Prediction function control method for coking heating furnace oxygen content

A technology for predicting function control and oxygen content, applied in the field of automation, can solve the problems of coke ovens limiting the applicability and efficiency of DMC control, interference, and affecting oxygen content

Active Publication Date: 2016-04-13
HANGZHOU DIANZI UNIV +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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,

Method used

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  • Prediction function control method for coking heating furnace oxygen content
  • Prediction function control method for coking heating furnace oxygen content
  • Prediction function control method for coking heating furnace oxygen content

Examples

Experimental program
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Effect test

Embodiment Construction

[0065] Take the oxygen content control process in the coking heating furnace as an example:

[0066] 1. Establish the model of the coking furnace

[0067] 1-1. Since the oxygen content in the coking furnace can be described as two parts, linear and nonlinear, the following expression can be obtained:

[0068] y(k)=y L (k)+y NL (k)(1)

[0069] where y L (k) is the output of the model under the step response, y NL (k) is through y L (k) and y(k)| i ,(i=1,2,…,N) The nonlinear module determined by the deviation between, y(k)| i , (i=1,2,...,N) is the output of the controlled object output y(k) in the actual process at time i, and N is the number of output sampling points.

[0070] 1-2. The linear part of the model can be obtained by step response, y(t) is the actual output of the model, y(∞) is the steady-state output of the model, U 0 is the magnification of the input signal. The output y(t) can be used Expressed in the form of , the gain of the model can be expressed...

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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...

Claims

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 徐卫德张日东
Owner HANGZHOU DIANZI UNIV
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