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Article

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

1
Department of Electronic Engineering, Hellenic Mediterranean University, Chania, 73100 Crete, Greece
2
Applied Chemistry Course, Department of Engineering, Kyushu Institute of Technology, Graduate School of Engineering, 1-1 Sensuicho, Tobata-ku, Kitakyushu 804-8550, Japan
3
Department of Applied Chemistry, Faculty of Engineering, Kyushu Institute of Technology, 1-1 Sensuicho, Tobata-ku, Kitakyushu 804-8550, Japan
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Department of Environmental Engineering, Gebze Technical University, 41400 Gebze, Turkey
5
Research Laboratory of Advanced Water and Wastewater Treatment Processes, Department of Applied Chemistry, Faculty of Chemistry, University of Tabriz, Tabriz 51666-16471, Iran
*
Author to whom correspondence should be addressed.
Academic Editor: M. Toufiq Reza
Energies 2021, 14(11), 3000; https://doi.org/10.3390/en14113000
Received: 19 April 2021 / Revised: 5 May 2021 / Accepted: 19 May 2021 / Published: 21 May 2021
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 NN1 (based on C, H, O content) exhibited HHV predicting performance with R2 = 0.974, another model, NN2, was also able to predict HHV with R2 = 0.936 using only C and H as input. Moreover, the inverse model of NN3 (based on H, O content, and HHV) could predict C content with an R2 of 0.939. View Full-Text
Keywords: sewage sludge; hydrothermal carbonization; hydrochar; artificial neural networks; machine learning; waste management; biomass sewage sludge; hydrothermal carbonization; hydrochar; artificial neural networks; machine learning; waste management; biomass
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MDPI and ACS Style

Kapetanakis, T.N.; Vardiambasis, I.O.; Nikolopoulos, C.D.; Konstantaras, A.I.; Trang, T.K.; Khuong, D.A.; Tsubota, T.; Keyikoglu, R.; Khataee, A.; Kalderis, D. Towards Engineered Hydrochars: Application of Artificial Neural Networks in the Hydrothermal Carbonization of Sewage Sludge. Energies 2021, 14, 3000. https://doi.org/10.3390/en14113000

AMA Style

Kapetanakis TN, Vardiambasis IO, Nikolopoulos CD, Konstantaras AI, Trang TK, Khuong DA, Tsubota T, Keyikoglu R, Khataee A, Kalderis D. Towards Engineered Hydrochars: Application of Artificial Neural Networks in the Hydrothermal Carbonization of Sewage Sludge. Energies. 2021; 14(11):3000. https://doi.org/10.3390/en14113000

Chicago/Turabian Style

Kapetanakis, Theodoros N., Ioannis O. Vardiambasis, Christos D. Nikolopoulos, Antonios I. Konstantaras, Trinh K. Trang, Duy A. Khuong, Toshiki Tsubota, Ramazan Keyikoglu, Alireza Khataee, and Dimitrios Kalderis. 2021. "Towards Engineered Hydrochars: Application of Artificial Neural Networks in the Hydrothermal Carbonization of Sewage Sludge" Energies 14, no. 11: 3000. https://doi.org/10.3390/en14113000

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