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Thesis (MEngineering)--University of South Australia, 2004.

Dry powder inhalers (DPIs) are used for delivering drugs to the airways. In addition to the drug, the formulations often contain a coarse carrier, most commonly alpha lactose monohydrate. The presence of fine lactose particles in the formulation is known to improve the formulation performance. The active site, drug-fines agglomeration and increased cohesion theories have been suggested to explain improved DPI performance upon addition of fine excipient particles. This project aimed to investigate the validity of those theories. The viability of the active sites theory in explaining the improved DPI performance was investigated by studying the impact of loaded drug dose on the in vitro performance for formulation series prepared with coarse carriers with different surface characteristics. The formulations prepared with the rougher lactose carrier were seen to outperform the formulations prepared with the smoother carrier at all drug concentrations. These findings were concluded to be non-compatible with the active sites theory. The impact of addition of lactose fines with different size distributions on powder flow and fluidisation properties and in vitro performance was studied. Powder cohesion increased independent of size distribution of the fines, but did not necessarily correspond to improved performance. Therefore, the increased cohesion theory was concluded not to be the sole explanation for the improvement in DPI performance in the presence of lactose fines. Instead, the increase in performance could be preliminarily attributed to the formation of agglomerated systems. The formation and co-deposition of drug-fines agglomerates, and consequential improvement in the DPI performance was proved using morphologically directed Raman spectroscopy. The project also aimed to develop a universal model for predicting DPI performance based on the lactose properties for a wide range of carriers with different properties. No simple linear correlations between any the lactose properties and the final DPI performance were found. Therefore no single parameter can be used as a universal predictor for DPI performance. To establish more complex relationships, artificial neural networks were used for modelling the importance of different lactose properties in determining DPI performance. The proportion of fine lactose particles (<4.5 μm) was identified as the most important parameter. However, this parameter was capable of explaining only approximately half of the variation seen in the formulation performance. The current study showed that to obtain more accurate predictions for the purposes of quality-by-design approach, also other lactose properties need to be characterised.

Submitted in fulfillment of the requirements for the Degree of Master of Technology: Chemical Engineering, Durban University of Technology. Durban. South Africa, 2015. / Cobalt powder is a grey metallic powder that is produced by the thermal decomposition and reduction of a cobalt compound. The challenge faced by Shu Powders Africa was that sub-micron cobalt powder had never been produced in a two-step furnace by any manufacturer in the cobalt powder industry. Hence there was no prior information to guide this type of processing. Therefore this research set out to investigate the production of sub-micron cobalt powder through a two-step furnace to determine the optimum parameters for this process. For the company to remain competitive, it was imperative to begin producing sub-micron cobalt powder. Sub-micron cobalt powder is much more valuable and profitable to produce. The second production line would be operational due to the production of sub-micron cobalt powder hence creating job opportunities for the local community. Sub-micron cobalt powder shares the same chemical composition and physical characteristics as cobalt powder. The only differences are particle size (0.60 - 0.90 µm), oxygen content (0.30 - 0.80%) and the microscopic structure which is the particle size distribution d90 (7 - 10 µm). The approach taken was to understand the variables that had a large effect on the powder. The effects needed to be established by determining how it impacted on the quality of the powder which is pertinent to making sub-micron cobalt powder. Due to the experience in producing cobalt powder, variables that had a large effect on normal cobalt powder production were assumed to be the same variables that would impact the production of sub-micron cobalt powder. Some of these effects were also confirmed by literature. A strategy of statistical design of experiments was used to evaluate the conditions for sub-micron cobalt powder production. Design of experiments assisted in planning the experimental design matrices for both experiments. For the furnace experimentation a 24 factor design was selected. For the jet mill experimentation a 23 factor design was selected. Response surface methodology was used to determine optimum ranges of the variables at various process conditions. The central composite rotatable design laid out the design in which the variables interacted with one another at different process conditions. Evaluation of results was based on the generated model. Models such as the 3D surface model, cubic model and the contour model were generated to graphically illustrate the effects that the variables have on the response. Analysis of furnace data indicated that the optimal response was achieved at a temperature range (445 - 460)°C, hydrogen gas range (225 - 250) Nm3/h, belt speed (80 - 90) mm/min, and carbon dioxide gas range (80 - 90) Nm3/h. Analysis of the jet mill experimental data indicated that the optimal response particle size distribution, was achieved at a classifier speed range of (5500 - 6000) rpm, AFG grinding bin range (30 - 35) kgs and grinding gas pressure of (4.0 - 4.5) bar. The study confirms the efficiency of a two-step furnace to produce sub-micron cobalt powder at high volumes. The advantage of the two-step furnace was the increased throughput of 2.3-2.7 tons/day whilst in industry furnace throughputs are 1.3-1.6 tons/day. This represented a 60% increase in productivity over conventional furnaces. The response surface methodology also proved to be a suitable technique for process optimization.

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