From Prediction to Operational Optimization: A Critical Review of Data-Driven Models and Digital Twins for Air-Cooled Steam Condensers
DOI:
https://doi.org/10.71229/3vc4m911Keywords:
air-cooled steam condenser, back pressure prediction, fan control, operational optimizationAbstract
Accuracy of prediction of backpressure does not automatically mean efficient consumption of energy by air-cooled steam condensers. It is required to close this gap in situations where the performance of the equipment depends on the heat rejection according to weather conditions and electricity consumption by fans. The critical review analyzes 23 papers dealing with the data-driven prediction, hybrid digital twins, and control of air-cooled steam condensers. The comparison was made with respect to the following unified criteria: the source of the data, the objective of validation, the type of operating decision, the reference conditions, and the validation of the benefits. The findings of synthesis comprise the following three conclusions. First of all, accuracy and consistency of the predictions and agreement with the parent simulation reflect the performance of the method, but not its operational efficiency. Second, the implementation of digital twins into an industry is a breakthrough in research, although the estimates of the fuel savings can still be made by consumption models and not by independent measurements of savings. Finally, adaptive and spatially distributed control of fans resolves a number of operational problems, although the validation of such a control may be conditioned by particular weather and disturbance conditions and not guarantee the stability of operation in various seasonal or disturbance situations. This review develops the framework for distinguishing between the predictions, the improvements in control via simulation, industrial implementation, and operations to assess the progress in research and deployment.
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