Developing an ANFIS-PSO model to predict mercury emissions in combustion flue gases

Shahaboddin Shamshirband, Masoud Hadipoor, Alireza Baghban, Amir Mosavi, Jozsef Bukor, Annamária R. Várkonyi-Kóczy

Research output: Contribution to journalArticle

9 Citations (Scopus)


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.

Original languageEnglish
Article number965
Issue number10
Publication statusPublished - Oct 1 2019


  • Adaptive neuro-fuzzy inference system (ANFIS)
  • Air pollution prediction
  • Air quality
  • Data science
  • Flue gas
  • Health hazards of air pollution
  • Hybrid machine learning model
  • Mercury emissions
  • Particle swarm optimization (PSO)
  • Particulate matter
  • Smart cities intelligent air quality monitoring

ASJC Scopus subject areas

  • Mathematics(all)

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