Abstract
In this work, an adaptive NOxemission model is proposed for a SCR system of a 660 MW utility boiler. First, 3-years operating data was collected from the plant SIS system as raw data, which was then filtered using the R-statistic method and clustered by the condensed nearest neighbor (CNN) rule to form a classified steady-state database. In addition, a sliding window approach was used to deal with the continuous data stream. As the newest steady state sample was introduced into the database, the most similar old sample in the same data class was replaced. The crowding distance (CD) operator was also used to eliminate the redundant samples. This new method RCNN-CD is proven to be a good tool to improve the representatives of the samples. Based on the selected samples, a fusion monotony support vector regression (FM-SVR) was used to establish the NOxemission model. The results show that, this model can reasonably reflect SCR mechanism and follow the degradation of SCR performance.
Highlights
Thermal process modelling can be divided into mechanism method and data-driven method
Taking the Selective Catalytic Reduction (SCR) system of the power station as an example, factors such as the modification of the deflector, the adjustment of the reducing agent distribution strategy, and the deactivation of the catalyst [1] will affect the accuracy of the mechanism model
Fig. 2. 3-years historical operating data for SCR system Fig. 3 shows the relationships between the DeNOx efficiency and the urea flow rate, each sub-figure displays the data of a month
Summary
Thermal process modelling can be divided into mechanism method and data-driven method. Taking the Selective Catalytic Reduction (SCR) system of the power station as an example, factors such as the modification of the deflector, the adjustment of the reducing agent distribution strategy, and the deactivation of the catalyst [1] will affect the accuracy of the mechanism model. Data-driven modelling method directly learns the relationship among the parameters, which resulting in a high predicting accuracy. Smrekar et al [2] performed data cleaning, steady-state screening, and variable selection on the operating data of a power station in the last 12 days, and based on the filtered data, established a main steam parameter model that can accurately reflect the current performance status of the unit. This paper used data-driven method to establish a prediction model of NOx emissions from a 660 MW power plant SCR system, and introduced mechanism knowledge and adaptive strategies into this model. The results show that the model can maintain the correct relationship among parameters, and can reasonably track the performance changes of the catalyst
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