Preprint Article Version 1 This version is not peer-reviewed

Developing an ANFIS-PSO Model to Estimate Mercury Emission in Combustion Flue Gases

Version 1 : Received: 30 June 2019 / Approved: 12 July 2019 / Online: 12 July 2019 (10:19:49 CEST)

How to cite: Shamshirband, S.; Baghban, A.; Hadipoor, M.; Mosavi, A. Developing an ANFIS-PSO Model to Estimate Mercury Emission in Combustion Flue Gases. Preprints 2019, 2019070165 (doi: 10.20944/preprints201907.0165.v1). Shamshirband, S.; Baghban, A.; Hadipoor, M.; Mosavi, A. Developing an ANFIS-PSO Model to Estimate Mercury Emission in Combustion Flue Gases. Preprints 2019, 2019070165 (doi: 10.20944/preprints201907.0165.v1).

Abstract

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 (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model include coal characteristics and the operational parameters of the boilers. The dataset has been collected from 82 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, which resulted 0.003266 and 0.013272 for training and testing respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1%, 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.

Subject Areas

ANFIS-PSO; air pollution prediction; flue gas, emission, mercury; adaptive neuro-fuzzy inference system (ANFIS); particle swarm optimization (PSO); hybrid machine learning model

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