skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Intelligent Machine Learning Analysis for Fuel Cell Operations

Abstract

A performance computational model for a 100 kW nominal solid oxide fuel cell generator system is described. The calculational methods are based on the FORTRAN programming language. Comprehensive parameter input options are presented, and constraints are identified. Example reactant, electrical, and efficiency outputs are demonstrated over the relevant operating ranges. A sample calculated output display at nominal operating conditions is given.

Authors:
;
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN
Sponsoring Org.:
USDOE Office of Fossil Energy (FE)
OSTI Identifier:
940376
Report Number(s):
ORNL96-0431; ORNL99-0564
TRN: US200902%%228
DOE Contract Number:
DE-AC05-00OR22725
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; EFFICIENCY; FORTRAN; FUEL CELLS; LEARNING; PERFORMANCE; PROGRAMMING LANGUAGES; SOLID OXIDE FUEL CELLS

Citation Formats

Murphy, R W, and Hoyt, W A. Intelligent Machine Learning Analysis for Fuel Cell Operations. United States: N. p., 2000. Web. doi:10.2172/940376.
Copy to clipboard
Murphy, R W, & Hoyt, W A. Intelligent Machine Learning Analysis for Fuel Cell Operations. United States. doi:10.2172/940376.
Copy to clipboard
Murphy, R W, and Hoyt, W A. Fri . "Intelligent Machine Learning Analysis for Fuel Cell Operations". United States. doi:10.2172/940376. https://www.osti.gov/servlets/purl/940376.
Copy to clipboard
@article{osti_940376,
title = {Intelligent Machine Learning Analysis for Fuel Cell Operations},
author = {Murphy, R W and Hoyt, W A},
abstractNote = {A performance computational model for a 100 kW nominal solid oxide fuel cell generator system is described. The calculational methods are based on the FORTRAN programming language. Comprehensive parameter input options are presented, and constraints are identified. Example reactant, electrical, and efficiency outputs are demonstrated over the relevant operating ranges. A sample calculated output display at nominal operating conditions is given.},
doi = {10.2172/940376},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Jun 30 00:00:00 EDT 2000},
month = {Fri Jun 30 00:00:00 EDT 2000}
}
Copy to clipboard
Technical Report:
View Technical Report (1.03 MB)
DOI:  10.2172/940376
Save / Share:
Export Metadata
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.

  • ; ; ; ...
    Several fuel cell types are available and are in various stages of technology development. The complex nature of the balance of plant and fuel cell interface poses many technical challenges to achieve proper system control under commercial operating conditions. Real-time predictive diagnostic computer systems based on advanced intelligent machine learning technologies offer a means to facilitate the detection, understanding, and control of fuel cell subsystems to avoid system instabilities and failures that can result in costly plant shutdowns. The objectives reported herein are the development of physical and empirical computer models for application and testing of predictive control strategies basedmore » on intelligent machine learning techniques for fuel cells. A physical/empirical model was built and validated using available operating data from commercial fuel cells. Neural networks were then used to build an empirical model from the original physical/empirical model. Using the neural network model, a predictive, feedforward strategy was developed to control the fuel flow for a phosphoric acid fuel cell physical/empirical model. The predictive control strategy was compared to traditional proportional integral derivative control schemes.« less

  • ; ; ; ...
    No abstract provided.

  • ; ; ; ...
    To ensure the usefulness of simulation technologies in practice, their credibility needs to be established with Uncertainty Quantification (UQ) methods. In this project, smart proxy is introduced to significantly reduce the computational cost of conducting large number of multiphase CFD simulations, which is typically required for non-intrusive UQ analysis. Smart proxy for CFD models are developed using pattern recognition capabilities of Artificial Intelligence (AI) and Data Mining (DM) technologies. Several CFD simulation runs with different inlet air velocities for a rectangular fluidized bed are used to create a smart CFD proxy that is capable of replicating the CFD results formore » the entire geometry and inlet velocity range. The smart CFD proxy is validated with blind CFD runs (CFD runs that have not played any role during the development of the smart CFD proxy). The developed and validated smart CFD proxy generates its results in seconds with reasonable error (less than 10%). Upon completion of this project, UQ studies that rely on hundreds or thousands of smart CFD proxy runs can be accomplished in minutes. Following figure demonstrates a validation example (blind CFD run) showing the results from the MFiX simulation and the smart CFD proxy for pressure distribution across a fluidized bed at a given time-step (the layer number corresponds to the vertical location in the bed).« less

  • Several technological and economic advances, however, must be made before fuel cell power plants can be used on the typical municipal bus. Further, it is highly possible that fuel cell-powered buses, representing a radical departure in design and operational needs, will require a different set of skills for the bus maintenance worker. As a joint effort between the city of Albuquerque and the Los Alamos National Laboratory (LANL), this study is a beginning effort to accelerate the use of fuel cell technology in actual bus applications. In recognition of the developing stage of fuel cell technology and its differences frommore » conventional power plants, this study was designed as an initial step necessary to move fuel cell research for buses from the theoretical stages to a more applied setting. The information produced by this project will be valuable to national fuel cell research for buses to verify existing knowledge and extrapolate with relative certainty the performance and operating cost of buses powered by fuel cells. The secondary benefit from this investigation has been the development of improved maintenance and operational data on Albuquerque's city bus system. The methodologies and information produced by this study should be useful not only to the city of Albuquerque and LANL, but to other transit operators and fuel cell researchers. The data will be made available through both the Urban Consortium Energy Task Force, and the vast networking capabilities of LANL. 1 ref., 9 figs., 7 tabs.« less