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

Ioannis O. Vardiambasis, Theodoros N. Kapetanakis, Christos D. Nikolopoulos, Trinh Kieu Trang, Toshiki Tsubota, Ramazan Keyikoglu, Alireza Khataee and Dimitrios Kalderis
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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
Dimitrios Kalderis: Department of Electronic Engineering, Hellenic Mediterranean University, Chania, 73100 Crete, Greece

Energies, 2020, vol. 13, issue 17, 1-20

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).

Keywords: hydrochar; hydrothermal carbonization; CiteSpace; scientometric analysis; artificial neural network; biofuels (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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