IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i17p2174-d630049.html
   My bibliography  Save this article

A Framework for Economically Optimal Operation of Explosive Waste Incineration Process to Reduce NOx Emission Concentration

Author

Listed:
  • Sunghyun Cho

    (Department of Chemical and Biomolecular Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea
    Green Materials & Processes R&D Group, Korea Institute of Industrial Technology, 55 Jongga-ro, Jung-gu, Ulsan 44413, Korea
    These authors contributed equally to this work.)

  • Dongwoo Kang

    (Department of Engineering Chemistry, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, Chungbuk 28644, Korea
    These authors contributed equally to this work.)

  • Joseph Sang-Il Kwon

    (Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77845, USA
    Texas A&M Energy Institute, Texas A&M University, College Station, TX 77845, USA)

  • Minsu Kim

    (Department of Chemical and Biomolecular Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Hyungtae Cho

    (Green Materials & Processes R&D Group, Korea Institute of Industrial Technology, 55 Jongga-ro, Jung-gu, Ulsan 44413, Korea)

  • Il Moon

    (Department of Chemical and Biomolecular Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Junghwan Kim

    (Green Materials & Processes R&D Group, Korea Institute of Industrial Technology, 55 Jongga-ro, Jung-gu, Ulsan 44413, Korea)

Abstract

Explosives, especially those used for military weapons, have a short lifespan and their performance noticeably deteriorates over time. These old explosives need to be disposed of safely. Fluidized bed incinerators (FBIs) are safe for disposal of explosive waste (such as TNT) and produce fewer gas emissions compared to conventional methods, such as the rotary kiln. However, previous studies on this FBI process have only focused on minimizing the amount of NOx emissions without considering the operating and unitality costs (i.e., total cost) associated with the process. It is important to note that, in general, a number of different operating conditions are available to achieve a target NOx emission concentration and, thus, it requires a significant computational requirement to compare the total costs among those candidate operating conditions using a computational fluid dynamics simulation. To this end, a novel framework is proposed to quickly determine the most economically viable FBI process operating condition for a target NOx concentration. First, a surrogate model was developed to replace the high-fidelity model of an FBI process, and utilized to determine a set of possible operating conditions that may lead to a target NOx emission concentration. Second, the candidate operating conditions were fed to the Aspen Plus™ process simulation program to determine the most economically competitive option with respect to its total cost. The developed framework can provide operational guidelines for a clean and economical incineration process of explosive waste.

Suggested Citation

  • Sunghyun Cho & Dongwoo Kang & Joseph Sang-Il Kwon & Minsu Kim & Hyungtae Cho & Il Moon & Junghwan Kim, 2021. "A Framework for Economically Optimal Operation of Explosive Waste Incineration Process to Reduce NOx Emission Concentration," Mathematics, MDPI, vol. 9(17), pages 1-12, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:17:p:2174-:d:630049
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/17/2174/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/17/2174/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Choi, Yeongryeol & Kim, Junghwan & Moon, Il, 2020. "Simulation and economic assessment of using H₂O₂ solution in wet scrubber for large marine vessels," Energy, Elsevier, vol. 194(C).
    2. Mehdi Rezaeian Zadeh & Seifollah Amin & Davar Khalili & Vijay Singh, 2010. "Daily Outflow Prediction by Multi Layer Perceptron with Logistic Sigmoid and Tangent Sigmoid Activation Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(11), pages 2673-2688, September.
    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. Mustafa Turan & Mehmet Yurdusev, 2014. "Predicting Monthly River Flows by Genetic Fuzzy Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4685-4697, October.
    2. Seyed Akrami & Vahid Nourani & S. Hakim, 2014. "Development of Nonlinear Model Based on Wavelet-ANFIS for Rainfall Forecasting at Klang Gates Dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 2999-3018, August.
    3. Mohammad R. Hassanvand & Hojat Karami & Sayed-Farhad Mousavi, 2018. "Investigation of neural network and fuzzy inference neural network and their optimization using meta-algorithms in river flood routing," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 94(3), pages 1057-1080, December.
    4. Andres Ticlavilca & Mac McKee, 2011. "Multivariate Bayesian Regression Approach to Forecast Releases from a System of Multiple Reservoirs," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(2), pages 523-543, January.
    5. Çağlayan-Akay, Ebru & Topal, Kadriye Hilal, 2024. "Forecasting Turkish electricity consumption: A critical analysis of single and hybrid models," Energy, Elsevier, vol. 305(C).
    6. Ayoub Zeroual & Mohamed Meddi & Ali A. Assani, 2016. "Artificial Neural Network Rainfall-Discharge Model Assessment Under Rating Curve Uncertainty and Monthly Discharge Volume Predictions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 3191-3205, July.
    7. Xuesong Zhang & Kaiguang Zhao, 2012. "Bayesian Neural Networks for Uncertainty Analysis of Hydrologic Modeling: A Comparison of Two Schemes," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(8), pages 2365-2382, June.
    8. Bonakdari, Hossein & Khozani, Zohreh Sheikh & Zaji, Amir Hossein & Asadpour, Navid, 2018. "Evaluating the apparent shear stress in prismatic compound channels using the Genetic Algorithm based on Multi-Layer Perceptron: A comparative study," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 400-411.
    9. Sinan Jasim Hadi & Mustafa Tombul, 2018. "Forecasting Daily Streamflow for Basins with Different Physical Characteristics through Data-Driven Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3405-3422, August.
    10. Hirad Abghari & Hojjat Ahmadi & Sina Besharat & Vahid Rezaverdinejad, 2012. "Prediction of Daily Pan Evaporation using Wavelet Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(12), pages 3639-3652, September.
    11. Coroianu, Lucian & Costarelli, Danilo & Gal, Sorin G. & Vinti, Gianluca, 2019. "The max-product generalized sampling operators: convergence and quantitative estimates," Applied Mathematics and Computation, Elsevier, vol. 355(C), pages 173-183.
    12. Mohammad Dorofki & Ahmed Elshafie & Othman Jaafar & Othman Karim & Sharifah Abdullah, 2014. "A GIS-ANN-Based Approach for Enhancing the Effect of Slope in the Modified Green-Ampt Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(2), pages 391-406, January.
    13. Syrodoy, S.V. & Kuznetsov, G.V. & Gutareva, N. Yu & Nigay (Ivanova), N.A., 2022. "Mathematical modeling of the thermochemical processes of sequestration of SOx when burning the particles of the coal and wood mixture," Renewable Energy, Elsevier, vol. 185(C), pages 1392-1409.
    14. Ozgur Kisi & Alireza Nia & Mohsen Gosheh & Mohammad Tajabadi & Azadeh Ahmadi, 2012. "Intermittent Streamflow Forecasting by Using Several Data Driven Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(2), pages 457-474, January.
    15. Jian Tang & Xin-An Yin & Pan Yang & ZhiFeng Yang, 2014. "Assessment of Contributions of Climatic Variation and Human Activities to Streamflow Changes in the Lancang River, China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 2953-2966, August.

    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:9:y:2021:i:17:p:2174-:d:630049. 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.