Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases
| Autor: | Shahaboddin Shamshirband, Masoud Hadipoor, Alireza Baghban, Amir Mosavi, Jozsef Bukor, Annamária R. Várkonyi-Kóczy |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
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 Mathematics QA1-939 |
| Zdroj: | Mathematics, Vol 7, Iss 10, p 965 (2019) |
| Druh dokumentu: | article |
| ISSN: | 2227-7390 |
| DOI: | 10.3390/math7100965 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |