A data-driven surrogate model is proposed for a 64-cell air-cooled condenser system at a power plant.The surro-gate model was developed using thermofluid simulation data from an existing detailed 1-D thermofluid netwo...A data-driven surrogate model is proposed for a 64-cell air-cooled condenser system at a power plant.The surro-gate model was developed using thermofluid simulation data from an existing detailed 1-D thermofluid network simulation model.The thermofluid network model requires a minimum of 20 min to solve for a single set of in-puts.With operating conditions fluctuating constantly,performance predictions are required in shorter intervals,leading to the development of a surrogate model.Simulation data covered three operating scopes across a range of ambient air temperatures,inlet steam mass flow rates,number of operating cells,and wind speeds.The surrogate model uses multi-layer perceptron deep neural networks in the form of a binary classifier network to avoid ex-trapolation from the simulation dataset,and a regression network to provide performance predictions,including the steady-state backpressure,heat rejections,air mass flowrates,and fan motor powers on a system level.The integrated surrogate model had an average relative error of 0.3%on the test set,while the binary classifier had a 99.85%classification accuracy,indicating sufficient generalisation.The surrogate model was validated using site-data covering 10 days of operation for the case-study ACC system,providing backpressure predictions for all 1967 input samples within a few seconds of compute time.Approximately 93.5%of backpressure predictions were within±6%of the recorded backpressures,indicating sufficient accuracy of the surrogate model with a significant decrease in compute time.展开更多
基金The authors would like to thank the National Research Foundation(NRF)[Grant Number 122957]the University of Cape Town,and the Eskom EPPEI program for funding this research.
文摘A data-driven surrogate model is proposed for a 64-cell air-cooled condenser system at a power plant.The surro-gate model was developed using thermofluid simulation data from an existing detailed 1-D thermofluid network simulation model.The thermofluid network model requires a minimum of 20 min to solve for a single set of in-puts.With operating conditions fluctuating constantly,performance predictions are required in shorter intervals,leading to the development of a surrogate model.Simulation data covered three operating scopes across a range of ambient air temperatures,inlet steam mass flow rates,number of operating cells,and wind speeds.The surrogate model uses multi-layer perceptron deep neural networks in the form of a binary classifier network to avoid ex-trapolation from the simulation dataset,and a regression network to provide performance predictions,including the steady-state backpressure,heat rejections,air mass flowrates,and fan motor powers on a system level.The integrated surrogate model had an average relative error of 0.3%on the test set,while the binary classifier had a 99.85%classification accuracy,indicating sufficient generalisation.The surrogate model was validated using site-data covering 10 days of operation for the case-study ACC system,providing backpressure predictions for all 1967 input samples within a few seconds of compute time.Approximately 93.5%of backpressure predictions were within±6%of the recorded backpressures,indicating sufficient accuracy of the surrogate model with a significant decrease in compute time.
