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Towards Engineered Hydrochars: Application of Artificial Neural Networks in the Hydrothermal Carbonization of Sewage Sludge
| Content Provider | MDPI |
|---|---|
| Author | Kapetanakis, Theodoros Vardiambasis, Ioannis Nikolopoulos, Christos Konstantaras, Antonios Trang, Trinh Khuong, Duy Tsubota, Toshiki Keyikoglu, Ramazan Khataee, Alireza Kalderis, Dimitrios |
| Copyright Year | 2021 |
| Description | 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. |
| Starting Page | 3000 |
| e-ISSN | 19961073 |
| DOI | 10.3390/en14113000 |
| Journal | Energies |
| Issue Number | 11 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-05-21 |
| Access Restriction | Open |
| Subject Keyword | Energies Paper and Wood Sewage Sludge Hydrothermal Carbonization Hydrochar Artificial Neural Networks Machine Learning Waste Management Biomass |
| Content Type | Text |
| Resource Type | Article |
