Data driven modelling of biomass pyrolysis
| dc.contributor.author | Sawant, Ruturaj Jayant | |
| dc.contributor.supervisor | Pareek, Vishnu | en_US |
| dc.contributor.supervisor | Gale, Julian | en_US |
| dc.contributor.supervisor | Rohl, Andrew | en_US |
| dc.date.accessioned | 2024-11-27T08:45:39Z | |
| dc.date.available | 2024-11-27T08:45:39Z | |
| dc.date.issued | 2024 | en_US |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/96428 | |
| dc.description.abstract |
A set of experiments to determine the composition of biomass samples were performed. Conversion profiles and rate of reaction profiles for biomass samples at different heating rates were studied. Existing kinetic methods were used to study the reaction kinetics of biomass pyrolysis. A novel predictive modelling approach was developed for biomass pyrolysis. Artificial neural networks were used to develop models capable of predicting conversion and rate of reaction profiles for unknown biomass samples. This approach has the potential for dynamic control of heterogenous feedstock and is applicable over wider heating rate range. | en_US |
| dc.publisher | Curtin University | en_US |
| dc.title | Data driven modelling of biomass pyrolysis | en_US |
| dc.type | Thesis | en_US |
| dcterms.educationLevel | PhD | en_US |
| curtin.department | WA School of Mines: Minerals, Energy and Chemical Engineering | en_US |
| curtin.accessStatus | Open access | en_US |
| curtin.faculty | Science and Engineering | en_US |
| curtin.contributor.orcid | Sawant, Ruturaj, Jayant [0000-0002-0013-6640] | en_US |