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Hydrochars as Emerging Biofuels: Recent Advances and Application of Artificial Neural Networks for the Prediction of Heating Values

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Listed:
  • Ioannis O. Vardiambasis

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

  • Theodoros N. Kapetanakis

    (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)

  • Trinh Kieu Trang

    (Applied Chemistry Course, Department of Engineering, Graduate School of Engineering, Kyushu Institute of Technology, 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

In this study, the growing scientific field of alternative biofuels was examined, with respect to hydrochars produced from renewable biomasses. Hydrochars are the solid products of hydrothermal carbonization (HTC) and their properties depend on the initial biomass and the temperature and duration of treatment. The basic (Scopus) and advanced (Citespace) analysis of literature showed that this is a dynamic research area, with several sub-fields of intense activity. The focus of researchers on sewage sludge and food waste as hydrochar precursors was highlighted and reviewed. It was established that hydrochars have improved behavior as fuels compared to these feedstocks. Food waste can be particularly useful in co-hydrothermal carbonization with ash-rich materials. In the case of sewage sludge, simultaneous P recovery from the HTC wastewater may add more value to the process. For both feedstocks, results from large-scale HTC are practically non-existent. Following the review, related data from the years 2014–2020 were retrieved and fitted into four different artificial neural networks (ANNs). Based on the elemental content, HTC temperature and time (as inputs), the higher heating values (HHVs) and yields (as outputs) could be successfully predicted, regardless of original biomass used for hydrochar production. ANN 3 (based on C, O, H content, and HTC temperature) showed the optimum HHV predicting performance (R 2 0.917, root mean square error 1.124), however, hydrochars’ HHVs could also be satisfactorily predicted by the C content alone (ANN 1 , R 2 0.897, root mean square error 1.289).

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4572-:d:408499
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    References listed on IDEAS

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    Cited by:

    1. Jiao Zhang & Qian Wang & Yiping Xia & Katsunori Furuya, 2022. "Knowledge Map of Spatial Planning and Sustainable Development: A Visual Analysis Using CiteSpace," Land, MDPI, vol. 11(3), pages 1-24, February.
    2. Ivan Brandić & Lato Pezo & Nikola Bilandžija & Anamarija Peter & Jona Šurić & Neven Voća, 2022. "Artificial Neural Network as a Tool for Estimation of the Higher Heating Value of Miscanthus Based on Ultimate Analysis," Mathematics, MDPI, vol. 10(20), pages 1-12, October.
    3. 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.

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