Development of a Phase Separation Strategy in Macrocyclization Reactions

La réaction de macrocyclisation est une transformation fondamentale en chimie organique de synthèse. Le principal défi associcé à la formation de macrocycles est la compétition inhérente avec la réaction d’oligomérisation qui mène à la formation de sousproduits indésirables. De plus, l’utilisation de conditions de dilutions élevées qui sont nécessaires afin d’obtenir une cyclisation “sélective”, sont souvent décourageantes pour les applications à l’échelle industrielle. Malgré cet intérêt pour les macrocycles, la recherche visant à développer des stratégies environnementalement bénignes, qui permettent d’utiliser des concentrations normales pour leur synthèse, sont encore rares. Cette thèse décrit le développement d’une nouvelle approche générale visant à améliorer l’efficacité des réactions de macrocyclisation en utilisant le contrôle des effets de dilution. Une stratégie de “séparation de phase” qui permet de réaliser des réactions à des concentrations plus élevées a été developpée. Elle se base sur un mélange de solvant aggrégé contrôlé par les propriétés du poly(éthylène glycol) (PEG). Des études de tension de surface, spectroscopie UV et tagging chimique ont été réalisées afin d’élucider le mécanisme de “séparation de phase”. Il est proposé que celui-ci fonctionne par diffusion lente du substrat organique vers la phase ou le catalyseur est actif. La nature du polymère co-solvant joue donc un rôle crutial dans le contrôle de l’aggrégation et de la catalyse La stratégie de “séparation de phase” a initiallement été étudiée en utilisant le couplage oxidatif d’alcynes de type Glaser-Hay co-catalysé par un complexe de cuivre et de nickel puis a été transposée à la chimie en flux continu. Elle fut ensuite appliquée à la cycloaddition d’alcynes et d’azotures catalysée par un complexe de cuivre en “batch” ainsi qu’en flux continu.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.