Preprint Article Version 2 This version is not peer-reviewed

Developing an ANFIS-PSO Based Model to Estimate Mercury Emission 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.; Baghban, A.; Hadipoor, M.; Mosavi, A. Developing an ANFIS-PSO Based Model to Estimate Mercury Emission in Combustion Flue Gases. Preprints 2019, 2019050124 (doi: 10.20944/preprints201905.0124.v2). Shamshirband, S.; Baghban, A.; Hadipoor, M.; Mosavi, A. Developing an ANFIS-PSO Based Model to Estimate Mercury Emission in Combustion Flue Gases. Preprints 2019, 2019050124 (doi: 10.20944/preprints201905.0124.v2).

Abstract

Accurate prediction of mercury content emitted from fossil-fueled power stations is of utmost important to environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas from boilers was predicted using an Adaptive Neuro-Fuzzy Inference System (ANFIS) integrated with particle swarm optimization (PSO). Input parameters were selected from coal characteristics and the operational configuration of boilers. The proposed ANFIS-PSO model is capable of developing a nonlinear model to represent the dependency of flue gas mercury content into the specifications of coal and also the boiler type. In this study, operational information from 82 power plants has been gathered and employed to educate and examine the proposed model. To evaluate the performance of the proposed 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 PSO-ANFIS model.

Subject Areas

ANFIS-PSO; air pollution prediction; flue gas; emission; mercury; adaptive neuro-fuzzy 27 inference system (ANFIS); particle swarm optimization (PSO); hybrid machine learning model

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