Construction Of Monitoring Platform For Crude Oil Heating Furnace Based On Neural Network PID Control

Crude oil is an indispensable strategic resource to ensure economic development and maintain social stability.Crude oil of our country has the characteristics of high wax content,high freezing point and high viscosity.In order to avoid pipe condensation in the process of pipeline transportation of crude oil,it is necessary to continuously heat the crude oil through the heating furnace.As one of the energy consuming equipment of pipeline transportation system,the fuel consumption of heating furnace accounts for a large proportion in the whole production system.In order to improve system control efficiency,shorten system control time and reduce fuel consumption,it is necessary to use intelligent control technology to build heating furnace monitoring platform.According to the basic structure and process flow of crude oil heating furnace,outlet temperature of crude oil is put forward as the main control index.In order to realize the combustion control of heating furnace system,three control directions of fuel volume,inlet and outlet air volume are summarized.By analyzing the characteristics of the outlet temperature control system of crude oil heating furnace,it is determined that the control difficulty lies in the strong coupling,pure lag,uncertainty and nonlinearity of the system.Based on the infrastructure of Supervisory Control And Data Acquisition(SCADA)and Distributed Control System(DCS),intelligent control technology is integrated,and finally formulate the design scheme of monitoring platform and determine the control strategy of the system.SSA-BP-PID controller is constructed by using intelligent control technology to control the load rate of intelligent burner,and then control the fuel quantity and inlet air volume of heating furnace.The outlet air volume is controlled by the opening of flue baffle and is less affected by other operating parameters.The traditional PID controller can ensure good control quality of outlet air volume.Aiming at the problem that traditional PID control depends on accurate mathematical model,BP neural network is used to identify the system model online,and the PID controller parameters are dynamically optimized according to the current running state of the equipment,so that the system control effect is always at the best level.Aiming at the problem that the training effect of BP neural network algorithm depends on the setting of parameters such as initial weight and learning rate,the sparrow search algorithm is applied to BP-PID controller to construct SSA-BP-PID controller to optimize initial super parameters of neural network.The function of SSA-BP-PID controller is realized through MATLAB,the outlet temperature control of crude oil heating furnace is simulated,and the advantages of this controller are analyzed from various angles.Finally,according to the design scheme and control strategy,through the two aspects of hardware composition and software configuration,a monitoring platform of crude oil heating furnace based on Neural Network PID control is constructed to provide solutions for energy saving and consumption reduction during production and operation.