Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1996. / Includes bibliographical references (p. 73-81). / Samir Mitragotri. / Ph.D.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2009. / Includes bibliographical references (leaves 182-185). / The objective of this thesis is to evaluate a new carbon dioxide compression technology - shock compression - applied specifically to capture-enabled power plants. Global warming has increased public interest in carbon capture and sequestration technologies (CCS), but these technologies add significant capital and operating cost at present, which creates a significant barrier to adoption. Carbon dioxide compression technology makes up a high proportion of the additional cost required, making it a focal point for engineering efforts to improve the economic feasibility of carbon capture. To this effect, shock compressors have the potential to reduce both operating and capital costs with supporting compression ratios of up to 10:1, requiring less stages and theoretically allowing for the possibility of heat integration with the rest of the plant, allowing waste heat to be recovered from hot interstage compressed carbon dioxide. This thesis first presents a technical context for carbon dioxide compression by providing an overview of capture technologies to build an understanding of the different options being investigated for efficient removal of carbon dioxide from power plant emissions. It then examines conventional compression technologies, and how they have each evolved over time. Sample engineering calculations are performed to model gas streams processed by these conventional compressors. An analysis of shock compression is carried out by first building a background in compressible flow theory, and then using this as a foundation for understanding shock wave theory, especially oblique shocks. The shock compressor design is carefully analyzed using patent information, and a simulation of the physics of the shock compressor is created using equations from the theory section described earlier. / (cont.) A heat integration analysis is carried out to compare how conventional compressor technologies compare against the new shock compressor in terms of cooling duty and power recovery when integrated with the carbon dioxide capture unit. Both precombustion IGCC using Selexol and post-combustion MEA configurations are considered and compared. Finally an economic analysis is conducted to determine whether shock compression technology should be attractive to investors and plant managers deciding to support it. Key factors such as market, macroeconomic and technical risk are analyzed for investors, whereas a comparison of capital and operating cost is carried out for plant managers. Relevant risks associated with new compression technologies are also analyzed. It is found that there is no significant operating cost benefit to the shock compressor over the conventional compressor, both costing $3,700/hr for an IGCC plant. Power recovery is simply too low to justify the high power requirements in operating a shock compressor with a 10:1 ratio. The technical claims of the shock compressor (such as projected discharge temperature and pressures) seem reasonable after basic modeling, which shows a higher temperature and pressure than claimed by Ramgen. / by Rajat Suri. / S.M.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2003. / Includes bibliographical references. / (cont.) As a model system, we have chosen the unicellular, photoautotrophic cyanobacteria Synechocystis sp. PCC6803 for study, as it is 1) fully sequenced, 2) has an easily manipulated input signal (light for photosynthesis), and 3) fixes carbon dioxide into the commercially interesting, biodegradable polymer polyhydroxyalkanoate (PHA). We have created DNA microarrays with [approximately]97% of the Synechocystis genome represented in duplicate to monitor the cellular transcriptional profile. These arrays are used in time-series experiments of differing light levels to measure dynamic transcriptional response to changing environmental conditions. We have developed networks of potential genetic regulatory interactions through time-series analysis based on the data from our studies. An algorithm for combining gene position information, clustering, and time-lagged correlations has been created to generate networks of hypothetical biological links. Analysis of these networks indicates that good correlation exists between the input signal and certain groups of photosynthesis- and metabolism-related genes. Furthermore, this analysis technique placed these in a temporal context, showing the sequence of potential effects from changes in the experimental conditions. This data and hypothetical interaction networks have been used to construct AutoRegressive with eXogenous input (ARX) models. These provide dynamic, state-space models for prediction of transcriptional profiles given a dynamically changing set of environmental perturbations... / Recent technological developments allow all the genes of a species to be monitored simultaneously at the transcriptional level. This necessitates a more global approach to biology that includes consideration of complex interactions between many genes and other intracellular species. The metaphor of a cell as a miniature chemical plant with inputs, outputs, and controls gives chemical engineers a foothold in this type of analysis. Networks of interacting genes are fertile ground for the application of the methods developed by engineers for the analysis and monitoring of industrial chemical processes. The DNA microarray has been established as a tool for efficient collection of mRNA expression data for a large number of genes simultaneously. Although great strides have been made in the methodology and instrumentation of this technique, the development of computational tools needed to interpret the results have received relatively inadequate attention. Existing analyses, such a clustering techniques applied to static data from cells at many different states, provide insight into co-expression of genes and are an important basis for exploration of the cell's genetic programming. We propose that an even greater level of regulatory detail may be gained by dynamically changing experimental conditions (the input signal) and measuring the time-delayed response of the genes (the output signal). The addition of temporal information to DNA microarray experiments should suggest potential cause/effect relationships among genes with significant regulatory responses to the conditions of interest. This thesis aims to develop computational techniques to maximize the information gained from such dynamic experiments. / by William A. Schmitt, Jr. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1989. / Vita. / Includes bibliographical references (leaves 192-200). / by Charles Owen Grigsby. / Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2013. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 215-224). / At any given moment, engineering and chemical companies have a host of projects that they are either trying to screen to advance to the next stage of research or select from for implementation. These choices could range from a relative few, like the expansion of production capacity of a particular plant, to a large number, such as the screening for candidate compounds for the active pharmaceutical ingredient in a drug development program. This choice problem is very often further complicated by the presence of uncertainty in the project outcomes and introduces an element of risk into the screening or decision process. It is the task of the process designer to prune the set of available options, or in some cases, generate a set of possible choices, in the presence of such uncertainties to provide recommendations that are in line with the objectives of the ultimate decision maker. Screening and decision rules already exist that do this but the problem with most of them is that they add more assumptions to the structure of the preferences of the decision maker, or to the form of the uncertain distribution that characterizes the project outcome, than is known at the time. These challenges may lead to the screening out of viable alternatives and may ultimately lead to the selection of inferior projects. This thesis aims to demonstrate the applicability of Stochastic Dominance as method that can overcome these obstacles. Stochastic Dominance has been shown to be a general method for incorporating risk preferences into the decision-making process. It is consistent with classical decision theory, it makes minimal assumptions of the structure of the utility functions of the decision makers and of the nature of the distributions of the uncertainty and under certain conditions can be shown to be equivalent to the other objectives. In this work, an up-to-date review and an implementation framework for Stochastic Dominance is presented. The performance of the method relative to some of the other screening and decision objectives is examined in the light of three case studies: the design of a reactor-separator system for the production of a chemical, the selection of a crop for biomass production and the design of a biomass to liquids process. The limitations of the method are also discussed together with suggestions for how they can be overcome to make the method more effective. / by Adekunle M. Adeyemo. / Ph.D.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1961. / Includes bibliographical references (leaves 31-34 / by Francis M. Pierce. / M.S.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references. / Growing awareness of climate change and the risks associated with our society's dependence on fossil fuels has motivated global initiatives to develop economically viable, renewable energy sources. However, the transportation sector remains a major hurdle. Although electric vehicles are becoming more mainstream, the transportation sector is expected to continue relying heavily on combustion engines, particularly in the freight and airline industries. Therefore, research efforts to develop cleaner combustion must continue. This includes the development of more efficient combustion engines, identification of compatible alternative fuels, and the streamlining of existing petroleum resources. These dynamic systems have complex chemistry and are often difficult and expensive to probe experimentally, making detailed chemical kinetic modeling an attractive option for simulating and predicting macroscopic observables such as ignition delay or CO₂ concentrations. This thesis presents several methods and applications towards high fidelity predictive modeling using Reaction Mechanism Generator (RMG), an open source software package which automatically constructs kinetic mechanisms. Several sources contribute to model error during automatic mechanism generation, including incomplete or incorrect handling of chemistry, poor estimation of thermodynamic and kinetics parameters, and uncertainty propagation. First, an overview of RMG is presented along with algorithmic changes for handling incomplete or incorrect chemistry. Completeness of chemistry is often limited by CPU speed and memory in the combinational problem of generating reactions for large molecules. A method for filtering reactions is presented for efficiently and accurately building models for larger systems. An extensible species representation was also implemented based on chemical graph theory, allowing chemistry to be extended to lone pairs, charges, and variable valencies. Several chemistries are explored in this thesis through modeling three combustion related processes. Ketone and cyclic ether chemistry are explored in the study of diisoproyl ketone and cineole, biofuel candidates produced by fungi in the decomposition of cellulosic biomass. Detailed kinetic modeling in conjunction with engine experiments and metabolic engineering form a collaborative feedback loop that efficiently screens biofuel candidates for use in novel engine technologies. Next, the challenge of modeling constrained cyclic geometries is tackled in generating a combustion model of JP-10, a synthetic jet fuel used in propulsion technologies. The model is validated against experimental and literature data and succeeds in capturing key product distributions, including aromatic compounds, which are precursors to polyaromatic hydrocarbons (PAHs) and soot. Finally, oil-to-gas cracking processes under geological conditions are studied through modeling the low temperature pyrolysis of the heavy oil analog phenyldodecane in the presence of diethyldisulfide. This system is used to gather mechanistic insight on the observation that sulfur-rich kerogens have accelerated oil-to-gas decomposition, a topic relevant to petroleum reservoir modeling. The model shows that free radical timescales matter in low temperature systems where alkylaromatics are relatively stable. Local and global uncertainty propagation methods are used to analyze error in automatically generated kinetic models. A framework for local uncertainty analysis was implemented using Cantera as a backend. Global uncertainty analysis was implemented using adaptive Smolyak pscudospcctral approximations to efficiently compute and construct polynomial chaos expansions (PCE) to approximate the dependence of outputs on a subset of uncertain inputs. Both local and global methods provide similar qualitative insights towards identifying the most influential input parameters in a model. The analysis shows that correlated uncertainties based on kinetics rate rules and group additivity estimates of thermochemistry drastically reduce a model's degrees of freedom and can have a large impact on model outputs. These results highlight the necessity of uncertainty analysis in the mechanism generation workflow. This thesis demonstrates that predictive chemical kinetics can aid in the mechanistic understanding of complex chemical processes and contributes new methods for refining and building high fidelity models in the automatic mechanism generation workflow. These contributions are available to the kinetics community through the RMG software package. / by Connie W. Gao. / Ph. D.

Massachusetts Institute of Technology. Dept. of Chemical Engineering. Thesis. 1945. Sc.D. / Bibliography: leaves 178-180. / Sc.D.

Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1909. / MIT copy bound with: A new method for determining the quality of galvanized iron / Charles L. Campbell -- The design of a gravimeter to automatically record the percentage of carbon dioxide in a flue gas / Bradley Dewey -- The manufacture of caustic soda from sodium sulphate and pyrite ash / John J. Elbert -- Investigation of the free energy of the reaction CaO + 3 C = CaC₂ + CO / Carl W. Gram -- Investigation of the methods of determining the dust content of moving gases and determination of the efficiency of the Howard Dust Chamber at the Merrimac Chemical Co. / Harold William Paine -- The dependence of the physical properties of calcium and magnesium glass on the chemical composition of the glass / C. M. Pritchard -- A microscopic investigation of broken steel rails / Clark S. Robinson -- The electrolytic determination of zinc / Edward E. Wells. / Includes bibliographical references (leaves 30-31). / by Elliot Quincy Adams. / B.S.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2011. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 281-292). / Nearly two-thirds of the United States' transportation fuels are derived from non-renewable fossil fuels. This demand of fossil fuels requires the United States to import ~ 60% of its total fuel consumption. Relying so heavily on foreign oil is a threat to national security, not to mention that burning all of these fossil fuels produces increased levels of CO₂, a greenhouse gas that contributes to global warming. This is not a sustainable model. The United States government has recently passed legislation that requires greenhouse gas emissions to be reduced to 80% of the 2005 level by the year 2050. Furthermore, new legislation under the Energy Independence and Security Act (EISA) requires that 36 billion gallons of renewable fuel be blended into transportation fuel by 2022. Solving these types of problems will require the fuel industry to shift away from petroleum fuels to biomass-derived oxygenated hydrocarbon fuels. These fuels are generated through different biological pathways, using different "bugs." The question of which fuel molecules should we be burning, and thus, which bugs should we be engineering, arises. To answer that question, a detailed understanding of the fuel chemistry under a wide range of operating conditions, i.e. temperature, pressure, fuel equivalence ratio, and fuel percentage, must be known. Understanding any fuel chemistry fully requires significant collaboration: experimental datasets that span a range of temperatures, pressures, and equivalence ratios, high-level ab initio quantum chemistry calculations for single species and reactions, and a comprehensive reaction mechanism and reactor model that utilizes the theoretical calculations to make predictions. A shortcoming in any of these three fields limits the knowledge gained from the others. This thesis addresses the third field of the collaboration, namely constructing accurate reaction mechanisms for chemical systems. In this thesis, reaction mechanisms are constructed automatically using a software package Reaction Mechanism Generator (RMG) that has been developed in the Green Group over the last decade. The predictive capability of any mechanism depends on the parameters employed. For kinetic models, these parameters consist of species thermochemistry and reaction rate coefficients. Many parameters have been reported in the literature, and it would be beneficial if RMG would utilize these values instead of relying on estimation routines purely. To this end, the PrIMe Warehouse C/H/O chemistry has been validated and a means of incorporating said data in the RMG database has been implemented. Thus, all kinetic models built by RMG may utilize the community's reported thermochemical parameters. / (cont.) A kinetic model is evaluated by how accurately it can predict experimental data. In this thesis, it was shown that the RMG software, with the PrIMe Warehouse data collaboration, constructs validated kinetic models by using RMG to predict the pyrolysis and combustion chemistry of the four butanol isomers. The kinetic model has been validated against many unique datasets, including: pyrolysis experiments in a flow reactor, opposed-flow and doped methane diffusion flames, jet-stirred reactors, shock tube and rapid compression machine experiments, and low-pressure and atmospheric premixed laminar flames. The mechanism predicts the datasets remarkably well across all operating conditions, including: speciation data within a factor of three, ignition delays within a factor of two, and laminar burning velocities within 20% of the experimental measurements. This accurate, comprehensivelyvalidated kinetic model for the butanol isomers is valuable itself, and even more so as a demonstration of the state-of-the-art in predictive chemical kinetics. Although the butanol kinetic model was validated against many datasets, the model contained no nitrogen-containing species, and also had limited pathways for benzene formation. These limitations were due to the RMG software, as RMG was initially written with only carbon, hydrogen, and oxygen chemistry in mind. While this functionality has been sufficient in modeling the combustion of hydrocarbons, the ability to make predictions for other chemical systems, e.g. nitrogen, sulfur, and silicon compounds, with the same tools is desired. As part of this thesis, the hardcoded C/H/O functional groups were removed from the source code and database, enabling our RMG software to model heteroatom chemistry. These changes in the RMG software also allows for robust modeling of aromatic compounds. The future in the transportation sector is uncertain, particularly regarding which fuels our engines will run on. Understanding the elementary chemistry of combustion will be critical in efficiently screening all potential fuel alternatives. This thesis demonstrates one method of understanding fuel chemistry, through detailed reaction mechanisms constructed automatically using the RMG software. Specifically, a method for data collaboration between the RMG software and the PrIMe Warehouse has been established, which will facilitate collaboration between researchers working on combustion experiments, theory, and modeling. The RMG software's algorithm of mechanism construction has been validated by comparing the RMG-generated model predictions for the combustion of the butanol isomers against many unique datasets from the literature; many new species thermochemistry and reaction rate kinetics were calculated and this validation shows RMG to be a capable tool in constructing reaction mechanisms for combustion. Finally, the RMG source code and database have been updated, to allow for robust modeling of heteroatom and aromatic chemistry; these two features will be especially important for future modeling of combustion systems as they relate to the formation of harmful pollutants such as NOx and soot. / by Michael Richard Harper, Jr. / Ph.D.