Yes / A reliable description of ion pair interactions for biological systems, particularly those involving polyatomic ions such as carboxylate and divalent ions such as Ca2+, using biomolecular force-fields is essential for making useful predictions for a range of protein functions. In particular, the interaction of divalent ions with the double carboxylate group present in γ-carboxyglutamic acid (Gla), relevant to the function of many proteins, is relatively understudied using biomolecular force-fields. Using force-field based metadynamics simulations to predict the free energy of binding between Ca2+ and the carboxylate group in liquid water, we show that a widely-used biomolecular force-field, CHARMM22*, substantially over-estimates the binding strength between Ca2+ and the side-chains of both glutamic acid (Glu) and Gla, compared with experimental data obtained for the analogous systems of aqueous calcium–acetate and calcium–malonate. To correct for this, we propose and test a range of modifications to the σ value of the heteroatomic Lennard–Jones interaction between Ca2+ and the oxygen of the carboxylate group. Our revised parameter set can recover the same three association modes of this aqueous ion pair as the standard parameter set, and yields free energies of binding for the carboxylate–Ca2+ interaction in good agreement with experimental data. The revised parameter set recovers other structural properties of the ion pair in agreement with the standard CHARMM22* parameter set.

The purpose of this study was to determine whether modifying photographic illustrations used for concept-learning by emphasizing defining attributes improves learning by individuals exhibiting various degrees of field-dependence. Concept-learning in this instance refers to identifying objects based on visual characteristics. The specific questions addressed were: 1) Does emphasis of attributes improve concept attainment in general? and 2) Does attribute emphasis provide a differential advantage to field-dependent students, who generally do not perform as well on visual tasks? The relative levels of field-dependence of 115 participants, recruited from the university and surrounding community, were assessed using the Group Embedded Figures Test (GEFT). Three levels of field-dependence were identified: an "indeterminate" level centered on the mean GEFT score for the group; field-dependent, those scoring more than 1/2 standard deviation below the mean; and field-independent, those scoring more than 1/2 standard deviation above the mean. Participants partook of one of two computer-based lessons on the identification of four maple tree species based on the appearance of leaves. One lesson (the control condition) was illustrated with plain photographs, the other with photographs which had been modified using digital image editing to emphasize the critical attributes of each leaf. Following the lesson, each participant took a computer-based, 15-item multiple choice test on identification, and then a second 20-item test requiring identification of mounted leaf specimens. Bartlett’s test for homogeneity of variance revealed that the values in the six cells were not equal. Therefore, the test scores were analyzed using two-way t-tests for samples with unequal variances. All analyses were performed at the 0.05 significance level. Results of the analyses suggest that attribute emphasis does improve learning by individuals in general, particularly when considering transfer. However, no evidence of specific benefit for field dependent learners was revealed by the results. The author concludes with suggestions for further investigation of the benefits derived by various types of attribute emphasis and also the extent to which attribute-emphasis techniques may improve learning for tasks other than concept attainment. / Ph. D.

Doctor of Philosophy / Department of Chemistry / Paul E. Smith / The force fields used in computer simulations play an important role in describing a particular system. In order to estimate the accuracy of a force field, physical or thermodynamic properties are usually compared with simulation results. Recently, we have been developing a force field which is called the Kirkwood-Buff Force Field (KBFF). This force field is established by transforming experimental data into Kirkwood-Buff (KB) integrals and then attempting to reproduce those KBIs with molecular dynamic (MD) simulations. Here we investigate a variety of intermolecular interactions in aqueous solutions through KB theory and molecular simulations. First, we describe a force field for the simulation of alkali halide aqueous solutions. These models are developed specifically to reproduce the experimentally determined Kirkwood-Buff integrals and solution activities as a function of molality. Additionally, other experimentally known properties including ion diffusion constants, relative permittivities, the densities and heats of mixing are reproduced by these models. Second, In an effort to understand the interactions which occur between amino acids in solution we have developed new force fields for simple amino acids and their analogs including glycine, betaine, β-alanine, dl-alanine, NH4Cl, NH4Br, N(CH3)4Cl, N(CH3)4Br, CH3NH3Cl, and CH3COONa. The new force fields reproduce the experimental Kirkwood-Buff integrals which describe the relative distribution of all the species in a solution mixture. Furthermore, it is shown that these simple amino acids can be understood in terms of the interactions of their functional groups and that, to a very good approximation, the transferability and additivity usually assumed in the development of biomolecular force fields appear to hold true. Third, an analysis of the effect of a cosolvent on the association of a solute in solution is presented by using the Kirkwood-Buff theory of solutions. The derived expressions provide a foundation for the investigation of cosolvent effects on molecular and biomolecular equilibria, including protein association, aggregation, and cellular crowding. Finally, in an effort to understand peptide aggregation at the atomic level we have performed simulations of polyglycine ((gly)n) using our recently developed force fields. Experimentally, the association of glycine polypeptides increases with n. Our force fields reproduce this behavior, and we investigated the reasons behind this trend. In addition to studying closed ensembles, we also simulate these systems in a semi-open ensemble that was designed to mimic cellular environments typically open to water, using a simple direct approach. The differences between the two ensembles are investigated and compared with our recent theoretical descriptions of aggregating systems using Kirkwood-Buff theory.

In this study, a three-dimensional, three-phase dynamic simulation model based on geological investigations of Korolev oilfield in Kazakhstan was used as a development planning tool in order to improve performance of three new wells. The model, developed previously by means of a seismic study, well log and core data, incorporating with characteristics of oilfield productivity, properties of reservoir, liquids and gases that are saturating the hydrocarbon-bearing horizon can be used to calculate development parameters for Korolev field, including production well locations, drilling schedules, and to facilitate both long-term and short-term forecasting for the purposes of optimizing the hydrocarbon recovery from the field. The objective of this work is to assess the impact of adding 3 extra producing wells and find ways to optimize cumulative production with the least impact on the existing development plan by means of deeper understanding subsurface dynamic processes of the Korolev field. The challenge is a high degree of connectivity between wells in the productive formation throughout the field so that any change of production parameters affects the whole field&rsquo / s cumulative production. Trying to find a solution to optimum production of the reservoir forecast studies were carried out, the impact of each new well on development parameters was defined, sub-surface processes changes due to extra producers lead-in were explained and as a result of this thesis two optimization models were proposed, one of which will bring nearly 9.7 million barrels more oil.

This thesis investigates how estimates of reservoir compartmentalization impact facility sizing decisions and the degree to which inaccurate estimates destroy project value. An uncertainty analysis workflow is proposed and an asset development optimization model is specified to simulate the decision making process during concept selection. The model endogenizes drilling decisions and includes a real option to expand facility capacity after the uncertain variables are realized. The value of information analysis suggests that cost of erroneous estimates of reservoir compartmentalization is significant and can reduce asset value by more than 30%. We also find that the negative impacts are larger when the degree of compartmentalization is underestimated (too optimistic) than when it is overestimated (too pessimistic). / text

Molecular modeling has come a long way during the past decades and in the current thesis modeling of biological membranes is the focus. The main method of choice has been classical Molecular Dynamics simulations and for this technique a model Hamiltonian, or force field (FF), has been developed for lipids to be used for biological membranes. Further, ways of more accurately simulate the interactions between solutes and membranes have been investigated. A FF coined Slipids was developed and validated against a range of experimental data (Papers I-III). Several structural properties such as area per lipid, scattering form factors and NMR order parameters obtained from the simulations are in good agreement with available experimental data. Further, the compatibility of Slipids with amino acid FFs was proven. This, together with the wide range of lipids that can be studied, makes Slipids an ideal candidate for large-scale studies of biologically relevant systems. A solute's electron distribution is changed as it is transferred from water to a bilayer, a phenomena that cannot be fully captured with fixed-charge FFs.  In Paper IV we propose a scheme of implicitly including these effects with fixed-charge FFs in order to more realistically model water-membrane partitioning. The results are in good agreement with experiments in terms of free energies and further the differences between using this scheme and the more traditional approach were highlighted. The free energy landscape (FEL) of solutes embedded in a model membrane is explored in Paper V. This was done using biased sampling methods with a reaction coordinate that included intramolecular degrees of freedom (DoF). These DoFs were identified in different bulk liquids and then used in studies with bilayers. The FELs describe the conformational changes necessary for the system to follow the lowest free energy path. Besides this, the pitfalls of using a one-dimensional reaction coordinate are highlighted.

The detrimental effects of rising CO₂ levels on the global climate have made carbon abatement technologies one of the most widely researched areas of recent times. In this thesis, we first present a techno-economic analysis of a novel approach to directly capture CO₂ from air (Air Capture) using highly selective adsorbents. Our process modeling calculations suggest that the monetary cost of Air Capture can be reduced significantly by identifying adsorbents that have high capacities and optimum heats of adsorption. The search for the best performing material is not limited to Air Capture, but is generally applicable for any adsorption-based separation. Recently, a new class of nanoporous materials, Metal-Organic Frameworks (MOFs), have been widely studied using both experimental and computational techniques. In this thesis, we use a combined quantum chemistry and classical simulations approach to predict macroscopic properties of MOFs. Specifically, we describe a systematic procedure for developing classical force fields that accurately represent hydrocarbon interactions with the MIL-series of MOFs using Density Functional Theory (DFT) calculations. We show that this force field development technique is easily extended for screening a large number of complex open metal site MOFs for various olefin/paraffin separations. Finally, we demonstrate the capability of DFT for predicting MOF topologies by studying the effect of ligand functionalization during CuBTC synthesis. This thesis highlights the versatility and opportunities of using multiscale modeling approach that combines process modeling, classical simulations and quantum chemistry calculations to study nanoporous materials for adsorptive separations.

No description available.

La quantité d’hydrocarbures récupérés peut être considérablement augmentée si un placement optimal des puits non conventionnels à forer, peut être trouvé. Pour cela, l’utilisation d’algorithmes d’optimisation, où la fonction objectif est évaluée en utilisant un simulateur de réservoir, est nécessaire. Par ailleurs, pour des réservoirs avec une géologie complexe avec des hétérogénéités élevées, le problème d’optimisation nécessite des algorithmes capables de faire face à la non-régularité de la fonction objectif. L’objectif de cette thèse est de développer une méthodologie efficace pour déterminer l’emplacement optimal des puits et leurs trajectoires, qui offre la valeur liquidative maximale en utilisant un nombre techniquement abordable de simulations de réservoir.Dans cette thèse, nous montrons une application réussie de l’algorithme “Covariance Matrix Adaptation - Evolution Strategy” (CMA-ES) qui est reconnu comme l’un des plus puissants optimiseurs sans-dérivés pour l’optimisation continue. Par ailleurs, afin de réduire le nombre de simulations de réservoir (évaluations de la fonction objectif), nous concevons deux nouveaux algorithmes. Premièrement, nous proposons une nouvelle variante de la méthode CMA-ES avec des méta-modèles, appelé le nouveau-local-méta-modèle CMA-ES (nlmm-CMA), améliorant la variante déjà existante de la méthode local-méta-modèle CMA-ES (lmm-CMA) sur la plupart des fonctions de benchmark, en particulier pour des tailles de population plus grande que celle par défaut. Ensuite, nous proposons d’exploiter la séparabilité partielle de la fonction objectif durant le processus d’optimisation afin de définir un nouvel algorithme appelé la partiellement séparable local-méta-modèle CMAES (p-sep lmm-CMA), conduisant à une réduction importante en nombre d’évaluations par rapport à la méthode CMA-ES standard.Dans cette thèse, nous appliquons également les algorithmes développés (nlmm-CMA et p-sep lmm-CMA) sur le problème de placement des puits pour montrer, à travers plusieurs exemples, une réduction significative du nombre de simulations de réservoir nécessaire pour trouver la configuration optimale des puits. Les approches proposées sont révélées prometteuses en considérant un budget restreint de simulations de réservoir, qui est le contexte imposé dans la pratique.Enfin, nous proposons une nouvelle approche pour gérer l’incertitude géologique pour le problème d’optimisation de placement des puits. L’approche proposée utilise seulement une réalisation, ainsi que le voisinage de chaque configuration, afin d’estimer sa fonction objectif au lieu d’utiliser multiples réalisations. L’approche est illustrée sur un cas de réservoir de benchmark, et se révèle être en mesure de capturer l’incertitude géologique en utilisant un nombre réduit de simulations de réservoir. / The amount of hydrocarbon recovered can be considerably increased by finding optimal placement of non-conventional wells. For that purpose, the use of optimization algorithms, where the objective function is evaluated using a reservoir simulator, is needed. Furthermore, for complex reservoir geologies with high heterogeneities, the optimization problem requires algorithms able to cope with the non-regularity of the objective function. The goal of this thesis was to develop an efficient methodology for determining optimal well locations and trajectories, that offers the maximum asset value using a technically feasible number of reservoir simulations.In this thesis, we show a successful application of the Covariance Matrix Adaptation - Evolution Strategy (CMA-ES) which is recognized as one of the most powerful derivative-free optimizers for continuous optimization. Furthermore, in order to reduce the number of reservoir simulations (objective function evaluations), we design two new algorithms. First, we propose a new variant of CMA-ES with meta-models, called the newlocal-meta-model CMA-ES (nlmm-CMA), improving over the already existing variant of the local-meta-model CMA-ES (lmm-CMA) on most benchmark functions, in particular for population sizes larger than the default one. Then, we propose to exploit the partial separability of the objective function in the optimization process to define a new algorithm called the partially separable local-meta-model CMA-ES (p-sep lmm-CMA), leading to an important speedup compared to the standard CMA-ES.In this thesis, we apply also the developed algorithms (nlmm-CMA and p-sep lmm-CMA) on the well placement problem to show, through several examples, a significant reduction of the number of reservoir simulations needed to find optimal well configurations. The proposed approaches are shown to be promising when considering a restricted budget of reservoir simulations, which is the imposed context in practice.Finally, we propose a new approach to handle geological uncertainty for the well placement optimization problem. The proposed approach uses only one realization together with the neighborhood of each well configuration in order to estimate its objective function instead of using multiple realizations. The approach is illustrated on a synthetic benchmark reservoir case, and is shown to be able to capture the geological uncertainty using a reduced number of reservoir simulations.

Exploration and production (E&P) involves the upstream activities from looking for promising reservoirs to extracting oil and selling it to downstream companies. E&P is the most profitable business in the oil industry. However, it is also the most capital-intensive and risky. Hence, the proper assessment of E&P projects with effective management of uncertainties is crucial to the success of any upstream business. This dissertation is concentrated on developing portfolio optimization models to manage E&P projects. The idea is not new, but it has been mostly restricted to the conceptual level due to the inherent complications to capture interactions among projects. We disentangle the complications by modeling the project portfolio optimization problem as multistage stochastic programs with mixed integer programming (MIP) techniques. Due to the disparate nature of uncertainties, we separately consider explored and unexplored oil fields. We model portfolios of real options and portfolios of decision trees for the two cases, respectively. The resulting project portfolio models provide rigorous and consistent treatments to optimally balance the total rewards and the overall risk. For explored oil fields, oil price fluctuations dominate the geologic risk. The field development process hence can be modeled and assessed as sequentially compounded options with our optimization based option pricing models. We can further model the portfolio of real options to solve the dynamic capital budgeting problem for oil projects. For unexplored oil fields, the geologic risk plays the dominating role to determine how a field is optimally explored and developed. We can model the E&P process as a decision tree in the form of an optimization model with MIP techniques. By applying the inventory-style budget constraints, we can pool multiple project-specific decision trees to get the multistage E&P project portfolio optimization (MEPPO) model. The resulting large scale MILP is efficiently solved by a decomposition-based primal heuristic algorithm. The MEPPO model requires a scenario tree to approximate the stochastic process of the geologic parameters. We apply statistical learning, Monte Carlo simulation, and scenario reduction methods to generate the scenario tree, in which prior beliefs can be progressively refined with new information. / text