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Towards Engineered Hydrochars: Application of Artificial Neural Networks in the Hydrothermal Carbonization of Sewage Sludge

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  • Theodoros N. Kapetanakis

    (Department of Electronic Engineering, Hellenic Mediterranean University, Chania, 73100 Crete, Greece)

  • Ioannis O. Vardiambasis

    (Department of Electronic Engineering, Hellenic Mediterranean University, Chania, 73100 Crete, Greece)

  • Christos D. Nikolopoulos

    (Department of Electronic Engineering, Hellenic Mediterranean University, Chania, 73100 Crete, Greece)

  • Antonios I. Konstantaras

    (Department of Electronic Engineering, Hellenic Mediterranean University, Chania, 73100 Crete, Greece)

  • Trinh Kieu Trang

    (Applied Chemistry Course, Department of Engineering, Kyushu Institute of Technology, Graduate School of Engineering, 1-1 Sensuicho, Tobata-ku, Kitakyushu 804-8550, Japan)

  • Duy Anh Khuong

    (Applied Chemistry Course, Department of Engineering, Kyushu Institute of Technology, Graduate School of Engineering, 1-1 Sensuicho, Tobata-ku, Kitakyushu 804-8550, Japan)

  • Toshiki Tsubota

    (Department of Applied Chemistry, Faculty of Engineering, Kyushu Institute of Technology, 1-1 Sensuicho, Tobata-ku, Kitakyushu 804-8550, Japan)

  • Ramazan Keyikoglu

    (Department of Environmental Engineering, Gebze Technical University, 41400 Gebze, Turkey)

  • Alireza Khataee

    (Department of Environmental Engineering, Gebze Technical University, 41400 Gebze, Turkey
    Research Laboratory of Advanced Water and Wastewater Treatment Processes, Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz 51666-16471, Iran)

  • Dimitrios Kalderis

    (Department of Electronic Engineering, Hellenic Mediterranean University, Chania, 73100 Crete, Greece)

Abstract

Sewage sludge hydrochars (SSHs), which are produced by hydrothermal carbonization (HTC), offer a high calorific value to be applied as a biofuel. However, HTC is a complex processand the properties of the resulting product depend heavily on the process conditions and feedstock composition. In this work, we have applied artificial neural networks (ANNs) to contribute to the production of tailored SSHs for a specific application and with optimum properties. We collected data from the published literature covering the years 2014–2021, which was then fed into different ANN models where the input data (HTC temperature, process time, and the elemental content of hydrochars) were used to predict output parameters (higher heating value, (HHV) and solid yield (%)). The proposed ANN models were successful in accurately predicting both HHV and contents of C and H. While the model NN 1 (based on C, H, O content) exhibited HHV predicting performance with R 2 = 0.974, another model, NN 2 , was also able to predict HHV with R 2 = 0.936 using only C and H as input. Moreover, the inverse model of NN 3 (based on H, O content, and HHV) could predict C content with an R 2 of 0.939.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3000-:d:559866
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    References listed on IDEAS

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    1. 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.
    2. Wang, Tengfei & Zhai, Yunbo & Zhu, Yun & Li, Caiting & Zeng, Guangming, 2018. "A review of the hydrothermal carbonization of biomass waste for hydrochar formation: Process conditions, fundamentals, and physicochemical properties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 223-247.
    3. Lu, Xiaoluan & Ma, Xiaoqian & Chen, Xinfei, 2021. "Co-hydrothermal carbonization of sewage sludge and lignocellulosic biomass: Fuel properties and heavy metal transformation behaviour of hydrochars," Energy, Elsevier, vol. 221(C).
    4. Akdeniz, Fikret & Biçil, Metin & Karadede, Yusuf & Özbek, Füreya Elif & Özdemir, Gültekin, 2018. "Application of real valued genetic algorithm on prediction of higher heating values of various lignocellulosic materials using lignin and extractive contents," Energy, Elsevier, vol. 160(C), pages 1047-1054.
    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).
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    1. Salah Jellali & Antonis A. Zorpas & Sulaiman Alhashmi & Mejdi Jeguirim, 2022. "Recent Advances in Hydrothermal Carbonization of Sewage Sludge," Energies, MDPI, vol. 15(18), pages 1-6, September.

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