CN113724795A - Method and device for calculating concentration of raffinate hydrogen peroxide, storage medium and processor - Google Patents
Method and device for calculating concentration of raffinate hydrogen peroxide, storage medium and processor Download PDFInfo
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- MHAJPDPJQMAIIY-UHFFFAOYSA-N Hydrogen peroxide Chemical compound OO MHAJPDPJQMAIIY-UHFFFAOYSA-N 0.000 title claims abstract description 441
- 238000000034 method Methods 0.000 title claims abstract description 48
- 238000013528 artificial neural network Methods 0.000 claims abstract description 35
- 238000012549 training Methods 0.000 claims abstract description 33
- 238000004364 calculation method Methods 0.000 claims abstract description 7
- 239000007789 gas Substances 0.000 claims description 50
- 230000001105 regulatory effect Effects 0.000 claims description 18
- 239000007788 liquid Substances 0.000 claims description 17
- 238000000605 extraction Methods 0.000 claims description 14
- 238000012545 processing Methods 0.000 claims description 12
- 238000001514 detection method Methods 0.000 claims description 8
- 238000012360 testing method Methods 0.000 claims description 8
- 239000000126 substance Substances 0.000 abstract description 4
- 238000010586 diagram Methods 0.000 description 10
- 238000004590 computer program Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 7
- 230000006870 function Effects 0.000 description 5
- 230000002159 abnormal effect Effects 0.000 description 4
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 4
- 239000001301 oxygen Substances 0.000 description 4
- 229910052760 oxygen Inorganic materials 0.000 description 4
- PYKYMHQGRFAEBM-UHFFFAOYSA-N anthraquinone Natural products CCC(=O)c1c(O)c2C(=O)C3C(C=CC=C3O)C(=O)c2cc1CC(=O)OC PYKYMHQGRFAEBM-UHFFFAOYSA-N 0.000 description 3
- 150000004056 anthraquinones Chemical class 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000007254 oxidation reaction Methods 0.000 description 3
- 238000005984 hydrogenation reaction Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000001590 oxidative effect Effects 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 1
- WUOFAZKBVWWYER-UHFFFAOYSA-N anthracene-9,10-dione;hydrogen peroxide Chemical compound OO.C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 WUOFAZKBVWWYER-UHFFFAOYSA-N 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000001311 chemical methods and process Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 239000001257 hydrogen Substances 0.000 description 1
- 229910052739 hydrogen Inorganic materials 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 230000003647 oxidation Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000012224 working solution Substances 0.000 description 1
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C10/00—Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/70—Machine learning, data mining or chemometrics
Abstract
The invention provides a method and a device for calculating the concentration of raffinate hydrogen peroxide, a storage medium and a processor, and belongs to the technical field of chemical industry. The calculation method comprises the following steps: detecting the current data of other sites except the concentration of the raffinate hydrogen peroxide of the hydrogen peroxide device; and calculating the raffinate hydrogen peroxide concentration of the hydrogen peroxide device based on a hydrogen peroxide concentration prediction model according to the current data of other sites, wherein the hydrogen peroxide concentration prediction model is obtained by performing neural network training on the fluctuation historical data of the raffinate hydrogen peroxide concentration and the historical data of other sites related to the fluctuation historical data of the raffinate hydrogen peroxide concentration. The invention does not depend on manpower and provides guarantee for safe operation.
Description
Technical Field
The invention relates to the technical field of chemical industry, in particular to a method and a device for calculating the concentration of raffinate hydrogen peroxide, a storage medium and a processor.
Background
Although the technological scale of the anthraquinone method for preparing hydrogen peroxide has the characteristics of small scale and low structural complexity compared with other chemical processes, the process device often has accidents due to poor reaction stability and high danger of products in the process, thereby causing serious loss of personnel and property. In the anthraquinone method hydrogen peroxide process, the hydrogen peroxide content in the raffinate is a key index for the safe operation of the device, for example, if the hydrogen peroxide concentration is too high, oxygen is generated by decomposition in a hydrogenation tower, and the risk of gas phase explosion is caused by mixing the hydrogen and the oxygen. The control of the concentration of the raffinate hydrogen peroxide has important significance for safe production. The existing hydrogen peroxide extraction tower does not have on-line measuring equipment for the concentration of the raffinate hydrogen peroxide, manual sampling is adopted, and the method has long sampling interval time and high cost. In addition, an automatic hydrogen peroxide concentration tester for raffinate in the existing technology for preparing hydrogen peroxide by the anthraquinone method is high in cost and large in measurement error, and emissions cannot reach the environmental protection standard and cannot meet the practical requirements of hydrogen peroxide enterprises in China.
Disclosure of Invention
The invention aims to provide a method and a device for calculating the concentration of raffinate hydrogen peroxide, a storage medium and a processor, which are independent of manpower and provide guarantee for safe operation.
In order to achieve the above object, the present invention provides a method for calculating a raffinate hydrogen peroxide concentration of a hydrogen peroxide solution device, the method comprising: detecting the current data of other sites except the concentration of the raffinate hydrogen peroxide of the hydrogen peroxide device; and calculating the raffinate hydrogen peroxide concentration of the hydrogen peroxide device based on a hydrogen peroxide concentration prediction model according to the current data of other sites, wherein the hydrogen peroxide concentration prediction model is obtained by performing neural network training on the fluctuation historical data of the raffinate hydrogen peroxide concentration and the historical data of other sites related to the fluctuation historical data of the raffinate hydrogen peroxide concentration.
Preferably, the fluctuation historical data is historical data in which the time length of the data which abnormally operates within a preset time length is greater than a preset percentage of the preset time length.
Preferably, the hydrogen peroxide concentration prediction model is obtained by: obtaining fluctuation historical data of the concentration of the raffinate hydrogen peroxide; calculating a gray level correlation coefficient of the fluctuation historical data and the historical data of each of other sites at the same operation time; extracting the fluctuation historical data and historical data of other sites of which the gray-scale correlation coefficient is larger than a correlation coefficient threshold value related to the site type; and training based on the extracted data and the neural network to obtain the hydrogen peroxide concentration prediction model.
Preferably, the gray scale correlation coefficient is calculated by the following formula:
Preferably, the other sites for which the gray scale correlation coefficient is greater than the correlation coefficient threshold value related to the site type include: the gas phase temperature behind the oxidized tail gas heat exchanger, the liquid level of the working liquid receiving tank, the opening degree of the gas phase temperature control regulating valve behind the oxidized tail gas heat exchanger and the opening degree of the extraction tower top boundary position control regulating valve.
Preferably, the neural network training uses a MATLAB neural network.
Preferably, 70% of the fluctuation history data of the raffinate hydrogen peroxide concentration and the history data of other sites related to the fluctuation history data of the raffinate hydrogen peroxide concentration are used for training the hydrogen peroxide concentration prediction model, 15% of the data are used for testing the hydrogen peroxide concentration prediction model, and 15% of the data are used for verifying the hydrogen peroxide concentration prediction model.
The invention also provides a device for calculating the raffinate hydrogen peroxide concentration of the hydrogen peroxide device, which comprises: the detection unit is used for detecting the current data of other sites except the raffinate hydrogen peroxide concentration of the hydrogen peroxide device; the processing unit is used for calculating the raffinate hydrogen peroxide concentration of the hydrogen peroxide device according to the current data of other sites based on a hydrogen peroxide concentration prediction model, wherein the hydrogen peroxide concentration prediction model is obtained by performing neural network training on the fluctuation historical data of the raffinate hydrogen peroxide concentration and the historical data of other sites related to the fluctuation historical data of the raffinate hydrogen peroxide concentration.
Preferably, the fluctuation historical data is historical data in which the time length of the data which abnormally operates within a preset time length is greater than a preset percentage of the preset time length.
Preferably, the hydrogen peroxide concentration prediction model is obtained by: obtaining fluctuation historical data of the concentration of the raffinate hydrogen peroxide; calculating a gray level correlation coefficient of the fluctuation historical data and the historical data of each of other sites at the same operation time; extracting the fluctuation historical data and historical data of other sites of which the gray-scale correlation coefficient is larger than a correlation coefficient threshold value related to the site type; and training based on the extracted data and the neural network to obtain the hydrogen peroxide concentration prediction model.
Preferably, the gray scale correlation coefficient is calculated by the following formula:
Preferably, the other sites for which the gray scale correlation coefficient is greater than the correlation coefficient threshold value related to the site type include: the gas phase temperature behind the oxidized tail gas heat exchanger, the liquid level of the working liquid receiving tank, the opening degree of the gas phase temperature control regulating valve behind the oxidized tail gas heat exchanger and the opening degree of the extraction tower top boundary position control regulating valve.
Preferably, the neural network training uses a MATLAB neural network.
Preferably, 70% of the fluctuation history data of the raffinate hydrogen peroxide concentration and the history data of other sites related to the fluctuation history data of the raffinate hydrogen peroxide concentration are used for training the hydrogen peroxide concentration prediction model, 15% of the data are used for testing the hydrogen peroxide concentration prediction model, and 15% of the data are used for verifying the hydrogen peroxide concentration prediction model.
The invention also provides a machine-readable storage medium, which stores instructions for causing a machine to execute the method for calculating the raffinate hydrogen peroxide concentration of the hydrogen peroxide device.
The invention also provides a processor for running a program, wherein the program is used for executing the method for calculating the raffinate hydrogen peroxide concentration of the hydrogen peroxide device when being run.
By adopting the technical scheme, the method, the device, the storage medium and the processor for calculating the concentration of the raffinate hydrogen peroxide provided by the invention comprise the following steps: detecting the current data of other sites except the concentration of the raffinate hydrogen peroxide of the hydrogen peroxide device; and calculating the raffinate hydrogen peroxide concentration of the hydrogen peroxide device based on a hydrogen peroxide concentration prediction model according to the current data of other sites, wherein the hydrogen peroxide concentration prediction model is obtained by performing neural network training on the fluctuation historical data of the raffinate hydrogen peroxide concentration and the historical data of other sites related to the fluctuation historical data of the raffinate hydrogen peroxide concentration. The invention does not depend on manpower and provides guarantee for safe operation.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
fig. 1 is a flowchart of a method for calculating a raffinate hydrogen peroxide concentration of a hydrogen peroxide apparatus according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for establishing a model for predicting hydrogen peroxide concentration according to an embodiment of the present invention;
fig. 3 is a block diagram of a raffinate hydrogen peroxide concentration calculation device of a hydrogen peroxide solution device according to an embodiment of the present invention.
Description of the reference numerals
1 detection unit 2 processing unit
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating embodiments of the invention, are given by way of illustration and explanation only, not limitation.
Example one
Fig. 1 is a flowchart of a method for calculating a raffinate hydrogen peroxide concentration of a hydrogen peroxide apparatus according to an embodiment of the present invention. As shown in fig. 1, the calculation method includes:
step S11, detecting the current data of other sites except the raffinate hydrogen peroxide concentration of the hydrogen peroxide device;
specifically, the sites may be different measurement objects of different chemical apparatuses, such as the oxygen content of the tail gas of the hydrogenation tower, the oxygen content of the tail gas of the oxidation tower, or the concentration of the raffinate hydrogen peroxide, and may also be different measurement objects in the same chemical apparatus, for example, for a hydrogen peroxide apparatus, particularly an anthraquinone hydrogen peroxide apparatus, the sites may be the concentration of the raffinate hydrogen peroxide, the temperature of the gas phase after oxidizing the tail gas heat exchanger, the liquid level of the working solution in the tank, the opening of the gas phase temperature control regulating valve after oxidizing the tail gas heat exchanger, or the opening of the extraction tower top boundary position control regulating valve, and the like. In the embodiment of the present invention, if the raffinate hydrogen peroxide concentration is desired to be detected, the current data of other sites than the raffinate hydrogen peroxide concentration may be detected.
Step S12, calculating the raffinate hydrogen peroxide concentration of the hydrogen peroxide device according to the current data of other sites based on a hydrogen peroxide concentration prediction model, wherein the hydrogen peroxide concentration prediction model is a model obtained by performing neural network training on fluctuation historical data of the raffinate hydrogen peroxide concentration and historical data of other sites related to the fluctuation historical data of the raffinate hydrogen peroxide concentration, and the fluctuation historical data is historical data of which the duration of abnormal operation data in a preset duration is greater than the preset percentage of the preset duration.
Specifically, the embodiment of the invention substitutes the current data of other detected sites to calculate the raffinate hydrogen peroxide concentration based on the hydrogen peroxide concentration prediction model. The hydrogen peroxide concentration prediction model is obtained by performing neural network training on the fluctuation historical data of the raffinate hydrogen peroxide concentration and the historical data of other sites related to the fluctuation historical data of the raffinate hydrogen peroxide concentration. The fluctuation history data refers to history data in which the duration of the data abnormally operated within a preset duration is greater than a preset percentage of the preset duration, and the preset percentage may be 30%, but is not limited thereto. For example, if the site has 3 months of historical data and the preset time is 1 month, all the historical data can be divided into three parts, and of the 1 st month of historical data, 10 days of historical data are data of abnormal operation (abnormal operation can be marked on the data), and then 10 days account for about 33% of the 1 st month, and are greater than the preset percentage, then the 1 st month of historical data are fluctuation historical data. In the historical data of month 2 and month 3, the historical data of day 5 and day 4 are abnormal operation data, the percentage of month 1 is less than 30%, and the historical data of month 2 and month 3 are not fluctuation historical data. The historical data of other sites related to the fluctuation historical data can be directly obtained, and can also be obtained according to the obtaining mode provided by the following embodiment of the invention.
Example two
In the present embodiment, a difference from the embodiment is that a method for establishing a hydrogen peroxide concentration prediction model is mainly provided, in particular, a more detailed method for obtaining historical data of other sites related to fluctuation historical data is provided.
Specifically, the hydrogen peroxide concentration prediction model is obtained by:
step S21, obtaining fluctuation historical data of the raffinate hydrogen peroxide concentration;
specifically, the data acquired in the embodiment of the present invention may be operation data stored in a Distributed Control System (DCS) or Laboratory Information Management System (LIMS) record data, but not all the history data of the site is acquired, and only the fluctuation history data is acquired. The meaning of the fluctuation history data is as described above.
Step S22, calculating the grey scale correlation coefficient of the fluctuation historical data and the historical data of each of other sites at the same operation time;
specifically, since the historical data of other sites may also have a certain influence on the historical data of the hydrogen peroxide concentration, the embodiment of the present invention further needs to determine the correlation between the fluctuation historical data and the historical data of other sites at the same operation time. The same time of operation means that if the fluctuation history data is data of 1 month in 2020 (even, in particular, several minutes and several seconds to several days), the history data of other points at which the gradation correlation coefficient is calculated should use the data of 1 month in 2020. Since there may be many other sites, the historical data of each site corresponding to the operation time can be used to calculate the gray scale correlation coefficient with the fluctuation historical data.
Step S23, extracting the fluctuation historical data and historical data of other sites with the gray scale correlation coefficient larger than the correlation coefficient threshold value related to the site type;
specifically, if the gray scale correlation coefficient calculated by the historical data of the H site and the fluctuation historical data is greater than the correlation coefficient threshold, it indicates that the historical data of the H site and the fluctuation historical data have a large correlation, and extraction is required, that is, on the basis of extracting the fluctuation historical data, the historical data of the H site is also required to be extracted. The threshold value of the correlation coefficient is different according to the site to be detected, for example, the site to be detected is the concentration of raffinate hydrogen peroxide, and the threshold value of the correlation coefficient can be 0.55.
And step S24, training based on the extracted data and the neural network to obtain the hydrogen peroxide concentration prediction model.
Specifically, for example, the MATLAB neural network toolbox is used for performing the operation, the number of nodes of an input layer of the neural network is the number of other sites, the number of nodes of an intermediate layer is 7, the number of nodes of each layer is 50, and the number of nodes of an output layer is 1, which corresponds to the output variable. 70% of the data samples were used to train the model, 15% of the data samples were used to test the model, and 15% of the data samples were used to validate the model.
EXAMPLE III
In this embodiment, the difference from the first and second embodiments is that a more detailed method for calculating the gray scale correlation coefficient between the fluctuation history data and the history data at one of the other sites at the same operation time is mainly provided, and the other calculation methods are similar to those in the first and second embodiments and will not be described again here.
Specifically, the gray scale correlation coefficient is calculated by the following formula:
Example four
In the present embodiment, different from the first to third embodiments, a preferable preset time length, a data interval, other points related to fluctuation history data calculated by the above formula, and a preferable hydrogen peroxide concentration prediction model obtained using the above method are mainly provided. Other extraction methods are similar to those in the first to third embodiments, and are not described herein again.
Specifically, for the preset time period and the data interval, the preset time period may be 10 days and the data interval may be 4 hours for the detection of the hydrogen peroxide concentration.
The sites that are preferably associated with fluctuation history data, i.e., other sites whose gray scale correlation coefficient is greater than the correlation coefficient threshold associated with the site category, include: the gas phase temperature behind the oxidized tail gas heat exchanger, the liquid level of the working liquid receiving tank, the opening degree of the gas phase temperature control regulating valve behind the oxidized tail gas heat exchanger and the opening degree of the extraction tower top boundary position control regulating valve. The four gray scale correlation coefficients calculated by the above formula are:
based on this, the obtained hydrogen peroxide concentration prediction model is:
X(t)＝0.2994·X1(t)+0.1012·X2(t)+0.6557·X3(t)+0.0224·X4(t)
wherein, X1Is the current post-oxidation tail gas heat exchanger gas phase temperature A, X2For the current working fluid receiving tank level, X3The current post-oxidation gas phase temperature B, X of the tail gas heat exchanger4Is the current boundary position of the top of the extraction tower, and X is the concentration of the raffinate hydrogen peroxide. The gas phase temperature A after the tail gas heat exchanger is oxidized and the gas phase temperature B after the tail gas heat exchanger is oxidized are gas phase temperatures measured at different positions after the gas passes through the tail gas heat exchanger.
Although the specific values are provided in the embodiment, the values are only preferable values in the embodiment of the present invention, and may be other values, which is not limited in the embodiment of the present invention.
EXAMPLE five
Fig. 3 is a block diagram of a raffinate hydrogen peroxide concentration calculation device of a hydrogen peroxide solution device according to an embodiment of the present invention. As shown in fig. 3, the computing device includes: the device comprises a detection unit 1 and a processing unit 2, wherein the detection unit 1 is used for detecting current data of other sites except for the raffinate hydrogen peroxide concentration of the hydrogen peroxide device; the processing unit 2 is configured to calculate the raffinate hydrogen peroxide concentration of the hydrogen peroxide device based on a hydrogen peroxide concentration prediction model according to the current data of the other sites, where the hydrogen peroxide concentration prediction model is obtained by performing neural network training on the fluctuation history data of the raffinate hydrogen peroxide concentration and the history data of the other sites related to the fluctuation history data of the raffinate hydrogen peroxide concentration.
Preferably, the fluctuation historical data is historical data in which the time length of the data which abnormally operates within a preset time length is greater than a preset percentage of the preset time length.
Preferably, the hydrogen peroxide concentration prediction model is obtained by: obtaining fluctuation historical data of the concentration of the raffinate hydrogen peroxide; calculating a gray level correlation coefficient of the fluctuation historical data and the historical data of each of other sites at the same operation time; extracting the fluctuation historical data and historical data of other sites of which the gray-scale correlation coefficient is larger than a correlation coefficient threshold value related to the site type; and training based on the extracted data and the neural network to obtain the hydrogen peroxide concentration prediction model.
Preferably, the gray scale correlation coefficient is calculated by the following formula:
Preferably, the other sites for which the gray scale correlation coefficient is greater than the correlation coefficient threshold value related to the site type include: the gas phase temperature behind the oxidized tail gas heat exchanger, the liquid level of the working liquid receiving tank, the opening degree of the gas phase temperature control regulating valve behind the oxidized tail gas heat exchanger and the opening degree of the extraction tower top boundary position control regulating valve.
Preferably, the neural network training uses a MATLAB neural network.
Preferably, 70% of the fluctuation history data of the raffinate hydrogen peroxide concentration and the history data of other sites related to the fluctuation history data of the raffinate hydrogen peroxide concentration are used for training the hydrogen peroxide concentration prediction model, 15% of the data are used for testing the hydrogen peroxide concentration prediction model, and 15% of the data are used for verifying the hydrogen peroxide concentration prediction model.
The embodiment provides a raffinate hydrogen peroxide concentration calculating device of a hydrogen peroxide device, which is similar to the first to fourth embodiments described above and is not described herein again.
The raffinate hydrogen peroxide concentration calculating device comprises a processor and a memory, wherein the detection unit, the processing unit and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. One or more than one inner core can be arranged, and the raffinate hydrogen peroxide concentration is detected by adjusting the inner core parameters.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
An embodiment of the present invention provides a storage medium having a program stored thereon, where the program is executed by a processor to implement the method for calculating the raffinate hydrogen peroxide concentration.
The embodiment of the invention provides a processor, which is used for running a program, wherein the method for calculating the raffinate hydrogen peroxide concentration is executed when the program runs.
The embodiment of the invention provides equipment, which comprises a processor, a memory and a program which is stored on the memory and can run on the processor, wherein the processor executes the program and realizes the following steps:
detecting the current data of other sites except the concentration of the raffinate hydrogen peroxide of the hydrogen peroxide device; and calculating the raffinate hydrogen peroxide concentration of the hydrogen peroxide device based on a hydrogen peroxide concentration prediction model according to the current data of other sites, wherein the hydrogen peroxide concentration prediction model is obtained by performing neural network training on the fluctuation historical data of the raffinate hydrogen peroxide concentration and the historical data of other sites related to the fluctuation historical data of the raffinate hydrogen peroxide concentration.
The fluctuation historical data is historical data of which the time length of the data which abnormally operates in a preset time length is greater than a preset percentage of the preset time length.
The hydrogen peroxide concentration prediction model is obtained by the following method: obtaining fluctuation historical data of the concentration of the raffinate hydrogen peroxide; calculating a gray level correlation coefficient of the fluctuation historical data and the historical data of each of other sites at the same operation time; extracting the fluctuation historical data and historical data of other sites of which the gray-scale correlation coefficient is larger than a correlation coefficient threshold value related to the site type; and training based on the extracted data and the neural network to obtain the hydrogen peroxide concentration prediction model.
The gray scale correlation coefficient is calculated by the following formula:
Other sites for which the gray scale correlation coefficient is greater than a correlation coefficient threshold associated with the site class include: the gas phase temperature behind the oxidized tail gas heat exchanger, the liquid level of the working liquid receiving tank, the opening degree of the gas phase temperature control regulating valve behind the oxidized tail gas heat exchanger and the opening degree of the extraction tower top boundary position control regulating valve.
The neural network training uses a MATLAB neural network.
70% of the fluctuation historical data of the raffinate hydrogen peroxide concentration and the historical data of other sites related to the fluctuation historical data of the raffinate hydrogen peroxide concentration are used for training the hydrogen peroxide concentration prediction model, 15% of the data are used for testing the hydrogen peroxide concentration prediction model, and 15% of the data are used for verifying the hydrogen peroxide concentration prediction model.
The device herein may be a server, a PC, a PAD, a mobile phone, etc.
The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device:
detecting the current data of other sites except the concentration of the raffinate hydrogen peroxide of the hydrogen peroxide device; and calculating the raffinate hydrogen peroxide concentration of the hydrogen peroxide device based on a hydrogen peroxide concentration prediction model according to the current data of other sites, wherein the hydrogen peroxide concentration prediction model is obtained by performing neural network training on the fluctuation historical data of the raffinate hydrogen peroxide concentration and the historical data of other sites related to the fluctuation historical data of the raffinate hydrogen peroxide concentration.
The fluctuation historical data is historical data of which the time length of the data which abnormally operates in a preset time length is greater than a preset percentage of the preset time length.
The hydrogen peroxide concentration prediction model is obtained by the following method: obtaining fluctuation historical data of the concentration of the raffinate hydrogen peroxide; calculating a gray level correlation coefficient of the fluctuation historical data and the historical data of each of other sites at the same operation time; extracting the fluctuation historical data and historical data of other sites of which the gray-scale correlation coefficient is larger than a correlation coefficient threshold value related to the site type; and training based on the extracted data and the neural network to obtain the hydrogen peroxide concentration prediction model.
The gray scale correlation coefficient is calculated by the following formula:
Other sites for which the gray scale correlation coefficient is greater than a correlation coefficient threshold associated with the site class include: the gas phase temperature behind the oxidized tail gas heat exchanger, the liquid level of the working liquid receiving tank, the opening degree of the gas phase temperature control regulating valve behind the oxidized tail gas heat exchanger and the opening degree of the extraction tower top boundary position control regulating valve.
The neural network training uses a MATLAB neural network.
70% of the fluctuation historical data of the raffinate hydrogen peroxide concentration and the historical data of other sites related to the fluctuation historical data of the raffinate hydrogen peroxide concentration are used for training the hydrogen peroxide concentration prediction model, 15% of the data are used for testing the hydrogen peroxide concentration prediction model, and 15% of the data are used for verifying the hydrogen peroxide concentration prediction model.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (16)
1. A method for calculating the concentration of hydrogen peroxide raffinate of a hydrogen peroxide device is characterized by comprising the following steps:
detecting the current data of other sites except the concentration of the raffinate hydrogen peroxide of the hydrogen peroxide device;
and calculating the raffinate hydrogen peroxide concentration of the hydrogen peroxide device based on a hydrogen peroxide concentration prediction model according to the current data of other sites, wherein the hydrogen peroxide concentration prediction model is obtained by performing neural network training on the fluctuation historical data of the raffinate hydrogen peroxide concentration and the historical data of other sites related to the fluctuation historical data of the raffinate hydrogen peroxide concentration.
2. The method for calculating the raffinate hydrogen peroxide concentration of a hydrogen peroxide apparatus according to claim 1, wherein the fluctuation history data is history data in which the duration of data that abnormally operates within a preset duration is greater than a preset percentage of the preset duration.
3. The method for calculating the raffinate hydrogen peroxide concentration of a hydrogen peroxide device according to claim 1, wherein the hydrogen peroxide concentration prediction model is obtained by:
obtaining fluctuation historical data of the concentration of the raffinate hydrogen peroxide;
calculating a gray level correlation coefficient of the fluctuation historical data and the historical data of each of other sites at the same operation time;
extracting the fluctuation historical data and historical data of other sites of which the gray-scale correlation coefficient is larger than a correlation coefficient threshold value related to the site type;
and training based on the extracted data and the neural network to obtain the hydrogen peroxide concentration prediction model.
4. The method for calculating the raffinate hydrogen peroxide concentration of a hydrogen peroxide device according to claim 2, wherein the gray scale correlation coefficient is calculated by the following formula:
5. The method for calculating the raffinate hydrogen peroxide concentration of a hydrogen peroxide apparatus according to claim 3, wherein the other sites at which the gray scale correlation coefficient is greater than the correlation coefficient threshold value related to the site type include:
the gas phase temperature behind the oxidized tail gas heat exchanger, the liquid level of the working liquid receiving tank, the opening degree of the gas phase temperature control regulating valve behind the oxidized tail gas heat exchanger and the opening degree of the extraction tower top boundary position control regulating valve.
6. The method for calculating the raffinate hydrogen peroxide concentration of a hydrogen peroxide apparatus according to claim 1, wherein the neural network training uses a MATLAB neural network.
7. The method for calculating the raffinate hydrogen peroxide concentration of a hydrogen peroxide apparatus according to claim 1, wherein 70% of the fluctuation history data of the raffinate hydrogen peroxide concentration and the history data of other sites related to the fluctuation history data of the raffinate hydrogen peroxide concentration are used for training the hydrogen peroxide concentration prediction model, 15% of the data are used for testing the hydrogen peroxide concentration prediction model, and 15% of the data are used for verifying the hydrogen peroxide concentration prediction model.
8. A hydrogen peroxide concentration computing device is left in extraction of hydrogen peroxide solution device, its characterized in that, this computing device includes:
a detection unit and a processing unit, wherein,
the detection unit is used for detecting the current data of other sites except the raffinate hydrogen peroxide concentration of the hydrogen peroxide device;
the processing unit is used for calculating the raffinate hydrogen peroxide concentration of the hydrogen peroxide device according to the current data of other sites based on a hydrogen peroxide concentration prediction model, wherein the hydrogen peroxide concentration prediction model is obtained by performing neural network training on the fluctuation historical data of the raffinate hydrogen peroxide concentration and the historical data of other sites related to the fluctuation historical data of the raffinate hydrogen peroxide concentration.
9. The raffinate hydrogen peroxide concentration calculating device of a hydrogen peroxide apparatus according to claim 8, wherein the fluctuation history data is history data in which a time length of data that abnormally operates within a preset time length is greater than a preset percentage of the preset time length.
10. The hydrogen peroxide raffinate concentration calculation device of claim 8, wherein the hydrogen peroxide concentration prediction model is obtained by:
obtaining fluctuation historical data of the concentration of the raffinate hydrogen peroxide;
calculating a gray level correlation coefficient of the fluctuation historical data and the historical data of each of other sites at the same operation time;
extracting the fluctuation historical data and historical data of other sites of which the gray-scale correlation coefficient is larger than a correlation coefficient threshold value related to the site type;
and training based on the extracted data and the neural network to obtain the hydrogen peroxide concentration prediction model.
11. The hydrogen peroxide raffinate concentration calculation device of claim 9, wherein the grey scale correlation coefficient is calculated by the following formula:
12. The apparatus for calculating raffinate hydrogen peroxide concentration according to claim 10, wherein the other sites at which the gray scale correlation coefficient is greater than the correlation coefficient threshold value related to the site type include:
the gas phase temperature behind the oxidized tail gas heat exchanger, the liquid level of the working liquid receiving tank, the opening degree of the gas phase temperature control regulating valve behind the oxidized tail gas heat exchanger and the opening degree of the extraction tower top boundary position control regulating valve.
13. The apparatus for calculating raffinate hydrogen peroxide concentration according to claim 8, wherein the neural network training uses a MATLAB neural network.
14. The raffinate hydrogen peroxide concentration calculating device of a hydrogen peroxide device according to claim 8, wherein 70% of the fluctuation history data of the raffinate hydrogen peroxide concentration and the history data of other sites related to the fluctuation history data of the raffinate hydrogen peroxide concentration are used for training the hydrogen peroxide concentration prediction model, 15% of the data are used for testing the hydrogen peroxide concentration prediction model, and 15% of the data are used for verifying the hydrogen peroxide concentration prediction model.
15. A machine-readable storage medium having stored thereon instructions for causing a machine to perform the method of calculating raffinate hydrogen peroxide concentration of a hydrogen peroxide solution apparatus according to any one of claims 1 to 7.
16. A processor configured to run a program, wherein the program is configured to execute the method for calculating the raffinate hydrogen peroxide concentration of a hydrogen peroxide apparatus according to any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115821265A (en) * | 2022-12-14 | 2023-03-21 | 苏州圆格电子有限公司 | Method and system for removing copper layer on surface of neodymium iron boron |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105621364A (en) * | 2014-11-03 | 2016-06-01 | 中国石油化工股份有限公司 | High-efficiency extraction process in hydrogen peroxide production process |
| CN105731385A (en) * | 2016-03-29 | 2016-07-06 | 福建永荣科技有限公司 | Hydrogen peroxide oxidization extracting tower and method |
| CN107730087A (en) * | 2017-09-20 | 2018-02-23 | 平安科技（深圳）有限公司 | Forecast model training method, data monitoring method, device, equipment and medium |
| CN109190314A (en) * | 2018-10-30 | 2019-01-11 | 河海大学 | The prediction technique of the power transformer top-oil temperature of neural network based on Adam optimization |
| CN109765331A (en) * | 2018-10-08 | 2019-05-17 | 陕西科技大学 | A kind of oxygen content of smoke gas hard measurement system based on least square method supporting vector machine |
-
2020
- 2020-05-25 CN CN202010451648.6A patent/CN113724795A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105621364A (en) * | 2014-11-03 | 2016-06-01 | 中国石油化工股份有限公司 | High-efficiency extraction process in hydrogen peroxide production process |
| CN105731385A (en) * | 2016-03-29 | 2016-07-06 | 福建永荣科技有限公司 | Hydrogen peroxide oxidization extracting tower and method |
| CN107730087A (en) * | 2017-09-20 | 2018-02-23 | 平安科技（深圳）有限公司 | Forecast model training method, data monitoring method, device, equipment and medium |
| CN109765331A (en) * | 2018-10-08 | 2019-05-17 | 陕西科技大学 | A kind of oxygen content of smoke gas hard measurement system based on least square method supporting vector machine |
| CN109190314A (en) * | 2018-10-30 | 2019-01-11 | 河海大学 | The prediction technique of the power transformer top-oil temperature of neural network based on Adam optimization |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115821265A (en) * | 2022-12-14 | 2023-03-21 | 苏州圆格电子有限公司 | Method and system for removing copper layer on surface of neodymium iron boron |
| CN115821265B (en) * | 2022-12-14 | 2023-12-19 | 苏州圆格电子有限公司 | Method and system applied to removing copper layer on surface of neodymium iron boron |
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