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. Preprints2019, 2019050124. https://doi.org/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. https://doi.org/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. Preprints2019, 2019050124. https://doi.org/10.20944/preprints201905.0124.v2
APA Style
Shamshirband, S., Baghban, A., Hadipoor, M., & Mosavi, A. (2019). Developing an ANFIS-PSO Based Model to Estimate Mercury Emission in Combustion Flue Gases. Preprints. https://doi.org/10.20944/preprints201905.0124.v2
Chicago/Turabian Style
Shamshirband, S., Masoud Hadipoor and Amir Mosavi. 2019 "Developing an ANFIS-PSO Based Model to Estimate Mercury Emission in Combustion Flue Gases" Preprints. https://doi.org/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.
Keywords
ANFIS-PSO; air pollution prediction; flue gas; emission; mercury; adaptive neuro-fuzzy 27 inference system (ANFIS); particle swarm optimization (PSO); hybrid machine learning model
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
