Integrated network-based thermofluid model of a once-through boiler at full- and part-load

dc.contributor.advisorRousseau, Pieter
dc.contributor.advisorLaubscher, Ryno
dc.contributor.authorFeng, Kai-Yu
dc.date.accessioned2023-03-07T07:54:34Z
dc.date.available2023-03-07T07:54:34Z
dc.date.issued2022
dc.date.updated2023-02-20T12:44:41Z
dc.description.abstractThe increased penetration of renewable energy sources in South Africa requires greater operational flexibility of existing coal-fired power plants (CFPPs). Operational flexibility implies that power plants need to operate intermittently or at low load for extended periods. Existing CFPPs are designed to operate at a steady baseload. Operating at these off-design conditions increase the risk of damaging the boiler's thick-walled components, leading to reduced life expectancy and/or failure. Given that extensive experimental investigations on operating plants are impractical due to the risks, costs and complexity involved, there is a need for an integrated boiler model that has the necessary detail to study off-design and low load operations of coal-fired power plants. For that reason, a 1D quasi-steady-state thermofluid network model of a tower type once-through boiler was developed using the Flownex simulation environment. The furnace model assumes complete, infinitely fast combustion with a specified value of unburned carbon and excess air. The radiation heat transfer in the furnace is modelled using the projected area approach (Gurvich/Blokh model) together with a high ash loading model. The gas-to-steam tube bank heat exchangers are discretised pass-by-pass, and the complex heat transfer phenomena in the heat exchangers and membrane water walls are represented by equivalent thermal networks. The model results for the as-designed cases show that the ash deposition resistances suggested in the literature are not applicable for the case study boiler. For that reason, the proposed model calibration methodology was therefore applied at full-load operation (100%), and the results show good accuracy compared to real-plant data. The average error of the predicted heat exchanger heat loads is 2.0% and the maximum error is 5.2%. The calibrated model was then validated by applying it to two part-load operational states in dry mode operation, as well as a wet mode (low-load) operational state. For the 81% load case, the average error in the heat exchanger duty is 2.0% and the maximum is 4.9%, while for the 63% load case, the average error is 3.6% and the maximum is 9.7%. For the low-load wet mode case at 35% load, the average error is 10.4% and the maximum is 18.1%. The cumulative heat transfer results for all the load cases correspond closely to the measured data, with the maximum error being 0.83% for the low load case. These results suggest that the calibrated model can capture the heat distribution in the boiler with sufficient accuracy to allow suitable ash deposition resistances to be obtained from the calibration process. Furthermore, the metal temperatures predicted by the model are also shown to be sufficiently accurate, which means that it can be used to identify the heat exchanger tube passes or membrane water walls that may be at risk during operation.
dc.identifier.apacitationFeng, K. (2022). <i>Integrated network-based thermofluid model of a once-through boiler at full- and part-load</i>. (). ,Faculty of Engineering and the Built Environment ,Department of Mechanical Engineering. Retrieved from http://hdl.handle.net/11427/37288en_ZA
dc.identifier.chicagocitationFeng, Kai-Yu. <i>"Integrated network-based thermofluid model of a once-through boiler at full- and part-load."</i> ., ,Faculty of Engineering and the Built Environment ,Department of Mechanical Engineering, 2022. http://hdl.handle.net/11427/37288en_ZA
dc.identifier.citationFeng, K. 2022. Integrated network-based thermofluid model of a once-through boiler at full- and part-load. . ,Faculty of Engineering and the Built Environment ,Department of Mechanical Engineering. http://hdl.handle.net/11427/37288en_ZA
dc.identifier.ris TY - Master Thesis AU - Feng, Kai-Yu AB - The increased penetration of renewable energy sources in South Africa requires greater operational flexibility of existing coal-fired power plants (CFPPs). Operational flexibility implies that power plants need to operate intermittently or at low load for extended periods. Existing CFPPs are designed to operate at a steady baseload. Operating at these off-design conditions increase the risk of damaging the boiler's thick-walled components, leading to reduced life expectancy and/or failure. Given that extensive experimental investigations on operating plants are impractical due to the risks, costs and complexity involved, there is a need for an integrated boiler model that has the necessary detail to study off-design and low load operations of coal-fired power plants. For that reason, a 1D quasi-steady-state thermofluid network model of a tower type once-through boiler was developed using the Flownex simulation environment. The furnace model assumes complete, infinitely fast combustion with a specified value of unburned carbon and excess air. The radiation heat transfer in the furnace is modelled using the projected area approach (Gurvich/Blokh model) together with a high ash loading model. The gas-to-steam tube bank heat exchangers are discretised pass-by-pass, and the complex heat transfer phenomena in the heat exchangers and membrane water walls are represented by equivalent thermal networks. The model results for the as-designed cases show that the ash deposition resistances suggested in the literature are not applicable for the case study boiler. For that reason, the proposed model calibration methodology was therefore applied at full-load operation (100%), and the results show good accuracy compared to real-plant data. The average error of the predicted heat exchanger heat loads is 2.0% and the maximum error is 5.2%. The calibrated model was then validated by applying it to two part-load operational states in dry mode operation, as well as a wet mode (low-load) operational state. For the 81% load case, the average error in the heat exchanger duty is 2.0% and the maximum is 4.9%, while for the 63% load case, the average error is 3.6% and the maximum is 9.7%. For the low-load wet mode case at 35% load, the average error is 10.4% and the maximum is 18.1%. The cumulative heat transfer results for all the load cases correspond closely to the measured data, with the maximum error being 0.83% for the low load case. These results suggest that the calibrated model can capture the heat distribution in the boiler with sufficient accuracy to allow suitable ash deposition resistances to be obtained from the calibration process. Furthermore, the metal temperatures predicted by the model are also shown to be sufficiently accurate, which means that it can be used to identify the heat exchanger tube passes or membrane water walls that may be at risk during operation. DA - 2022_ DB - OpenUCT DP - University of Cape Town KW - boiler modelling KW - thermofluid network modelling LK - https://open.uct.ac.za PY - 2022 T1 - Integrated network-based thermofluid model of a once-through boiler at full- and part-load TI - Integrated network-based thermofluid model of a once-through boiler at full- and part-load UR - http://hdl.handle.net/11427/37288 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/37288
dc.identifier.vancouvercitationFeng K. Integrated network-based thermofluid model of a once-through boiler at full- and part-load. []. ,Faculty of Engineering and the Built Environment ,Department of Mechanical Engineering, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/37288en_ZA
dc.language.rfc3066eng
dc.publisher.departmentDepartment of Mechanical Engineering
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.subjectboiler modelling
dc.subjectthermofluid network modelling
dc.titleIntegrated network-based thermofluid model of a once-through boiler at full- and part-load
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationlevelMSc
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