Browsing by Subject "mathematical modeling"
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Item Catalytic Membrane Reactor for Extraction of Hydrogen from Bioethanol Reforming(2013-11-26) Kuncharam, Bhanu VardhanThis research explores a novel application of catalytic membrane reactors for high- purity hydrogen extraction from bioethanol reforming. Conventional membrane systems employ hydrogen permselective materials such as palladium, polymer membranes, which present several material challenges including embrittlement, thermal degradation and poisoning by hydrocarbons when used for high-temperature hydrocarbon reforming. Thus, the present work is motivated by an interest in employing reactor design concepts to alleviate our reliance upon permselective materials. Catalytic membrane reactor with segregated reactant(s) is employed to demonstrate the hypothesis that high-purity hydrogen with competitive hydrogen recoveries can be achieved by manipulating the reaction and diffusion phenomena, and corresponding thermal gradients inside the catalytic membrane, in the absence of any permselective materials. The hypothesis is demonstrated in two designs: (1) a single functional layer design for water-gas-shift catalytic membrane reactor, and (2) a multi-layer design for bioethanol reforming. A two-dimensional model is developed to describe reaction and diffusion in the catalytic membrane coupled with plug-flow equations in the retentate and permeate volumes using shell and tube architecture. Simulation results for a typical diesel reformate mixture (9 mol% CO, 3 mol% CO2, 28 mol% H2 and 15 mol% H2O) demonstrate that H2:CO permselectivities of 90:1 to > 200:1 with permeate hydrogen recoveries of 20% to 40% can be achieved through appropriate catalytic membrane design. This single reaction simulation results are used to establish a clear rubric of design rules that are then used as a base for designing catalytic membrane reactor for extraction of hydrogen from bioethanol (16 mol% ethanol). The two-dimensional catalytic membrane reactor for bioethanol reforming is simulated, using a network of ethanol reforming reactions and a composite iicatalyst with unique catalytic layers active for one or more reactions. The isothermal simulation results show that an apparent H2:CO permselectivity of 100:1 with hydrogen recovery of 15% can be achieved at appropriate design and flow configuration. This model is extended to a non-isothermal design, which predicted a decrease in membrane performance owing to endothermic reforming reaction. An autothermal design with an additional combustion catalyst layer to counteract the endothermic thermal gradients enhanced the non-isothermal membrane performance. Experiments were conducted to validate the water-gas-shift catalytic membrane reactor model using a gas permeation system; results qualitatively agree with the modeling results and quantitively with an error.Item Network television dynamics: a conceptual mathematical modelMaceyko, Aimee EItem Parameter Estimation of Complex Systems from Sparse and Noisy Data(2011-02-22) Chu, YunfeiMathematical modeling is a key component of various disciplines in science and engineering. A mathematical model which represents important behavior of a real system can be used as a substitute for the real process for many analysis and synthesis tasks. The performance of model based techniques, e.g. system analysis, computer simulation, controller design, sensor development, state filtering, product monitoring, and process optimization, is highly dependent on the quality of the model used. Therefore, it is very important to be able to develop an accurate model from available experimental data. Parameter estimation is usually formulated as an optimization problem where the parameter estimate is computed by minimizing the discrepancy between the model prediction and the experimental data. If a simple model and a large amount of data are available then the estimation problem is frequently well-posed and a small error in data fitting automatically results in an accurate model. However, this is not always the case. If the model is complex and only sparse and noisy data are available, then the estimation problem is often ill-conditioned and good data fitting does not ensure accurate model predictions. Many challenges that can often be neglected for estimation involving simple models need to be carefully considered for estimation problems involving complex models. To obtain a reliable and accurate estimate from sparse and noisy data, a set of techniques is developed by addressing the challenges encountered in estimation of complex models, including (1) model analysis and simplification which identifies the important sources of uncertainty and reduces the model complexity; (2) experimental design for collecting information-rich data by setting optimal experimental conditions; (3) regularization of estimation problem which solves the ill-conditioned large-scale optimization problem by reducing the number of parameters; (4) nonlinear estimation and filtering which fits the data by various estimation and filtering algorithms; (5) model verification by applying statistical hypothesis test to the prediction error. The developed methods are applied to different types of models ranging from models found in the process industries to biochemical networks, some of which are described by ordinary differential equations with dozens of state variables and more than a hundred parameters.