The focus of this thesis is on building models by utilizing process information: from data, from our knowledge of physics, or both. The closer the model approximates reality, the better is the expected performance in forecasting, soft-sensing, process monitoring, optimization and advanced process control. In the domain of batch and continuous manufacturing, quality models can help in ensuring tightly controlled product quality, having safe and reliable operating conditions and reducing production/operation costs. To this end, first a parallel grey box model was built which makes use of a mechanistic model, and a subspace identification model for modeling a batch poly methyl methacrylate (PMMA) polymerisation process. The efficacy of such a parallel hybrid model in the context of a control problem was illustrated thereafter for reducing the volume of fines. Real-time implementation of models in many cases demand the model to be tractable and simple enough, and thus the parallel hybrid model was next adapted to have a linear representation, and then used for control computations. While the parallel hybrid modelling strategy shows great advantages in many applications, there can be other avenues of using fundamental process knowledge in conjunction with historical data. In one such approach, a unique way of adding mechanistic knowledge to improve the estimation ability of PLS models was proposed. The predictor matrix of PLS was augmented with additional trajectory information coming strategically from a mechanistic model. This augmented model was used as a soft-sensor to estimate batch end quality for a seeded batch crystallizer process. In a collaborative work with an industrial partner focusing on estimating important variables of a hydroprocessing unit, an operational data based input-output model was chosen as the right fit in the absence of available mechanistic knowledge. The usefulness of linear dynamic modeling tools for such applications was demonstrated. / Thesis / Doctor of Philosophy (PhD)

Modern organic synthesis typically centers around the use of expensive, complex, homogeneous catalyst systems in organic solvents which often generate copious amounts of hazardous waste. Therefore, the development of water-tolerant catalysts capable of performing reactions in aqueous solutions has become a growing area of scientific inquiry. To this end, we have designed and optimized a water-stable catalyst (Mn[TMPyP4]I5) capable of generating aziridines from olefins in aqueous solutions. Aziridines are valuable synthetic building blocks that have been used to generate various biologically active compounds, though synthetic techniques for aziridine synthesis are not well-established. Our ultimate goal was developing a catalytic system, which could be paired with DNA in order to perform asymmetric transformation in aqueous solutions. Herein we report the optimization of reaction conditions using Mn[TMPyP4]I5 paired with various DNA types, in the hopes of generating chiral aziridines from several olefinic substrates.

Increasing population equals increase in agricultural product consumption for which continuous food production is not a viable option. Solar drying, on the other hand is a promising method to preserve agricultural products for longer durations. This thesis focuses on calculating the predictability of the independent factors and a comprehensive risk assessment to improve the performance of Solar Hybrid Kiln. Biochar samples with different moisture content were selected for 3 tests. Principal component analysis and multiple linear regression analysis were conducted on the gathered data using Minitab 18R platform. Risk response plans associated with the kiln were provided through failure mode effects analysis. Results exhibited 3 significant principal components and reliable prediction model limits were obtained for both training and testing datasets. A total of 41 risks were identified and risk response plans were proposed for them. These results can be further used to increase the efficiency of biochar drying processes.

In this dissertation, a novel method to extrude near-body meshes from surface meshes of arbitrary topology that exploits topologically adaptive generalized elements to improve mesh quality is presented. Specifically, an advancing layer algorithm to generate near-body meshes which are appropriate for viscous fluid flows is discussed. First, an orthogonal two-layer algebraic reference mesh is generated. The reference mesh is then smoothed using a locally three-dimensional Poisson-type mesh generation equation that is generalized to smooth extruded meshes of arbitrary surface topology. Local quality improvement operations such as edge collapse, face refinement, and local reconnection are performed in each layer to drive the mesh toward isotropy. An automatic marching thickness reduction algorithm is used to extrude from multiple geometries in close proximity. A global face refinement algorithm is used to improve the transition from the extruded mesh to the voidilling tetrahedral mesh. A few example meshes along with quality plots are presented to demonstrate the efficacy of the algorithms developed.

Computational investigations were carried out to evaluate the effectiveness and usability of hybrid RANS/LES techniques for predicting the unsteady separated flow over wings with ice accretion. RANS and hybrid RANS/LES computations were performed using the viscous flow solver CHEM with the SST turbulence model. Two configurations were considered during the study: an extruded wing with a glaze-ice shape and a swept wing with a simulated glaze-ice accretion. Hybrid RANS/LES results, in general, predict a less active shear layer ``roll up' than seen in the experimental data. Qualitative improvements are seen in the hybrid RANS/LES results over corresponding RANS results. The extruded wing results show that the CHEM hybrid RANS/LES results are similar to the AVUS DES results. The use of preconditioning and a different turbulent model in CHEM showed a slight improvement in results.

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