IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v245y2022ics0360544222001980.html
   My bibliography  Save this article

Random forest-based modeling for insights on phosphorus content in hydrochar produced from hydrothermal carbonization of sewage sludge

Author

Listed:
  • Djandja, Oraléou Sangué
  • Salami, Adekunlé Akim
  • Wang, Zhi-Cong
  • Duo, Jia
  • Yin, Lin-Xin
  • Duan, Pei-Gao

Abstract

The hydrochar produced from hydrothermal carbonization(HTC) of sewage sludge (SS) usually has a high phosphorous (P) content, and that would result in fouling and energy efficiency reduction. Therefore, it is important to monitor the P content during the hydrochar production process. This work suggests a data-driven Random Forest-based model to predict the total P content in the hydrochar (TP-hc) from the HTC of SS. Various configurations of inputs features were examined, including the data of proximate analysis, ultimate analysis, ultimate and proximate analyses, and for each configuration, either if the total P in the SS (TP-ss) was known or not. Overall, the models including TP-ss as input have accurately predicted the TP-hc with an R2 located in [92–95%]. Features’ importance approach and partial dependence analysis pointed out that the TP-ss, ash content, reaction temperature (T), reaction time (t), and initial pH of feedwater exhibit positive effect on the TP-hc. In contrast, contribution of the volatile matter (VM) of SS was mostly negative. Dry matter loading exhibits no obvious monotonicity with TP-hc. This work could guide the production of SS-hydrochar with the desired P content, and thus avoid time and resources consuming for many trials.

Suggested Citation

  • Djandja, Oraléou Sangué & Salami, Adekunlé Akim & Wang, Zhi-Cong & Duo, Jia & Yin, Lin-Xin & Duan, Pei-Gao, 2022. "Random forest-based modeling for insights on phosphorus content in hydrochar produced from hydrothermal carbonization of sewage sludge," Energy, Elsevier, vol. 245(C).
    • Handle: RePEc:eee:energy:v:245:y:2022:i:c:s0360544222001980
    DOI: 10.1016/j.energy.2022.123295
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222001980
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.123295?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wen, Shaoting & Buyukada, Musa & Evrendilek, Fatih & Liu, Jingyong, 2020. "Uncertainty and sensitivity analyses of co-combustion/pyrolysis of textile dyeing sludge and incense sticks: Regression and machine-learning models," Renewable Energy, Elsevier, vol. 151(C), pages 463-474.
    2. Aragón-Briceño, C.I. & Grasham, O. & Ross, A.B. & Dupont, V. & Camargo-Valero, M.A., 2020. "Hydrothermal carbonization of sewage digestate at wastewater treatment works: Influence of solid loading on characteristics of hydrochar, process water and plant energetics," Renewable Energy, Elsevier, vol. 157(C), pages 959-973.
    3. Wang, Liping & Chang, Yuzhi & Li, Aimin, 2019. "Hydrothermal carbonization for energy-efficient processing of sewage sludge: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 423-440.
    4. Antoniadis, Anestis & Lambert-Lacroix, Sophie & Poggi, Jean-Michel, 2021. "Random forests for global sensitivity analysis: A selective review," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    5. Li, Jie & Pan, Lanjia & Suvarna, Manu & Tong, Yen Wah & Wang, Xiaonan, 2020. "Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning," Applied Energy, Elsevier, vol. 269(C).
    6. Li, Liang & Flora, Joseph R.V. & Berge, Nicole D., 2020. "Predictions of energy recovery from hydrochar generated from the hydrothermal carbonization of organic wastes," Renewable Energy, Elsevier, vol. 145(C), pages 1883-1889.
    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. David Puga-Gil & Gonzalo Astray & Enrique Barreiro & Juan F. Gálvez & Juan Carlos Mejuto, 2022. "Global Solar Irradiation Modelling and Prediction Using Machine Learning Models for Their Potential Use in Renewable Energy Applications," Mathematics, MDPI, vol. 10(24), pages 1-21, December.
    2. Wang, Ruikun & Lin, Zhaohua & Meng, Shu & Liu, Senyang & Zhao, Zhenghui & Wang, Chunbo & Yin, Qianqian, 2022. "Effect of lignocellulosic components on the hydrothermal carbonization reaction pathway and product properties of protein," Energy, Elsevier, vol. 259(C).
    3. Cheng, Xiong & Lv, Xin & Li, Xianshan & Zhong, Hao & Feng, Jia, 2023. "Market power evaluation in the electricity market based on the weighted maintenance object," Energy, Elsevier, vol. 284(C).
    4. Joanna Mikusińska & Monika Kuźnia & Klaudia Czerwińska & Małgorzata Wilk, 2023. "Hydrothermal Carbonization of Digestate Produced in the Biogas Production Process," Energies, MDPI, vol. 16(14), pages 1-18, July.
    5. Djandja, Oraléou Sangué & Kang, Shimin & Huang, Zizhi & Li, Junqiao & Feng, Jiaqi & Tan, Zaiming & Salami, Adekunlé Akim & Lougou, Bachirou Guene, 2023. "Machine learning prediction of fuel properties of hydrochar from co-hydrothermal carbonization of sewage sludge and lignocellulosic biomass," Energy, Elsevier, vol. 271(C).
    6. Leng, Lijian & Li, Tanghao & Zhan, Hao & Rizwan, Muhammad & Zhang, Weijin & Peng, Haoyi & Yang, Zequn & Li, Hailong, 2023. "Machine learning-aided prediction of nitrogen heterocycles in bio-oil from the pyrolysis of biomass," Energy, Elsevier, vol. 278(PB).

    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. Aragón-Briceño, C.I. & Ross, A.B. & Camargo-Valero, M.A., 2021. "Mass and energy integration study of hydrothermal carbonization with anaerobic digestion of sewage sludge," Renewable Energy, Elsevier, vol. 167(C), pages 473-483.
    2. Śliz, Maciej & Wilk, Małgorzata, 2020. "A comprehensive investigation of hydrothermal carbonization: Energy potential of hydrochar derived from Virginia mallow," Renewable Energy, Elsevier, vol. 156(C), pages 942-950.
    3. Aragon-Briceño, Christian & Pożarlik, Artur & Bramer, Eddy & Brem, Gerrit & Wang, Shule & Wen, Yuming & Yang, Weihong & Pawlak-Kruczek, Halina & Niedźwiecki, Łukasz & Urbanowska, Agnieszka & Mościcki,, 2022. "Integration of hydrothermal carbonization treatment for water and energy recovery from organic fraction of municipal solid waste digestate," Renewable Energy, Elsevier, vol. 184(C), pages 577-591.
    4. Djandja, Oraléou Sangué & Duan, Pei-Gao & Yin, Lin-Xin & Wang, Zhi-Cong & Duo, Jia, 2021. "A novel machine learning-based approach for prediction of nitrogen content in hydrochar from hydrothermal carbonization of sewage sludge," Energy, Elsevier, vol. 232(C).
    5. Ioannis O. Vardiambasis & Theodoros N. Kapetanakis & Christos D. Nikolopoulos & Trinh Kieu Trang & Toshiki Tsubota & Ramazan Keyikoglu & Alireza Khataee & Dimitrios Kalderis, 2020. "Hydrochars as Emerging Biofuels: Recent Advances and Application of Artificial Neural Networks for the Prediction of Heating Values," Energies, MDPI, vol. 13(17), pages 1-20, September.
    6. Czerwińska, Klaudia & Śliz, Maciej & Wilk, Małgorzata, 2022. "Hydrothermal carbonization process: Fundamentals, main parameter characteristics and possible applications including an effective method of SARS-CoV-2 mitigation in sewage sludge. A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    7. Wilk, Małgorzata & Śliz, Maciej & Lubieniecki, Bogusław, 2021. "Hydrothermal co-carbonization of sewage sludge and fuel additives: Combustion performance of hydrochar," Renewable Energy, Elsevier, vol. 178(C), pages 1046-1056.
    8. Theodoros N. Kapetanakis & Ioannis O. Vardiambasis & Christos D. Nikolopoulos & Antonios I. Konstantaras & Trinh Kieu Trang & Duy Anh Khuong & Toshiki Tsubota & Ramazan Keyikoglu & Alireza Khataee & D, 2021. "Towards Engineered Hydrochars: Application of Artificial Neural Networks in the Hydrothermal Carbonization of Sewage Sludge," Energies, MDPI, vol. 14(11), pages 1-15, May.
    9. Li, Jie & Suvarna, Manu & Pan, Lanjia & Zhao, Yingru & Wang, Xiaonan, 2021. "A hybrid data-driven and mechanistic modelling approach for hydrothermal gasification," Applied Energy, Elsevier, vol. 304(C).
    10. Wądrzyk, Mariusz & Korzeniowski, Łukasz & Plata, Marek & Janus, Rafał & Lewandowski, Marek & Michalik, Marek & Magdziarz, Aneta, 2023. "Pyrolysis of hydrochars obtained from blackcurrant pomace in single and binary solvent systems," Renewable Energy, Elsevier, vol. 214(C), pages 383-394.
    11. Yamashiro, Hirochika & Nonaka, Hirofumi, 2021. "Estimation of processing time using machine learning and real factory data for optimization of parallel machine scheduling problem," Operations Research Perspectives, Elsevier, vol. 8(C).
    12. Pablo J. Arauzo & María Atienza-Martínez & Javier Ábrego & Maciej P. Olszewski & Zebin Cao & Andrea Kruse, 2020. "Combustion Characteristics of Hydrochar and Pyrochar Derived from Digested Sewage Sludge," Energies, MDPI, vol. 13(16), pages 1-15, August.
    13. Kim, Jun Young & Kim, Dongjae & Li, Zezhong John & Dariva, Claudio & Cao, Yankai & Ellis, Naoko, 2023. "Predicting and optimizing syngas production from fluidized bed biomass gasifiers: A machine learning approach," Energy, Elsevier, vol. 263(PC).
    14. Mehdi Dasineh & Amir Ghaderi & Mohammad Bagherzadeh & Mohammad Ahmadi & Alban Kuriqi, 2021. "Prediction of Hydraulic Jumps on a Triangular Bed Roughness Using Numerical Modeling and Soft Computing Methods," Mathematics, MDPI, vol. 9(23), pages 1-24, December.
    15. Song, Weiming & Zhou, Jianan & Li, Yujie & Yang, Jian & Cheng, Rijin, 2021. "New technology for producing high-quality combustible gas by high-temperature reaction of dust-removal coke powder in mixed atmosphere," Energy, Elsevier, vol. 233(C).
    16. Torii, André Jacomel & Novotny, Antonio André, 2021. "A priori error estimates for local reliability-based sensitivity analysis with Monte Carlo Simulation," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    17. González-Arias, Judith & González-Castaño, Miriam & Sánchez, Marta Elena & Cara-Jiménez, Jorge & Arellano-García, Harvey, 2022. "Valorization of biomass-derived CO2 residues with Cu-MnOx catalysts for RWGS reaction," Renewable Energy, Elsevier, vol. 182(C), pages 443-451.
    18. Aragón-Briceño, C.I. & Pozarlik, A.K. & Bramer, E.A. & Niedzwiecki, Lukasz & Pawlak-Kruczek, H. & Brem, G., 2021. "Hydrothermal carbonization of wet biomass from nitrogen and phosphorus approach: A review," Renewable Energy, Elsevier, vol. 171(C), pages 401-415.
    19. Vishwajeet & Halina Pawlak-Kruczek & Marcin Baranowski & Michał Czerep & Artur Chorążyczewski & Krystian Krochmalny & Michał Ostrycharczyk & Paweł Ziółkowski & Paweł Madejski & Tadeusz Mączka & Amit A, 2022. "Entrained Flow Plasma Gasification of Sewage Sludge–Proof-of-Concept and Fate of Inorganics," Energies, MDPI, vol. 15(5), pages 1-14, March.
    20. Siti Zaharah Roslan & Siti Fairuz Zainudin & Alijah Mohd Aris & Khor Bee Chin & Mohibah Musa & Ahmad Rafizan Mohamad Daud & Syed Shatir A. Syed Hassan, 2023. "Hydrothermal Carbonization of Sewage Sludge into Solid Biofuel: Influences of Process Conditions on the Energetic Properties of Hydrochar," Energies, MDPI, vol. 16(5), pages 1-16, March.

    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:eee:energy:v:245:y:2022:i:c:s0360544222001980. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.