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,
Dimitrios Kalderis
Affiliations
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
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.
