Development of a process modelling methodology and condition monitoring platform for air-cooled condensers

Master Thesis


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Air-cooled condensers (ACCs) are a type of dry-cooling technology that has seen an increase in implementation globally, particularly in the power generation industry, due to its low water consumption. Unfortunately, ACC performance is susceptible to changing ambient conditions, such as dry bulb temperatures, wind direction, and wind speeds. This can result in performance reduction under adverse ambient conditions, which leads to increased turbine back pressures and in turn, a decrease in generated electricity. Therefore, this creates a demand to monitor and predict ACC performance under changing ambient conditions. This study focuses on modelling a utility-scale ACC system at steady-state conditions applying a 1-D network modelling approach and using a component-level discretization approach. This approach allowed for each cell to be modelled individually, accounting for steam duct supply behaviour, and for off-design conditions to be investigated. The developed methodology was based on existing empirical correlations for condenser cells and adapted to model double-row dephlegmators. A utility-scale 64-cell ACC system based in South Africa was selected for this study. The thermofluid network model was validated using site data with agreement in results within 1%; however, due to a lack of site data, the model was not validated for off-design conditions. The thermofluid network model was also compared to the existing lumped approach and differences were observed due to the steam ducting distribution. The effect of increasing ambient air temperature from 25 35  −  C C was investigated, with a heat rejection rate decrease of 10.9 MW and a backpressure increase of 7.79 kPa across the temperature range. Condensers' heat rejection rate decreased with higher air temperatures, while dephlegmators' heat rejection rate increased due to the increased outlet vapour pressure and flow rates from condensers. Off-design conditions were simulated, including hot air recirculation and wind effects. For wind effects, the developed model predicted a decrease in heat rejection rate of 1.7 MW for higher wind speeds, while the lumped approach predicted an increase of 4.9 . MW For practicality, a data-driven surrogate model was developed through machine learning techniques using data generated by the thermofluid network model. The surrogate model predicted systemlevel ACC performance indicators such as turbine backpressure and total heat rejection rate. Multilayer perceptron neural networks were developed in the form of a regression network and binary classifier network. For the test sets, the regression network had an average relative error of 0.3%, while the binary classifier had a 99.85% classification accuracy. The surrogate model was validated to site data over a 3 week operating period, with 93.5% of backpressure predictions within 6% of site data backpressures. The surrogate model was deployed through a web-application prototype which included a forecasting tool to predict ACC performance based on a weather forecast.