Application of probabilistic deep learning models to simulate thermal power plant processes
| dc.contributor.advisor | Laubscher, Ryno | |
| dc.contributor.author | Raidoo, Renita Anand | |
| dc.date.accessioned | 2023-04-20T11:13:20Z | |
| dc.date.available | 2023-04-20T11:13:20Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2023-04-18T09:33:03Z | |
| dc.description.abstract | Deep learning has gained traction in thermal engineering due to its applications to process simulations, the deeper insights it can provide and its abilities to circumvent the shortcomings of classic thermodynamic simulation approaches by capturing complex inter-dependencies. This works sets out to apply probabilistic deep learning to power plant operations using historic plant data. The first study presented, entails the development of a steady-state mixture density network (MDN) capable of predicting effective heat transfer coefficients (HTC) for the various heat exchanger components inside a utility scale boiler. Selected directly controllable input features, including the excess air ratio, steam temperatures, flow rates and pressures are used to predict the HTCs. In the second case study, an encoder-decoder mixturedensity network (MDN) is developed using recurrent neural networks (RNN) for the prediction of utility-scale air-cooled condenser (ACC) backpressure. The effects of ambient conditions and plant operating parameters, such as extraction flow rate, on ACC performance is investigated. In both case studies, hyperparameter searches are done to determine the best performing architectures for these models. Comparisons are drawn between the MDN model versus standard model architecture in both case studies. The HTC predictor model achieved 90% accuracy which equates to an average error of 4.89 W m2K across all heat exchangers. The resultant time-series ACC model achieved an average error of 3.14 kPa, which translate into a model accuracy of 82%. | |
| dc.identifier.apacitation | Raidoo, R. A. (2022). <i>Application of probabilistic deep learning models to simulate thermal power plant processes</i>. (). ,Faculty of Engineering and the Built Environment ,Department of Mechanical Engineering. Retrieved from http://hdl.handle.net/11427/37790 | en_ZA |
| dc.identifier.chicagocitation | Raidoo, Renita Anand. <i>"Application of probabilistic deep learning models to simulate thermal power plant processes."</i> ., ,Faculty of Engineering and the Built Environment ,Department of Mechanical Engineering, 2022. http://hdl.handle.net/11427/37790 | en_ZA |
| dc.identifier.citation | Raidoo, R.A. 2022. Application of probabilistic deep learning models to simulate thermal power plant processes. . ,Faculty of Engineering and the Built Environment ,Department of Mechanical Engineering. http://hdl.handle.net/11427/37790 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Raidoo, Renita Anand AB - Deep learning has gained traction in thermal engineering due to its applications to process simulations, the deeper insights it can provide and its abilities to circumvent the shortcomings of classic thermodynamic simulation approaches by capturing complex inter-dependencies. This works sets out to apply probabilistic deep learning to power plant operations using historic plant data. The first study presented, entails the development of a steady-state mixture density network (MDN) capable of predicting effective heat transfer coefficients (HTC) for the various heat exchanger components inside a utility scale boiler. Selected directly controllable input features, including the excess air ratio, steam temperatures, flow rates and pressures are used to predict the HTCs. In the second case study, an encoder-decoder mixturedensity network (MDN) is developed using recurrent neural networks (RNN) for the prediction of utility-scale air-cooled condenser (ACC) backpressure. The effects of ambient conditions and plant operating parameters, such as extraction flow rate, on ACC performance is investigated. In both case studies, hyperparameter searches are done to determine the best performing architectures for these models. Comparisons are drawn between the MDN model versus standard model architecture in both case studies. The HTC predictor model achieved 90% accuracy which equates to an average error of 4.89 W m2K across all heat exchangers. The resultant time-series ACC model achieved an average error of 3.14 kPa, which translate into a model accuracy of 82%. DA - 2022_ DB - OpenUCT DP - University of Cape Town KW - Air-cooled condensers KW - Natural convection boilers Time-series prediction KW - Deep learning KW - Mixture density networks KW - Recurrent neural networks LK - https://open.uct.ac.za PY - 2022 T1 - Application of probabilistic deep learning models to simulate thermal power plant processes TI - Application of probabilistic deep learning models to simulate thermal power plant processes UR - http://hdl.handle.net/11427/37790 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/37790 | |
| dc.identifier.vancouvercitation | Raidoo RA. Application of probabilistic deep learning models to simulate thermal power plant processes. []. ,Faculty of Engineering and the Built Environment ,Department of Mechanical Engineering, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/37790 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Mechanical Engineering | |
| dc.publisher.faculty | Faculty of Engineering and the Built Environment | |
| dc.subject | Air-cooled condensers | |
| dc.subject | Natural convection boilers Time-series prediction | |
| dc.subject | Deep learning | |
| dc.subject | Mixture density networks | |
| dc.subject | Recurrent neural networks | |
| dc.title | Application of probabilistic deep learning models to simulate thermal power plant processes | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |