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

Theodoros N. Kapetanakis, Ioannis O. Vardiambasis, Christos D. Nikolopoulos, Antonios I. Konstantaras, Trinh Kieu Trang, Duy Anh Khuong, Toshiki Tsubota, Ramazan Keyikoglu, Alireza Khataee and Dimitrios Kalderis
Additional contact information
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
Dimitrios Kalderis: Department of Electronic Engineering, Hellenic Mediterranean University, Chania, 73100 Crete, Greece

Energies, 2021, vol. 14, issue 11, 1-15

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.

Keywords: sewage sludge; hydrothermal carbonization; hydrochar; artificial neural networks; machine learning; waste management; biomass (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/11/3000/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/11/3000/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:11:p:3000-:d:559866

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().