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- Creators: Debussy, Claude, 1862-1918
ContributorsDebussy, Claude, 1862-1918 (Composer)
ContributorsDebussy, Claude, 1862-1918 (Composer)
ContributorsDebussy, Claude, 1862-1918 (Composer)
ContributorsDebussy, Claude, 1862-1918 (Composer)
ContributorsDebussy, Claude, 1862-1918 (Composer)
Description
Excessive weight gain during pregnancy is a significant public health concern and has been the recent focus of novel, control systems-based interventions. Healthy Mom Zone (HMZ) is an intervention study that aims to develop and validate an individually tailored and intensively adaptive intervention to manage weight gain for overweight or obese pregnant women using control engineering approaches. Motivated by the needs of the HMZ, this dissertation presents how to use system identification and state estimation techniques to assist in dynamical systems modeling and further enhance the performance of the closed-loop control system for interventions.
Underreporting of energy intake (EI) has been found to be an important consideration that interferes with accurate weight control assessment and the effective use of energy balance (EB) models in an intervention setting. To better understand underreporting, a variety of estimation approaches are developed; these include back-calculating energy intake from a closed-form of the EB model, a Kalman-filter based algorithm for recursive estimation from randomly intermittent measurements in real time, and two semi-physical identification approaches that can parameterize the extent of systematic underreporting with global/local modeling techniques. Each approach is analyzed with intervention participant data and demonstrates potential of promoting the success of weight control.
In addition, substantial efforts have been devoted to develop participant-validated models and incorporate into the Hybrid Model Predictive Control (HMPC) framework for closed-loop interventions. System identification analyses from Phase I led to modifications of the measurement protocols for Phase II, from which longer and more informative data sets were collected. Participant-validated models obtained from Phase II data significantly increase predictive ability for individual behaviors and provide reliable open-loop dynamic information for HMPC implementation. The HMPC algorithm that assigns optimized dosages in response to participant real time intervention outcomes relies on a Mixed Logical Dynamical framework which can address the categorical nature of dosage components, and translates sequential decision rules and other clinical considerations into mixed-integer linear constraints. The performance of the HMPC decision algorithm was tested with participant-validated models, with the results indicating that HMPC is superior to "IF-THEN" decision rules.
Underreporting of energy intake (EI) has been found to be an important consideration that interferes with accurate weight control assessment and the effective use of energy balance (EB) models in an intervention setting. To better understand underreporting, a variety of estimation approaches are developed; these include back-calculating energy intake from a closed-form of the EB model, a Kalman-filter based algorithm for recursive estimation from randomly intermittent measurements in real time, and two semi-physical identification approaches that can parameterize the extent of systematic underreporting with global/local modeling techniques. Each approach is analyzed with intervention participant data and demonstrates potential of promoting the success of weight control.
In addition, substantial efforts have been devoted to develop participant-validated models and incorporate into the Hybrid Model Predictive Control (HMPC) framework for closed-loop interventions. System identification analyses from Phase I led to modifications of the measurement protocols for Phase II, from which longer and more informative data sets were collected. Participant-validated models obtained from Phase II data significantly increase predictive ability for individual behaviors and provide reliable open-loop dynamic information for HMPC implementation. The HMPC algorithm that assigns optimized dosages in response to participant real time intervention outcomes relies on a Mixed Logical Dynamical framework which can address the categorical nature of dosage components, and translates sequential decision rules and other clinical considerations into mixed-integer linear constraints. The performance of the HMPC decision algorithm was tested with participant-validated models, with the results indicating that HMPC is superior to "IF-THEN" decision rules.
ContributorsGuo, Penghong (Author) / Rivera, Daniel E. (Thesis advisor) / Peet, Matthew M. (Committee member) / Forzani, Erica (Committee member) / Deng, Shuguang (Committee member) / Pavlic, Theodore P. (Committee member) / Arizona State University (Publisher)
Created2018
Description
In Nepal, a viable solution for environmental management, food and water security is the production of biochar, a carbon material made of plants burned in low oxygen conditions. Currently, the biochar is manufactured into charcoal briquettes and sold on the market for energy usage, however this may not provide the best value for community members who make less than a dollar a day and sell the biochar for as little as 16 cents per kilogram. This thesis seeks to improve the price of biochar and help their livelihoods as well as explore innovative solutions. One way to improve biochar while addressing water security problems is to create activated carbon, which uses its heightened porosity to adsorb contaminants from water or air. Activated carbon is also worth 100x the price of biochar. This thesis evaluates the mass content of biochar produced in Nepal, comparing it to literature values, and performed gravimetric and thermogravimetric analysis, comparing it to Activated Charcoal. Analysis of the biochar system used in Nepal reveals that the byproduct of biochar, biofuels, is highly underutilized. The higher heating value of biochar is 17.95 MJ/kg, which is much lower than other charcoals which burn around 30 MJ/kg. Low volatile content, less than 5% in biochar, provides a smokeless briquette, which is favorable on the market, however low heating value and misutilizations of biofuels in the solution indicate that creating a briquette is not the best use for biochar. Ash content is really high in this biochar, averaging around 12% and it may be due to the feedstock, a composite between Mikania and Lantana, which have 5.23% and 10.77% ash content respectively. This does not necessarily indicate a poor quality biochar, since ash values can vary widely between charcoals. Producing activated charcoal from this biochar is a favored solution; it will increase the price of the biochar, provide water security solutions, and be an appropriate process for this biochar, where heating value and underutilization of biofuel byproducts pose a problem.
ContributorsCayer, Joelle Marie Caroline (Author) / Chhetri, Netra (Thesis director) / Henderson, Mark (Committee member) / Deng, Shuguang (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Separation of carbon dioxide and methane for the upgrade of natural gas through use of pressure swing adsorption could potentially save large amounts of energy from the current, costly process of cryogenic distillation and provides greater cost effectiveness for carbon dioxide capture, and provide larger product flowrates than membrane permeation separation. The purpose of this study is to analyze the effects of varying initial conditions of a MatLab simulation, courtesy of Mai Xu, a graduate student at ASU, designed to use Langmuir isotherms, mass transfer equations, and adsorbent and gas properties to simulate a pressure swing adsorption process with a mixture of methane and carbon dioxide gas feed. The effects that will be varied are the adsorption/desorption time, pressurization/depressurization time, adsorption feed composition, desorption purge composition, adsorption pressure, desorption pressure, adsorption flow rate, and desorption flow rate. The study found that the trends in methane purity and production generally follow the trends predicted by literature and relevant equations, with pressure boundaries being the largest impacting factor. In addition there was a markedly inverse correlation between purity of methane product and the productivity of the system. This trend was only violated in one instance, at very low vacuum pressure during desorption, which could indicate an area that requires further study. Overall, the main areas of improvement in pressure swing adsorption for this system would be improving the selectivity of adsorption of carbon dioxide over methane, which requires improvement and change of the adsorbent, and more extreme vacuum pressures during desorption, both of which will increase methane yield and reduce operating costs.
ContributorsCook, Alexander Charles (Author) / Deng, Shuguang (Thesis director) / Mu, Bin (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Carbon capture is an essential way to reduce greenhouse gas emissions. One way to decrease the emissions is through the use of adsorbents such as zeolites. Dr. Dong-Kyun Seo’s group (School of Molecular Sciences, Arizona State University) synthesized the nanostructured faujasite (NaX). The zeolite was characterized using Scanning Electron Microscopy (SEM) and the physisorption properties were determined using ASAP 2020. ASAP 2020 tests of the nano-zeolite pellets at 77K in a liquid N2 bath determined the BET surface area of 547.1 m2/mol, T-plot micropore volume of 0.2257 cm3/g, and an adsorption average pore width of 5.9 Å. The adsorption isotherm (equilibrium) of CH4, N2, and CO2 were measured at 25ºC. Adsorption isotherm experiments concluded that the linear isotherm was the best fit for N2, and CH4 and the Sips isotherm was a better fit than the Langmuir and Freundlich isotherm for CO2. At 25ºC and 1 atm the zeolite capacity for CO2 is 4.3339 mmol/g, 0.1948 mmol/g for CH4, and 0.3534 mmol/g for N2. The zeolite has a higher CO2 capacity than the conventional NaX zeolite. Breakthrough experiments were performed in a fixed bed 22in, 0.5 in packing height and width at 1 atm and 298 K with nano-zeolite pellets. The gas chromatographer tested and recorded the data every two minutes with a flow rate of 10 cm3/min for N2 and 10 cm3/min CO2. Breakthrough simulations of the zeolite in a fixed bed adsorber column were conducted on MATLAB utilizing varying pressures, flow rates, and fed ratios of various CO2, N2 and CH4. Simulations using ideal adsorbed solution theory (IAST) calculations determined that the selectivity of CO2 in flue gas (15% CO2 + 85% N2) is 571.79 at 1 MPa, significantly higher than commercial zeolites and literature. The nanostructured faujasite zeolite appears to be a very promising adsorbent for CO2/N2 capture from flue gas and the separation of CO2/N2.
ContributorsClark, Krysta D. (Author) / Deng, Shuguang (Thesis director) / Green, Matthew (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Hydrothermal Liquefaction of Algae represents one of many pathways for the sustainable replacement of fossil fuels in transportation. When processing and researching algal biofuel, determination of the higher heating value (HHV) is paramount. Bomb calorimetry represents to current method for direct determination of HHV. When determining HHV’s indirectly, the industry standard is using one of many linear correlations relating elemental composition to HHV. Most of these correlations were developed from coal industry data, meaning that they do not necessarily fit algal product data well. In this study bomb calorimetry data and CHNS/O elemental composition data were collected for Chlorella, Micract, GS 5587.1, Kirchnella, and Gal 87.1 MM8 algae species. This data was added to CHNS/O and HHV values for other algal products in literature, and utilized to test the accuracy of the Dulong, Gumz, Vandralek and Boie correlations for algae products. Several preliminary algae specific correlations were proposed through a linear regression model of the data. Of the 5 samples tested, Kirchnella exhibited the highest HHV (23.2405 ± 0.0216 MJ/kg) and Chlorella exhibited the lowest (20.2055 ± 0.0484 MJ/kg). For both the experimental, and literature CHNS/O vs HHV data, the Vandralek and Boie correlations provided the best approximations in this study. For the totality of the data collected and researched in this study, 6 of 8 proposed correlations outperformed the Vandralek equation for HHV approximation. The most promising proposed correlations incorporated multiple linear regressions for elemental fractions of CHS, CHSO and CHNSO. Being that only 20 distinct algal product samples were regressed to create the proposed correlations, more data should be incorporated before publication of a final correlation. This study should serve as a starting point for the compilation of an exhaustive database for algal product assay and HHV data.
ContributorsCopp, Connor Joseph (Author) / Deng, Shuguang (Thesis director) / Muppaneni, Tapaswy (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05