Dynamic modeling, model-based control, and optimization of solid oxide fuel cells

Solid oxide fuel cells are a promising option for distributed stationary power generation that offers efficiencies ranging from 50% in stand-alone applications to greater than 80% in cogeneration. To advance SOFC technology for widespread market penetration, the SOFC should demonstrate improved cell...

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Main Author: Spivey, Benjamin James
Format: Others
Language:English
Published: 2011
Subjects:
Online Access:http://hdl.handle.net/2152/ETD-UT-2011-08-4325
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spelling ndltd-UTEXAS-oai-repositories.lib.utexas.edu-2152-ETD-UT-2011-08-43252015-09-20T17:03:40ZDynamic modeling, model-based control, and optimization of solid oxide fuel cellsSpivey, Benjamin JamesModel predictive controlLinear system identificationFirst principles modelingSolid oxide fuel cellsEconomic optimizationNonlinear programmingSolid oxide fuel cells are a promising option for distributed stationary power generation that offers efficiencies ranging from 50% in stand-alone applications to greater than 80% in cogeneration. To advance SOFC technology for widespread market penetration, the SOFC should demonstrate improved cell lifetime and load-following capability. This work seeks to improve lifetime through dynamic analysis of critical lifetime variables and advanced control algorithms that permit load-following while remaining in a safe operating zone based on stress analysis. Control algorithms typically have addressed SOFC lifetime operability objectives using unconstrained, single-input-single-output control algorithms that minimize thermal transients. Existing SOFC controls research has not considered maximum radial thermal gradients or limits on absolute temperatures in the SOFC. In particular, as stress analysis demonstrates, the minimum cell temperature is the primary thermal stress driver in tubular SOFCs. This dissertation presents a dynamic, quasi-two-dimensional model for a high-temperature tubular SOFC combined with ejector and prereformer models. The model captures dynamics of critical thermal stress drivers and is used as the physical plant for closed-loop control simulations. A constrained, MIMO model predictive control algorithm is developed and applied to control the SOFC. Closed-loop control simulation results demonstrate effective load-following, constraint satisfaction for critical lifetime variables, and disturbance rejection. Nonlinear programming is applied to find the optimal SOFC size and steady-state operating conditions to minimize total system costs.text2011-10-12T16:36:13Z2011-10-12T16:36:13Z2011-082011-10-12August 20112011-10-12T16:36:37Zthesisapplication/pdfhttp://hdl.handle.net/2152/ETD-UT-2011-08-43252152/ETD-UT-2011-08-4325eng
collection NDLTD
language English
format Others
sources NDLTD
topic Model predictive control
Linear system identification
First principles modeling
Solid oxide fuel cells
Economic optimization
Nonlinear programming
spellingShingle Model predictive control
Linear system identification
First principles modeling
Solid oxide fuel cells
Economic optimization
Nonlinear programming
Spivey, Benjamin James
Dynamic modeling, model-based control, and optimization of solid oxide fuel cells
description Solid oxide fuel cells are a promising option for distributed stationary power generation that offers efficiencies ranging from 50% in stand-alone applications to greater than 80% in cogeneration. To advance SOFC technology for widespread market penetration, the SOFC should demonstrate improved cell lifetime and load-following capability. This work seeks to improve lifetime through dynamic analysis of critical lifetime variables and advanced control algorithms that permit load-following while remaining in a safe operating zone based on stress analysis. Control algorithms typically have addressed SOFC lifetime operability objectives using unconstrained, single-input-single-output control algorithms that minimize thermal transients. Existing SOFC controls research has not considered maximum radial thermal gradients or limits on absolute temperatures in the SOFC. In particular, as stress analysis demonstrates, the minimum cell temperature is the primary thermal stress driver in tubular SOFCs. This dissertation presents a dynamic, quasi-two-dimensional model for a high-temperature tubular SOFC combined with ejector and prereformer models. The model captures dynamics of critical thermal stress drivers and is used as the physical plant for closed-loop control simulations. A constrained, MIMO model predictive control algorithm is developed and applied to control the SOFC. Closed-loop control simulation results demonstrate effective load-following, constraint satisfaction for critical lifetime variables, and disturbance rejection. Nonlinear programming is applied to find the optimal SOFC size and steady-state operating conditions to minimize total system costs. === text
author Spivey, Benjamin James
author_facet Spivey, Benjamin James
author_sort Spivey, Benjamin James
title Dynamic modeling, model-based control, and optimization of solid oxide fuel cells
title_short Dynamic modeling, model-based control, and optimization of solid oxide fuel cells
title_full Dynamic modeling, model-based control, and optimization of solid oxide fuel cells
title_fullStr Dynamic modeling, model-based control, and optimization of solid oxide fuel cells
title_full_unstemmed Dynamic modeling, model-based control, and optimization of solid oxide fuel cells
title_sort dynamic modeling, model-based control, and optimization of solid oxide fuel cells
publishDate 2011
url http://hdl.handle.net/2152/ETD-UT-2011-08-4325
work_keys_str_mv AT spiveybenjaminjames dynamicmodelingmodelbasedcontrolandoptimizationofsolidoxidefuelcells
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