This thesis addressed two main issues. First, using an inter-temporal optimisation framework, the thesis analysed the tradeoffs between short and long-term objectives of soil use that smallholder farmers’ face in their production decisions. Second, using econometric models that account for simultaneity of choices and plot level data, the thesis explored the determinants of soil fertility and soil conservation adoption decision behaviour of smallholder farmers in the Central highlands of Ethiopia. Four major conclusions are drawn from the optimization results. First, steady state optimal output and input levels under the dynamic decision rule are found to be significantly higher than the static solutions suggesting that the static decision rule is sub-optimal. Second, current soil nutrient inputs and conservation efforts are well above the requirements of the static solutions suggesting smallholder farmers consider some of the long-term (dynamic) costs of soil degradation. Third, current farmer practices involve net nitrogen extraction of 16.2 kg/ha from bottomlands and 56.7 kg/ha from slopping lands entailing a total soil user cost of Birr 255 per ha and Birr 928 per ha, respectively. This suggests that current smallholder farmer practices discount the future heavily and hence over exploit the soil resource stock. Fourth, a comparison of steady state dynamic solutions when nutrient stocks are the sole determinant of soil quality with a situation where both nutrient stocks and rooting depth impinge on soil quality confirm the main hypothesis that the socially optimal path of soil use also depends on the nature of soil degradation smallholder farmers face on their plots. The econometric analysis of soil fertility and soil conservation adoption confirmed that awareness of soil degradation, public assistance with sharing initial costs of constructing soil conservation structures, improved security of land tenure and farmers’ education and access to information on soil degradation were found essential for farmers to adopt soil fertility management practices and invest in soil conservation. On the other hand, improved small farmers’ access to short-term credit for the purchase of inorganic fertilizers present a disincentive for long-term conservation practices, an important trade off with serious policy implications. / Thesis (PhD (Environmental Economics))--University of Pretoria, 2007. / Agricultural Economics, Extension and Rural Development / unrestricted

Hydraulic fracturing has been increasingly used in the USA to economically extract natural gas and oil from newly discovered shale plays. Despite new, more severe, and long term impacts of hydraulic fracturing compared to conventional drilling, regulatory practices are mostly implemented by states that regulate with older regulations that were were written before the widespread use of hydraulic fracturing. This dissertation presents three essays on the economics of hydraulic fracturing. A standard renewable lease in hydraulic fracturing runs for a five-year primary term. The first essay examines the effect of initial contract length on extraction behavior and social costs. It finds that the rate of extraction decreases over time for both, the social planner and the private extractor. In addition, the social planner has a more stable extraction path compared to the private extractor. Holding other things equal, if the social planner seeks to induce a private extractor to leave a higher in situ stock un-extracted, then the optimal contract duration is longer. Simulations illustrate the magnitude of social costs inherent in hydraulic fracturing and non-optimal fixed contract lengths. The second essay investigates the impact of the significantly increased bonding requirements for horizontal wells introduced in West Virginia in December, 2011, on the probability of violation committed by those wells. Results suggest that the increased bonding requirement has reduced the probability of violation by 2.6 to 3.2 percentage points. Moreover, it slightly reduces the number of violations done by horizontal wells. Finally, the third essay explores several aspects of Act-13, introduced on February 14, 2012, by Pennsylvania. This act imposes new fees that are assessed annually for fifteen years, on all unconventional gas wells in Pennsylvania. This chapter explores the impacts of Act-13 on the likelihood of an unconventional well's shut-down, rate of extraction, and probability of violation. Results suggest that wells incurring this increased fee schedule have a significantly higher likelihood (more than three times) of shut-down. Also, Act-13 have reduced the extraction rate, and the probability of violation committed by unconventional wells in Pennsylvania. / Ph. D.

Distillation technology has been evolving for many decades for a variety of reasons, with the most important ones being energy efficiency and cost. As a part of the evolution, semicontinuous distillation was conceived, which has the advantages of both batch and continuous distillation. The economic benefits of this intensified process compared to batch and continuous distillation were expounded in many of the previous studies. Semicontinuous distillation of ternary mixtures, which is the main focus of this thesis, is carried out in a single distillation column with a tightly integrated external middle vessel and the operation is driven by a control system. The system operation does not include any start-up or shut-down phases of the column and has three periodically repeating operating modes. In the status quo design procedure, called the ‘sequential design methodology,’ an imaginary continuous distillation system design was used to design the semicontinuous distillation system. In this methodology, dynamic simulations of the process were used to find the values of the controller tuning parameters based on the design of the continuous system. Afterwards, black-box optimization was used to find better controller tuning parameter values that minimized cost. However, after analyzing the dynamics of the system for different cases, it was found that the heuristics used in this design methodology yielded suboptimal designs. Therefore, the primary goal of the thesis is to improve these heuristics by incorporating more knowledge of the system and thereby develop a better design methodology. Firstly, the setpoint trajectories generated by the ideal side draw recovery arrangement for side stream flowrate control, which was standard in most semicontinuous distillation studies, was modified. In this thesis, the performance of the status quo as compared to the modified version, based on the criteria, cycle time and cost for different case studies, was presented. Results showed that the modified-ideal side draw recovery arrangement for side stream flowrate control performed better with a 10-20% lower separating cost while maintaining product purities. Furthermore, to reap more cost benefits, dynamic optimization was used to seek the flow rate trajectory that minimized cost. However, it was found that the additional cost savings, which is in addition to the benefits gained by using the modified version, were at the most 2% from different case studies. Subsequently, the impact of changing the imaginary continuous distillation system design on the nature of the semicontinuous distillation limit cycle, specifically, its period was studied. Results revealed the necessity for a new design procedure, and thus the back-stepping design methodology was proposed. This design methodology was used to find better limit cycles of zeotropic ternary semicontinuous distillation using the aspenONE Engineering suite. The proposed methodology was applied to three different case studies using feed mixtures with different chemical components. A comparison with the sequential design methodology for the two case studies indicates that the new method outperforms the state-of-the-art by finding limit cycles that were 4% to 57% lower in terms of cost. Furthermore, the designs obtained from this procedure were guaranteed to have feasible column operation with stable periodic steady-state behaviour. Semicontinuous distillation design using the design methodology with heuristic components involves guessing, checking and then using black-box optimization to find the values of the design variables to meet some performance criteria. Furthermore, mathematical guarantees of either local or global optimality of the designs obtained from the design procedure do not exist. Therefore, to address these issues, in this thesis, the application of using the shooting method for designing the semicontinuous distillation process was demonstrated using two case studies, which involve the separation of hexane, heptane and octane. This method has the potential to be combined with gradient-based optimization algorithms for optimization of the process design in the future. / Thesis / Doctor of Philosophy (PhD)

No / The use of neural networks (NNs) in all aspects of process engineering activities, such as modelling, design, optimization and control has considerably increased in recent years (Mujtaba and Hussain, 2001). In this work, three different types of nonlinear control strategies are developed and implemented in batch reactors using NN techniques. These are generic model control (GMC), direct inverse model control (DIC) and internal model control (IMC) strategies. Within the control strategies, NNs have been used as dynamic estimator, dynamic model (forward model) and control (inverse model). An exothermic complex reaction scheme in a batch reactor is considered to explain all these control strategies and their robustness. A dynamic optimization problem with a simple model is solved a priori to obtain optimal operation policy in terms of the reactor temperature with an objective to maximize the desired product in a given batch time. The resulting optimal temperature policy is used as set-point in the control study. All types of controllers performed well in tracking the optimal temperature profile and achieving target conversion to the desired product. However, the NNs used in DIC and IMC controllers need training beyond the nominal operating condition to cope with uncertainties better.

Application profiling is an important step in the design and optimization of embedded systems. Accurately identifying and analyzing the execution of frequently executed computational kernels is needed to effectively optimize the system implementation, both at design time and runtime. In a traditional design process, it suffices to perform the profiling and optimization steps offline, during design time. The offline profiling guides the design space exploration, hardware software codesign, or power and performance optimizations. When the system implementation can be finalized at design time, this approach works well. However, dynamic optimization techniques, which adapt and reconfigure the system at runtime, require dynamic profiling with minimum runtime overheads. Existing profiling methods are usually software based and incur significant overheads that may be prohibitive or impractical for profiling embedded systems at runtime. In addition, these profiling methods typically focus on profiling the execution of specific tasks executing on a single processor core, but do not consider accurate and holistic profiling across multiple processor cores. Directly utilizing existing profiling approaches and naively combining isolated profiles from multiple processor cores can lead to significant profile inaccuracies of up to 35%. To address these challenges, a hardware-based dynamic application profiler for non-intrusively and accurately profiling software applications in multicore embedded systems is presented. The profiler provides a detailed execution profile for computational kernels and maintains profile accuracy across multiple processor cores. The hardware-based profiler achieves an average error of less than 0.5% for the percentage execution time of profiled applications while being area efficient.

Approved for public release, distribution is unlimited / problem, with engine thrust as the only possible perturbation. The optimal control problems are solved using the general purpose dynamic optimization software, DIDO. The dynamical model together with the fuel optimal control problem is validated by simulating several well known orbit transfers. By replicating the single satellite model, this thesis shows that a multi-satellite model which optimizes all vehicles concurrently can be easily built. The specific scenario under study involves the injection of multiple satellites from a common launch vehicle; however, the methods and model are applicable to spacecraft formation problems as well. / Major, United States Air Force

Dans cette thèse, nous avons étudié le contrôle et l'optimisation de systèmes dynamiques sujets à des retards variables.L'existence de retards, de commande ou d'état, est un problème classique en automatique, susceptible de réduire les performances du système en régime transitoire, voire de remettre en cause la stabilité de contrôleurs en boucle fermée. De tels phénomènes de retards variables jouent un rôle important dans de nombreuses applications en génie des procédés.Dans une première partie, nous avons étudié la régulation en boucle fermée d'un système soumis à des retards de métrologie variables et incertains. Nous avons établi de nouveaux résultats garantissant la stabilité robuste sous certaines conditions explicites sur le gain du contrôleur. Dans une seconde partie, nous avons abordé le problème de l'optimisation dynamique de systèmes présentant des retards variables dépendant de la commande liés à des phénomènes de transport dans des réseaux hydrauliques. Nous avons proposé un algorithme itératif d'optimisation et garanti sa convergence grâce à une analyse détaillée. / This Ph.D. work studied the control and optimization of dynamical systems subject to varying time delays.State and control time delays are a well-known problem in control theory, with a potential to decrease performances during transient regimes, or even to jeopardize controllers closed-loop stability. Such variable delays play a key role in many applications in process industries.In a first part, we studied the closed-loop control of a system subject to varying and uncertain metrology delays. We established new results on robust stability under explicit conditions on the controller gain. In a second part, we tackled the problem of the dynamic optimization of systems exhibiting input dependent delays due to transport phenomena in complex hydraulic architectures. We designed an iterative optimization algorithm and guaranteed its convergence through a detailed analysis.

The increasing trend of world-wide energy consumption emphasizes the importance of ongoing optimization of new and existing technologies. In this dissertation, two energy–intensive systems are simulated and optimized. Advanced estimation, optimization, and control techniques such as a moving horizon estimator and a model predictive controller are developed to enhance the profitability, product quality, and reliability of the systems. An enabling development is presented for the solution of complex dynamic optimization problems. The strategy involves an initialization approach to large–scale system models that both enhance the computational performance as well as the ability of the solver to converge to an optimal solution. One particular application of this approach is the modeling and optimization of a batch distillation column. For estimation of unknown parameters, an L1-norm method is utilized that is less sensitive to outliers than a squared error objective. The results obtained from the simple model match the experimental data and model prediction for a more rigorous model. A nonlinear statistical analysis and a sensitivity analysis are also implemented to verify the reliability of the estimated parameters. The reduced–order model developed for the batch distillation column is computationally fast and reasonably accurate and is applicable for real time control and online optimization purposes. Similar to estimation, an L1-norm objective function is applied for optimization of the column operation. Application of an L1-norm permits explicit prioritization of the multi–objective problems and adds only linear terms to the problem. Dynamic optimization of the column results in a 14% increase in the methanol product obtained from the column with 99% purity. In a second application of the methodology, the results obtained from optimization of the hybrid system of a cryogenic carbon capture (CCC) and power generation units are presented. Cryogenic carbon capture is a novel technology for CO2 removal from power generation units and has superior features such as low energy consumption, large–scale energy storage, and fast response to fluctuations in electricity demand. Grid–level energy storage of the CCC process enables 100% utilization of renewable power sources while 99% of the CO2 produced from fossil–fueled power plants is captured. In addition, energy demand of the CCC process is effectively managed by deploying the energy storage capability of this process. By exploiting time–of–day pricing, the profit obtained from dynamic optimization of this hybrid energy system offsets a significant fraction of the cost of construction of the cryogenic carbon capture plant.

In this paper we examine models that incorporate CBDT. In the first chapter, we will examine CBDT more thoroughly including a reinterpretation of the standard labor supply problem under a wage tax in a partial equilibrium model where preferences exhibit characteristics of CBDT. In the second chapter, we extend the labor supply decision under a wage tax by incorporating a household production function. Utility maximization by repeated substitution is applied as a novel approach to solving dynamic optimization problems. This approach allows us to find labor supply elasticities that evolve over the life cycle. In the third chapter, CBDT will be explored in more depth focusing on its applicability in representing people's preferences over movie rentals in the Netflix competition. This chapter builds on the theoretical model introduced in chapter 1, among other things, expressing the rating of any customer movie pair using the ratings of similar movies that the customer rated and the ratings of the movie in question by similar customers. We will also explore in detail the econometric model used in the Netflix competition which utilizes machine learning and spatial regression to estimate customer's preferences.

Optimal control methods are applied to mechanical models in order to predict the control strategies in human arm movements. Optimality criteria are used to determine unique controls for a biomechanical model of the human upper-limb with redundant actuators. The motivation for this thesis is to provide a non-task-specific method of motion prediction as a tool for movement researchers and for controlling human models within virtual prototyping environments. The current strategy is based on determining the muscle activation levels (control signals) necessary to perform a task that optimizes several physical determinants of the model such as muscular and joint stresses, as well as performance timing. Currently, the initial and final location, orientation, and velocity of the hand define the desired task. Several models of the human arm were generated using a graph-theoretical method in order to take advantage of similar system topology through the evolution of arm models. Within this framework, muscles were modelled as non-linear actuator components acting between origin and insertion points on rigid body segments. Activation levels of the muscle actuators are considered the control inputs to the arm model. Optimization of the activation levels is performed via a hybrid genetic algorithm (GA) and a sequential quadratic programming (SQP) technique, which provides a globally optimal solution without sacrificing numerical precision, unlike traditional genetic algorithms. Advantages of the underlying genetic algorithm approach are that it does not require any prior knowledge of what might be a 'good' approximation in order for the method to converge, and it enables several objectives to be included in the evaluation of the fitness function. Results indicate that this approach can predict optimal strategies when compared to benchmark minimum-time maneuvers of a robot manipulator. The formulation and integration of the aforementioned components into a working model and the simulation of reaching and lifting tasks represents the bulk of the thesis. Results are compared to motion data collected in the laboratory from a test subject performing the same tasks. Discrepancies in the results are primarily due to model fidelity. However, more complex models are not evaluated due to the additional computational time required. The theoretical approach provides an excellent foundation, but further work is required to increase the computational efficiency of the numerical implementation before proceeding to more complex models.