Developing an ANFIS PSO Model to Estimate Mercury Emission in Combustion Flue Gases
This work addresses environmental pollution assessment for power plants, but it is incremental as it applies a hybrid method to a specific domain.
The paper tackled predicting mercury emissions from power plant flue gases using an ANFIS PSO model, achieving accurate predictions with statistical metrics like MARE and low relative errors to handle nonlinear dependencies.
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.