What is it about?

Previously established and validated [16-1-6-1] ANN model,whichwas published in the Part 1 of this paper was employed to predict SO2 emissions fromcoal combustion in a large-scale 261MWe CFB COMPACT-type boiler as well as in a pilot-scale 0.1 MWth OxyFuel–CFB test rig.

Featured Image

Why is it important?

The simulations are carried out using artificial neural network approach for different combustion environments, both in atmospheric and pressurized conditions. The study is conducted for air-firing, oxygen-enriched and oxy-fired combustion conditions. Therefore, four different combustion atmospheres are considered in the paper, where combustion runs in air and air enriched with oxygen (O2/N2 mode) as well as in oxycombustion (oxygen-fired combustion) conditions, which mean the mixture of oxygen with CO2 or recycled flue gas (RFG) with various fractions of oxygen (O2/CO2 mode and O2/RFG mode, respectively).

Perspectives

The ANN model, which is worked out by the use of a neurocomputing approach, proved to be an easy to use and useful tool. This tool allows mapping the areas of operating conditions in regions where the real measurements are unavailable, as neural networks have the ability to generalize knowledge acquired during the training stage.

Jaroslaw Krzywanski
Jan Dlugosz University in Czestochowa

Read the Original

This page is a summary of: A generalized model of SO2 emissions from large- and small-scale CFB boilers by artificial neural network approach Part 2. SO2 emissions from large- and pilot-scale CFB boilers in O2/N2, O2/CO2 and O2/RFG combustion atmospheres, Fuel Processing Technology, November 2015, Elsevier,
DOI: 10.1016/j.fuproc.2015.08.009.
You can read the full text:

Read

Contributors

The following have contributed to this page