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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 were studied.  The data collected from parametric field experiments were used to build a feed-forward back-propagation artificial neural net (ANN). The coal combustion parameters were used as inputs and NOx emission as outputs of the model. The ANN model was developed for full load condition and its predicted values were verified with the actual values. The algebraic equation containing weights and biases of the trained net was used as fitness function in sequential quadratic programming (SQP) to find the optimum level of input operating conditions for low NOx emission. The results proved that the proposed approach could be used for generating feasible operating conditions.

ANN - SQP Approach For NOx Emission Reduction In Coal Fired Boilers