Abstract
Taking a 1000MW ultra-supercritical once-through boiler as an example for research, two kinds of soft measurement methods of once-through boiler water-coal ratio is studied: With Auto Regressive Integrated Moving Average (ARIMA) time series method, water-coal ratio data after filtering is processed with parameter estimation and dynamic prediction, then the error analysis verified the model can accurately predict water-coal ratio; By analyzing several procedure variables which are involved with water-coal ratio and applying the method of principal component regression, a prediction measurement about water-coal ratio was formed. The two methods respectively used time series analysis and principal component regression to calculate and predict water-coal ratio, and results proved that the two soft-measuring methods can to some extent overcome the large delay and big error which are the characteristics of traditional monitoring methods.
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