Dynamic optimization algorithm of cement firing system based on differential evolution

Abstract

Aiming at the problem of resource waste in the process of cement firing and the difficulty of establishing an effective mathematical mechanism model,a dynamic energy optimization method based on the cement industry firing system was proposed.The method used the convolutional neural network to construct the objective function of power consumption and coal consumption of the firing system.The differential evolution algorithm was used to solve the control parameters in reverse,and the better operating index was obtained according to the current working conditions.Since the actual production conditions will change with time,the operating indicators and power consumption and coal consumption in the future will be saved,and then input into the neural network for training,and the constraint range will be determined by the actual running index value at the current time.The optimization value can meet the actual operation index adjustment requirements.Furthermore,the goal optimization of the dynamic energy consumption state of the cement firing process was realized.It effectively reduces the energy consumption of cement firing process.

Keywords

• convolutional neural network;differential evolution algorithm;energy optimization;energy consumption forecast