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Open AccessArticle

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

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Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
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Department of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran
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Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Mahshahr Campus, Mahshahr, Iran
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Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
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Faculty of Health, Queensland University of Technology, Victoria Park Road, Kelvin Grove 4059, Australia
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School of Built the Environment, Oxford Brookes University, Oxford OX30BP, UK
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Department of Mathematics and Informatics, J. Selye University, 94501 Komarno, Slovakia
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Author to whom correspondence should be addressed.
Mathematics 2019, 7(10), 965; https://doi.org/10.3390/math7100965
Received: 30 June 2019 / Revised: 16 September 2019 / Accepted: 10 October 2019 / Published: 14 October 2019
Accurate prediction of mercury content emitted from fossil-fueled power stations is of the 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 included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type. View Full-Text
Keywords: air pollution prediction; flue gas; mercury emissions; adaptive neuro-fuzzy inference system (ANFIS); particle swarm optimization (PSO); ANFIS-PSO; hybrid machine learning model; smart cities intelligent air quality monitoring; data science; particulate matter; health hazards of air pollution; air quality air pollution prediction; flue gas; mercury emissions; adaptive neuro-fuzzy inference system (ANFIS); particle swarm optimization (PSO); ANFIS-PSO; hybrid machine learning model; smart cities intelligent air quality monitoring; data science; particulate matter; health hazards of air pollution; air quality
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MDPI and ACS Style

Shamshirband, S.; Hadipoor, M.; Baghban, A.; Mosavi, A.; Bukor, J.; Várkonyi-Kóczy, A.R. Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases. Mathematics 2019, 7, 965.

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