Intelligent optimal control method and device for sintered brick tunnel kiln roasting process

An intelligent optimization, tunnel kiln technology, applied in the fields of instrumentation, electrical equipment and material science, and industrial automatic control, can solve the problems of uneven product quality, high production energy consumption, low production efficiency, etc., to achieve stable sintered brick quality, reduce Energy consumption, the effect of real-time monitoring

Active Publication Date: 2016-01-13
GUANGXI UNIV
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  • Abstract
  • Description
  • Claims
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Problems solved by technology

[0010] In order to solve the problems of low production efficiency, uneven product quality, unstable yield, high production energy consumption, and heavy pollution in the e

Method used

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  • Intelligent optimal control method and device for sintered brick tunnel kiln roasting process
  • Intelligent optimal control method and device for sintered brick tunnel kiln roasting process
  • Intelligent optimal control method and device for sintered brick tunnel kiln roasting process

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Embodiment 1

[0051] This embodiment mainly describes the intelligent optimization control method for the sintered brick tunnel kiln roasting process of the present invention, such as figure 2 As shown, the method includes:

[0052] Step S201: Train the extreme learning machine ELM based on the genetic algorithm GA, construct the offline data of the tunnel kiln as the input (including the temperature of the temperature collection points in each zone of the tunnel kiln, the pressure in the kiln, the moisture content of the green body, and the calorific value of the coal blending), and the output is the sintering Brick quality (including pass rate, strength, water absorption rate) intelligent prediction model for fired brick tunnel kiln firing.

[0053] Step S202: Summarize the initial cases according to the operation experience of the fired brick tunnel kiln production process, and establish an optimized case base containing multiple groups of historical firing cases of excellent fired bric...

Embodiment 2

[0058] This embodiment describes in detail the method for creating an intelligent prediction model for sintered brick tunnel kiln firing in step S201, as follows: image 3 shown, including:

[0059] Step S301: separately collect the offline detection parameters, expected process indicators and online detection parameters of the firing process of the sintered brick tunnel kiln;

[0060] The collected offline detection parameters include: raw body moisture, coal blending calorific value, strength of fired bricks, water absorption rate of fired bricks, frost resistance, pass rate and other test data; expected process indicators include pass rate of fired bricks, strength, water absorption rate, etc.; The online detection parameters include: the temperature data and pressure values ​​of each zone of the tunnel kiln transmitted by the thermocouple and pressure probe through the I / O module of the DCS system.

[0061] Step S302: Use data processing software to analyze each detection...

Embodiment 3

[0067] Such as Figure 4 As shown, this embodiment describes the steps of optimizing the parameters in the extreme learning machine using the genetic algorithm in step S303, including:

[0068] Step S401: Initialize the genetic algorithm, mainly the process of parameter control: there are four parameters in the genetic algorithm that need to be set in advance, and in practical applications, it takes multiple tests to determine the reasonable values ​​of these parameters, mainly including the following four parameter.

[0069] M: It is called the population size. The efficiency of the genetic algorithm is obviously affected by M. If the population size is too small, the diversity of the population will be reduced, and if it is too large, the efficiency of the algorithm will be reduced. For different problems, the population size is also different, and the generally recommended value range is 20-100.

[0070] N: It is called the terminal evolution algebra, and the generally re...

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Abstract

The invention discloses an intelligent optimal control method and device for the sintered brick tunnel kiln roasting process. The method comprises the steps that A, an extreme learning machine (ELM) is trained based on a genetic algorithm (GA), and a sintered brick tunnel kiln roasting intelligent prediction model with input as tunnel kiln offline and online data and output as quality of sintered bricks is established; B, according to the initial case of operating experience summary of the sintered brick tunnel kiln production process, an optimal case library comprising multiple sets of historical roasting cases of good finished sintered bricks is established; C, the historical roasting case with the highest similarity to the current working condition is worked out according to a case-based reasoning method, and the optimal temperature value of each zone of a tunnel kiln on the current working condition is obtained; and D, according to the intelligent prediction model and the optimal temperature valve of each zone of the tunnel kiln on the current working condition, the corresponding frequency of a frequency converter is calculated, and the frequency of the frequency converter on the current working condition is adjusted according to the calculation result.

Description

technical field [0001] The invention relates to the technical fields of industrial automatic control, instrumentation, electrical equipment, material science, etc., and in particular relates to an intelligent optimization control method and device for the firing process of a sintered brick tunnel kiln. Background technique [0002] Sintered porous brick is the leading product in the composition of new wall materials. At present, some enterprises in my country have adopted relatively advanced tunnel kiln brick production technology. Even so, when using sintered brick tunnel kilns to fire products, especially internal combustion bricks, most enterprises are still in the extensive production management stage of controlling kilns based on experience, depending on the technical level and sense of responsibility of pyrotechnics. It determines the output, energy consumption and product qualification rate of the tunnel kiln. [0003] The actual production status shows that some ent...

Claims

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Application Information

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IPC IPC(8): F27B9/40
Inventor 宋绍剑王尧黄清宝林小峰宋春宁廖碧莲
Owner GUANGXI UNIV
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