CN116516710A - Beating degree soft measurement and control method based on edge calculation of Internet of things - Google Patents
Beating degree soft measurement and control method based on edge calculation of Internet of things Download PDFInfo
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- CN116516710A CN116516710A CN202310493459.9A CN202310493459A CN116516710A CN 116516710 A CN116516710 A CN 116516710A CN 202310493459 A CN202310493459 A CN 202310493459A CN 116516710 A CN116516710 A CN 116516710A
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- 238000010009 beating Methods 0.000 title claims abstract description 112
- 238000004364 calculation method Methods 0.000 title claims abstract description 40
- 238000005259 measurement Methods 0.000 title claims abstract description 37
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000004537 pulping Methods 0.000 claims abstract description 29
- 238000000227 grinding Methods 0.000 claims description 49
- 239000002023 wood Substances 0.000 claims description 16
- 238000013528 artificial neural network Methods 0.000 claims description 10
- 238000007599 discharging Methods 0.000 claims description 6
- 238000005457 optimization Methods 0.000 claims description 4
- 239000002002 slurry Substances 0.000 claims description 4
- 238000012549 training Methods 0.000 claims description 4
- 230000000694 effects Effects 0.000 abstract description 2
- 239000007787 solid Substances 0.000 abstract description 2
- 230000002596 correlated effect Effects 0.000 description 4
- 230000001276 controlling effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 238000007670 refining Methods 0.000 description 2
- 230000004913 activation Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000003801 milling Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000008961 swelling Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- D—TEXTILES; PAPER
- D21—PAPER-MAKING; PRODUCTION OF CELLULOSE
- D21D—TREATMENT OF THE MATERIALS BEFORE PASSING TO THE PAPER-MAKING MACHINE
- D21D1/00—Methods of beating or refining; Beaters of the Hollander type
- D21D1/002—Control devices
-
- D—TEXTILES; PAPER
- D21—PAPER-MAKING; PRODUCTION OF CELLULOSE
- D21D—TREATMENT OF THE MATERIALS BEFORE PASSING TO THE PAPER-MAKING MACHINE
- D21D1/00—Methods of beating or refining; Beaters of the Hollander type
- D21D1/20—Methods of refining
-
- 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
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/05—Programmable logic controllers, e.g. simulating logic interconnections of signals according to ladder diagrams or function charts
- G05B19/054—Input/output
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5061—Partitioning or combining of resources
- G06F9/5072—Grid computing
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- 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
- G06N3/0895—Weakly supervised learning, e.g. semi-supervised or self-supervised learning
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
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Abstract
The invention discloses a beating degree soft measurement and control method based on internet of things edge calculation. The cloud server stores a large amount of data related to beating degree, and has a strong function in complex calculation, so that important intermediate coefficients are calculated by the cloud server. The edge calculation power is strong, the real-time performance of data exchange with the PLC is high, and real-time data support is provided for PLC control. The method has good effect in practical application, reduces the workload of workers in factories, saves cost, greatly improves the quality of pulping while improving the pulping efficiency, and lays a solid foundation for producing high-quality paper.
Description
Technical Field
The invention relates to the technical field of papermaking, in particular to a beating degree soft measurement and control method based on edge calculation of the Internet of things.
Background
The pulping machine is a key device in the papermaking pulping process, and has the function of physically changing pulp fibers through the mechanical action of the pulping machine, so that specific properties are obtained, and the main index of the pulping machine is beating degree, which comprehensively reflects the degree of cutting, swelling, brooming and filament separation of the fibers. Whether the beating degree reaches the standard and is stable plays a decisive role in the quality of the final finished paper. However, the method for measuring the beating degree in China is carried out manually until now: manual sampling, manual testing in a laboratory, and manual adjustment if deviation occurs. The efficiency is low, the control and adjustment time is too long, and the beating degree is unstable.
The factors influencing the beating degree are more than ten, the forms and data of the influence are different, and the prior automatic control of industry 3.0 cannot overcome the difficult problem. A small amount of paper was introduced abroad that purportedly enabled online automatic measurement of freeness: an online beating degree measuring instrument is disclosed, but the real-time measurement and control errors of beating degree are larger in the actual trial process, and the equipment is expensive.
Disclosure of Invention
The invention aims to provide a beating degree soft measurement and control method based on the edge calculation of the Internet of things, so as to solve the problems that the online beating degree tester in the prior art is provided in the background art, the real-time measurement and control error of the beating degree is found to be larger in the actual trial process, and the equipment cost is high.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the beating degree soft measurement and control method based on the edge calculation of the Internet of things comprises the following steps:
acquiring parameters related to beating degree in the pulping process, and transmitting the acquired related parameters to an edge box;
transmitting the acquired related parameters to a cloud server through an edge box, calculating an intermediate coefficient required by edge calculation by the cloud server, and transmitting the intermediate coefficient to the edge box;
calculating a beating degree soft measurement value SRr by the edge box through the acquired data and the intermediate coefficient;
and controlling a grinding disc cutter feeding and discharging motor according to the calculated beating degree to realize constant beating degree beating of the grinding disc.
Further preferably, the parameters related to freeness in the refining process include the concentration of incoming pulp, the flow rate of incoming pulp, the running power of the mill, the specific beating pressure, the beating time, the beating temperature, the distance between the plates, the performance parameters of the plates, the life cycle of the mill, the properties of the wood chips and the PH of the pulp.
Further preferably, the pulp inlet concentration, the pulp inlet flow, the millstone running power, the pulping specific pressure, the pulping time, the pulping temperature, the distance between the milling sheets and the pulp PH value are obtained through calculation and instrument measurement; the characteristics of the refiner plates, the life cycle of the refiner plates and the wood chip source are fixed values obtained by manual input.
Further preferably, the cloud server stores performance parameters of the grinding disc of different manufacturers, life cycle of the grinding disc and properties of the wood chips in different areas, and can calculate intermediate coefficients affecting the beating degree according to the performance parameters of the grinding disc, the life cycle of the grinding disc and the properties of the wood chips, and then transmits the calculated intermediate coefficients back to the edge box to participate in calculation of the soft beating degree measured value SRr by the edge box.
Further preferably, when the grinding disc feeding and discharging motor is controlled according to the calculated beating degree to realize the constant beating degree beating of the grinding disc, the beating degree soft measurement value SRr obtained by the edge box is compared with a set value, the deviation value delta is calculated by the control unit to obtain an adjustment value, and the feeding amount of the grinding disc feeding and discharging motor is adjusted, so that the gap of the grinding disc can be adjusted, and the beating degree of the slurry is changed until delta-0.
Further preferably, the edge box has integrated therein a data fitting and neural network self-learning algorithm.
Further preferably, the calculating of the edge box comprises the steps of:
inputting the beating degree measured in a laboratory;
the edge box obtains an initial beating degree algorithm model through a data fitting algorithm according to the beating degree measured in a laboratory, and the beating degree formula is as follows:
SR is beating degree, K is coefficient, W is active power of working of disc mill, C is concentration of disc mill, F is outlet flow of disc mill, T 1 To disc mill run time as a percentage of the total life cycle,
further obtains the nonlinear relation between the power and the freeness, and the relation is curved, so that the freeness formula is
B is a constant;
according to the formula model, data fitting is carried out to obtain relevant parameters, and an accurate model is obtained;
training an initial beating degree algorithm model;
the trained beating degree algorithm is further inserted into an optimization calculation model through a Back production neural network self-learning algorithm, and an accurate beating degree soft measurement value SRr is obtained;
compared with the prior art, the invention has the beneficial effects that:
the method utilizes a cloud server calculation method, an edge box calculation method and a PLC (programmable logic controller) combined method to realize online soft measurement and accurate control of the beating degree. The cloud server stores a large amount of data related to beating degree, and has a strong function in complex calculation, so that important intermediate coefficients are calculated by the cloud server. The edge calculation power is strong, the real-time performance of data exchange with the PLC is high, and real-time data support is provided for PLC control. The method has good effect in practical application, reduces the workload of workers in factories, saves cost, greatly improves the quality of pulping while improving the pulping efficiency, and lays a solid foundation for producing high-quality paper.
Drawings
FIG. 1 is a schematic diagram of a beating degree soft measurement and control method based on the edge calculation of the Internet of things;
FIG. 2 is a flow chart of edge box computation of the present invention;
FIG. 3 is a schematic diagram of a control system according to the present invention;
FIG. 4 is a flowchart of a neural network algorithm of the present invention;
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-4, the present invention provides a technical solution:
the beating degree soft measurement and control method based on the edge calculation of the Internet of things comprises the following steps:
acquiring parameters related to beating degree in the pulping process, and transmitting the acquired related parameters to an edge box;
transmitting the acquired related parameters to a cloud server through an edge box, calculating an intermediate coefficient required by edge calculation by the cloud server, and transmitting the intermediate coefficient to the edge box;
calculating a beating degree soft measurement value SRr by the edge box through the acquired data and the intermediate coefficient;
and controlling a grinding disc cutter feeding and discharging motor according to the calculated beating degree to realize constant beating degree beating of the grinding disc.
In the invention, parameters related to the beating degree in the pulping process comprise the pulp inlet concentration, the pulp inlet flow, the grinding disc running power, the specific beating pressure, the beating time, the beating temperature, the distance between grinding sheets, the performance parameters of the grinding sheets, the life cycle of the grinding disc, the properties of wood chips and the pH value of pulp. The pulp inlet concentration, the pulp inlet flow, the millstone running power, the pulping specific pressure, the pulping time, the pulping temperature, the distance between the millstones and the pulp PH value are obtained through calculation and instrument measurement; the characteristics of the refiner plates, the life cycle of the refiner plates and the wood chip source are fixed values obtained by manual input.
According to the invention, the cloud server stores performance parameters of the grinding disc of different manufacturers, the life cycle of the grinding disc and the properties of wood chips in different areas, intermediate coefficients affecting the beating degree can be calculated according to the performance parameters of the grinding disc, the life cycle of the grinding disc and the properties of the wood chips, and then the calculated intermediate coefficients are transmitted back to the edge box to participate in calculation of the beating degree soft measurement value SRr of the edge box.
According to the invention, the grinding disc cutter feeding and retracting motor is controlled according to the calculated beating degree, when the constant beating degree beating of the grinding disc is realized, the beating degree soft measurement value SRr obtained by the edge box is compared with a set value, the deviation value delta is calculated by the control unit to obtain an adjustment quantity, and the cutter feeding quantity of the grinding disc cutter feeding and retracting motor is adjusted, so that the gap of a grinding disc can be adjusted, and the beating degree of the slurry is changed until delta-0.
In the invention, a data fitting and neural network self-learning algorithm is integrated in the edge box. The calculation of the edge box includes the following steps:
inputting the beating degree measured in a laboratory;
the edge box obtains an initial beating degree algorithm model through a data fitting algorithm according to the beating degree measured in a laboratory, the beating degree is positively correlated with the pulping power, is inversely correlated with the concentration flow, and is in percentage T of the total life cycle with the disc grinding operation time 1 In inverse correlation, the freeness formula is:
SR is beating degree, K is coefficient, W is active power of working of disc mill, C is concentration of disc mill, F is outlet flow of disc mill, T 1 To disc mill run time as a percentage of the total life cycle,
further obtains the nonlinear relation between the power and the freeness, and the relation is curved, so that the freeness formula is
B is a constant;
according to the formula model, data fitting is carried out to obtain relevant parameters, and an accurate model is obtained;
training an initial beating degree algorithm model;
the trained beating degree algorithm is further inserted into an optimization calculation model through a BP neural network self-learning algorithm, and an accurate beating degree soft measurement value SRr is obtained;
example 1,
The PLC acquires parameters related to the beating degree in the pulping process, wherein the parameters related to the beating degree in the pulping process comprise the concentration of pulp entering into the mill, the flow rate of pulp entering into the mill, the running power of the mill, the specific beating pressure, the beating time, the beating temperature, the distance between the grinding sheets, the performance parameters of the grinding sheets, the life cycle of the mill, the attribute of the wood chips and the pH value of the pulp. The pulp inlet concentration, the pulp inlet flow, the millstone running power, the pulping specific pressure, the pulping time, the pulping temperature, the distance between the millstones and the pulp PH value are obtained through calculation and instrument measurement; the characteristics of the refiner plates, the life cycle of the refiner plates and the wood chip source are fixed values obtained by manual input. Transmitting the acquired related parameters to an edge box;
the obtained related parameters are transmitted to a cloud server through an edge box, the cloud server calculates the middle coefficients required by edge calculation, the cloud server stores performance parameters of grinding plates of different manufacturers, life cycles of the grinding plates and attributes of wood chips in different areas, the middle coefficients affecting the beating degree can be calculated according to the performance parameters of the grinding plates, the life cycles of the grinding plates and the attributes of the wood chips, and then the calculated middle coefficients are transmitted back to the edge box to participate in calculation of the beating degree soft measurement value SRr of the edge box. And transmitting the intermediate coefficients to the edge box;
calculating a beating degree soft measurement value SRr by the edge box through the acquired data and the intermediate coefficient; the edge box integrates a data fitting and neural network self-learning algorithm inside. The calculation of the edge box includes the following steps:
inputting the beating degree measured in a laboratory;
the edge box obtains an initial beating degree algorithm model through a data fitting algorithm according to the beating degree measured in a laboratory, the beating degree is positively correlated with the pulping power, is inversely correlated with the concentration flow, and is in percentage T of the total life cycle with the disc grinding operation time 1 In inverse correlation, the freeness formula is:
SR is beating degree, K is coefficient, W is active power of working of disc mill, C is concentration of disc mill, F is outlet flow of disc mill, T 1 To disc mill run time as a percentage of the total life cycle,
further obtains the nonlinear relation between the power and the freeness, and the relation is curved, so that the freeness formula is
B is a constant;
according to the formula model, data fitting is carried out to obtain relevant parameters, and an accurate model is obtained;
the data recorded in the table are parameters relating to freeness during refining. The degree of beating in the table is the degree of beating described herein.
Training an initial beating degree algorithm model;
the trained beating degree algorithm is further inserted into an optimization calculation model through a BP neural network self-learning algorithm, and an accurate beating degree soft measurement value SRr is obtained;
the neural network formula is:
y: outputting; x is x i : inputting a variable; n: the number of variables; w (w) i : weighting; θ: the function is activated and the function is activated,
the activation function is a relu function,
f(x)＝max(0,x)。
the PLC controls the grinding disc cutter feeding and retracting motor according to the calculated beating degree, when the constant beating degree beating of the grinding disc is realized, the beating degree soft measured value SRr obtained by the edge box is compared with a set value, the deviation value delta is calculated by the control unit through PID to obtain an adjustment quantity, and the cutter feeding quantity of the grinding disc cutter feeding and retracting motor is adjusted, so that the gap of a grinding disc can be adjusted, and the beating degree of the slurry is changed until delta-0.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (7)
1. The beating degree soft measurement and control method based on the edge calculation of the Internet of things is characterized by comprising the following steps of:
acquiring parameters related to beating degree in the pulping process, and transmitting the acquired related parameters to an edge box;
transmitting the acquired related parameters to a cloud server through an edge box, calculating an intermediate coefficient required by edge calculation by the cloud server, and transmitting the intermediate coefficient to the edge box;
calculating a beating degree soft measurement value SRr by the edge box through the acquired related parameters and the intermediate coefficients;
and controlling a grinding disc cutter feeding and discharging motor according to the calculated beating degree to realize constant beating degree beating of the grinding disc.
2. The soft measurement and control method of freeness based on internet of things edge calculation according to claim 1, wherein the method is characterized in that: the parameters related to the beating degree in the pulping process comprise the pulp inlet concentration, the pulp inlet flow, the grinding disc running power, the specific beating pressure, the beating time, the beating temperature, the distance between grinding sheets, the performance parameters of the grinding sheets, the life cycle of the grinding disc, the properties of wood chips and the pH value of pulp.
3. The soft measurement and control method of freeness based on internet of things edge calculation according to claim 2, wherein the method is characterized in that: the pulp concentration, pulp flow, millstone running power, specific pulping pressure, pulping time, pulping temperature, distance between the millstones and pulp PH value are obtained through calculation and instrument measurement; the characteristics of the refiner plates, the life cycle of the refiner plates and the wood chip source are fixed values obtained by manual input.
4. The soft measurement and control method of freeness based on internet of things edge calculation according to claim 1, wherein the method is characterized in that: the cloud server stores performance parameters of the grinding disc of different manufacturers, the life cycle of the grinding disc and the attributes of wood chips in different areas, can calculate intermediate coefficients affecting the beating degree according to the performance parameters of the grinding disc, the life cycle of the grinding disc and the attributes of the wood chips, and then transmits the calculated intermediate coefficients back to the edge box to participate in the calculation of the soft beating degree measured value SRr of the edge box.
5. The soft measurement and control method of freeness based on internet of things edge calculation according to claim 1, wherein the method is characterized in that: when the constant beating degree beating of the grinding disc is realized, the beating degree soft measurement value SRr obtained by the edge box is compared with a set value, the deviation value delta is calculated by the control unit to obtain an adjustment quantity, and the feeding quantity of the grinding disc feeding and discharging motor is adjusted, so that the gap of the grinding disc can be adjusted, and the beating degree of the slurry is changed until delta-0.
6. The soft measurement and control method of freeness based on internet of things edge calculation according to claim 1, wherein the method is characterized in that: and the edge box is internally integrated with a data fitting and neural network self-learning algorithm.
7. The soft measurement and control method of freeness based on internet of things edge calculation according to claim 6, wherein the soft measurement and control method is characterized in that: the calculation of the edge box comprises the following steps:
inputting the beating degree measured in a laboratory;
the edge box obtains an initial beating degree algorithm model through a data fitting algorithm according to the beating degree measured in a laboratory, and the beating degree formula is as follows:
SR is beating degree, K is coefficient, W is active power of doing work of disc mill, C is concentration of disc mill, F is outlet flow of disc mill, T1 is percentage of running time of disc mill in total life cycle;
further obtains the nonlinear relation between the power and the freeness, and the relation is curved, so that the freeness formula is
B is a constant;
according to the formula model, data fitting is carried out to obtain relevant parameters, and an accurate model is obtained;
training an initial beating degree algorithm model;
and the trained freeness algorithm is further inserted into an optimization calculation model through a neural network self-learning algorithm, so that an accurate freeness soft measurement value SRr is obtained.
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