  Accurate prediction of mercury content emitted from fossil fueled power
stations is of utmost important for environmental pollution assessment and
hazard mitigation. In this paper, mercury content in the output gas of power
stations boilers was predicted using adaptive neuro fuzzy inference system
method integrated with particle swarm optimization. The input parameters of the
model include coal characteristics and the operational parameters of the
boilers. The dataset has been collected from a number of power plants and
employed to educate and examine the proposed model. To evaluate the performance
of the proposed ANFIS PSO model the statistical meter of MARE was implemented.
Furthermore, relative errors between acquired data and predicted values
presented, which confirm the accuracy of the model to deal nonlinearity and
representing the dependency of flue gas mercury content into the specifications
of coal and the boiler type.
