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Gas turbines are used to generate electricity in thermal power plants and are also used as a backup for renewable energy. Recently, following various environmental regulations, interest in technology for predicting the state of gas turbines for power generation and controlling the emission of air pollutants such as nitrogen oxides is increasing. Indices for predicting the state of a gas turbine include the turbine inlet temperature and exhaust temperature. Predicting the exhaust temperature according to the fluctuating operation method makes it possible to know the efficiency and condition of the gas turbine and enables more efficient operation. In this study, deep learning is used to predict the exhaust temperature of a gas turbine. The gas turbine data set consists of various variables, and by learning the data, it learns the trend of irregular changes in the exhaust temperature. A deep learning model is constructed by combining CNN and RNN algorithms, which are accessible for time series prediction. In particular, LSTM and GRU algorithms among RNN algorithms are applied respectively to obtain a more robust and accurate model. Accurate prediction of exhaust temperature can contribute to diagnosing the condition of a gas turbine and operating it efficiently.

Kori Unit 1 is planning a system decontamination project to reduce radiation exposure of decommissioning workers, prevent the spread of contamination and down-grade the level of classification of radioactive waste. The system decontamination range for Kori Unit 1 will be the entire primary system, including RCS, CVCS and RHRS. Some system design modifications are required for the system decontamination operation. In this paper, major system design modifications were evaluated based on the conditions that system restoration is needed after completion of system decontamination. The major system design modifications are CIDF connection location to system, system decontamination operating pressure control, RCP seal water injection and formation of letdown flow. It was evaluated that there was no negative effect on the system due to the system design modifications. However, as the RCP seal water is injected into the system in the oxidation process, the concentration of the oxidizing agent is diluted. Therefore, the oxidizing agent injection and system decontamination operation procedures should be developed to address the dilution effect of the oxidizing agent. The system design modifications dealt in this paper will be finally confirmed through on-site investigation in the future, and if necessary, the system design modifications will be re-evaluated.

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