CN111125951B - Optimization method and device for evaporator scaling prediction model - Google Patents
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Abstract
The application is applicable to the technical field of evaporators, and provides an optimization method and device of an evaporator scaling prediction model, wherein the method comprises the following steps: determining scaling prediction data through historical data, periodic data and internet of things data of an evaporator by using a preset first prediction model; descaling the evaporator according to scale prediction data; obtaining model optimization test data by using the descaled evaporator; determining a model optimization index of the first prediction model according to the standard data of the evaporator and the model optimization test data; and when the model optimization index meets the preset condition, correcting the first prediction model to obtain a second prediction model. The evaporator scaling prediction method provided by the application enables a user to effectively avoid the technical problems of evaporator scaling, reduced heat exchange efficiency of the evaporator and energy waste, and brings benefit improvement to factories.
Description
Technical Field
The application belongs to the technical field of evaporators, and particularly relates to an optimization method and device for an evaporator scaling prediction model.
Background
The evaporator heats the solution containing non-volatile solute in the raw material liquid to boiling state by heating, so that part of the solvent is vaporized and removed, thereby improving the concentration of the solute in the solvent, and the evaporator is widely applied to the industries such as chemical industry, food industry, pharmacy and the like. But the evaporator may scale during long-term use. Once the evaporator is fouled, the steam consumption will increase significantly, wasting a lot of energy. Predictive maintenance of evaporator fouling is necessary. Not only can save a large amount of energy sources, but also the efficiency of the equipment is improved. The descaling is reasonably arranged, the emergency stop under extreme conditions is effectively avoided, and the benefit is improved for factories.
In the prior art, in-situ inspection is mostly adopted at present, and then maintenance such as cleaning and the like is arranged on key parts of the evaporator according to a preset scheme after the energy consumption is obviously increased through post judgment. In this way, during the post-maintenance, the heat exchange efficiency of the evaporator is reduced, the energy waste is a fact, and the production and operation benefits are adversely affected. However, the device mechanism modeling is difficult to realize under the constraint of the field internet of things condition.
Disclosure of Invention
In view of the above, the embodiments of the present application provide an optimization method, an apparatus, a terminal device, and a computer readable storage medium for an evaporator scaling prediction model, so as to solve the technical problem that in the prior art, the evaporator scaling cannot be effectively and accurately determined and timely removed due to the restriction of the on-site internet of things condition.
A first aspect of an embodiment of the present application provides a method for optimizing an evaporator scaling prediction model, including:
determining scaling prediction data through historical data, periodic data and internet of things data of an evaporator by using a preset first prediction model;
descaling the evaporator according to scale prediction data;
obtaining model optimization test data by using the descaled evaporator;
determining a model optimization index of the first prediction model according to the standard data of the evaporator and the model optimization test data;
and when the model optimization index meets the preset condition, correcting the first prediction model to obtain a second prediction model.
A second aspect of an embodiment of the present application provides an optimization apparatus for an evaporator scaling prediction model, including:
the scaling prediction data module is used for determining scaling prediction data through historical data, periodic data and internet of things data of the evaporator by utilizing a preset first prediction model;
the descaling instruction module is used for descaling the evaporator according to the scaling prediction data;
the test data module is used for obtaining model test data by utilizing the descaled evaporator;
the optimization index module is used for determining a model optimization index of the first prediction model according to the standard data of the evaporator and the model test data when the model test data meets preset conditions;
and the correction module is used for correcting the first prediction model according to the model optimization index to obtain a second prediction model.
In a third aspect of the embodiment of the present application, a terminal device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements steps of the data simulation method of the internet of things device when executing the computer program.
In a fourth aspect of the embodiments of the present application, there is provided a computer readable storage medium storing a computer program, which when executed by a processor, implements the steps of the data simulation method of an internet of things device.
The data acquisition method provided by the embodiment of the application has the beneficial effects that: according to the embodiment of the application, firstly, the preset first prediction model is utilized, the scaling prediction data is determined through the historical data, the periodic data and the internet of things data of the evaporator, secondly, the evaporator is subjected to scale removal according to the scaling prediction data, then the model test data is obtained through the evaporator subjected to scale removal, then, when the model test data meets preset conditions, the model optimization index of the first prediction model is determined according to the standard data of the evaporator and the model test data, and finally, the first prediction model is corrected according to the model optimization index to obtain the second prediction model, so that a user can perform scale prediction more accurately and efficiently, cost is effectively saved, and energy waste is avoided.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an implementation of an optimization method of an evaporator scaling prediction model provided by an embodiment of the application;
FIG. 2 is a schematic illustration of determining fouling prediction data provided by an embodiment of the present application;
FIG. 3 is a schematic illustration of determining fouling prediction data provided by an embodiment of the present application;
FIG. 4 is a schematic illustration of determining fouling prediction data provided by an embodiment of the present application;
FIG. 5 is a schematic diagram of obtaining initial parameters provided by an embodiment of the present application;
FIG. 6 is a schematic diagram of determining model optimization metrics for a first predictive model provided by an embodiment of the application;
FIG. 7 is a schematic structural diagram of an optimization device for an evaporator fouling prediction model provided by an embodiment of the application;
fig. 8 is a schematic diagram of a terminal device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail. All other embodiments, which are obtained by a person skilled in the art based on the described embodiments of the application, fall within the scope of protection of the application. The technical means used in the examples are conventional means well known to those skilled in the art unless otherwise indicated.
It should be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
As used in this specification and the appended claims, the term "if" may be interpreted as "when..once" or "in response to a determination" or "in response to detection" depending on the context. Similarly, the phrase "if a determination" or "if a [ described condition or event ] is detected" may be interpreted in the context of meaning "upon determination" or "in response to determination" or "upon detection of a [ described condition or event ]" or "in response to detection of a [ described condition or event ]".
In order to illustrate the technical scheme of the application, the following description is made by specific examples.
As shown in fig. 1, a schematic implementation flow chart of an optimization method of an evaporator scaling prediction model according to an embodiment of the present application is shown, where the method includes:
step S10: and determining scaling prediction data through historical data, periodic data and internet of things data of the evaporator by using a preset first prediction model.
Wherein the historical data are steam consumption, finished liquid generation amount, descaling critical value and the like.
The cycle data is the data of each cycle of the evaporator, and the method for collecting the cycle data is to use the first day of normal operation of the descaling evaporator as a cycle starting point and the first day of normal operation after the next descaling as an end point.
The internet of things data are valve switch, environment data, steam flow, pipeline diameter, liquid completion amount and the like.
And calculating the historical data, the periodic data and the Internet of things data through a preset first prediction model to obtain the scaling prediction data.
Step S20: and descaling the evaporator according to the scale prediction data.
Common ways of removing scale from evaporators are chemical cleaning and physical cleaning. Most cleaning methods employ chemical cleaning-acid cleaning, which is effective for various depositions and saves time compared with physical cleaning. The disadvantage is that chemical cleaning is corrosive to systems and other metal components, is prone to corrosion of equipment lines, and pollutes the environment during discharge.
And (3) according to the scaling prediction data obtained in the step (S10), obtaining a time node for scaling and the scaling degree, and timely scaling. Effectively avoiding energy waste caused by not timely descaling.
Step S30: and obtaining model test data by using the descaled evaporator.
And taking the numerical values such as steam consumption obtained after the first use of the evaporator after descaling and the finished liquid generation amount as the model test data. Because the test data obtained after the first descaling can more accurately show how the descaling effect is, and the interference accuracy of other factors can be avoided.
Step S40: and when the model test data meet preset conditions, determining the model optimization index of the first prediction model according to the standard data of the evaporator and the model test data.
The preset condition is that the model test data is smaller than the standard data, and when the preset condition is not met, namely the test data is larger than or equal to the standard data, the first prediction model is not required to be optimized; and when the preset condition is met, namely the predicted data is smaller than standard data, optimizing the first prediction model.
And taking the test data as a model optimization index of the first prediction model, wherein the test data is superior to the standard data.
Step S50: and correcting the first prediction model according to the model optimization index to obtain a second prediction model.
Calculating the percentages of the test data and the standard data through the model optimization index, carrying out average calculation through a plurality of percentages to obtain the total percentage, multiplying the descaling critical value by the total percentage, thereby finishing correction of the first model data, and obtaining a new prediction model, namely the second prediction model after updating model parameters.
As shown in fig. 2, an embodiment of the present application provides a method for optimizing an evaporator scaling prediction model, wherein step S10 includes:
step S11: and obtaining a corresponding predicted value through at least one prediction algorithm in the prediction model.
The prediction model is formed by combining a plurality of prediction algorithms, and can comprise, for example, a linear regression model, an exponential regression model, a multiple regression model, an elastic network regression model and the like. And obtaining a predicted value corresponding to the historical data, the periodic data and the internet of things data through the plurality of prediction algorithms by utilizing the historical data, the periodic data and the internet of things data included in the step S10. Each predicted value is a result predicted by the prediction algorithm corresponding to the corresponding data.
Step S12: and determining the scaling prediction data according to each prediction value.
And (3) calculating all the predicted values obtained in the step (S11) through the prediction effectiveness degree to obtain the scaling prediction data.
As shown in fig. 3, an embodiment of the present application provides a method for optimizing an evaporator scaling prediction model, where step S12 includes:
step S121: and weighting each predicted value by using a weight configured for each prediction algorithm to determine the scaling prediction data.
And (3) carrying out weighted calculation on the weight according to the weight obtained in the step S120 to obtain the structure prediction data.
As shown in fig. 4, an embodiment of the present application provides a method for optimizing an evaporator scaling prediction model, where before step S121, the method further includes:
and step S120, determining the weight of each prediction algorithm by using a prediction validity algorithm.
The prediction effectiveness algorithm is essentially-the sum of squares of combined prediction errors over a period of time is minimal. The prediction validity algorithm is used for weighting each prediction model.
e i,t ＝y t -y i,t Wherein e is i,t Is the error of the predicted value of the ith prediction algorithm at the t moment, y t Is the actual value at time t, y i,t The predicted value of the ith model at the moment t;
error e according to the above predicted value i,t Obtaining F i ＝[e i,1 ,e i,2 ,...,e i,n ] T ，F i The vector of the error of the predicted value of the ith prediction algorithm is represented by T, which is the matrix vector transposition;
according to the vector F i Obtaining an error matrix e, e= [ F ] 1 ,F 2 ,...,F r ]R is r different prediction algorithms;
obtaining an information error matrix E according to the error matrix E r ，
R r Let R be a standard vector r ＝[1,1,...,1] T The r-dimensional vector with the elements of all 1 is the weight vector of r prediction algorithms;
let W be the weight of each of the prediction algorithms, the sum of squares of the errors of the model test data be S,
obtaining linear programming from formula (1)
Obtaining the weight according to the formula (2)
As shown in fig. 5, an embodiment of the present application provides a method for optimizing an evaporator scaling prediction model, where in any step of fig. 1 to fig. 4, the method further includes:
step S100: and configuring initial parameters of the first prediction model by using the historical data.
And initializing the historical data such as the evaporation consumption of the evaporator, the generation amount of the finished liquid, the descaling critical value and the like to obtain the initial parameters of the first prediction model. Initializing the data can effectively avoid the model training speed from slowing down and collapsing until failure.
As shown in fig. 6, an embodiment of the present application provides a method for optimizing an evaporator scaling prediction model, wherein step S40 includes:
step S41: the performance parameters of the evaporator without fouling were determined as standard data.
The values of the steam consumption, the amount of produced liquid, and the like when the evaporator was not fouled were used as the standard data. This ensures the accuracy of the standard values, and is minimally affected by other conditions.
Step S42: and when the model test data is smaller than the standard data, determining the model optimization index according to the ratio of the test data to the standard data.
When the model test data is smaller than the standard data, the model optimization index is: the test data is divided by the standard data to obtain a percentage value, average calculation is carried out through a plurality of percentage values to obtain an overall percentage value, and the descaling critical value in the model is multiplied by the overall percentage to obtain the model optimization index.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
As shown in fig. 7, an object of an embodiment of the present application is to provide an optimization apparatus for an evaporator scaling prediction model, which includes a scaling prediction data module 61, a scaling instruction module 62, a test data module 63, an optimization index module 64, and a correction module 65. The scaling prediction data module 61 is configured to determine scaling prediction data according to historical data, periodic data and internet of things data of the evaporator by using a preset first prediction model; a descaling instruction module 62 for descaling the evaporator according to the scale prediction data; a test data module 63, configured to obtain model test data by using the descaled evaporator; an optimization index module 64, configured to determine a model optimization index of the first prediction model according to the standard data of the evaporator and the model test data when the model test data meets a preset condition; and the correction module 65 is configured to correct the first prediction model according to the model optimization index to obtain a second prediction model.
As shown in fig. 8, fig. 8 is a schematic diagram of a terminal device according to an embodiment of the present application. As shown in fig. 8, the terminal device 7 includes a memory 71, a processor 70, and a computer program 72 stored in the memory 71 and executable on the processor 70, wherein the processor 70 implements the steps of the data simulation method of the internet of things device when executing the computer program 72. Such as steps S10 to S50 shown in fig. 1-6.
The terminal device 7 may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The terminal device may include, but is not limited to, a processor 70, the memory 71. It will be appreciated by those skilled in the art that fig. 8 is merely an example of the terminal device 7 and does not constitute a limitation of the terminal device 7, and may include more or less components than illustrated, or may combine certain components, or different components, e.g., the terminal device may further include an input-output device, a network access device, a bus, etc.
The processor 70 may be a central processing unit (Central Processing Unit, CPU), or may be another general purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a Field-programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 71 may be an internal storage unit of the terminal device 7, such as a hard disk or a memory of the terminal device 7. The memory 71 may be an external storage device of the terminal device 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal device 7. Further, the memory 71 may also include both an internal storage unit and an external storage device of the terminal device 7. The memory 71 is used for storing the computer program as well as other programs and data required by the terminal device. The memory 71 may also be used for temporarily storing data that has been output or is to be output.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
Specifically, the embodiment of the present application further provides a computer readable storage medium, which may be a computer readable storage medium contained in the memory in the above embodiment; or may be a computer-readable storage medium, alone, that is not incorporated into the terminal device. The computer readable storage medium stores one or more computer programs:
a computer readable storage medium, comprising the computer readable storage medium storing a computer program, which when executed by a processor, implements the steps of the internet of things device data simulation method.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other manners. For example, the apparatus/terminal device embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (8)
1. A method of optimizing an evaporator fouling prediction model, comprising:
determining scaling prediction data through historical data, periodic data and internet of things data of an evaporator by using a preset first prediction model;
descaling the evaporator according to scale prediction data;
obtaining model test data by using the descaled evaporator; the model test data comprise steam consumption obtained by first use after the evaporator is descaled and finished liquid generation amount;
determining standard data of the evaporator, wherein the standard data are performance parameters of the evaporator without scaling, and at least comprise steam consumption and finished liquid generation amount of the evaporator without scaling;
when the model test data is smaller than the standard data, determining a model optimization index of the first prediction model according to the ratio of the standard data of the evaporator to the model test data; correcting the first prediction model according to the model optimization index to obtain a second prediction model; specific: and calculating the percentage of the test data and the standard data, carrying out average calculation through a plurality of percentages to obtain the total percentage, and multiplying the descaling critical value by the total percentage so as to finish the correction of the first prediction model.
2. The method for optimizing an evaporator fouling prediction model of claim 1, wherein determining fouling prediction data from historical data, cycle data and thing networking data of the evaporator using a preset first prediction model comprises:
obtaining a corresponding predicted value through at least one prediction algorithm in the prediction model;
and determining the scaling prediction data according to each prediction value.
3. A method of optimizing an evaporator fouling prediction model as set forth in claim 2 wherein said determining said fouling prediction data from each of said prediction values includes:
and weighting each predicted value by using a weight configured for each prediction algorithm to determine the scaling prediction data.
4. A method of optimizing an evaporator fouling prediction model as set forth in claim 3 wherein said weighting each of said predicted values with weights configured for each of said prediction algorithms further comprises, prior to determining said fouling prediction data:
and determining the weight of each prediction algorithm by using the prediction validity algorithm.
5. The method according to any one of claims 1 to 4, further comprising:
and configuring initial parameters of the first prediction model by using the historical data.
6. An optimization apparatus for an evaporator fouling prediction model, comprising:
the scaling prediction data module is used for determining scaling prediction data through historical data, periodic data and internet of things data of the evaporator by utilizing a preset first prediction model;
the descaling instruction module is used for descaling the evaporator according to the scaling prediction data;
the test data module is used for obtaining model test data by utilizing the descaled evaporator; the model test data comprise steam consumption obtained by first use after the evaporator is descaled and finished liquid generation amount;
the optimization index module is used for determining standard data of the evaporator, wherein the standard data are performance parameters of the evaporator when no scaling exists, and at least comprise steam consumption and finished liquid generation amount of the evaporator when no scaling exists; when the model test data is smaller than the standard data, determining a model optimization index of the first prediction model according to the ratio of the standard data of the evaporator to the model test data;
the correction module is used for correcting the first prediction model according to the model optimization index so as to adjust a descaling starting threshold of the first prediction model and obtain a second prediction model; specific: and calculating the percentage of the test data and the standard data, carrying out average calculation through a plurality of percentages to obtain the total percentage, and multiplying the descaling critical value by the total percentage so as to finish the correction of the first prediction model.
7. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 5 when the computer program is executed.
8. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1 to 5.
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