CN111766778A - Cotton production automatic control system of flame based on neural network - Google Patents
Cotton production automatic control system of flame based on neural network Download PDFInfo
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- CN111766778A CN111766778A CN202010458330.0A CN202010458330A CN111766778A CN 111766778 A CN111766778 A CN 111766778A CN 202010458330 A CN202010458330 A CN 202010458330A CN 111766778 A CN111766778 A CN 111766778A
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 49
- 238000013528 artificial neural network Methods 0.000 title claims abstract description 28
- 229920000742 Cotton Polymers 0.000 title claims abstract description 15
- 239000000835 fiber Substances 0.000 claims abstract description 52
- 238000002485 combustion reaction Methods 0.000 claims abstract description 33
- 238000012549 training Methods 0.000 claims abstract description 16
- 238000003062 neural network model Methods 0.000 claims abstract description 14
- 238000004364 calculation method Methods 0.000 claims abstract description 10
- 239000007921 spray Substances 0.000 claims abstract description 5
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 52
- 239000003345 natural gas Substances 0.000 claims description 26
- 239000007788 liquid Substances 0.000 claims description 15
- 230000001276 controlling effect Effects 0.000 claims description 9
- 230000001105 regulatory effect Effects 0.000 claims description 8
- 238000000034 method Methods 0.000 abstract description 19
- 230000008569 process Effects 0.000 abstract description 15
- 238000004134 energy conservation Methods 0.000 abstract description 5
- 238000005265 energy consumption Methods 0.000 abstract description 5
- 238000002347 injection Methods 0.000 abstract description 5
- 239000007924 injection Substances 0.000 abstract description 5
- 230000009467 reduction Effects 0.000 abstract description 5
- 230000003647 oxidation Effects 0.000 description 8
- 238000007254 oxidation reaction Methods 0.000 description 8
- 230000009471 action Effects 0.000 description 4
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 4
- 229910052782 aluminium Inorganic materials 0.000 description 4
- 239000011521 glass Substances 0.000 description 4
- 239000003365 glass fiber Substances 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 238000007664 blowing Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000010438 heat treatment Methods 0.000 description 3
- 238000013461 design Methods 0.000 description 2
- 239000011491 glass wool Substances 0.000 description 2
- 238000009413 insulation Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 239000002994 raw material Substances 0.000 description 2
- 210000002268 wool Anatomy 0.000 description 2
- 229910000838 Al alloy Inorganic materials 0.000 description 1
- 239000004411 aluminium Substances 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000013529 biological neural network Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000001149 cognitive effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 239000011162 core material Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000011490 mineral wool Substances 0.000 description 1
- 239000006060 molten glass Substances 0.000 description 1
- 230000036403 neuro physiology Effects 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 238000005312 nonlinear dynamic Methods 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
- 238000010301 surface-oxidation reaction Methods 0.000 description 1
- 238000005491 wire drawing Methods 0.000 description 1
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Classifications
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D27/00—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00
- G05D27/02—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00 characterised by the use of electric means
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D29/00—Simultaneous control of electric and non-electric variables
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The invention discloses an automatic control system for flame cotton production based on a neural network, which consists of a kiln, a bushing plate, a combustion chamber, a flame spray port, a rubber roller, a data acquisition system, a data calculation system, a data training system, an electrical control system and a database; the data acquisition system reads the production process parameters and stores the production process parameters in a database; the data training system utilizes the neural network to train and learn the database; the data calculation system calculates the average diameter of the corresponding primary fiber and various production process parameters according to the input average diameter requirement of the secondary fiber; and the electrical control system respectively regulates and controls various production process parameters calculated by the neural network model. The system realizes automatic control of the flame injection process, effectively avoids experience errors caused by manual operation, stably controls various process parameters, reduces energy consumption, and realizes energy conservation and emission reduction.
Description
Technical Field
The invention relates to the technical field of automatic control, in particular to a system and a method for automatically controlling a superfine glass wool felt production line based on a neural network.
Background
The production process of the glass wool mainly comprises a centrifugal blowing process and a flame blowing process, wherein the flame blowing method is characterized in that glass balls or glass blocks are used as raw materials and are melted in a kiln to form homogeneous glass liquid, the glass liquid flows out through a drain plate under the action of self gravity and viscosity, and primary fibers are formed under the action of a wire drawing device; forming glass fiber cotton under the action of high-temperature air flow of a flame nozzle; the glass fiber cotton enters the cotton collecting chamber through the cotton guide cylinder under the traction action of high-speed airflow generated by the negative pressure fan. The glass fiber produced by the flame injection process has small diameter, the average diameter can reach below 2 microns, and the glass fiber is mainly used for preparing sound and heat insulation cotton felts, AGM partition plates, filter paper, vacuum heat insulation plate core materials and the like. However, the existing flame injection process has the disadvantages of simple and crude production equipment, low automation degree, unstable process parameters, incapability of ensuring product performance, especially average fiber diameter, low productivity and high energy consumption, and is not favorable for popularization and application of flame cotton, and the product quality mainly depends on experience and manual control of operators.
The artificial neural network is an important branch of artificial intelligence science, is an ultra-large-scale nonlinear dynamic system formed by interconnection of a large number of neurons, and is a cross-border subject and a new high technology formed by integrating the subject achievements of neurophysiology, cognitive science, mathematical science, biophysics, information science, management science, computer science and the like. The neural network, as a consultation processing and computing system simulating a biological neural network, has the outstanding advantages of high-speed computing capability, large memory capacity, association capability, self-adaption capability, fault tolerance, fuzzy reasoning and the like, and is making a great deal of progress and achievement in the fields of a plurality of technologies and industries, such as sensing technology, knowledge and signal processing, automatic control, aerospace, transportation, communication, market analysis, medical diagnosis and the like.
The utility model discloses a chinese utility model patent of application number 201520325952.0 discloses an aluminium bar heating furnace combustion temperature automatic control device based on neural network contains heating furnace combustion system and temperature automatic control electrical system, and temperature automatic control electrical system contains neural network control module, links to each other with the PID controller. The neural network is divided into a forward propagation part and a backward learning part, the forward propagation of the neural network is used for outputting various parameters in PID control, and the backward learning of the neural network is used for self-adaptively adjusting the weighting coefficient of the neural network. The method can ensure that the combustion temperature of the aluminum bar heating furnace can be controlled to meet the process requirements, and the working efficiency of aluminum bar pressurization is improved; and the adjustment time required by the temperature reaching the steady state can be reduced, the energy consumption loss is reduced, and the purposes of energy conservation and emission reduction are achieved.
The Chinese patent with application number 201510417189.9 discloses an automatic control system for aluminum profile surface oxidation based on a neural network, wherein a neural network model is constructed for each oxidation tank and is divided into a forward propagation part and a reverse learning part. Firstly, training a neural network by utilizing a reverse learning part, taking production parameters influencing oxidation time as input parameters and corresponding oxidation time as output parameters, and adaptively adjusting a weighting coefficient through back propagation to enable a neural network model to be matched with the actual production process of an oxidation tank; then, the required thickness of the oxide film and corresponding production parameters are given, the required oxidation time is calculated by using a trained neural network model, and when the oxidation time is up, the production of the oxidation tank is automatically stopped by the control circuit module. The method can ensure that the thickness of the aluminum alloy oxide film meets the requirement of a stable process, reduce energy consumption loss caused by excessive oxidation and achieve the purposes of energy conservation and emission reduction.
The automatic control function of the system is realized by utilizing the neural network tool, so that the process parameters are stabilized, the production efficiency is improved, and the purposes of energy conservation and emission reduction are achieved. However, no one has applied the neural network to the production field of mineral wool materials, especially to the manufacturing field of flame wool, so that it is necessary to invent an automatic control system for flame wool production based on the neural network.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an automatic control system for flame cotton production, so as to stabilize process parameters and accurately control the average fiber diameter of the flame cotton.
The technical scheme adopted for realizing the purpose of the invention is as follows: the automatic control system is characterized by consisting of a kiln, a bushing plate, a combustion chamber, a flame spray opening, a rubber roll, a data acquisition system, a data calculation system, a data training system, an electrical control system and a database, wherein production process parameters comprise kiln natural gas flow, kiln liquid level height, bushing plate current, rubber roll rotating speed, combustion chamber natural gas flow and combustion chamber air flow; the production process parameters controlled by the electrical control system comprise kiln natural gas flow, kiln liquid level height, bushing plate current, rubber roll rotating speed, combustion chamber natural gas flow and combustion chamber air flow; the data acquisition system reads various production process parameters in real time and stores the parameters in a database; the data training system reads the production process parameter records in the database, utilizes the neural network to carry out training and learning, respectively constructs an influence relation model of the kiln natural gas flow, the kiln liquid level height, the bushing current and the rubber roll rotating speed on the average diameter of the primary fiber and an influence relation model of the average diameter of the primary fiber, the combustion chamber natural gas flow and the combustion chamber air flow on the average diameter of the secondary fiber, and outputs the influence relation models to the data computing system; the data calculation system calculates the corresponding average diameter of the primary fiber and various production process parameters by using the trained neural network model according to the requirement on the average diameter of the secondary fiber input by the operator on the day, and outputs the average diameter and various production process parameters to the electric control system; and the electrical control system respectively regulates and controls various production process parameters calculated by the neural network model.
Further, the data acquisition system comprises a high-definition camera, the average diameter of the primary fiber is detected on line in real time, when the electric control system regulates and controls the flow rate of the natural gas of the kiln, the height of the liquid level of the kiln, the current of the bushing plate and the rotating speed of the rubber covered roller, the high-definition camera feeds the detected average diameter of the primary fiber back to the electric control system in real time, and when the average diameter of the primary fiber reaches a set value in the control system, the electric control system stops regulating and controlling the parameters.
And further, the data acquisition system also comprises a laser fineness meter which detects the average diameter of the secondary fibers on line in real time, when the electrical control system regulates and controls the natural gas flow and the air flow of the combustion chamber, the laser fineness meter feeds the detected diameter of the secondary fibers back to the electrical control system in real time, and when the average diameter of the secondary fibers reaches a set value in the control system, the electrical control system stops regulating and controlling the parameters.
The application effect is as follows: compared with the prior art, the invention has the following advantages: (1) an automatic control system based on a neural network is adopted, a neural network model is constructed according to historical production data, the average fiber diameter value required to be obtained is input into the system before production every time, the system automatically calculates various process parameters, and then automatic control is carried out; (2) new data can be added into the database after production is finished every time, the neural network is used for training and learning again, and the neural network model is continuously corrected, so that the accuracy of the control system is continuously improved; (3) the system realizes automatic control of the flame injection process, effectively avoids experience errors caused by manual operation, stably controls various process parameters, reduces energy consumption, and realizes energy conservation and emission reduction.
Drawings
FIG. 1 is a schematic diagram of an automatic control system for production of flame cotton based on neural network according to the present invention;
fig. 2 is a schematic view of the flame injection process of the present invention, wherein fig. 10 is a kiln, 20 is molten glass, 30 is a bushing, 40 is primary fiber, 50 is a rubber roll, 60 is a combustion chamber, 70 is a flame jet, and 80 is secondary fiber.
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be purely exemplary and are not intended to limit the scope of the invention, as various equivalent modifications of the invention will occur to those skilled in the art upon reading the present specification and which fall within the limits of the appended claims.
Example 1
An automatic control system for flame cotton production based on a neural network is composed of a kiln, a bushing, a combustion chamber, a flame spray opening, a rubber roll, a data acquisition system, a data calculation system, a data training system, an electrical control system and a database, wherein production process parameters comprise kiln natural gas flow, kiln liquid level height, bushing current, rubber roll rotating speed, combustion chamber natural gas flow and combustion chamber air flow; the data acquisition system reads various production process parameters in real time and stores the parameters in a database; the data training system reads the production process parameter records in the database, utilizes the neural network to carry out training and learning, respectively constructs an influence relation model of the kiln natural gas flow, the kiln liquid level height, the bushing current and the rubber roll rotating speed on the average diameter of the primary fiber and an influence relation model of the average diameter of the primary fiber, the combustion chamber natural gas flow and the combustion chamber air flow on the average diameter of the secondary fiber, and outputs the influence relation models to the data computing system; the operator inputs that the requirement of the average diameter of the secondary fiber on the current day is 1.8 mu m, and the data calculation system calculates the corresponding average diameter of the primary fiber and various production process parameters by using the trained neural network model and outputs the parameters to the electric control system; and the electrical control system respectively regulates and controls various production process parameters calculated by the neural network model.
The data acquisition system comprises a high-definition camera, the average diameter of the primary fiber is detected on line in real time, when the electric control system regulates and controls the natural gas flow of the kiln, the liquid level height of the kiln, the bushing plate current and the rotating speed of the rubber covered roller, the high-definition camera feeds the detected average diameter of the primary fiber back to the electric control system in real time, and when the average diameter of the primary fiber reaches a set value in the control system, the electric control system stops regulating and controlling the parameters.
The data acquisition system also comprises a laser fineness meter which detects the average diameter of the secondary fiber on line in real time, when the electrical control system regulates and controls the natural gas flow of the combustion chamber and the air flow of the combustion chamber, the laser fineness meter feeds the detected diameter of the secondary fiber back to the electrical control system in real time, and when the average diameter of the secondary fiber reaches 1.8 mu m, the electrical control system stops regulating and controlling the parameters.
Example 2
An automatic control system for flame cotton production based on a neural network is composed of a kiln, a bushing, a combustion chamber, a flame spray opening, a rubber roll, a data acquisition system, a data calculation system, a data training system, an electrical control system and a database, wherein production process parameters comprise kiln natural gas flow, kiln liquid level height, bushing current, rubber roll rotating speed, combustion chamber natural gas flow and combustion chamber air flow; the data acquisition system reads various production process parameters in real time and stores the parameters in a database; the data training system reads the production process parameter records in the database, utilizes the neural network to carry out training and learning, respectively constructs an influence relation model of the kiln natural gas flow, the kiln liquid level height, the bushing current and the rubber roll rotating speed on the average diameter of the primary fiber and an influence relation model of the average diameter of the primary fiber, the combustion chamber natural gas flow and the combustion chamber air flow on the average diameter of the secondary fiber, and outputs the influence relation models to the data computing system; the operator inputs that the requirement of the average diameter of the secondary fiber on the current day is 2.5 mu m, and the data calculation system calculates the corresponding average diameter of the primary fiber and various production process parameters by using the trained neural network model and outputs the parameters to the electric control system; and the electrical control system respectively regulates and controls various production process parameters calculated by the neural network model.
The data acquisition system comprises a high-definition camera, the average diameter of the primary fiber is detected on line in real time, when the electric control system regulates and controls the natural gas flow of the kiln, the liquid level height of the kiln, the bushing plate current and the rotating speed of the rubber covered roller, the high-definition camera feeds the detected average diameter of the primary fiber back to the electric control system in real time, and when the average diameter of the primary fiber reaches a set value in the control system, the electric control system stops regulating and controlling the parameters.
The data acquisition system also comprises a laser fineness meter which detects the average diameter of the secondary fiber on line in real time, when the electrical control system regulates and controls the natural gas flow of the combustion chamber and the air flow of the combustion chamber, the laser fineness meter feeds the detected diameter of the secondary fiber back to the electrical control system in real time, and when the average diameter of the secondary fiber reaches 2.5 mu m, the electrical control system stops regulating and controlling the parameters.
The above description is only two specific embodiments of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept should fall within the scope of infringing the protection scope of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention are still within the protection scope of the technical solution of the present invention.
Claims (1)
1. An automatic control system for flame cotton production based on a neural network is characterized by comprising a kiln, a bushing, a combustion chamber, a flame spray port, a rubber roll, a data acquisition system, a data calculation system, a data training system, an electrical control system and a database, wherein production process parameters comprise kiln natural gas flow, kiln liquid level height, bushing current, rubber roll rotating speed, combustion chamber natural gas flow and combustion chamber air flow; the data acquisition system reads various production process parameters in real time and stores the parameters in a database; the data training system reads the production process parameter records in the database, utilizes the neural network to carry out training and learning, respectively constructs an influence relation model of the kiln natural gas flow, the kiln liquid level height, the bushing current and the rubber roll rotating speed on the average diameter of the primary fiber and an influence relation model of the average diameter of the primary fiber, the combustion chamber natural gas flow and the combustion chamber air flow on the average diameter of the secondary fiber, and outputs the influence relation models to the data computing system; the data calculation system calculates the corresponding average diameter of the primary fiber and various production process parameters by using the trained neural network model according to the requirement on the average diameter of the secondary fiber input by the operator on the day, and outputs the average diameter and various production process parameters to the electric control system; the electrical control system respectively regulates and controls various production process parameters calculated according to the neural network model; the data acquisition system comprises a high-definition camera, the average diameter of primary fibers is detected on line in real time, when the electric control system regulates and controls the natural gas flow of the kiln, the liquid level height of the kiln, the bushing current and the rotating speed of the rubber covered roller, the high-definition camera feeds the detected average diameter of the primary fibers back to the electric control system in real time, and when the average diameter of the primary fibers reaches a set value in the control system, the electric control system stops regulating and controlling the parameters; the data acquisition system also comprises a laser fineness meter which detects the average diameter of the secondary fiber on line in real time, when the electrical control system regulates and controls the natural gas flow and the air flow of the combustion chamber, the laser fineness meter feeds the detected diameter of the secondary fiber back to the electrical control system in real time, and when the average diameter of the secondary fiber reaches a set value in the control system, the electrical control system stops regulating and controlling the parameters.
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| CN202010458330.0A CN111766778A (en) | 2020-05-20 | 2020-05-20 | Cotton production automatic control system of flame based on neural network |
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| CN202010458330.0A CN111766778A (en) | 2020-05-20 | 2020-05-20 | Cotton production automatic control system of flame based on neural network |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115185191A (en) * | 2022-09-13 | 2022-10-14 | 钛科优控(江苏)工业科技有限公司 | Self-learning control system and method for thickness of copper foil of foil forming machine |
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2020
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115185191A (en) * | 2022-09-13 | 2022-10-14 | 钛科优控(江苏)工业科技有限公司 | Self-learning control system and method for thickness of copper foil of foil forming machine |
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