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:
Shahab, S.; Baghban, A.; Hadipoor, M. Developing an ANFIS-PSO Based Model to Estimate Mercury Emission in Combustion Flue Gases. Preprints2019, 2019050124. https://doi.org/10.20944/preprints201905.0124.v1
Shahab, S.; Baghban, A.; Hadipoor, M. Developing an ANFIS-PSO Based Model to Estimate Mercury Emission in Combustion Flue Gases. Preprints 2019, 2019050124. https://doi.org/10.20944/preprints201905.0124.v1
Shahab, S.; Baghban, A.; Hadipoor, M. Developing an ANFIS-PSO Based Model to Estimate Mercury Emission in Combustion Flue Gases. Preprints2019, 2019050124. https://doi.org/10.20944/preprints201905.0124.v1
APA Style
Shahab, S., Baghban, A., & Hadipoor, M. (2019). Developing an ANFIS-PSO Based Model to Estimate Mercury Emission in Combustion Flue Gases. Preprints. https://doi.org/10.20944/preprints201905.0124.v1
Chicago/Turabian Style
Shahab, S., Alireza Baghban and Masoud Hadipoor. 2019 "Developing an ANFIS-PSO Based Model to Estimate Mercury Emission in Combustion Flue Gases" Preprints. https://doi.org/10.20944/preprints201905.0124.v1
Abstract
Mercury content in the output gas from boilers was predicted using an Adaptive Neuro-Fuzzy Inference System (ANFIS). Input parameters were selected from coal characteristics and the operational configuration of boilers. The ANFIS approach 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 ANFIS model. Resulted values from the model were compared to the collected data and it indicates that the model possesses an extraordinary level of precision with a correlation coefficient of unity. The MARE% for training and testing parts were 0.003266 and 0.013272, 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
Flue gas; Emission; Mercury; ANFIS; PSO
Subject
Engineering, Energy and Fuel Technology
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.
