CN118349053A - Automatic control method and system for lithium carbonate production - Google Patents
Automatic control method and system for lithium carbonate production Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 90
- XGZVUEUWXADBQD-UHFFFAOYSA-L lithium carbonate Chemical compound [Li+].[Li+].[O-]C([O-])=O XGZVUEUWXADBQD-UHFFFAOYSA-L 0.000 title claims abstract description 38
- 229910052808 lithium carbonate Inorganic materials 0.000 title claims abstract description 38
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 30
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 32
- 238000005984 hydrogenation reaction Methods 0.000 claims abstract description 28
- 238000009826 distribution Methods 0.000 claims description 24
- 230000008569 process Effects 0.000 claims description 22
- 230000000694 effects Effects 0.000 claims description 17
- 230000035772 mutation Effects 0.000 claims description 15
- 230000006870 function Effects 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 4
- 238000000195 production control method Methods 0.000 claims 9
- 230000000875 corresponding effect Effects 0.000 description 54
- 238000006243 chemical reaction Methods 0.000 description 19
- 230000008859 change Effects 0.000 description 11
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 8
- 229910002092 carbon dioxide Inorganic materials 0.000 description 6
- 238000004422 calculation algorithm Methods 0.000 description 5
- 230000007423 decrease Effects 0.000 description 4
- 238000003860 storage Methods 0.000 description 4
- 230000001276 controlling effect Effects 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 239000001569 carbon dioxide Substances 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000007327 hydrogenolysis reaction Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000012847 fine chemical Substances 0.000 description 1
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- 230000004048 modification Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D23/00—Control of temperature
- G05D23/19—Control of temperature characterised by the use of electric means
- G05D23/20—Control of temperature characterised by the use of electric means with sensing elements having variation of electric or magnetic properties with change of temperature
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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
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- 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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Abstract
The invention relates to the technical field of data processing. In particular to an automatic control method and system for lithium carbonate production. The method comprises the following steps: acquiring the temperature in the hydrogenation reactor, thereby acquiring a temperature data sequence; determining the most preferable extension length of the temperature data sequence, and marking the most preferable extension length as the optimal extension length; decomposing the temperature data sequence by adopting an empirical mode decomposition method so as to obtain an IMF component; denoising each IMF component, and reconstructing the denoised IMF component to obtain a denoised temperature data sequence; acquiring temperature data closest to the current moment from the denoised temperature data sequence, and recording the temperature data as the current temperature; and PID control is carried out on the temperature in the hydrogenation reactor according to the difference value between the current temperature and the target temperature. The method can greatly improve the accuracy and efficiency of temperature control in the hydrogenation reactor in the production process of preparing lithium carbonate.
Description
Technical Field
The present invention relates generally to the field of data processing technology. More particularly, the invention relates to an automatic control method and system for lithium carbonate production.
Background
In the production process of preparing lithium carbonate by a hydrogenation decomposition method, the method relates to fine chemical reaction condition control and high purity requirements, is particularly sensitive to temperature management, and is particularly important to temperature automation control in production in order to improve production efficiency, ensure product quality and improve safety in the production process. In order to improve the accuracy of temperature control, the acquired temperature data needs to be denoised. A common method for denoising data is wavelet threshold denoising, when the wavelet threshold denoising is adopted, the problems of wavelet base and decomposition layer number are involved, if the wavelet base and decomposition layer number are not proper, the quality of denoising can be influenced, and a new method is proposed by students. For example, the journals entitled "empirical mode decomposition and application thereof in noise reduction" published by the university of martial arts and university of energy and motion, wang Saiwen and Zheng Weigang disclose an EMD-based wavelet threshold noise reduction method, which can solve the defect existing in the direct use of wavelet threshold noise reduction, but an end point effect occurs in the process of performing decomposition of IMF components, that is, since the EMD method is based on searching and interpolation of local extrema, data points at the end points (a starting point and an ending point of a data sequence) cannot obtain enough adjacent extrema to perform interpolation, thus resulting in end point effect, abnormal phenomenon occurs in EMD decomposition, and finally affecting the effect of data denoising.
The existing solution to the end-point effect is a symmetrical extension method provided by Shu Zhongping, the end-point effect is solved by extending the extreme points of a plurality of periods outwards symmetrically at the end point of the original data, but in the temperature control in the process of preparing lithium carbonate by a hydrogenation decomposition method, the temperature data does not show a period change mode, so the existing symmetrical extension method can be adopted to solve the problem that the extension length is too long or too short; if the extension length is too long, a large amount of noise is generated in the extension part, the reliability of data is low, if the extension length is too short, information can be omitted, and the denoising effect can be influenced by the too long or too short extension length.
Disclosure of Invention
In order to solve the technical problems that the existing symmetrical extension method has a large amount of noise in an extension part caused by overlong extension length and missing information caused by excessively short extension length, the invention provides a scheme in the following aspects.
In a first aspect, the present invention provides an automated control method for lithium carbonate production, comprising:
acquiring the temperature in the hydrogenation reactor, thereby acquiring a temperature data sequence;
Determining the extension length with the highest preference degree of the temperature data sequence, and recording the extension length as the optimal extension length, wherein the preference degree is in negative correlation with the noise expression degree of the data sequence of the extension part, and in positive correlation with the consistency degree between the extreme point distribution of the extension part and the extreme point distribution of the whole temperature data sequence;
Decomposing the temperature data sequence by adopting an empirical mode decomposition method so as to obtain an IMF component, and carrying out extension on the end points of the temperature data sequence according to the optimal extension length and the symmetrical extension method in the decomposition process so as to solve the end point effect in the EMD decomposition process;
denoising each IMF component, and reconstructing the denoised IMF component to obtain a denoised temperature data sequence;
Acquiring temperature data closest to the current moment from the denoised temperature data sequence, and recording the temperature data as the current temperature;
And PID control is carried out on the temperature in the hydrogenation reactor according to the difference value between the current temperature and the target temperature.
The beneficial effects are as follows: the automatic control method for lithium carbonate production of the invention carries out the denoising of the collected temperature data sequence before carrying out PID control on the temperature in the hydrogenation reactor, adopts an empirical mode decomposition method during denoising, can keep the physical meaning of signals as much as possible compared with other denoising algorithms, removes noise without damaging signal characteristics, keeps temperature change information related to the production process in the temperature sequence and ensures the accuracy of the denoised temperature data sequence, thereby improving the accuracy during controlling the temperature in the hydrogenation reactor; in addition, when the temperature data sequence is subjected to empirical mode decomposition, the end effect is solved by using a symmetrical extension method, and the extension length with the highest preference degree is selected to carry out extension on the temperature data sequence, so that the self-adaptive adjustment is carried out on the extension length, the robustness of an algorithm can be effectively improved, the problems that the information is possibly missed due to the too short extension length caused by the fixed length and the noise is introduced due to the too long extension length are avoided, the decomposition precision is improved, the denoising effect on the temperature data sequence is further improved, and the precision and the efficiency of temperature control in a hydrogenation reactor in the production process of preparing lithium carbonate by using a hydrogenation decomposition method are further improved.
In one embodiment, the method of determining the optimal extension length comprises:
setting an initial value of the extension length, and calculating a corresponding preference degree;
Iterating the extension length by taking the distance of one extreme point as a step length, and calculating the preference degree corresponding to the extension length after each iteration; iterating the extension length means that the extension length is increased according to a preset step length; the distance of one extreme point is the data length of the next extreme point from the end point of the data sequence corresponding to the extension length before the iteration in the temperature data sequence;
And stopping iteration if the preference degree corresponding to a certain iteration is smaller than the preference degree corresponding to the last iteration, and taking the extension length corresponding to the last iteration as the optimal extension length.
Because the consistency degree and the preference degree between the extreme point distribution of the extended part and the extreme point distribution of the whole temperature data sequence are positively correlated, if the extended length is iterated by taking the distance corresponding to a plurality of data points as a step length, the situation that the number of the extreme points of the extended part after extension is unchanged can occur, and the calculated preference degree is smaller, so that the extended length with the highest preference degree takes longer time to find.
In one embodiment, the degree of consistency is calculated according to a difference between the frequency of occurrence of the extreme point in the extended portion data sequence and the frequency of occurrence of the extreme point in the temperature data sequence, and is inversely related to the absolute value of the difference, and the degree of noise performance of the extended portion data sequence is calculated according to an average value of the degree of noise performance of each temperature data point in the extended portion data sequence, and is inversely related to the average value.
The smaller the difference value between the occurrence frequency of the extreme points of the extended part data sequence and the occurrence frequency of the extreme points in the temperature data sequence, the more consistent the occurrence frequency of the extreme points of the extended part data sequence and the temperature data sequence, which indicates that the degree of consistency of the extreme point distribution of the extended part data sequence and the temperature data sequence is higher, so that the degree of consistency between the extreme point distribution of the extended part and the extreme point distribution of the temperature data sequence can be accurately calculated according to the difference value between the occurrence frequency of the extreme points of the extended part data sequence and the occurrence frequency of the extreme points in the temperature data sequence; the larger the average value of the noise expression degree of each data point in the extended part data sequence is, the larger the noise expression degree of the whole extended part data sequence is, so that the noise expression degree of the extended part data sequence can be accurately calculated according to the average value of the noise expression degree of each data point in the extended part data sequence.
In one embodiment, the computational expression of the preference is:
；
in the method, in the process of the invention, Indicating the preference degree of the extended partial data sequence,Representing the frequency of occurrence of extreme points in the temperature data sequence,Representing the frequency of occurrence of extreme points in the extended partial data sequence,For the difference between the frequency of occurrence of the extreme point in the extended partial data sequence and the frequency of occurrence of the extreme point in the temperature data sequence,Representing the degree of consistency; A mean value representing a degree of noise performance of each data point in the extended portion data sequence; b is the ultrasonic parameter.
In one embodiment, the occurrence frequency of the extreme points in the data sequence may be calculated according to a time difference corresponding to the adjacent extreme points and a sum of noise performance degrees of the adjacent extreme points, and the time difference and the sum of the noise performance degrees are all in negative correlation; the time difference corresponding to the adjacent extreme points refers to the difference value between the acquisition moments corresponding to the two adjacent extreme points.
In one embodiment, the frequency of occurrence of an extreme point in the data sequence is calculated as:
；
in the method, in the process of the invention, Represents the occurrence frequency of extreme points in the data sequence, N represents the number of extreme points in the data sequence,、Respectively representing the time corresponding to the jth extreme point and the (j+1) th extreme point in the data sequence,、The noise performance degree of the temperature data point corresponding to the jth extreme point and the noise performance degree of the temperature data point corresponding to the (j+1) th extreme point in the data sequence are respectively represented.
The larger the time difference corresponding to the adjacent data points is, the longer the time spent for the next extreme point to appear after one extreme point appears, and the lower the frequency of occurrence of the extreme point is; the greater the degree of noise performance of an extremum point, the greater the likelihood that the extremum point is noise, i.e., the greater the likelihood that the extremum point is a false extremum point, and therefore, the greater the sum of the degrees of noise performance of adjacent extremum points, the lower the frequency of occurrence of the corresponding extremum point. Therefore, the frequency of occurrence of the extreme points can be accurately and efficiently calculated by adopting the frequency of occurrence calculation expression of the extreme points in the data sequence.
In one embodiment, the method for obtaining the noise performance degree of the temperature data point includes:
acquiring a temperature data sequence in a neighborhood range of the temperature data point according to the temperature data sequence, and recording the temperature data sequence as a neighborhood temperature data sequence;
Obtaining the pressure in the hydrogenation reactor at each temperature acquisition moment in a time period corresponding to the neighborhood temperature data sequence, thereby forming a neighborhood pressure data sequence;
Calculating the noise expression degree of the temperature data point according to the pearson correlation coefficient between the neighborhood temperature data sequence and the neighborhood pressure data sequence and the mutation degree of the temperature data point, wherein the noise expression degree of the temperature data point is in negative correlation with the pearson correlation coefficient and in positive correlation with the corresponding mutation degree; the degree of mutation is used to characterize the magnitude of the fluctuation of the temperature data point.
In one embodiment, the noise performance level calculation expression of the temperature data point is:
；
in the method, in the process of the invention, Representing the noise performance level of the ith temperature data point, exp () is an exponential function; Take a linear value at the ith temperature data point, Representing the actual value of the ith temperature data point,Indicating the degree of mutation of the ith temperature data point; a neighborhood temperature data sequence representing an ith temperature data point, A neighborhood pressure data sequence for the ith temperature data point,Representing pearson correlation coefficients between the neighborhood temperature data sequence and the neighborhood pressure data sequence.
The temperature data acquired by the temperature sensor is usually mixed with noise, and noise points are usually shown as abrupt points, so that the degree of noise expression of the temperature data points can be calculated by utilizing the abrupt degree of the temperature data points. However, in the process of hydrogenolysis, there are endothermic reactions and exothermic reactions (i.e., li 2CO3+CO2+H2O→2LiHCO3 is an endothermic reaction, the temperature decreases when this reaction occurs, and at the same time, carbon dioxide is absorbed in the reaction process to cause the pressure in the container to decrease, while 2LiHCO 3→Li2CO3↓+CO2 ++h2o is an exothermic reaction, the temperature increases when this reaction occurs and gas is evolved to cause the pressure to increase), and such endothermic or exothermic reactions cause abrupt points in the temperature data, but such abrupt points are normal data changes, so it is necessary to correct the noise expression level by using pearson correlation coefficients (i.e., linear correlation degree) of the temperature data and the pressure data, the larger the value of pearson correlation coefficient is [ -1, +1], the larger the value of Xiang Pier correlation coefficient is, the more likely to be the abrupt change of the pressure data, the smaller the noise expression level of the temperature data is, and conversely, the more likely to be the abrupt change of the temperature data is the noise, the noise expression level of the temperature data is large, and the more the pearson correlation coefficient is the more likely to be the noise expression level of the temperature data. Therefore, the noise performance degree calculating method of the temperature data point can calculate the noise performance degree of the temperature data point more accurately and efficiently.
In one embodiment, the temperature within the hydrogenation reactor is collected at a frequency of 50HZ.
In a second aspect, the present invention provides an automated control system for lithium carbonate production comprising a processor and a memory, the memory storing computer program instructions which, when executed by the processor, implement the automated control method for lithium carbonate production of the present invention.
Drawings
The above, as well as additional purposes, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description when read in conjunction with the accompanying drawings. In the drawings, embodiments of the invention are illustrated by way of example and not by way of limitation, and like reference numerals refer to similar or corresponding parts and in which:
Fig. 1 is a flowchart schematically illustrating an automated control method of lithium carbonate production according to an embodiment of the present invention;
FIG. 2 is a flow chart schematically illustrating a method of determining the optimal extension length according to an embodiment of the present invention;
FIG. 3 is a schematic diagram schematically illustrating a temperature data sequence and a continuation of an embodiment of the present invention;
FIG. 4 is a flow chart of a method of calculating the noise performance level of temperature data points schematically illustrating an embodiment of the present invention;
FIG. 5 is a schematic diagram schematically illustrating a method of linear value acquisition of temperature data points according to an embodiment of the present invention;
fig. 6 is a schematic view schematically showing the construction of an automated control system for lithium carbonate production according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. 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.
Specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.
Examples of automated control methods for lithium carbonate production:
As shown in fig. 1, the automatic control method for lithium carbonate production of the present invention comprises:
S101, acquiring the temperature in the hydrogenation reactor, thereby acquiring a temperature data sequence.
A temperature sensor may be provided in the hydrogenation reactor to enable the temperature in the hydrogenation reactor to be collected. The acquisition frequency may be 50HZ or other suitable frequency.
S102, acquiring the optimal extension length, which specifically comprises the following steps: and determining the extension length with the highest preference degree of the temperature data sequence, and recording the extension length as the optimal extension length, wherein the preference degree is in negative correlation with the noise expression degree of the extension part data sequence, and in positive correlation with the consistency degree between the extreme point distribution of the extension part and the extreme point distribution of the whole temperature data sequence.
When denoising temperature data, the temperature data is decomposed into a plurality of connotation modal components by adopting empirical mode decomposition, and each connotation modal component is denoised respectively, so that in order to solve the problem of end-point effect generated when the empirical mode decomposition is performed on the temperature data sequence, symmetrical extension is performed on the temperature data sequence to obtain an extension part, and the extension length refers to the length occupied by the extension part.
Many chemical reactions are involved in the preparation of lithium carbonate by the hydrogenation decomposition method, the endothermic exothermic reaction of which causes data in the hydrogenation reactor to change, and the characteristics of the data change are necessarily different from time to time, which results in difficult determination of the extension length when the symmetrical extension method is used for solving the port effect in EMD decomposition. Specifically, a longer extension length is helpful to better preserve detailed information in the data, but more noise may be introduced in the process, and the excessive noise may also cause calculation errors, so that the noise level in the extension data segment in the extension process needs to be as low as possible, thereby ensuring that the extension part of the data has higher reliability.
The consistency degree between the extreme point distribution of the extension part and the extreme point distribution of the whole temperature data sequence is as high as possible, so that the decomposed inherent mode function can better reflect local characteristics, and the symmetrical extension method can better solve the end point effect of an EMD algorithm; and the EMD decomposition precision is improved, and the denoising effect is improved. And the optimal extension length is calculated so that after the temperature data sequence is subjected to symmetrical extension in the follow-up process, the extension part data has higher reliability and ensures that the decomposed inherent mode function better reflects local characteristics.
The extreme point distribution of the extension part and the consistency degree of the extreme point distribution of the whole temperature data sequence are determined according to various methods, for example, the determination is carried out according to the positions of all the extreme points of the extension part and the proportion of the extreme points of the temperature data sequence, which are symmetrical about the extension center, is analyzed, and the greater the proportion is, the higher the corresponding consistency degree is.
The determination may also be performed according to a deviation between the ordinate of the extreme point of the extended portion and the ordinate of the extreme point of the temperature data sequence, for example, a mean value of the ordinate of the extreme point of the extended portion and a mean value of the ordinate of the extreme point of the temperature data sequence may be calculated, respectively, and the degree of consistency may be determined by the deviation between the two mean values.
The determination may also be made based on a deviation between the frequency of occurrence of the extreme point of the continuation portion and the frequency of occurrence of the extreme point of the temperature data sequence, the smaller the deviation, the higher the degree of consistency.
S103, performing empirical mode decomposition on the temperature data sequence, wherein the empirical mode decomposition specifically comprises the following steps of: and decomposing the temperature data sequence by adopting an empirical mode decomposition method so as to obtain an IMF component, and carrying out extension on the end points of the temperature data sequence according to the optimal extension length and the symmetrical extension method in the decomposition process so as to solve the end point effect in the EMD decomposition process.
S104, denoising each IMF component, and reconstructing the denoised IMF components to obtain a denoised temperature data sequence.
Wavelet thresholding, or other suitable methods may be used in denoising the IMF components, and is preferably used in this embodiment. The reconstruction may be performed using a least squares method or a regularization method.
S105, acquiring temperature data closest to the current moment from the denoised temperature data sequence, and recording the temperature data as the current temperature.
S106, PID control is carried out on the temperature in the hydrogenation reactor according to the difference value between the current temperature and the target temperature.
The automatic control method for lithium carbonate production of the invention carries out the denoising of the collected temperature data sequence before carrying out PID control on the temperature in the hydrogenation reactor, adopts an empirical mode decomposition method during denoising, can keep the physical meaning of signals as much as possible compared with other denoising algorithms, removes noise without damaging signal characteristics, keeps temperature change information related to the production process in the temperature sequence and ensures the accuracy of the denoised temperature data sequence, thereby improving the accuracy during controlling the temperature in the hydrogenation reactor; in addition, when the temperature data sequence is subjected to empirical mode decomposition, the end effect is solved by using a symmetrical extension method, and the extension length with the highest preference degree is selected to carry out extension on the temperature data sequence, so that the self-adaptive adjustment is carried out on the extension length, the robustness of an algorithm can be effectively improved, the problems that the information is possibly missed due to the too short extension length caused by the fixed length and the noise is introduced due to the too long extension length are avoided, the decomposition precision is improved, the denoising effect on the temperature data sequence is further improved, and the precision and the efficiency of controlling the temperature in a hydrogenation reactor in the production process of preparing lithium carbonate by using the hydrogenation decomposition method are further improved.
As shown in fig. 2, in one embodiment, the method for determining the optimal extension length includes:
S201, setting an initial value of the extension length, and calculating the corresponding preference degree.
S202, iterating the extension length, and calculating the preference degree corresponding to the extension length after each iteration, wherein the preference degree is specifically as follows: iterating the extension length by taking the distance of one extreme point as a step length, and calculating the preference degree corresponding to the extension length after each iteration; iterating the extension length means that the extension length is increased according to a preset step length; the distance of one extreme point refers to the data length of the next extreme point from the end point of the data sequence corresponding to the extension length before the current iteration in the temperature data sequence.
As shown in fig. 3, it is assumed that the collected temperature data sequence is a data sequence from a data point D to a data point E, the extension part before the current iteration is a data sequence from a data point C to a data point B, q, where the data point D is a center point of extension, the data point B and the data point B are symmetrical about the data point D, the data sequence corresponding to the extension length before the current iteration is also symmetrical about the data point D, the data sequence corresponding to the extension length before the current iteration is a data sequence from the data point D to the data point C, the right end point is a data point C, the data point C searches to the right, the next extreme point is a data point a, and the data point from the data point C to the data point a is a distance of the extreme point.
And S203, stopping iteration if the preference degree corresponding to a certain iteration is smaller than the preference degree corresponding to the last iteration, and taking the extension length corresponding to the last iteration as the optimal extension length.
Because the consistency degree and the preference degree between the extreme point distribution of the extended part and the extreme point distribution of the whole temperature data sequence are in positive correlation, if the extended length is iterated by taking the distance corresponding to a plurality of data points as a step length, the situation that the number of the extreme points of the extended part after extension is unchanged can occur, and the calculated preference degree is smaller, so that the extended length with the highest preference degree takes longer time to find.
In the above embodiment, it is mentioned that the preference degree is inversely related to the noise expression degree of the extended portion data sequence, and is positively related to the consistency degree between the extreme point distribution of the extended portion and the extreme point distribution of the temperature data sequence, and in one embodiment, the consistency degree is calculated according to the difference between the occurrence frequency of the extreme point of the extended portion data sequence and the occurrence frequency of the extreme point in the temperature data sequence, and is inversely related to the absolute value of the difference, and the noise expression degree of the extended portion data sequence is calculated according to the average value of the noise expression degrees of the data points in the extended portion data sequence, and is inversely related to the average value.
The smaller the difference value between the occurrence frequency of the extreme points of the extended part data sequence and the occurrence frequency of the extreme points in the temperature data sequence, the more consistent the occurrence frequency of the extreme points of the extended part data sequence and the temperature data sequence, which indicates that the degree of consistency of the extreme point distribution of the extended part data sequence and the temperature data sequence is higher, so that the degree of consistency between the extreme point distribution of the extended part and the extreme point distribution of the temperature data sequence can be accurately calculated according to the difference value between the occurrence frequency of the extreme points of the extended part data sequence and the occurrence frequency of the extreme points in the temperature data sequence; the larger the average value of the noise expression degree of each data point in the extended part data sequence is, the larger the noise expression degree of the whole extended part data sequence is, so that the noise expression degree of the extended part data sequence can be accurately calculated according to the average value of the noise expression degree of each data point in the extended part data sequence.
As can be seen from the above embodiments, the preference degree is inversely related to the mean value of the noise performance degree of each data point in the extended portion data sequence, and is inversely related to the difference between the occurrence frequency of the extreme point of the extended portion and the occurrence frequency of the extreme point in the temperature data sequence, and in one embodiment, the calculation expression of the preference degree is as follows:
in the method, in the process of the invention, Indicating the preference degree of the extended partial data sequence,Representing the frequency of occurrence of extreme points in the temperature data sequence,Represents the frequency of occurrence of extreme points in the extended partial data sequence,To extend the difference between the frequency of occurrence of extreme points in a partial data sequence and the frequency of occurrence of extreme points in the entire temperature data sequence,Representing the consistency of the extreme point distribution of the extension part and the extreme point distribution of the whole temperature data sequence; Means for representing the noise performance level of each data point in the extended partial data sequence; b is a super parameter, and prevents denominator from being zero.
In one embodiment, the occurrence frequency of the extreme points in the data sequence may be calculated according to the time difference corresponding to the adjacent extreme points and the sum of the noise performance degrees of the adjacent extreme points, and the time difference and the sum of the noise performance degrees are all in negative correlation; the time difference corresponding to the adjacent extreme points refers to the difference between the acquisition moments corresponding to the two adjacent extreme points, namely the duration between the acquisition moments corresponding to the adjacent data points. The frequency of occurrence calculation expression of the extreme points in the data sequence is as follows:
in the method, in the process of the invention, Represents the occurrence frequency of extreme points in the data sequence, N represents the number of extreme points in the data sequence,、Respectively representing the time corresponding to the jth extreme point and the (j+1) th extreme point in the data sequence,、The noise performance degree of the temperature data point corresponding to the jth extreme point and the noise performance degree of the temperature data point corresponding to the (j+1) th extreme point in the data sequence are respectively represented. If the data sequence is a continuation part data sequence, the temperature data point corresponding to the jth extreme point refers to a data point in the temperature data sequence symmetrical to the extreme point about the continuation center.
The larger the time difference corresponding to the adjacent data points is, the longer the time spent for the next extreme point to appear after one extreme point appears, and the lower the frequency of occurrence of the extreme point is; the greater the degree of noise performance of an extremum point, the greater the likelihood that the extremum point is noise, i.e., the greater the likelihood that the extremum point is a false extremum point, and therefore, the greater the sum of the degrees of noise performance of adjacent extremum points, the lower the frequency of occurrence of the corresponding extremum point. Therefore, the frequency of occurrence of the extreme point can be accurately and efficiently calculated by using the frequency of occurrence calculation expression of the extreme point in the data sequence in the present embodiment.
As shown in fig. 4, the noise performance degree calculating method of the temperature data point includes:
s401, acquiring a temperature data sequence in a neighborhood range of the temperature data point according to the temperature data sequence, and recording the temperature data sequence as a neighborhood temperature data sequence.
In this embodiment, a data sequence formed by a plurality of data centered on the temperature data point in the temperature data sequence is a neighborhood temperature data sequence of the temperature data point.
S402, acquiring a neighborhood pressure data sequence, which specifically comprises the following steps: and acquiring the pressure in the hydrogenation reactor at each temperature acquisition moment in a time period corresponding to the neighborhood temperature data sequence, thereby forming a neighborhood pressure data sequence.
The time period corresponding to the neighborhood temperature data sequence refers to a time period between the acquisition time of the first temperature data and the acquisition time of the last temperature data in the neighborhood temperature data sequence.
S403, calculating the noise expression degree of the temperature data point, specifically: calculating the noise expression degree of the temperature data point according to the pearson correlation coefficient between the neighborhood temperature data sequence and the neighborhood pressure data sequence and the mutation degree of the temperature data point, wherein the noise expression degree of the temperature data point is in negative correlation with the pearson correlation coefficient and in positive correlation with the corresponding mutation degree; the degree of mutation is used to characterize the magnitude of the fluctuation of the temperature data point.
In this embodiment, the method for calculating the mutation degree of the temperature data point includes:
a. The linear value of the temperature data point is obtained, specifically: and acquiring a function corresponding to a straight line where the previous data point and the next data point of the temperature data point are located, further acquiring the value of the function at the acquisition time corresponding to the temperature data point, and recording the value as the linear value of the temperature data point.
As shown in fig. 5, assuming that the temperature data point is a data point H, the previous data point and the next data point are respectively a data point P and a data point Q, the corresponding acquisition time of the data point H is T2, and the corresponding temperature value is T2; the corresponding acquisition time of the data point P isThe corresponding temperature value is T1; the collection time corresponding to the data point Q isThe corresponding temperature value is T3. Establishing a rectangular coordinate system by taking the acquisition time as the horizontal axis and the corresponding temperature value as the vertical axis, wherein the coordinates corresponding to the data point H, the data point P and the data point Q are respectively [ (x-ray) and [ (x-ray) respectively，) ; Assuming that the function corresponding to the straight line where the data point P and the data point Q are located is f (t), the linear value of the data point H is f (t 2).
B. The mutation degree of the temperature data points is obtained, specifically: the difference between the linear value of the temperature data point and the actual value of the temperature data point is calculated and recorded as the degree of abrupt change of the temperature data point.
In fact, the degree to which the temperature data point deviates from the midpoint of the line segment is the degree of abrupt change in the temperature data point.
Since the point at the end point is a symmetrical point in the extension method, the calculation of the noise manifestation degree at the left and right end points of the temperature data sequence is not needed in the automatic control method for lithium carbonate production of the present invention.
The temperature data acquired by the temperature sensor is usually mixed with noise, and noise points are usually shown as abrupt points, so that the degree of noise expression of the temperature data points can be calculated by utilizing the abrupt degree of the temperature data points. However, in the process of hydrogenolysis, there are endothermic reactions and exothermic reactions (i.e., li 2CO3+CO2+H2O→2LiHCO3 is an endothermic reaction, the temperature decreases when this reaction occurs, carbon dioxide is absorbed during the reaction, resulting in a decrease in the pressure in the container, 2LiHCO 3→Li2CO3↓+CO2↑+H2 O is an exothermic reaction, the temperature increases when this reaction occurs, and gas is evolved, resulting in a pressure increase), and such endothermic or exothermic reactions cause abrupt changes in temperature data, but such abrupt changes are normal data changes, so it is necessary to correct the noise expression level by using pearson correlation coefficients (i.e., linear correlation levels) of the temperature data and the pressure data, the larger the value of pearson correlation coefficient is in the range of [ -1, +1], the smaller the value of Xiang Pier is, the smaller the value of the correlation coefficient is, the more likely to be the abrupt change in the pressure data, the larger the noise expression level of the temperature data is, and the more likely to be the noise expression level of the temperature data is the noise expression level of the temperature data. Therefore, the noise performance degree calculating method of the temperature data point can calculate the noise performance degree of the temperature data point more accurately and efficiently.
As can be seen from the above embodiments, the noise performance level of a certain temperature data point is inversely related to the corresponding pearson correlation coefficient and is positively related to the mutation level of the temperature data point, and in one embodiment, the noise performance level of a certain temperature data point is calculated by the following expression:
in the method, in the process of the invention, Representing the noise performance level of the ith temperature data point, exp () is an exponential function; Take a linear value at the ith temperature data point, Representing the actual value of the ith temperature data point, thenThe degree of mutation of the ith temperature data point is shown.A neighborhood temperature data sequence representing an ith temperature data point,A neighborhood pressure data sequence for the ith temperature data point,Representing pearson correlation coefficients between the neighborhood temperature data sequence and the neighborhood pressure data sequence.
Because the value of the pearson correlation coefficient ranges from-1 to 1, in order to satisfy the size relation of the correlation and facilitate normalization, the pearson correlation coefficient is limited to be positive by adding 1, namelyThenThat is, the higher the pearson correlation coefficient is, the smaller the noise expression is, so that the noise expression in the embodiment is adopted to calculate the noise expression of the temperature data point more accurately and efficiently.
Lithium carbonate production automation control system example:
The invention also provides an automatic control system for lithium carbonate production. As shown in fig. 6, the lithium carbonate production automation control system includes a processor and a memory storing computer program instructions that, when executed by the processor, implement the lithium carbonate production automation control method according to the first aspect of the present invention.
The lithium carbonate production automation control system further comprises other components such as a communication bus and a communication interface, which are well known to those skilled in the art, and the arrangement and function of which are known in the art, and thus are not described herein.
In the context of this patent, the foregoing memory may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, the computer-readable storage medium may be any suitable magnetic or magneto-optical storage medium, such as, for example, resistance change Memory RRAM (Resistive Random Access Memory), dynamic Random Access Memory DRAM (Dynamic Random Access Memory), static Random Access Memory SRAM (Static Random-Access Memory), enhanced dynamic Random Access Memory EDRAM (ENHANCED DYNAMIC Random Access Memory), high-Bandwidth Memory HBM (High-Bandwidth Memory), hybrid storage cube HMC (Hybrid Memory Cube), or the like, or any other medium that may be used to store the desired information and that may be accessed by an application, a module, or both. Any such computer storage media may be part of, or accessible by, or connectable to, the device. Any of the applications or modules described herein may be implemented using computer-readable/executable instructions that may be stored or otherwise maintained by such computer-readable media.
In the description of the present specification, the meaning of "a plurality", "a number" or "a plurality" is at least two, for example, two, three or more, etc., unless explicitly defined otherwise.
While various embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Many modifications, changes, and substitutions will now occur to those skilled in the art without departing from the spirit and scope of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention.
Claims (10)
1. An automatic control method for lithium carbonate production, which is characterized by comprising the following steps:
acquiring the temperature in the hydrogenation reactor, thereby acquiring a temperature data sequence;
Determining the extension length with the highest preference degree of the temperature data sequence, and recording the extension length as the optimal extension length, wherein the preference degree is in negative correlation with the noise expression degree of the data sequence of the extension part, and in positive correlation with the consistency degree between the extreme point distribution of the extension part and the extreme point distribution of the whole temperature data sequence;
Decomposing the temperature data sequence by adopting an empirical mode decomposition method so as to obtain an IMF component, and carrying out extension on the end points of the temperature data sequence according to the optimal extension length and the symmetrical extension method in the decomposition process so as to solve the end point effect in the EMD decomposition process;
denoising each IMF component, and reconstructing the denoised IMF component to obtain a denoised temperature data sequence;
Acquiring temperature data closest to the current moment from the denoised temperature data sequence, and recording the temperature data as the current temperature;
And PID control is carried out on the temperature in the hydrogenation reactor according to the difference value between the current temperature and the target temperature.
2. The automated lithium carbonate production control method of claim 1, wherein the method of determining the optimal extension length comprises:
setting an initial value of the extension length, and calculating a corresponding preference degree;
Iterating the extension length by taking the distance of one extreme point as a step length, and calculating the preference degree corresponding to the extension length after each iteration; iterating the extension length means that the extension length is increased according to a preset step length; the distance of one extreme point is the data length of the next extreme point from the end point of the data sequence corresponding to the extension length before the iteration in the temperature data sequence;
And stopping iteration if the preference degree corresponding to a certain iteration is smaller than the preference degree corresponding to the last iteration, and taking the extension length corresponding to the last iteration as the optimal extension length.
3. The automated lithium carbonate production control method of claim 1, wherein the degree of consistency is calculated based on a difference between a frequency of occurrence of an extreme point in the extended portion data sequence and a frequency of occurrence of an extreme point in the temperature data sequence, and is inversely related to an absolute value of the difference, and the degree of noise performance of the extended portion data sequence is calculated based on an average of the degrees of noise performance of the respective temperature data points in the extended portion data sequence, and is inversely related to the average.
4. The automated lithium carbonate production control method of claim 3, wherein the calculated expression of the preference degree is:
；
in the method, in the process of the invention, Indicating the preference degree of the extended partial data sequence,Representing the frequency of occurrence of extreme points in the temperature data sequence,Representing the frequency of occurrence of extreme points in the extended partial data sequence,For the difference between the frequency of occurrence of the extreme point in the extended partial data sequence and the frequency of occurrence of the extreme point in the temperature data sequence,Representing the degree of consistency; A mean value representing a degree of noise performance of each data point in the extended portion data sequence; b is the ultrasonic parameter.
5. The automated lithium carbonate production control method of claim 3, wherein the occurrence frequency of the extreme points in the data sequence is calculated according to a time difference corresponding to the adjacent extreme points and a sum of noise performance degrees of the adjacent extreme points, and is inversely related to the time difference and the sum of the noise performance degrees; the time difference corresponding to the adjacent extreme points refers to the difference value between the acquisition moments corresponding to the two adjacent extreme points.
6. The automated lithium carbonate production control method of claim 5, wherein the frequency of occurrence of the extreme points in the data sequence is calculated as:
；
in the method, in the process of the invention, Represents the occurrence frequency of extreme points in the data sequence, N represents the number of extreme points in the data sequence,、Respectively representing the time corresponding to the jth extreme point and the (j+1) th extreme point in the data sequence,、The noise performance degree of the temperature data point corresponding to the jth extreme point and the noise performance degree of the temperature data point corresponding to the (j+1) th extreme point in the data sequence are respectively represented.
7. The automated lithium carbonate production control method of claim 3, wherein the noise performance level acquisition method of the temperature data points comprises:
acquiring a temperature data sequence in a neighborhood range of the temperature data point according to the temperature data sequence, and recording the temperature data sequence as a neighborhood temperature data sequence;
Obtaining the pressure in the hydrogenation reactor at each temperature acquisition moment in a time period corresponding to the neighborhood temperature data sequence, thereby forming a neighborhood pressure data sequence;
Calculating the noise expression degree of the temperature data point according to the pearson correlation coefficient between the neighborhood temperature data sequence and the neighborhood pressure data sequence and the mutation degree of the temperature data point, wherein the noise expression degree of the temperature data point is in negative correlation with the pearson correlation coefficient and in positive correlation with the corresponding mutation degree; the degree of mutation is used to characterize the magnitude of the fluctuation of the temperature data point.
8. The automated lithium carbonate production control method of claim 3, wherein the noise performance level calculation expression of the temperature data points is:
；
in the method, in the process of the invention, Representing the noise performance level of the ith temperature data point, exp () is an exponential function; Take a linear value at the ith temperature data point, Representing the actual value of the ith temperature data point,Indicating the degree of mutation of the ith temperature data point; a neighborhood temperature data sequence representing an ith temperature data point, A neighborhood pressure data sequence for the ith temperature data point,Representing pearson correlation coefficients between the neighborhood temperature data sequence and the neighborhood pressure data sequence.
9. The automated lithium carbonate production control method of any one of claims 1-8, wherein the temperature within the hydrogenation reactor is collected at a frequency of 50HZ.
10. An automated lithium carbonate production control system comprising a processor and a memory, the memory storing computer program instructions, wherein the computer program instructions, when executed by the processor, implement the automated lithium carbonate production control method of any one of claims 1-9.
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