Theses - Chemical Engineering
http://hdl.handle.net/1721.1/7787
2018-05-25T18:57:47ZRobust simulation and optimization methods for natural gas liquefaction processes
http://hdl.handle.net/1721.1/115702
Robust simulation and optimization methods for natural gas liquefaction processes
Watson, Harry Alexander James
Natural gas is one of the world's leading sources of fuel in terms of both global production and consumption. The abundance of reserves that may be developed at relatively low cost, paired with escalating societal and regulatory pressures to harness low carbon fuels, situates natural gas in a position of growing importance to the global energy landscape. However, the nonuniform distribution of readily-developable natural gas sources around the world necessitates the existence of an international gas market that can serve those regions without reasonable access to reserves. International transmission of natural gas via pipeline is generally cost-prohibitive beyond around two thousand miles, and so suppliers instead turn to the production of liquefied natural gas (LNG) to yield a tradable commodity. While the production of LNG is by no means a new technology, it has not occupied a dominant role in the gas trade to date. However, significant growth in LNG exports has been observed within the last few years, and this trend is expected to continue as major new liquefaction operations have and continue to become operational worldwide. Liquefaction of natural gas is an energy-intensive process requiring specialized cryogenic equipment, and is therefore expensive both in terms of operating and capital costs. However, optimization of liquefaction processes is greatly complicated by the inherently complex thermodynamic behavior of process streams that simultaneously change phase and exchange heat at closely-matched cryogenic temperatures. The determination of optimal conditions for a given process will also generally be nontransferable information between LNG plants, as both the specifics of design (e.g. heat exchanger size and configuration) and the operation (e.g. source gas composition) may have significantly variability between sites. Rigorous evaluation of process concepts for new production facilities is also challenging to perform, as economic objectives must be optimized in the presence of constraints involving equipment size and safety precautions even in the initial design phase. The absence of reliable and versatile software to perform such tasks was the impetus for this thesis project. To address these challenging problems, the aim of this thesis was to develop new models, methods and algorithms for robust liquefaction process simulation and optimization, and to synthesize these advances into reliable and versatile software. Recent advances in the sensitivity analysis of nondifferentiable functions provided an advantageous foundation for the development of physically-informed yet compact process models that could be embedded in established simulation and optimization algorithms with strong convergence properties. Within this framework, a nonsmooth model for the core unit operation in all industrially-relevant liquefaction processes, the multi-stream heat exchanger, was first formulated. The initial multistream heat exchanger model was then augmented to detect and handle internal phase transitions, and an extension of a classic vapor-liquid equilibrium model was proposed to account for the potential existence of solutions in single-phase regimes, all through the use of additional nonsmooth equations. While these initial advances enabled the simulation of liquefaction processes under the conditions of simple, idealized thermodynamic models, it became apparent that these methods would be unable to handle calculations involving nonideal thermophysical property models reliably. To this end, robust nonsmooth extensions of the celebrated inside-out algorithms were developed. These algorithms allow for challenging phase equilibrium calculations to be performed successfully even in the absence of knowledge about the phase regime of the solution, as is the case when model parameters are chosen by a simulation or optimization algorithm. However, this still was not enough to equip realistic liquefaction process models with a completely reliable thermodynamics package, and so new nonsmooth algorithms were designed for the reasonable extrapolation of density from an equation of state under conditions where a given phase does not exist. This procedure greatly enhanced the ability of the nonsmooth inside-out algorithms to converge to physical solutions for mixtures at very high temperature and pressure. These models and submodels were then integrated into a flowsheeting framework to perform realistic simulations of natural gas liquefaction processes robustly, efficiently and with extremely high accuracy. A reliable optimization strategy using an interior-point method and the nonsmooth process models was then developed for complex problem formulations that rigorously minimize thermodynamic irreversibilities. This approach significantly outperforms other strategies proposed in the literature or implemented in commercial software in terms of the ease of initialization, convergence rate and quality of solutions found. The performance observed and results obtained suggest that modeling and optimizing such processes using nondifferentiable models and appropriate sensitivity analysis techniques is a promising new approach to these challenging problems. Indeed, while liquefaction processes motivated this thesis, the majority of the methods described herein are applicable in general to processes with complex thermodynamic or heat transfer considerations embedded. It is conceivable that these models and algorithms could therefore inform a new, robust generation of process simulation and optimization software.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2018.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 313-324).
2018-01-01T00:00:00ZOptimal control of dynamical systems with time-invariant probabilistic parametric uncertainties
http://hdl.handle.net/1721.1/115701
Optimal control of dynamical systems with time-invariant probabilistic parametric uncertainties
Shen, Dongying Erin
The importance of taking model uncertainties into account during controller design is well established. Although this theory is well developed and quite mature, the worst-case uncertainty descriptions assumed in robust control formulations are incompatible with the uncertainty descriptions generated by commercial model identification software that produces time-invariant parameter uncertainties typically in the form of probability distribution functions. This doctoral thesis derives rigorous theory and algorithms for the optimal control of dynamical systems with time-invariant probabilistic uncertainties. The main contribution of this thesis is new feedback control design algorithms for linear time-invariant systems with time-invariant probabilistic parametric uncertainties and stochastic noise. The originally stochastic system of equations is transformed into an equivalent deterministic system of equations using polynomial chaos (PC) theory. In addition, the H2- and H[infinity symbol]-norms commonly used to describe the effect of stochastic noise on output are transformed such that the eventual closed-loop performance is insensitive to parametric uncertainties. A robustifying constant is used to enforce the closed-loop stability of the original system of equations. This approach results in the first PC-based feedback control algorithm with proven closed-loop stability, and the first PC-based feedback control formulation that is applicable to the design of fixed-order state and output feedback control designs. The numerical algorithm for the control design is formulated as optimization over bilinear matrix inequality (BMI) constraints, for which commercial software is available. The effectiveness of the approach is demonstrated in two case studies that include a continuous pharmaceutical manufacturing process. In addition to model uncertainties, chemical processes must operate within constraints, such as upper and lower bounds on the magnitude and rate of change of manipulated and/or output variables. The thesis also demonstrates an optimal feedback control formulation that explicitly addresses both constraints and time-invariant probabilistic parameter uncertainties for linear time-invariant systems. The H2-optimal feedback controllers designed using the BMI formulations are incorporated into a fast PC-based model predictive control (MPC) formulation. A numerical case study demonstrates the improved constraint satisfaction compared to past polynomial chaos-based formulations for model predictive control.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, February 2018.; Cataloged from PDF version of thesis. "September 2017." Handwritten on title page "February 2018."; Includes bibliographical references (pages 117-121).
2018-01-01T00:00:00ZImproving efficacy of therapeutics by enhancing delivery using chemical engineering
http://hdl.handle.net/1721.1/115700
Improving efficacy of therapeutics by enhancing delivery using chemical engineering
Tam, Hok Hei
In the past decades, many new and interesting modalities for therapeutics have been discovered, including nucleic acid therapeutics such as siRNA and mRNA. However, one of the limiting challenges in developing these technologies into medicines is delivering the therapeutics to the correct location in the body or in the cell. Furthermore, many older modalities for therapeutics, such as vaccines and chemotherapeutics, could become more efficacious with optimization of delivery. By using chemical engineering principles, we can develop better delivery methods, materials, and formulations to improve the treatment of a wide range of diseases. In this thesis, I report on applications to vaccines and cancer. Vaccines are currently the vanguard of public health efforts; unfortunately, a wide range of diseases have no effective vaccine. This includes devastating diseases such as HIV, malaria, and others. One area of vaccination that few people have considered optimizing is the kinetics by which the vaccine is delivered. We found that using an exponential increasing dosing profile, we could produce over 7 times more antibodies compared to the current prime-boost profile using the same amount and type of vaccine. The antibodies generated were also of higher affinity. By improving antibody affinity and titer, this work may make existing vaccines for diseases such as HIV sufficiently efficacious to use in humans. Cancer is one of the leading causes of death in both developed and developing countries, and is extremely difficult to cure due to its high variability. Furthermore, current cancer therapeutics cause severe toxicity. By delivering more of the cancer therapeutics to the tumor, we can reduce the side effects. Some tumors, because of their location, are even harder to access: brain tumors, such as glioblastoma, are protected from most drugs by the blood-brain barrier or blood-brain-tumor barrier. Circumventing these challenges allow us to develop safer and more efficacious therapies. We found that conjugates of siRNA with chlorotoxin could knock down levels of a housekeeping gene in vitro and in vivo in a mouse brain tumor model. Furthermore, we developed prostate-cancer targeting ligands that demonstrate in vitro efficacy and tested them in vivo.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2018.; Cataloged from PDF version of thesis.; Includes bibliographical references.
2018-01-01T00:00:00ZPredictive chemical kinetics for auto ignition of fuel blends
http://hdl.handle.net/1721.1/115698
Predictive chemical kinetics for auto ignition of fuel blends
Yee, Nathan W. (Nathan Wa-Wai)
Predictive chemical kinetics plays an important part in the study of chemical systems by reducing the need for expensive experiments. The size and complexity of modem chemical mechanisms increasingly require the use of automated mechanism generators, such as the Reaction Mechanism Generator (RMG). Use of these automated generators for creating quality chemical mechanisms necessitates accurate reaction rates. Unfortunately, the vast majority of kinetic parameters governing rate constants are not known. The goals of this thesis are the accurate estimation of kinetic parameters and its application to the prediction of auto ignition in fuel blends. At the molecular scale, quantum chemical methods can give kinetic coefficients with accuracy nearing those of experiments. Even when specific kinetic parameters are unavailable, rates can be evaluated by analogy to similar molecules. RMG uses an averaging scheme based on arranging functional groups in a hierarchical tree structure. We have been able to continue expansion of the database to species with nitrogen and sulfur, improve methods for structural representation, and showcase validation for thermochemistry and kinetic parameter estimates. Studying kinetics at the mechanistic level allows insight into the interaction between chemical reactions. Specifically, we have been interested in finding and analyzing the reaction pathways relevant to auto ignition, simplifying well-studied fuel mechanisms for propane and methanol. We were able to define clear stages of ignition and report the controlling chemistry during each stage. Understanding of these base fuels provides the basis to analyzing ignition for larger and more novel fuels. Finally, from a macroscopic perspective we studied ignition for blends of phenolic additives in gasoline. Chemical mechanisms generated by RMG were modeled in a variable volume reactor that emulate end gas conditions of the CRF engine used to evaluate Research Octane Number (RON). We predicted the effect each additive has on the timing of ignition, which were later proven to be reasonably accurate by experimental validation. The chemical pathways that affect the ignition were analyzed and discussed. Finally, we developed a framework for predicting several different aspects of potential fuel additives, which could help eliminate costly experiments by identifying unsuitable candidates before they are even synthesized.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2018.; Cataloged from PDF version of thesis.; Includes bibliographical references.
2018-01-01T00:00:00Z