The Intersection of Economics and Politics in a Small OPEC Country: the Case of Ecuador, 1990 - March 2014

Today, crude oil remains a vital resource all around the world. This non-renewable resource powers countries worldwide. Besides serving as an energy source, crude oil is also the most important component for different world economies, especially in developing countries. Ecuador, a small member of the OPEC oil cartel, presents a case where its economy is oil dependent. A great percentage of the country¿s GDP and government¿s budget comes from oil revenues. Ecuador has always been a primary exporter of raw materials. In the last centuries, the country experienced three important economic booms: cacao, bananas, and, ultimately, crude oil. In this sense, the country has not been able to fully industrialize and begin to export manufactured goods, i.e., Ecuador suffers from the Dutch disease. The latter has deterred Ecuador from achieving broad-based economic development. Given crude oil¿s importance for the Ecuadorian economy, the government has always tried to influence the oil industry in search of profits and benefits. Therefore, this thesis, explores the question: how and to what extent have political interventions affected the oil industry in Ecuador from 1990 until March 2014? In general, this thesis establishes an economic history context during the last twenty-four years, attempting to research how political interventions have shaped Ecuador¿s oil industry and economy. In the analysis, it covers a period where political instability prevailed, until Rafael Correa became president. The thesis examines Ecuador¿s participation in OPEC, trying to find explanations as to why the country voluntarily left the organization in 1992, only to rejoin in 2007 when Correa rose to power. During the ¿Revolución Ciudadana¿ period, the thesis researches reforms to the Law of Hydrocarbons, variations in the relations with other nations, the controversy surrounding the Yasuní-ITT oil block, and the ¿Refinería del Pacífico¿ construction. The thesis is an Industrial Organization detailed case study that analyzes, updates, and evaluates the intersection of economics and politics in Ecuador¿s crude oil industry during the last 24 years. In this sense I have consulted past theses, newspaper articles, books, and other published data about the petroleum industry, both from a global and Ecuadorian perspective. In addition to published sources, I was able to interview sociologists, public figures, history and economics academics, and other experts, accessing unique unpublished data about Ecuador¿s oil industry. I made an effort to collect information that shows the private and public side of the industry, i.e., from government-related and independent sources. I attempted to remain as objective as possible to make conclusions about the appropriate Industrial Organization policy for Ecuador¿s oil industry, addressing the issue from an economic, social, political, and environmental point of view. I found how Ecuador¿s political instability caused public policy to fail, molding the conduct and market structure of the crude oil industry. Throughout history, developed nations have benefited from low oil prices, but things shifted since oil prices began to rise, which is more beneficial for the developing nations that actually possess and produce the raw material. Nevertheless, Ecuador, a victim of the Dutch disease due to its heavy reliance on crude oil as a primary product, has not achieved broad-based development.

Development of Dynamic Constraint Models for a Model Based Transient Calibration Process

Model based calibration has gained popularity in recent years as a method to optimize increasingly complex engine systems. However virtually all model based techniques are applied to steady state calibration. Transient calibration is by and large an emerging technology. An important piece of any transient calibration process is the ability to constrain the optimizer to treat the problem as a dynamic one and not as a quasi-static process. The optimized air-handling parameters corresponding to any instant of time must be achievable in a transient sense; this in turn depends on the trajectory of the same parameters over previous time instances. In this work dynamic constraint models have been proposed to translate commanded to actually achieved air-handling parameters. These models enable the optimization to be realistic in a transient sense. The air handling system has been treated as a linear second order system with PD control. Parameters for this second order system have been extracted from real transient data. The model has been shown to be the best choice relative to a list of appropriate candidates such as neural networks and first order models. The selected second order model was used in conjunction with transient emission models to predict emissions over the FTP cycle. It has been shown that emission predictions based on air-handing parameters predicted by the dynamic constraint model do not differ significantly from corresponding emissions based on measured air-handling parameters.

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 1: data requirements, processing, and analysis

This is the first part of a study investigating a model-based transient calibration process for diesel engines. The motivation is to populate hundreds of parameters (which can be calibrated) in a methodical and optimum manner by using model-based optimization in conjunction with the manual process so that, relative to the manual process used by itself, a significant improvement in transient emissions and fuel consumption and a sizable reduction in calibration time and test cell requirements is achieved. Empirical transient modelling and optimization has been addressed in the second part of this work, while the required data for model training and generalization are the focus of the current work. Transient and steady-state data from a turbocharged multicylinder diesel engine have been examined from a model training perspective. A single-cylinder engine with external air-handling has been used to expand the steady-state data to encompass transient parameter space. Based on comparative model performance and differences in the non-parametric space, primarily driven by a high engine difference between exhaust and intake manifold pressures (ΔP) during transients, it has been recommended that transient emission models should be trained with transient training data. It has been shown that electronic control module (ECM) estimates of transient charge flow and the exhaust gas recirculation (EGR) fraction cannot be accurate at the high engine ΔP frequently encountered during transient operation, and that such estimates do not account for cylinder-to-cylinder variation. The effects of high engine ΔP must therefore be incorporated empirically by using transient data generated from a spectrum of transient calibrations. Specific recommendations on how to choose such calibrations, how many data to acquire, and how to specify transient segments for data acquisition have been made. Methods to process transient data to account for transport delays and sensor lags have been developed. The processed data have then been visualized using statistical means to understand transient emission formation. Two modes of transient opacity formation have been observed and described. The first mode is driven by high engine ΔP and low fresh air flowrates, while the second mode is driven by high engine ΔP and high EGR flowrates. The EGR fraction is inaccurately estimated at both modes, while EGR distribution has been shown to be present but unaccounted for by the ECM. The two modes and associated phenomena are essential to understanding why transient emission models are calibration dependent and furthermore how to choose training data that will result in good model generalization.

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 2: modelling and optimization

This is the second part of a study investigating a model-based transient calibration process for diesel engines. The first part addressed the data requirements and data processing required for empirical transient emission and torque models. The current work focuses on modelling and optimization. The unexpected result of this investigation is that when trained on transient data, simple regression models perform better than more powerful methods such as neural networks or localized regression. This result has been attributed to extrapolation over data that have estimated rather than measured transient air-handling parameters. The challenges of detecting and preventing extrapolation using statistical methods that work well with steady-state data have been explained. The concept of constraining the distribution of statistical leverage relative to the distribution of the starting solution to prevent extrapolation during the optimization process has been proposed and demonstrated. Separate from the issue of extrapolation is preventing the search from being quasi-static. Second-order linear dynamic constraint models have been proposed to prevent the search from returning solutions that are feasible if each point were run at steady state, but which are unrealistic in a transient sense. Dynamic constraint models translate commanded parameters to actually achieved parameters that then feed into the transient emission and torque models. Combined model inaccuracies have been used to adjust the optimized solutions. To frame the optimization problem within reasonable dimensionality, the coefficients of commanded surfaces that approximate engine tables are adjusted during search iterations, each of which involves simulating the entire transient cycle. The resulting strategy, different from the corresponding manual calibration strategy and resulting in lower emissions and efficiency, is intended to improve rather than replace the manual calibration process.

Design and Development of an FPGA-based Distributed Computing Processing Platform

This thesis presents two frameworks- a software framework and a hardware core manager framework- which, together, can be used to develop a processing platform using a distributed system of field-programmable gate array (FPGA) boards. The software framework providesusers with the ability to easily develop applications that exploit the processing power of FPGAs while the hardware core manager framework gives users the ability to configure and interact with multiple FPGA boards and/or hardware cores. This thesis describes the design and development of these frameworks and analyzes the performance of a system that was constructed using the frameworks. The performance analysis included measuring the effect of incorporating additional hardware components into the system and comparing the system to a software-only implementation. This work draws conclusions based on the provided results of the performance analysis and offers suggestions for future work.