A soft sensing method for operation optimization of coke dry quenching process

Based on the actual data analysis, it is found in this paper that the supplementary air flow rate in the CDQ operation didn't follow the variation of the discharge rate of incandescent coke well, which results in the concentration increase of combustible gas in the exhaust gas and the decrease of economic efficiency. The correlation analysis results show that the introduced derived variables are more useful than some plain variables for the purpose of prediction. Next, to handle the contradiction between the steam productivity and the coke burning loss, a new economic efficiency index is introduced by synthesizing the two competing aspects. A kind of soft sensor of economic efficiency is put forward by combining nonnegative garrote (NNG) variable selection algorithm with the autoregressive integrated moving average (ARIMA) model, which gives a good solution to the economic efficiency real-time prediction problem of CDQ system. Then, the implementation of model-based optimization is studied based on the actual operation data. The results show that there exists large room for economic efficiency promotion.