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  • Modelling of NOx emission from oil refinery furnaces.

    Author(s)
    Mullins, BJ
    Braddock, RD
    Agranovski, IE
    Griffith University Author(s)
    Braddock, Roger D.
    Agranovski, Igor E.
    Year published
    2002
    Metadata
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    Abstract
    The current paper investigates the possibility of establishing an empirically based model for predicting the emission rate of nitrogen oxides (NO x ) from oil refinery furnaces, in order to continually track emissions with respect to environmental licence limits. Model input data were collected by direct stack monitoring using an electrochemical cell NO x analyser, as well as a range of telemetry sensors to obtain refinery process parameters. Principal Component Analysis (PCA), in conjunction with Partial Least Squares (PLS) regression was then used to build a series of models able to predict NO x emissions from the ...
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    The current paper investigates the possibility of establishing an empirically based model for predicting the emission rate of nitrogen oxides (NO x ) from oil refinery furnaces, in order to continually track emissions with respect to environmental licence limits. Model input data were collected by direct stack monitoring using an electrochemical cell NO x analyser, as well as a range of telemetry sensors to obtain refinery process parameters. Principal Component Analysis (PCA), in conjunction with Partial Least Squares (PLS) regression was then used to build a series of models able to predict NO x emissions from the furnaces. The models produced were proven to be robust, with a relatively high accuracy, and are able to predict NO x levels over the range of operating conditions which were sampled. It was found that due to structural/operational variations a separate model is usually required for each furnace. The models can be integrated with the refinery operating system to predict NO x emission rates on a continuous basis. Two models representing structurally different furnaces are considered in this paper.
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    Journal Title
    Environmental Modeling and Assessment
    Volume
    7
    Issue
    1
    DOI
    https://doi.org/10.1023/A:1015257211432
    Publication URI
    http://hdl.handle.net/10072/6701
    Collection
    • Journal articles

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