What is it about?

The paper introduces the artificial neural network (ANN) approach for the prediction of SO2 emissions from CFB boilers. The model considers a wide range of parameters influencing SO2 emissions. The [16-1-6-1] ANN model was successfully applied to predict SO2 emissions from coal combustion in several large- and small-scale CFB boilers, over a wide range of operating conditions, both in air-firing as well as oxygen-enriched and oxycombustion conditions.

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Why is it important?

Since the method constitutes a quick and easy to run technique this approach makes a complementary tool in relation to the experimental procedures and the programmed computing approach. The ANN model is time-efficient to run and therefore can be easily applied for calculations of SO2 concentration in the flue gas via the, so-called, non-iterative procedure. The model can be also applied as a submodel or a separate module in engineering calculations to predict SO2 emissions from coal combustion in CFB units.

Perspectives

The simulations of sulfur dioxide emissions from coal combustion in large- and pilot-scale CFB boilers, operating in air-fired as well as in oxygen-enriched and oxycombustion O2/CO2 and O2/RFG atmospheres are described in the Part 2 of the paper.

Jaroslaw Krzywanski
Jan Dlugosz University in Czestochowa

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This page is a summary of: A generalized model of SO2 emissions from large- and small-scale CFB boilers by artificial neural network approach, Fuel Processing Technology, September 2015, Elsevier,
DOI: 10.1016/j.fuproc.2015.04.012.
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