Multimodel Operability Framework for Design of Modular and Intensified Energy Systems

In this dissertation, a novel operability framework is introduced for the process design of modular and intensified energy systems that are challenged by complexity and highly constrained environments. Previously developed process operability approaches are reviewed and further developed in terms of theory, application, and software infrastructure. An optimization-based multilayer operability framework is introduced for process design of nonlinear energy systems. In the first layer of this framework, a mixed-integer linear programming (MILP)-based iterative algorithm considers the minimization of footprint and achievement of process intensification targets. Then, in the second layer, an operability analysis is performed to incorporate key features of optimality and feasibility accounting for the system achievability and flexibility. The outcome of this framework consists of a set of modular designs, considering both the aspects of size and process operability. For this study and throughout this dissertation, the nonlinear system is represented by multiple linearized models, which results in lower computational expense and more efficient quantification of operability regions. A systematic techno-economic analysis framework is also proposed for costing intensified modular systems. Conventional costing techniques are extended to allow estimation of capital and operating costs of modular units. Economy of learning concepts are included to consider the effect of experience curves on purchase costs. Profitability measures are scaled with respect to production of a chemical of interest for comparison with plants of traditional scale. Scenarios in which the modular technology presents break-even or further reduction in cost when compared to the traditional process are identified as a result. A framework for the development of process operability algorithms is provided as a software infrastructure outcome. Generated codes from the developed approaches are included in an open-source platform that will give researchers from academia and industry access to the algorithms. This platform has the purpose of dissemination and future improvement of process operability algorithms and methods. To show versatility and efficacy of the developed approaches, a variety of applications are considered as follows: a membrane reactor for direct methane aromatization conversion to hydrogen and benzene (DMA-MR), the classical shower problem in process operability, a power plant cycling application for power generation with penetration of renewable energy sources, and a newly developed modular hydrogen unit. Applications to DMA-MR subsystems demonstrate employment of the multilayer framework to find a region with modular design candidates, which are then ranked according to an operability index. The most operable design is determined and contrasted with the optimal design with respect to process intensification in terms of footprint minimization, showing that optimality at fixed nominal operations does not necessarily ensure the best system operability. For the modular hydrogen unit application, the developed process operability framework provides guidelines for obtaining modular designs that are highly integrated and flexible with respect to disturbances in inlet natural gas composition. The modular hydrogen unit is also used for demonstration of the proposed techno-economic analysis framework. A comparison with a benchmark conventional steam methane reforming plant shows that the modular hydrogen unit can benefit from the economy of learning. An assembled modular steam methane reforming plant is used to map the decrease in natural gas price that must be needed for the plant to break even when compared to traditional technologies. Scenarios in which the natural gas price is low allow break-even cost for both individual hydrogen units and the assembled modular plant. The economy of learning must produce a reduction of 40% or less in capital cost when the natural gas price is under 0.02 US$/Sm3. This result suggests that the synthesized modular hydrogen process has potential to be economically feasible under these conditions. The developed tools can be used to accelerate the deployment and manufacturing of standardized modular energy systems

Optimal Selection of Measurements and Manipulated Variables for Production Control

The main objective in a chemical plant is to improve profit while assuring products meet required specifications and satisfy environmental and operational constraints. A sub-objective that directly affects profit (main objective) is to improve the control performance of key economic variables in the plant, such as production rate and quality. An optimal control-based approach is proposed to determine a set of measurements and manipulated variables (dominant variables) and to structure them to improve plant profitability. This approach is model-based, and it uses optimal control theory to find the dominant variables that affect economic variables in the plant. First, the measurements and manipulated variables that affect product flow and quality are identified. Then, a decentralized control structure is designed to pair these measurements with the manipulated variables. Finally, a model predictive control (MPC) is built on top of the resulting control structure. This is done to manipulate the set point of these loops in order to change the production rate and product quality. Another sub-objective that affects the profit in the plant is to improve the control of inerts. In general, the inventory of the inerts is controlled using a purge. A new methodology to optimally control inerts is presented. This methodology aims to reduce the losses that occur throughout the purge by solving an optimization problem to determine the maximum amount of inert that can be handled in the plant without having shut down of the plant due to inert accumulation. The methodology is successfully applied to the Tennessee Eastman Plant where the operating cost was reduced approximately 4%. This methodology solves an approximation to an optimal economic problem. First, it improves the control performance of key economic variables in the plant. Therefore, tighter control of these economic variables is achieved and the plant can be operated closer to operational constraints. Second, it minimizes purge which is a variable that generally causes significant costs in the plant. This approach is applied to the Tennessee Eastman and the Vinyl Acetate Processes. Results demonstrating the effectiveness of this method are presented and compared with the results from other authors

Novi algoritam za kompresiju seizmičkih podataka velike amplitudske rezolucije

Renewable sources cannot meet energy demand of a growing global market. Therefore, it is expected that oil & gas will remain a substantial sources of energy in a coming years. To find a new oil & gas deposits that would satisfy growing global energy demands, significant efforts are constantly involved in finding ways to increase efficiency of a seismic surveys. It is commonly considered that, in an initial phase of exploration and production of a new fields, high-resolution and high-quality images of the subsurface are of the great importance. As one part in the seismic data processing chain, efficient managing and delivering of a large data sets, that are vastly produced by the industry during seismic surveys, becomes extremely important in order to facilitate further seismic data processing and interpretation. In this respect, efficiency to a large extent relies on the efficiency of the compression scheme, which is often required to enable faster transfer and access to data, as well as efficient data storage. Motivated by the superior performance of High Efficiency Video Coding (HEVC), and driven by the rapid growth in data volume produced by seismic surveys, this work explores a 32 bits per pixel (b/p) extension of the HEVC codec for compression of seismic data. It is proposed to reassemble seismic slices in a format that corresponds to video signal and benefit from the coding gain achieved by HEVC inter mode, besides the possible advantages of the (still image) HEVC intra mode. To this end, this work modifies almost all components of the original HEVC codec to cater for high bit-depth coding of seismic data: Lagrange multiplier used in optimization of the coding parameters has been adapted to the new data statistics, core transform and quantization have been reimplemented to handle the increased bit-depth range, and modified adaptive binary arithmetic coder has been employed for efficient entropy coding. In addition, optimized block selection, reduced intra prediction modes, and flexible motion estimation are tested to adapt to the structure of seismic data. Even though the new codec after implementation of the proposed modifications goes beyond the standardized HEVC, it still maintains a generic HEVC structure, and it is developed under the general HEVC framework. There is no similar work in the field of the seismic data compression that uses the HEVC as a base codec setting. Thus, a specific codec design has been tailored which, when compared to the JPEG-XR and commercial wavelet-based codec, significantly improves the peak-signal-tonoise- ratio (PSNR) vs. compression ratio performance for 32 b/p seismic data. Depending on a proposed configurations, PSNR gain goes from 3.39 dB up to 9.48 dB. Also, relying on the specific characteristics of seismic data, an optimized encoder is proposed in this work. It reduces encoding time by 67.17% for All-I configuration on trace image dataset, and 67.39% for All-I, 97.96% for P2-configuration and 98.64% for B-configuration on 3D wavefield dataset, with negligible coding performance losses. As a side contribution of this work, HEVC is analyzed within all of its functional units, so that the presented work itself can serve as a specific overview of methods incorporated into the standard

Error resilience and concealment techniques for high-efficiency video coding

This thesis investigates the problem of robust coding and error concealment in High Efficiency Video Coding (HEVC). After a review of the current state of the art, a simulation study about error robustness, revealed that the HEVC has weak protection against network losses with significant impact on video quality degradation. Based on this evidence, the first contribution of this work is a new method to reduce the temporal dependencies between motion vectors, by improving the decoded video quality without compromising the compression efficiency. The second contribution of this thesis is a two-stage approach for reducing the mismatch of temporal predictions in case of video streams received with errors or lost data. At the encoding stage, the reference pictures are dynamically distributed based on a constrained Lagrangian rate-distortion optimization to reduce the number of predictions from a single reference. At the streaming stage, a prioritization algorithm, based on spatial dependencies, selects a reduced set of motion vectors to be transmitted, as side information, to reduce mismatched motion predictions at the decoder. The problem of error concealment-aware video coding is also investigated to enhance the overall error robustness. A new approach based on scalable coding and optimally error concealment selection is proposed, where the optimal error concealment modes are found by simulating transmission losses, followed by a saliency-weighted optimisation. Moreover, recovery residual information is encoded using a rate-controlled enhancement layer. Both are transmitted to the decoder to be used in case of data loss. Finally, an adaptive error resilience scheme is proposed to dynamically predict the video stream that achieves the highest decoded quality for a particular loss case. A neural network selects among the various video streams, encoded with different levels of compression efficiency and error protection, based on information from the video signal, the coded stream and the transmission network. Overall, the new robust video coding methods investigated in this thesis yield consistent quality gains in comparison with other existing methods and also the ones implemented in the HEVC reference software. Furthermore, the trade-off between coding efficiency and error robustness is also better in the proposed methods

Theory and Computation of Optimal Low- and Medium- Thrust Orbit Transfers

This report presents new theoretical results which lead to new algorithms for the computation of fuel-optimal multiple-burn orbit transfers of low and medium thrust. Theoretical results introduced herein show how to add burns to an optimal trajectory and show that the traditional set of necessary conditions may be replaced with a much simpler set of equations. Numerical results are presented to demonstrate the utility of the theoretical results and the new algorithms. Two indirect methods from the literature are shown to be effective for the optimal orbit transfer problem with relatively small numbers of burns. These methods are the Minimizing Boundary Condition Method (MBCM) and BOUNDSCO. Both of these methods make use of the first-order necessary conditions exactly as derived by optimal control theory. Perturbations due to Earth's oblateness and atmospheric drag are considered. These perturbations are of greatest interest for transfers that take place between low Earth orbit altitudes and geosynchronous orbit altitudes. Example extremal solutions including these effects and computed by the aforementioned methods are presented. An investigation is also made into a suboptimal multiple-burn guidance scheme. The FORTRAN code developed for this study has been collected together in a package named ORBPACK. ORBPACK's user manual is provided as an appendix to this report

Fault recovery of an under-actuated quadrotor aerial vehicle

The research on autonomous flying robots has intensified considerably due to recent growth of civilian and military interests in Unmanned Aerial Vehicles (UAV). Miniature UAVs with the ability to vertically take off and land such as quadrotor aerial vehicles exhibit further advantages and features in maneuverability that have recently gained interest among the research community. Reliability of control systems require robustness and fault tolerance in presence of anomalies and unexpected failures in actuators, sensors or subsystems. Autonomy of dynamical systems that are vulnerable to the above failures has been an important topic of research during the past several years. Particularly, in small aerial vehicles due to hardware redundancy limitations design of a reliable control system plays an important role in ensuring acceptable and efficient performance. In view of the above, an autonomous recovery from actuators faults in under-actuated quadrotor aerial vehicles constitutes the main focus of the research investigated in this dissertation. A self-recovery mechanism, which extends the capabilities of the quadrotor system to operate under the presence of actuator faults is developed. The solution proposed takes into account the management of the control authority in the system by taking advantage of the post-fault model of an actuator. The first step in accomplishing this task is achieved by developing a controller under healthy condition that guarantees the stability of the quadrotor system in response to the commanded trajectories. This controller is then extended to incorporate the effects of a certain type of actuators fault by estimating the post-fault model of the system and then by properly commanding the faulty actuators accordingly. The performance of the proposed fault recovery scheme in presence of noise in the input and output channels and under different fault severities is evaluated through numerical simulations. It is shown that a significant reduction in the average tracking steady state errors are obtained through the application of the proposed recovery mechanism. The proposed scheme is applicable to rotorcraft systems even in presence of multiple faults in actuators