Control of denitration system in cement calcination process: A Novel method of Deep Neural Network Model Predictive Control

Abstract

Denitration is an important measure to reduce pollutant emissions in cement calcination prosses. The purpose of accurate control of the denitration system in cement calcination is to control the concentration of nitrogen oxides (NOx) emissions while reducing the use of ammonia. However, it is difficult to precisely control the denitration system because of the problems of multivariable, large time delay and uncertainty in the cement calcination process. In order to solve these problems, we propose a denitration process control strategy based on Deep Neural Network Model Predictive Control (DNN-MPC) for cement calcination system. This method proposes a time series deep belief network (TS-DBN) prediction model, which solves the problems of long delay, nonlinearity and high data complexity in the denitration system, and realizes a more precise forecast of NOx emission. The model predictive control framework is applied to solve the problems of volatility and uncertainty in the denitration system. The ammonia flow rate is introduced into the objective function to reduce ammonia escape and achieve the purpose of energy saving. In addition, we use differential evolution (DE) algorithm for rolling optimization solution, and add variable constraints that meet the actual running conditions in the solution process. Compared with PID controller dynamic setting time is reduced to 10% and ammonia consumption is saved by 1.7%. The experimental results show that the method proposed in this paper has higher accuracy and stronger robustness in controlling the NOx emission in the denitrification system of cement calcination, and at the same time reduces the amount of ammonia used.