IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i19p5115-d422627.html
   My bibliography  Save this article

Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes

Author

Listed:
  • Xiongchao Lin

    (School of Chemical & Environmental Engineering, China University of Mining and Technology (Beijing), D11 Xueyuan Road, Haidian District, Beijing 100083, China)

  • Wenshuai Xi

    (School of Chemical & Environmental Engineering, China University of Mining and Technology (Beijing), D11 Xueyuan Road, Haidian District, Beijing 100083, China)

  • Jinze Dai

    (Department of Chemical Engineering, The University of Utah, 50 South Central Campus Drive, MEB Room 3290, Salt Lake City, UT 84112, USA)

  • Caihong Wang

    (School of Chemical & Environmental Engineering, China University of Mining and Technology (Beijing), D11 Xueyuan Road, Haidian District, Beijing 100083, China)

  • Yonggang Wang

    (School of Chemical & Environmental Engineering, China University of Mining and Technology (Beijing), D11 Xueyuan Road, Haidian District, Beijing 100083, China)

Abstract

Molten gasification is considered as a promising technology for the processing and safe disposal of hazardous wastes. During this process, the organic components are completely converted while the hazardous materials are safely embedded in slag via the fusion-solidification-vitrification transformation. Ideally, the slag should be glassy with low viscosity to ensure the effective immobilization and steady discharge of hazardous materials. However, it is very difficult to predict the characteristics of slag using existing empirical equations or conventional mathematical methods, due to the complex non-linear relationship among the phase transformation, vitrification transition and chemical composition of slag. Equipped with a strong nonlinear mapping ability, an artificial neural network may be able to predict the properties of slags if a large amount of data is available for training. In this work, over 10,000 experimental data points were used to train and develop a slag classification model (glassy vs. non-glassy) based on a neural network. The optimal structure of the neural network was figured out and validated. The results suggest that the classification accuracy for the independent test samples reached 93.3%. Using 1 and 0 as model inputs to represent mildly reducing and inert atmospheres, a double hidden layer structure in the neural network enabled the accurate classification of slags under various atmospheres. Furthermore, the neural network for the prediction of glassy slag viscosity was optimized; it featured a double hidden layer structure. Under a mildly reducing atmosphere, the absolute error from the independent test data was generally within 4 Pa·s. By adding a gas atmosphere into the input of the neural network using a simple normalization method, a multi-atmosphere slag viscosity prediction model was developed. Said model is much more accurate than its counterpart that does not consider the effect of the atmosphere. In summary, the artificial neural network proved to be an effective approach to predicting the slag properties under different atmospheres. The data-driven models developed in this work are expected to facilitate the commercial deployment of molten gasification technology.

Suggested Citation

  • Xiongchao Lin & Wenshuai Xi & Jinze Dai & Caihong Wang & Yonggang Wang, 2020. "Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes," Energies, MDPI, vol. 13(19), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5115-:d:422627
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/19/5115/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/19/5115/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wei Li & Ben Wang & Jun Nie & Wu Yang & Linlin Xu & Lushi Sun, 2018. "Migration and Transformation of Vanadium and Nickel in High Sulfur Petroleum Coke during Gasification Processes," Energies, MDPI, vol. 11(8), pages 1-14, August.
    2. Hongbo Li & Hao Sun & Juncang Tian & Qiuning Yang & Qingqing Wan, 2019. "Mechanical and Ultrasonic Testing of Self-Compacting Concrete," Energies, MDPI, vol. 12(11), pages 1-15, June.
    3. Wei Han & Lingbo Nan & Min Su & Yu Chen & Rennian Li & Xuejing Zhang, 2019. "Research on the Prediction Method of Centrifugal Pump Performance Based on a Double Hidden Layer BP Neural Network," Energies, MDPI, vol. 12(14), pages 1-14, July.
    4. Min Xiang & Jie Min & Zaiqian Wang & Pan Gao, 2017. "A Novel Fault Early Warning Model Based on Fault Gene Table for Smart Distribution Grids," Energies, MDPI, vol. 10(12), pages 1-22, November.
    5. Lei Song & Lijun Huang & Bo Long & Fusheng Li, 2020. "A Genetic-Algorithm-Based DC Current Minimization Scheme for Transformless Grid-Connected Photovoltaic Inverters," Energies, MDPI, vol. 13(3), pages 1-18, February.
    6. Tilia Dahou & Françoise Defoort & Sébastien Thiéry & Maguelone Grateau & Matthieu Campargue & Simona Bennici & Mejdi Jeguirim & Capucine Dupont, 2018. "The Influence of Char Preparation and Biomass Type on Char Steam Gasification Kinetics," Energies, MDPI, vol. 11(8), pages 1-15, August.
    7. Chen, Xiaodong & Kong, Lingxue & Bai, Jin & Dai, Xin & Li, Huaizhu & Bai, Zongqing & Li, Wen, 2017. "The key for sodium-rich coal utilization in entrained flow gasifier: The role of sodium on slag viscosity-temperature behavior at high temperatures," Applied Energy, Elsevier, vol. 206(C), pages 1241-1249.
    8. Jan Stąsiek & Marek Szkodo, 2020. "Thermochemical Conversion of Biomass and Municipal Waste into Useful Energy Using Advanced HiTAG/HiTSG Technology," Energies, MDPI, vol. 13(16), pages 1-17, August.
    9. Natarianto Indrawan & Betty Simkins & Ajay Kumar & Raymond L. Huhnke, 2020. "Economics of Distributed Power Generation via Gasification of Biomass and Municipal Solid Waste," Energies, MDPI, vol. 13(14), pages 1-18, July.
    10. Rian Zan & Wei Wang & Runsheng Xu & Johannes Schenk & Heng Zheng & Haoxiang Wang, 2019. "Gasification Characteristics and Kinetics of Unburned Pulverized Coal in Blast Furnaces," Energies, MDPI, vol. 12(22), pages 1-14, November.
    11. Huichao Ji & Junyou Yang & Haixin Wang & Kun Tian & Martin Onyeka Okoye & Jiawei Feng, 2019. "Electricity Consumption Prediction of Solid Electric Thermal Storage with a Cyber–Physical Approach," Energies, MDPI, vol. 12(24), pages 1-18, December.
    12. Jan Najser & Petr Buryan & Sergej Skoblia & Jaroslav Frantik & Jan Kielar & Vaclav Peer, 2019. "Problems Related to Gasification of Biomass—Properties of Solid Pollutants in Raw Gas," Energies, MDPI, vol. 12(6), pages 1-14, March.
    13. Ana Ramos & Carlos Afonso Teixeira & Abel Rouboa, 2019. "Environmental Assessment of Municipal Solid Waste by Two-Stage Plasma Gasification," Energies, MDPI, vol. 12(1), pages 1-16, January.
    14. Peng Liu & Peijun Zheng & Ziyu Chen, 2019. "Deep Learning with Stacked Denoising Auto-Encoder for Short-Term Electric Load Forecasting," Energies, MDPI, vol. 12(12), pages 1-15, June.
    15. Lanjun Wan & Hongyang Li & Yiwei Chen & Changyun Li, 2020. "Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory," Energies, MDPI, vol. 13(5), pages 1-23, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tomasz Tietze & Piotr Szulc & Daniel Smykowski & Andrzej Sitka & Romuald Redzicki, 2021. "Application of Phase Change Material and Artificial Neural Networks for Smoothing of Heat Flux Fluctuations," Energies, MDPI, vol. 14(12), pages 1-17, June.
    2. Sanaz Sabzevari & Rasool Heydari & Maryam Mohiti & Mehdi Savaghebi & Jose Rodriguez, 2021. "Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters," Energies, MDPI, vol. 14(8), pages 1-12, April.

    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. Min Yi & Wei Xie & Li Mo, 2021. "Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO," Energies, MDPI, vol. 14(20), pages 1-17, October.
    2. Jerzy Chojnacki & Jan Najser & Krzysztof Rokosz & Vaclav Peer & Jan Kielar & Bogusława Berner, 2020. "Syngas Composition: Gasification of Wood Pellet with Water Steam through a Reactor with Continuous Biomass Feed System," Energies, MDPI, vol. 13(17), pages 1-14, August.
    3. Matheus Oliveira & Ana Ramos & Tamer M. Ismail & Eliseu Monteiro & Abel Rouboa, 2022. "A Review on Plasma Gasification of Solid Residues: Recent Advances and Developments," Energies, MDPI, vol. 15(4), pages 1-21, February.
    4. Jia Li & Xin Wang & Yue Wang & Wancheng Wang & Baibing Chen & Xiaolong Chen, 2020. "Effects of a Combination Impeller on the Flow Field and External Performance of an Aero-Fuel Centrifugal Pump," Energies, MDPI, vol. 13(4), pages 1-16, February.
    5. Ji, Huichao & Wang, Haixin & Yang, Junyou & Feng, Jiawei & Yang, Yongyue & Okoye, Martin Onyeka, 2021. "Optimal schedule of solid electric thermal storage considering consumer behavior characteristics in combined electricity and heat networks," Energy, Elsevier, vol. 234(C).
    6. Sanghyuk Yoo & Sangyong Jeon & Seunghwan Jeong & Heesoo Lee & Hosun Ryou & Taehyun Park & Yeonji Choi & Kyongjoo Oh, 2021. "Prediction of the Change Points in Stock Markets Using DAE-LSTM," Sustainability, MDPI, vol. 13(21), pages 1-15, October.
    7. Jose R. Cedeño González & Juan J. Flores & Claudio R. Fuerte-Esquivel & Boris A. Moreno-Alcaide, 2020. "Nearest Neighbors Time Series Forecaster Based on Phase Space Reconstruction for Short-Term Load Forecasting," Energies, MDPI, vol. 13(20), pages 1-24, October.
    8. Li, Chao & Sun, Yifan & Yi, Zijun & Zhang, Lijun & Zhang, Shu & Hu, Xun, 2022. "Co-pyrolysis of coke bottle wastes with cellulose, lignin and sawdust: Impacts of the mixed feedstock on char properties," Renewable Energy, Elsevier, vol. 181(C), pages 1126-1139.
    9. Xiaosheng Peng & Kai Cheng & Jianxun Lang & Zuowei Zhang & Tao Cai & Shanxu Duan, 2021. "Short-Term Wind Power Prediction for Wind Farm Clusters Based on SFFS Feature Selection and BLSTM Deep Learning," Energies, MDPI, vol. 14(7), pages 1-18, March.
    10. Wu, Guixuan & Seebold, Sören & Yazhenskikh, Elena & Tanner, Joanne & Hack, Klaus & Müller, Michael, 2019. "Slag mobility in entrained flow gasifiers optimized using a new reliable viscosity model of iron oxide-containing multicomponent melts," Applied Energy, Elsevier, vol. 236(C), pages 837-849.
    11. Valentina Segneri & Jean Henry Ferrasse & Antonio Trinca & Giorgio Vilardi, 2022. "An Overview of Waste Gasification and Syngas Upgrading Processes," Energies, MDPI, vol. 15(17), pages 1-7, September.
    12. Stanislaw Osowski & Robert Szmurlo & Krzysztof Siwek & Tomasz Ciechulski, 2022. "Neural Approaches to Short-Time Load Forecasting in Power Systems—A Comparative Study," Energies, MDPI, vol. 15(9), pages 1-21, April.
    13. Vasileios Ntouros & Ioannis Kousis & Dimitra Papadaki & Anna Laura Pisello & Margarita Niki Assimakopoulos, 2021. "Life Cycle Assessment on Different Synthetic Routes of ZIF-8 Nanomaterials," Energies, MDPI, vol. 14(16), pages 1-22, August.
    14. Huican Luo & Peijian Zhou & Lingfeng Shu & Jiegang Mou & Haisheng Zheng & Chenglong Jiang & Yantian Wang, 2022. "Energy Performance Curves Prediction of Centrifugal Pumps Based on Constrained PSO-SVR Model," Energies, MDPI, vol. 15(9), pages 1-19, May.
    15. Sungwoo Park & Jihoon Moon & Seungwon Jung & Seungmin Rho & Sung Wook Baik & Eenjun Hwang, 2020. "A Two-Stage Industrial Load Forecasting Scheme for Day-Ahead Combined Cooling, Heating and Power Scheduling," Energies, MDPI, vol. 13(2), pages 1-23, January.
    16. Liu, Yingzu & He, Yong & Wang, Zhihua & Xia, Jun & Wan, Kaidi & Whiddon, Ronald & Cen, Kefa, 2018. "Characteristics of alkali species release from a burning coal/biomass blend," Applied Energy, Elsevier, vol. 215(C), pages 523-531.
    17. Zihao Wang & Kun Li & Steve Q. Xia & Hongfu Liu, 2021. "Economic Recession Prediction Using Deep Neural Network," Papers 2107.10980, arXiv.org.
    18. Zhang, Yiming & Li, Jingxiang & Fei, Liangyu & Feng, Zhiyan & Gao, Jingzhou & Yan, Wenpeng & Zhao, Shengdun, 2023. "Operational performance estimation of vehicle electric coolant pump based on the ISSA-BP neural network," Energy, Elsevier, vol. 268(C).
    19. Ali Eliasu & Nana Sarfo Agyemang Derkyi & Samuel Gyamfi, 2022. "Techno-Economic Analysis of Municipal Solid Waste Gasification for Electricity Generation," International Journal of Energy Economics and Policy, Econjournals, vol. 12(1), pages 342-348.
    20. Mejdi Jeguirim & Lionel Limousy, 2019. "Biomass Chars: Elaboration, Characterization and Applications II," Energies, MDPI, vol. 12(3), pages 1-6, January.

    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:jeners:v:13:y:2020:i:19:p:5115-:d:422627. 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.