunami and storm surge without substantial loss of accuracy compared to the accurate physics-based computer models. Three computer models are as follows: (1) A computer model that can rapidly predict the maximum ground elevation wetted by the tsunami along the coastline from earthquake information, (2) A computer model that can reversely predict a tsunami source and its impact from the observations of the maximum ground elevation wetted by the tsunami, (3) A computer model that can rapidly predict peak storm surges across a wide range of coastal areas from the tropical cyclone's track position over time. These new computer models have the potential to improve forecasting capabilities, advance understanding of historical tsunami and storm surge events, and lead to better preparedness plans for possible future tsunamis and storm surges.

In the past few years, the demands for high speed semiconductor integrated circuits have warranted new techniques in their fabrication process which will meet the ever-shrinking dimensions. The gaseous plasma assisted etching is one of these revolutionary processes. However, the plasma and the etch process are very complex in nature. It has been very difficult to understand various species present in the plasma and their role in the etch reaction. In addition, the submicron geometries also require interconnect materials which will satisfy the necessary properties such as thermal stability and low electrical resistance. Titanium (Ti) and titanium nitride (TiN) are widely used as barriers between aluminum (Al) and silicon (Si) to prevent the destructive intermixing of these two materials. The process of patterning of the interconnect containing Ti and TiN along with Al has been a challenge to the semiconductor process engineers. Therefore, complete characterization of the plasma etch process of Ti and TiN films and development of mathematical models to represent the responses such as the etch rate and uniformity is necessary for a good understanding of the etching process. A robust and well controlled metal etch process usually results in good die yield per wafer and hence can translate into higher profits for the semiconductor manufacturer. The objective of this dissertation is to characterize the plasma etch processes of Ti and TiN films in chlorine containing plasmas such as BCl₃ and Cl₂/N₂ and to develop mathematical models for the etch processes using statistical experimental design and analysis technique known as Response Surface Methodology (RSM). In this work, classical experiments are conducted on the plasma etch process of Ti and TiN films by varying the process parameters, such as gas flow, radio frequency (RF) power, reaction pressure, and temperature, one parameter at a time, while maintaining the other parameters constant. The variation in the etch rate with the change in the process parameter of the film is studied and the results were explained in terms of the concepts of plasma. These experiments, while providing very good understanding of the main effects of the parameters, yield little or no information on the higher order effects or interaction between the process parameters. Therefore, modern experimental design and analysis techniques using computerized statistical methods need to be employed for developing mathematical models for these complex plasma etch processes. The second part of this dissertation concentrates on the Design and Analysis of Experiments using Response Surface Methodology (RSM) and development of models for the etch rate and the etch uniformity of the Ti and TiN films in chlorine-containing plasmas such as Cl₂/N₂ and Cl₂/N₂/BCl₃. A complete characterization of the plasma etch process of Ti and TiN films is achieved with the RSM technique and a well fitting and statistically significant models have been developed for the process responses, such as the etch rate and the etch uniformity. These models also provide a means for quantitative comparison of main effects, which are also known as first order effects, second order effects and two factor interactions. The models, thus developed, can be effectively used for an etch process optimization, prediction of the responses without actually conducting the experiments, and the determination of process window. This dissertation work has achieved a finite study of the plasma etch process of Ti and TiN films. There is tremendous potential and scope for further research in this area, limited only by the available resources for wafer processing. A few of the possibilities for further research is discussed in the next few sentences. The optimized process derived from the RSM technique needs to be implemented in the actual production process of the semiconductor ICs and its effects on the wafer topography, etch residue and the resulting die yield have to be studied. More research studies are needed to examine the effect of process parameters such as temperature, the size and shape of the etch chamber, the quality of the film being etched, among other parameters. It is worth emphasizing in this respect that this dissertation marks beginning of research work into the ever-increasing complexities of gas plasma. / Ph. D.