Preprint Article Version 3 This version is not peer-reviewed

Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Version 1 : Received: 6 May 2019 / Approved: 10 May 2019 / Online: 10 May 2019 (13:54:51 CEST)
Version 2 : Received: 27 June 2019 / Approved: 29 June 2019 / Online: 29 June 2019 (15:47:43 CEST)
Version 3 : Received: 9 August 2019 / Approved: 12 August 2019 / Online: 12 August 2019 (05:19:37 CEST)

How to cite: Shamshirband, S.; Hadipoor, M.; Baghban, A.; Mosavi, A.; Bukor, J.; R. Várkonyi-Kóczy, A. Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases. Preprints 2019, 2019050124 (doi: 10.20944/preprints201905.0124.v3). Shamshirband, S.; Hadipoor, M.; Baghban, A.; Mosavi, A.; Bukor, J.; R. Várkonyi-Kóczy, A. Developing ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases. Preprints 2019, 2019050124 (doi: 10.20944/preprints201905.0124.v3).

Abstract

Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an 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 hybrid model of ANFIS-PSO model, the statistical meter of MARE% was implemented, which resulted in 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

air pollution prediction; flue gas; mercury emissions; adaptive neuro-fuzzy inference system (ANFIS); particle swarm optimization (PSO); ANFIS-PSO; hybrid machine learning model; data science; particulate matter; health hazards of air pollution; air quality

Comments (1)

Comment 1
Received: 12 August 2019
Commenter: Amir Mosavi
Commenter's Conflict of Interests: Author
Comment: The work has been substantially improved. The literature review, model development, results have been highly revised. The order of the coauthors have been changes and two professors are added to the paper.
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