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Predictive modelling and optimization of nitrogen oxides emission in coal power plant using Artificial Neural Network and Simulated Annealing

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Author(s): Ilamathi P | V. Selladurai | K. Balamurugan

Journal: IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN 2252-8938

Volume: 1;
Issue: 1;
Start page: 11;
Date: 2012;
Original page

Keywords: coal | environmental study | simulated annealing | optimization | Emission Control | Back propagation neural network

ABSTRACT
In this research paper, predictive modelling of NOx emission of a 210 MW capacity pulverized coal-fired boiler and combustion parameter optimization to reduce NOx emission in flue gas is proposed. The effects of oxygen concentration in flue gas, coal properties, coal flow, boiler load, air distribution scheme, flue gas outlet temperature and nozzle tilt are studied.  The data collected from parametric field experiments are used to build a feed-forward back-propagation artificial neural net (ANN). The coal combustion parameters are used as inputs and NOx emission as outputs of the model. The ANN model is developed for full load condition and its predicted values are verified with the actual values. The algebraic equation containing weights and biases of the trained net is used as fitness function in simulated annealing (SA) to find the optimum level of input operating conditions for low NOx emission. The result proves that the proposed approach could be used for generating feasible operating conditions.
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