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

Shahaboddin Shamshirband, Masoud Hadipoor, Alireza Baghban, Amir Mosavi, Jozsef Bukor and Annamária R. Várkonyi-Kóczy
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Shahaboddin Shamshirband: Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Masoud Hadipoor: Department of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran
Alireza Baghban: Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Mahshahr Campus, Mahshahr, Iran
Amir Mosavi: Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
Jozsef Bukor: Department of Mathematics and Informatics, J. Selye University, 94501 Komarno, Slovakia
Annamária R. Várkonyi-Kóczy: Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary

Mathematics, 2019, vol. 7, issue 10, 1-16

Abstract: 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.

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 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
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