Master of Science

thesisThis thesis focused on exploring the economic limitations for the development of western oil shale. The analysis was developed by scaling a known process and simulating in ProMax some of the chemical processes implicated in the production of oil shale, obtaining the capital and operating costs to develop these processes and performing an economic evaluation. The final results are a detailed breakdown of the components of the supply cost of syn crude produced. Two technologies were considered in this project: air-fired combustors and oxyfired combustors with a CO2 capture course of action. Additionally, in each of the scenarios, a sensitivity analysis was performed based on the resource quality and the taxation of CO2 emissions for the air-fired combustion and the price of CO2 for oxy-fired combustion. This project revealed that the total capital invested to develop oil shale projects is gargantuan: a total depreciable capital cost of $3.34 and$3.39 billion for the air and oxyfired case, respectively, for a shale quality of 25 gal/ton. It was shown that the geological resource significantly impacts the cost of production. For different shale grades of 20, 25 and 35 gal/ton, the supply cost varied from $124/bbl,$112/bbl and $97/bbl, respectively. Moreover, this analysis showed that the oil shale project profitability is highly dependent on governmental policies. The potential taxation of CO2 increased the supply cost by 1.75$112/bbl and with CO2 taxation increased to $120/bbl. From these results, it can be concluded that oil shale projects have higher technical, economic and government policy risks which limit their use by industry. For more projects to move forward, these risks must be lowered. It also is clear from the supply cost analysis that royalties are a major component as are taxes and interest charges

Predicting pharmaceutical powder flow from microscopy images using deep learning

The powder flowability of active pharmaceutical ingredients and excipients is a key parameter in the manufacturing of solid dosage forms used to inform the choice of tabletting methods. Direct compression is the favoured tabletting method; however, it is only suitable for materials that do not show cohesive behaviour. For materials that are cohesive, processing methods before tabletting, such as granulation, are required. Flowability measurements require large quantities of materials, significant time and human investments and repeat testing due to a lack of reproducible results when taking experimental measurements. This process is particularly challenging during the early-stage development of a new formulation when the amount of material is limited. To overcome these challenges, we present the use of deep learning methods to predict powder flow from images of pharmaceutical materials. We achieve 98.9% validation accuracy using images which by eye are impossible to extract meaningful particle or flowability information from. Using this approach, the need for experimental powder flow characterization is reduced as our models rely on images which are routinely captured as part of the powder size and shape characterization process. Using the imaging method recorded in this work, images can be captured with only 500 mg of material in just 1 hour. This completely removes the additional 30 g of material and extra measurement time needed to carry out repeat testing for traditional flowability measurements. This data-driven approach can be better applied to early-stage drug development which is by nature a highly iterative process. By reducing the material demand and measurement times, new pharmaceutical products can be developed faster with less material, reducing the costs, limiting material waste and hence resulting in a more efficient, sustainable manufacturing process. This work aims to improve decision-making for manufacturing route selection, achieving the key goal for digital design of being able to better predict properties while minimizing the amount of material required and time to inform process selection during early-stage development

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio