IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v7y2019i10p965-d276112.html
   My bibliography  Save this article

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Author

Listed:
  • Shahaboddin Shamshirband

    (Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
    Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

  • Masoud Hadipoor

    (Department of Petroleum Engineering, Ahwaz Faculty of Petroleum Engineering, Petroleum University of Technology (PUT), Ahwaz, Iran)

  • Alireza Baghban

    (Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Mahshahr Campus, Mahshahr, Iran)

  • Amir Mosavi

    (Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
    Faculty of Health, Queensland University of Technology, Victoria Park Road, Kelvin Grove 4059, Australia
    School of Built the Environment, Oxford Brookes University, Oxford OX30BP, UK)

  • Jozsef Bukor

    (Department of Mathematics and Informatics, J. Selye University, 94501 Komarno, Slovakia)

  • Annamária R. Várkonyi-Kóczy

    (Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
    Department of Mathematics and Informatics, J. Selye University, 94501 Komarno, Slovakia)

Abstract

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.

Suggested Citation

  • Shahaboddin Shamshirband & Masoud Hadipoor & Alireza Baghban & Amir Mosavi & Jozsef Bukor & Annamária R. Várkonyi-Kóczy, 2019. "Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases," Mathematics, MDPI, vol. 7(10), pages 1-16, October.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:10:p:965-:d:276112
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/7/10/965/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/7/10/965/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Majid Dehghani & Hossein Riahi-Madvar & Farhad Hooshyaripor & Amir Mosavi & Shahaboddin Shamshirband & Edmundas Kazimieras Zavadskas & Kwok-wing Chau, 2019. "Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 12(2), pages 1-20, January.
    2. Pravitra Oyjinda & Nopparat Pochai, 2017. "Numerical Simulation to Air Pollution Emission Control near an Industrial Zone," Advances in Mathematical Physics, Hindawi, vol. 2017, pages 1-7, October.
    3. Lee, Chia-Yen & Wang, Ke, 2019. "Nash marginal abatement cost estimation of air pollutant emissions using the stochastic semi-nonparametric frontier," European Journal of Operational Research, Elsevier, vol. 273(1), pages 390-400.
    4. Adrienn Dineva & Amir Mosavi & Sina Faizollahzadeh Ardabili & Istvan Vajda & Shahaboddin Shamshirband & Timon Rabczuk & Kwok-Wing Chau, 2019. "Review of Soft Computing Models in Design and Control of Rotating Electrical Machines," Energies, MDPI, vol. 12(6), pages 1-28, March.
    5. Sina Faizollahzadeh Ardabili & Bahman Najafi & Meysam Alizamir & Amir Mosavi & Shahaboddin Shamshirband & Timon Rabczuk, 2018. "Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters," Energies, MDPI, vol. 11(11), pages 1-19, October.
    6. Fan, Junliang & Wu, Lifeng & Ma, Xin & Zhou, Hanmi & Zhang, Fucang, 2020. "Hybrid support vector machines with heuristic algorithms for prediction of daily diffuse solar radiation in air-polluted regions," Renewable Energy, Elsevier, vol. 145(C), pages 2034-2045.
    7. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
    8. Samuel Atuahene & Yukun Bao & Yao Yevenyo Ziggah & Patricia Semwaah Gyan & Feng Li, 2018. "Short-Term Electric Power Forecasting Using Dual-Stage Hierarchical Wavelet- Particle Swarm Optimization- Adaptive Neuro-Fuzzy Inference System PSO-ANFIS Approach Based On Climate Change," Energies, MDPI, vol. 11(10), pages 1-19, October.
    9. Li, Qingwei & Wu, Jiang & Wei, Hongqi, 2018. "Reduction of elemental mercury in coal-fired boiler flue gas with computational intelligence approach," Energy, Elsevier, vol. 160(C), pages 753-762.
    10. You, C.F. & Xu, X.C., 2010. "Coal combustion and its pollution control in China," Energy, Elsevier, vol. 35(11), pages 4467-4472.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
    2. Saeed Nosratabadi & Amir Mosavi & Shahaboddin Shamshirband & Edmundas Kazimieras Zavadskas & Andry Rakotonirainy & Kwok Wing Chau, 2019. "Sustainable Business Models: A Review," Sustainability, MDPI, vol. 11(6), pages 1-30, March.
    3. Md Mijanur Rahman & Mohammad Shakeri & Sieh Kiong Tiong & Fatema Khatun & Nowshad Amin & Jagadeesh Pasupuleti & Mohammad Kamrul Hasan, 2021. "Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks," Sustainability, MDPI, vol. 13(4), pages 1-28, February.
    4. Sina Ardabili & Amir Mosavi & Asghar Mahmoudi & Tarahom Mesri Gundoshmian & Saeed Nosratabadi & Annamaria R. Varkonyi-Koczy, 2020. "Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks," Papers 2010.02673, arXiv.org.
    5. Saeed Nosratabadi & Amir Mosavi & Ramin Keivani & Sina Ardabili & Farshid Aram, 2020. "State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability," Papers 2010.02670, arXiv.org.
    6. Nosratabadi, Saeed & Mosavi, Amir & Shamshirband, Shahaboddin & Zavadskas, Edmundas Kazimieras & Rakotonirainy, Andry & Chau, Kwok Wing, 2020. "Sustainable Business Models: A Review," OSF Preprints u4xw3, Center for Open Science.
    7. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    8. Rabia Tehseen & Muhammad Shoaib Farooq & Adnan Abid, 2020. "Earthquake Prediction Using Expert Systems: A Systematic Mapping Study," Sustainability, MDPI, vol. 12(6), pages 1-32, March.
    9. Nosratabadi, Saeed & Mosavi, Amir & Shamshirband, Shahaboddin & Zavadskas, Edmundas Kazimieras & Rakotonirainy, Andry & Chau, Kwok Wing, 2020. "Sustainable Business Models: A Review," OSF Preprints ts54m, Center for Open Science.
    10. Chen Shi & Yujiao Xian & Zhixin Wang & Ke Wang, 2023. "Marginal abatement cost curve of carbon emissions in China: a functional data analysis," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 28(2), pages 1-25, February.
    11. Lv, You & Lv, Xuguang & Fang, Fang & Yang, Tingting & Romero, Carlos E., 2020. "Adaptive selective catalytic reduction model development using typical operating data in coal-fired power plants," Energy, Elsevier, vol. 192(C).
    12. Jiaxin Yu & Jun Wang, 2020. "Optimization Design of a Rain-Power Utilization System Based on a Siphon and Its Application in a High-Rise Building," Energies, MDPI, vol. 13(18), pages 1-18, September.
    13. Vanchinathan Kumarasamy & Valluvan KarumanchettyThottam Ramasamy & Gokul Chandrasekaran & Gnanavel Chinnaraj & Padhmanabhaiyappan Sivalingam & Neelam Sanjeev Kumar, 2023. "A review of integer order PID and fractional order PID controllers using optimization techniques for speed control of brushless DC motor drive," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(4), pages 1139-1150, August.
    14. Liu, Lan-Cui & Wu, Gang, 2013. "Relating five bounded environmental problems to China's household consumption in 2011–2015," Energy, Elsevier, vol. 57(C), pages 427-433.
    15. Li, Chen & Kies, Alexander & Zhou, Kai & Schlott, Markus & Sayed, Omar El & Bilousova, Mariia & Stöcker, Horst, 2024. "Optimal Power Flow in a highly renewable power system based on attention neural networks," Applied Energy, Elsevier, vol. 359(C).
    16. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    17. Vasallo, Manuel Jesús & Cojocaru, Emilian Gelu & Gegúndez, Manuel Emilio & Marín, Diego, 2021. "Application of data-based solar field models to optimal generation scheduling in concentrating solar power plants," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 1130-1149.
    18. Han, Yongming & Liu, Shuang & Cong, Di & Geng, Zhiqiang & Fan, Jinzhen & Gao, Jingyang & Pan, Tingrui, 2021. "Resource optimization model using novel extreme learning machine with t-distributed stochastic neighbor embedding: Application to complex industrial processes," Energy, Elsevier, vol. 225(C).
    19. Alvaro Furlani Bastos & Surya Santoso, 2021. "Optimization Techniques for Mining Power Quality Data and Processing Unbalanced Datasets in Machine Learning Applications," Energies, MDPI, vol. 14(2), pages 1-21, January.
    20. Zhai, Yijie & Ma, Xiaotian & Gao, Feng & Zhang, Tianzuo & Hong, Jinglan & Zhang, Xu & Yuan, Xueliang & Li, Xiangzhi, 2020. "Is energy the key to pursuing clean air and water at the city level? A case study of Jinan City, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:7:y:2019:i:10:p:965-:d:276112. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.