Prediction function control method for coking heating furnace oxygen content

Abstract

The invention discloses a prediction function control method for coking heating furnace oxygen content. The prediction function control method comprises steps of modeling for an industrial process through the substantial data collected during the industrial process, dividing the model into a linear part and a non-linear part, using a counterpropagation nerve network to perform modeling on the non-linear portion, using a traditional linear two-point method to perform modeling on the linear part, using the prediction function control method to perform rolling optimization on the established model, and returning the feedback of correction so as to determine the control variable outputted at the next moment. The invention uses the prediction function, which, compared with the traditional PID control, can more effectively improve the dynamic performance and stability of the system.

Description

technical field [0001] The invention belongs to the technical field of automation, and relates to a predictive function control method for the oxygen content of a coking heating furnace. Background technique [0002] Coking plays an important role in boosting the economics of the petrochemical industry. In the control system of coal coke oven, the control of oxygen content is a very important issue, it will directly affect the pressure in the room, the temperature of radiation and so on. Because the coke oven, distillation tower and coke tower are an integral process that interacts with each other. The volume of gaseous oil in the distillation column is closely related to the oil circulation temperature flowing into the coke oven, while the oil temperature has a direct effect on the oxygen content. The residual oil temperature in the coke oven and the coking rate in the coke drum also affect the amount of fuel required to be fed into the oven, and these factors are also cl...

AI Technical Summary

Problems solved by technology

Disturbances in the process, time delays, and nonlinearity affect the oxygen content in the coke oven, which leads to a lot of periodic oscillations in the process due to the switch in the coke drum

It is precisely because of the nonlinearity, complex dynamics and variable interference in the process that it is difficult for ordinary PID controllers to control the oxygen content at a set value

Although model predictive control (DMC) has been widely used in industrial processes, complex problems in coke ovens limit the applicability and efficiency of DMC control

Although multi-model strategies and nonlinear control can be used, multi-model strategies require a large number of operations and extensive experiments, which leads to control performance greatly dependent on the form of operation. For nonlinear control, there must be sufficient effective nonlinear model and the corresponding nonlinear model, which is also a major problem facing the industry

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