1,721,072 research outputs found
Two-phase thermofluidic engines for low-grade heat recovery: system analysis and supporting algorithms
Get PDFThis thesis considers a two-phase thermofluidic oscillator known as the non-inertive thermofluidic engine (NIFTE), that is capable of utilizing heat supplied at a steady temperature to induce persistent thermal-fluid oscillations. The NIFTE is appealing for its simplicity and ability to operate across small temperature differences, as low as 30 °C on current prototypes. But it is also expected for these prototypes to exhibit low efficiencies relative to conventional heat recovery technologies that target higher-grade heat conversion.
The first part of this work involves a system analysis based on a nonlinear model of the NIFTE, which we extend to encompass irreversible thermal losses. The NIFTE is predicted to exhibit multiple cyclic steady states (CSS) for certain design configurations, either stable or unstable, a behavior that had never been hypothesized. A parametric analysis of the main design parameters of the NIFTE is conducted using local optimization techniques based on randomized multistart. The results confirm that failure to include the irreversible thermal losses in the NIFTE model can grossly overpredict its performance, especially over extended parameter domains. The optimization potential of this technology is also assessed by conducting a multi-objective optimization. Our results reveal that most of the optimization potential is achievable via targeted modifications of three design parameters only. The Pareto frontier between exergetic efficiency and power output is also found to be highly sensitive to these optimized parameters.
Rigorous analysis and optimization of the NIFTE calls for the implementation of global optimization due to the presence of multiple cyclic steady state and other uncertainties, and this is notoriously difficult. The second part of the work focuses on improving ODE bounding methods that form an essential step in global dynamic optimization. Unlike most available bounding methods, we follow a discretization approach to convert the differential equations to sparse algebraic equation systems; and we take advantage of their block structure. A simple block diagonal decomposition strategy is shown to result in significant overestimation due to the wrapping effect. Therefore, we develop a recursive block decomposition strategy, which fully retains the inter-dependency between the blocks. Numerical case studies reveal that the2 proposed discretization approach can potentially outperform the state-of-the-art set-propagation methods for nonlinear dynamic ODE bounding, both in terms of bounds tightness and CPU time. However, for larger systems with more complex dynamics, such as the NIFTE, our method may end up being more computationally demanding than the set-propagation method due to the much larger number of operations involved. Moreover, all of these methods are prone to fail when either a large uncertainty set or a long time horizon is considered.
To circumvent the numerical challenges associated with solving nonlinear ODEs, a switched-linear version of the NIFTE model is developed and validated. This model has three distinct modes for its dynamics, between which the NIFTE switches continuously. The conventional approach of bounding such linear hybrid automata using set-propagation methods proves useful for small-scale problems, but not for the NIFTE. Instead, we develop a new optimization-based approach in the final chapter of this thesis. The switched-linear dynamical system is first discretized. The resulting algebraic system is then reformulated using mixed-integer linear constraints, which can be solved using state-of-the-art MILP solvers. This proposed optimization-based approach is shown to generate tight state enclosures, especially in the presence of large uncertainties where set-propagation ODE bounding methods fail. However, the run-time complexity grows exponentially with the length of the time horizon.
All the numerical methods developed in this thesis are implemented in the in-house toolkit CRONOS, which is publicly available.Open Acces
Dual Modifier Adaptation Methodology For the On-line Optimization of Uncertain Processes
Get PDFThe current industry standard in real-time optimization (RTO) is the two-step method. In this approach, mismatch between the plant and process model is compensated for by continuously updating a subset of the parameters in the process model. It is suitably resistant to measurement noise, however it is not guaranteed to move toward the plant optimum if structural plant-model mismatch exists. Due to this deficiency, a number of alternative methods have been developed over the years, including ISOPE and modifier adaptation. These methods, however, utilize plant derivative information, which must be estimated because a precise plant model is typically not known in practice. This makes these methods particularly susceptible to measurement noise. Therefore, in this thesis, the development of an RTO technology which is both optimum seeking and resistant to measurement noise is considered. This research can be separated into two parts. In the first phase, the current state-of-the-art modifier adaptation algorithm is modified by employing Broyden's method to estimate the plant output derivatives. A pair of deficiencies of Broyden's method are then detailed, and a modification to the algorithm, designed to mitigate these deficiencies, is proposed. This consists of the inclusion of additional constraints in the model-based optimization problem, designed to limit both offset and variance in the Broyden derivative estimates. Since the new algorithm possesses two distinct goals, optimality and the accuracy of the Broyden estimates, it is referred to as dual modifier adaptation. In the second phase of this research, the design of dual modifier adaptation systems is considered. The design methodology is built around the design cost criterion, a metric which had previously been developed for the two-step approach of RTO. The calculation procedure for the metric is adapted in this research in order to address dual modifier adaptation systems. In addition, an approach designed to compute the constraint back-off necessary to ensure a certain level of feasibility is developed. The concepts discussed in both the first and second phases of the research are illustrated using the Williams-Otto Reactor case study. This is a benchmark problem that has been used in the RTO literature for many years. A more involved case study, a propane furnace, is introduced in the last main chapter of this thesis. Both the performance of the dual modifier adaptation algorithm itself and the design of dual modifier adaptation systems are discussed for this case study.Master of Engineering (ME
Deterministic global flowsheet optimization for the design of energy conversion processes
No full textReducing anthropogenic greenhouse gas emissions requires a new generation of energy conversion processes that make efficient use of renewable resources. Flowsheet optimization is a tool that can aid the design of such processes. Since the resulting optimization problems are nonconvex, deterministic global optimization is desirable. However, many problems remain computationally intractable for global optimization. In this thesis, two approaches for expediting the global solution of flowsheet optimization problems are investigated and applied to power-to-fuel processes as an example for a new type of energy conversion processes. First, factorable reduced-space formulations are considered as a way of reducing the size of the optimization problems arising from flowsheet optimization. In these reduced-space formulations, optimization variables are eliminated from the problem using equality constraints that can be rearranged to compute the variables as a factorable function of other variables. In flowsheet optimization, such formulations can be achieved at the modeling stage in analogy to established methods from flowsheet simulation and local optimization, where they can be interpreted as hybrids between equation-oriented and sequential-modular methods. The reduced-space formulations enable significant savings in computational time compared to fully equation-oriented approaches, both in the state-of-the-art global solver BARON and the newly developed open-source solver MAiNGO. The conducted analyses suggest that the savings are due to effects resembling selective branching and constraint propagation, as well as the reduced size of the subproblems for lower and upper bounding and bound tightening. Second, tight relaxations are developed for two classes of thermodynamic property models that occur in flowsheet optimization problems: general pure component models that are used when modeling multicomponent systems, and the IAPWS-IF97 model for water. The developed relaxations are significantly tighter than the relaxations obtained with general purpose methods and result in significant reductions in computational time for several case studies. Finally, methylal (also known as OME1) is considered as an example product of power-to-fuel processes because of its attractive combustion properties. In detailed process simulations, OME1 production in a power-to-fuel process chain based on a combination of existing processes is found to be less efficient than the production of other fuels. Therefore, an alternative process based on direct oxidation of methanol to OME1 is considered and globally optimized with the above methods. The results demonstrate that the developed methods make optimization of relatively complex flowsheets with few degrees of freedom tractable. However, they also highlight remaining limitations regarding the complexity of certain unit operation models as well as the importance of using realistic boundary conditions, in particular regarding heat integration and available utilities
Design of multi-parametric NCO-tracking controllers for linear continuous-time systems
Get PDFProcess optimization for industrial applications aims to achieve performance enhancements while satisfying system constraints. A major challenge for any such method lies in the problem of uncertainty stemming from model mismatch and process disturbances. Classical approaches such as model predictive control usually handle the uncertainty by repeatedly solving the optimization problem on-line, which may prove a rather computationally demanding task nonetheless and cause serious delays for fast dynamic systems. Existing approaches for mitigating the on-line computational burden via off-line optimization include multi-parametric programming and NCO-tracking. Multi-parametric programming aims to generate a mapping of control strategies as a function of given parameters; whereas NCO-tracking involves tracking the necessary conditions of optimality (NCOs) based on a precomputed control switching structure, which enables a dynamic real-time optimization problem to be transferred into an on-line tracking problem using a feedback controller. A methodology, called multi-parametric (mp-)NCO-tracking is developed in this thesis, whereby multi-parametric dynamic optimization and NCO-tracking methods are combined into a unified framework.
An algorithm for the design of mp-NCO-tracking controllers for continuous-time, linear-quadratic optimal control problems is presented in Chapter 2. The off-line step defines the multi-parametric control structure mapped to given uncertain (measurable) parameters in terms of so-called critical regions and feedback laws. Specifically, each critical region corresponds to a unique control switching structure in terms of the sequence of active constraints. The on-line step involves determining the current critical region once the parameter value has been revealed, and then applying the corresponding feedback control laws in a receding horizon manner. The mp-NCO-tracking approach provides a means for relaxing the invariant switching structure assumption in NCO-tracking by constructing critical regions for various switching structures. Moreover, addressing the problem directly in continuous-time can potentially reduce the number of critical regions compared with standard multi-parametric programming based on a time discretization and a control vector parameterization. The methodology and its benefits are illustrated for a number of simple case studies.
To obtain the mathematical representation of the generally nonlinear critical regions, Chapter 3 investigates a machine learning model as a classifier, based on deep neural network. This feed-forward network is selected for its representational power as a universal approximator for arbitrary continuous functions. Here, the classifier takes the unknown parameter as input and maps the corresponding critical regions in terms of their switching structures. An algorithm for training the classifier is presented, which involves generating the training data set, setting up a neural network architecture, and applying optimization based training. By using a Softmax classifier in the output layer of the network, a normalized probability distribution is obtained, which consist of a vector with as many elements as the total number of critical regions, and each element representing the likelihood for a region to be the correct one. The classifier is conveniently embedded into the multi-parametric NCO-tracking controller for choosing the real-time switching structure in on-line control.
Lastly, a robustification of the mp-NCO-tracking methodology is developed in Chapter 4, where constraints are guaranteed to be satisfied under all possible uncertainty scenarios, which leads to a min-max formulation. A robust counterpart formulation of the multi-parametric dynamic optimization problem is presented, which considers both additive or multiplicative time-varying disturbances. The approach involves backing-off the path and terminal constraints of the linear-quadratic optimal control problem based on a worst-case uncertainty propagation computed using either interval or ellipsoidal reachability tubes. The uncertain system state is decomposed into a nominal reference and a perturbed component, and a convex enclosure of the reachable set for the perturbed component is precomputed via some auxiliary differential equations. Conservative constraint back-offs are obtained from the precomputed reachability tubes, which enables the controller design procedure in the nominal case to be directly applied for the robust control problem, and to retain the same computational effort as in the nominal case. These developments are demonstrated by numerical case studies, and ways of extending this approach to more general, nonlinear optimal control problems are discussed in Chapter 5.Open Acces
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Towards ethylene production from carbon dioxide: Economic and global warming potential assessment
Get PDFCurrently, ethylene is the most important chemical with the largest global demand: it is mainly produced by ethane or naphtha cracking but, this is characterized by significant carbon dioxide emissions. For this reason, starting from carbon dioxide and water, different routes for ethylene production have been proposed and investigated in the literature but a complete comparative analysis is missing.
In this research, we analyze ethylene production via carbon dioxide electroreduction and methanol-to-olefin process, with methanol obtained in several ways. After the modelling of these systems, economic and environmental (in term of global warming potential) analyses are conducted to develop a comparison among the investigated processes and a conventional one based on naphtha cracking.
Results, located in the UK, show that the tandem process could be economically competitive (with the lowest production cost of $ 1.34 per kg of ethylene), while the methanol-to-olefin process with methanol obtained from syngas (produced through carbon dioxide-water co-electrolysis) has the best advantage for carbon dioxide emissions (with the lowest impact of −3.08 kg of CO2eq per kg of ethylene). Moreover, the most preferred energy source for the electricity supply is the nuclear one with a small-scale plant because, economic and greenhouse gas emission advantages are provided while, worse conditions are obtained when solar energy is used.
Our main finding is that electrochemical processes are likely to play an important role in the future when performance improvements are realized
Towards an integrated wide approach for upstream field recovery
No full textIntegrated asset modelling is the modelling of an entire production facility comprising of both subsurface and surface elements. Historically, asset modelling has entailed discrete reservoir, well and facility models accompanied by silo discipline ownership. Adopting an integrated wide approach provides a holistic overview capturing the complex interactions between subcomponents thereby enabling the assessment of system constraints and identification of production optimisation opportunities.
The objective of this research study is to develop a viable, representative alternative to the industry state of the art integrated asset modelling tool which can be deployed for short term surveillance and medium to long term field optimisation.
A series of integrated asset frameworks were constructed using an industry integrated asset modelling tool; this served as a basis for the development of proxy models via traditional approaches such as the response surface methodology and regression to complex techniques such as Artificial Neural Networks, RS-HDMR and ALAMO. Functional relationships defining production and pressure responses were postulated for a gas field case and an oil field supplemented by water injection drive case. Global sensitivity analyses of selected input parameters were investigated to validate the postulated functional relationships and smoothing splines were deployed to alleviate instabilities in the pressure prediction outputs and improve the surrogate response.
The optimisation of the surrogates via a constrained nonlinear optimisation framework in addition to gradient free optimisation techniques of neural networks yielded favourable results. Whilst the surrogates provided suitably accurate predictions, deviations were noted at late field life conditions suggesting suitability in early phases of the field development or production life cycle.
The developed surrogate models are proven to predict production and pressure profiles within specified production windows at a fraction of the simulation time and computational efforts of the industry counterpart and can be extended to further field development types in future workflows.Open Acces
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Multi-scale modeling of light-limited growth in microalgae production systems
Get PDFMicroalgae are often seen as important candidates for biofuel production. Claimed advantages over conventional oil crops include their fast growth rate and high lipid content as well as an independence from arable land and fresh water. Commercial viability of microalgae-derived biofuel is currently hindered by the high nutrient requirement, the trade-off between growth and lipid accumulation, sub-optimal growth conditions in large-scale culturing systems and difficulties related to the lipid extraction process. This thesis is concerned with the effect that the light conditions have on microalgae growth. The main contributions are related to the development of multi-scale mathematical models that span several orders of magnitude in both time and space and are suitable for predictions of photosynthetic production of microalgae in laboratory as well as industrial scale systems. Advanced mathematical techniques have been used along with state-of-the-art experimental methods in order to accurately represent microalgae cultures. The first three chapters focus on the development and identification of laboratory-scale models, while the last chapter develops a multi-physics modeling framework, where laboratory-scale predictions are extrapolated to industrial scale. More specifically, Chapter 3 presents a model that couples nutrient- and light-limitation, simultaneously accounting for photoacclimation and photoinhibition. This model is able to predict photosynthesis-irradiance (PI) response curves by accounting for different photoacclimation strategies. A self-developed Monte Carlo method has been used to estimate the exact confidence regions of the model parameters. The results show that even though a statistically meaningful coupling between photoacclimation and photoinhibition can be established, the exclusive use of PI curves is insufficient for the estimation of the parameters that describe the fast time-scale photosynthetic processes. Moreover, it is concluded that a quasi steady-state assumption in PI curve modeling may lead to confusing interpretation of the experimental observations. Chapter 4 attempts to resolve the aforementioned issues with the development of a model of chlorophyll fluorescence that couples photosynthetic production, photoinhibition and photoregulation to predict the light-limited photosynthetic operation of microalgae. This model achieves a significant improvement in the utilization of experimental information that is suitable for model identification and enables the quantitative characterization of the state of the reaction centers of photosystem II (PSII) from fluorescence fluxes, giving thereby a detailed description of the photoinhibition dynamics. Moreover, a theoretical connection between fluorescence and PI experimentation is established. In Chapter 5, model-based design of experiments, along with a more advanced description of photoregulation, practically demonstrate the capabilities of the fluorescence model in simultaneously predicting fluorescence fluxes and photosynthesis rate measurements. Additionally, the followed approach leads to the accurate estimation of the parameters representing the fast time-scale photosynthetic processes. Overall, the fluorescence model successfully combines fluorescence, photosynthesis rate and antenna size measurements, enabling thereby the accurate estimation of a large number of parameters. The prediction accuracy of the photosynthesis rate especially, suggests that fluorescence can be used to screen the photosynthetic performance of different microalgae strains as well as predict the photosynthetic productivity of culturing systems. In Chapter 6 the fluorescence model is extended to account for photoacclimation and is integrated with physics models that characterize hydrodynamics and light attenuation in large-scale cultivation systems. More specifically, the hydrodynamic conditions of a raceway pond are characterized in terms of individual cell trajectories using computational fluid dynamics (CFD). Large-scale productivity predictions are then obtained by averaging over all the trajectories. Analysis of the outcomes shows that both mixing and light attenuation affect the photosynthetic productivity in raceway ponds, while photoacclimation and photoinhibition have a significant impact too. The thesis concludes with Chapter 7 where significant contributions and future directions of research are discussed. The focus is on microalgae growth modeling extensions, possible applications of the developed fluorescence models in industrial aquaculture and model-based optimization of light-limited culturing systems.Open Acces
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