CN113722973A - Correction system and correction method of computer simulation model - Google Patents
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Abstract
The invention provides a correction system and a correction method of a computer simulation model, belonging to the technical field of chemical engineering. A correction system for a computer simulation model for chemical process anomaly identification includes a processing module for performing the following operations: determining the accuracy of the initial computer simulation model according to the working condition data related to the chemical process; and in case the accuracy is not as expected, performing the steps of: correcting the initial computer simulation model according to the working condition data to obtain a corrected computer simulation model; or correcting the mathematical model used for forming the initial computer simulation model according to the working condition data to obtain a corrected mathematical model, and determining the corrected computer simulation model constructed by the corrected mathematical model. The computer simulation model obtained by adopting the technical scheme provided by the invention has the advantages of good reliability and high accuracy.
Description
Technical Field
The invention relates to the technical field of chemical engineering, in particular to a correction system and a correction method of a computer simulation model for chemical process anomaly identification.
Background
For a chemical process, the production process by using a chemical device is a continuous and dynamic production process, each process variable can float up and down around a set value, and the process variables are more.
The deep learning network essentially belongs to a data-driven method, and can complete modeling only by using data information, so that the deep learning network can be effectively applied to anomaly detection of a large-scale industrial process.
However, data-driven based approaches lack a precise description of the process dynamics and logical judgment of human experience, and may give ambiguous and even erroneous detection results. However, the existing models for recognizing the abnormality by using deep learning are static models, and do not suggest that the models need to be corrected and optimized.
Disclosure of Invention
An object of the embodiments of the present invention is to provide a system and a method for correcting a computer simulation model for chemical process anomaly identification, which are used to solve one or more of the above technical problems.
In order to achieve the above object, an embodiment of the present invention provides a correction system for a computer simulation model for chemical process anomaly identification, the correction system including a processing module for performing the following operations: determining the accuracy of the initial computer simulation model according to the working condition data related to the chemical process; and in case the accuracy is not as expected, performing the steps of: correcting the initial computer simulation model according to the working condition data to obtain a corrected computer simulation model; or correcting the mathematical model used for forming the initial computer simulation model according to the working condition data to obtain a corrected mathematical model, and determining the corrected computer simulation model constructed by the corrected mathematical model.
Optionally, the initial computer simulation model is determined by a first computer simulation model, the first computer simulation model is established by a mathematical model related to the working conditions of the chemical process, and the processing module determines the initial computer simulation model according to the following steps: acquiring an output result of the mathematical model and an output result of the first computer simulation model; comparing the output of the mathematical model with the output of the first computer simulation model; under the condition that the output result of the mathematical model is inconsistent with the output result of the first computer simulation model, correcting the first computer simulation model by taking the mathematical model as a basis to obtain the initial computer simulation model; and taking the first computer simulation model as the initial computer simulation model under the condition that the output result of the mathematical model is consistent with the output result of the first computer simulation model.
Optionally, the processing module is further configured to perform the following operations: storing simulation data of the computer model with the label and/or chemical process working condition data with the label to a data platform; and correcting the mathematical model and/or correcting the computer simulation model constructed by the mathematical model by using the simulation data of the computer model with the label and/or the chemical process working condition data with the label.
Optionally, the operating condition data related to the chemical process is derived from actual production data and/or simulation data of a physical model.
Optionally, the working condition data related to the chemical process is stored in a data platform, where the data platform includes an expert knowledge database, an actual working condition database of the chemical process, a simulation working condition database of the simulation model, a database of the physical model, and a database in which data is stored in advance.
Optionally, the type of the mathematical model is one or more of the following: CNN model, R-CNN model, CNN-DAE model, and SSLN model.
Correspondingly, the embodiment of the invention also provides a correction method of the computer simulation model for chemical process anomaly identification, which comprises the following steps: determining the accuracy of the initial computer simulation model according to the working condition data related to the chemical process; under the condition that the accuracy does not reach the expectation, correcting the computer simulation model according to the working condition data to obtain a corrected computer simulation model; or correcting the mathematical model used for forming the initial computer simulation model according to the working condition data to obtain a corrected mathematical model, and determining the corrected computer simulation model constructed by the corrected mathematical model.
Optionally, the initial computer simulation model is determined by a first computer simulation model, the first computer simulation model is established by a mathematical model related to the working conditions of the chemical process, and the correction method further determines the initial computer simulation model by: acquiring an output result of the mathematical model and an output result of the first computer simulation model; comparing the output result of the mathematical model with the output result of the first computer simulation model; under the condition that the output result of the mathematical model is inconsistent with the output result of the first computer simulation model, correcting the first computer simulation model by taking the mathematical model as a basis to obtain the initial computer simulation model; and taking the first computer simulation model as the initial computer simulation model under the condition that the output result of the mathematical model is consistent with the output result of the first computer simulation model.
Optionally, the correction method further includes: storing simulation data of the computer model with the label and/or chemical process working condition data with the label to a data platform; and correcting the mathematical model and/or correcting the computer simulation model formed by the mathematical model by using the simulation data of the computer model with the label and/or the chemical process working condition data with the label.
Optionally, the operating condition data related to the chemical process is derived from actual production data and/or simulation data of a physical model.
Optionally, the working condition data related to the chemical process is stored in a data platform, where the data platform includes an expert knowledge database, an actual working condition database of the chemical process, a simulation working condition database of the simulation model, a database of the physical model, and a database in which data is stored in advance.
Optionally, the type of the mathematical model is one or more of the following: CNN model, R-CNN model, CNN-DAE model, and SSLN model.
In another aspect, the present disclosure provides a machine-readable storage medium having stored thereon instructions for causing a machine to perform any one of the methods for modifying a computer simulation model for chemical process anomaly identification described herein above.
By the technical scheme, on the basis of establishing the computer simulation model, the method for correcting the computer simulation model by using the working condition data related to the chemical process is provided, and the reliability of the computer simulation model and the accuracy of the identification result can be improved.
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 schematic flow chart of a modification to a computer simulation model performed by a process module according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a computer simulation model modification provided by an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a modification system provided with a data platform according to an embodiment of the present invention;
FIG. 4 is a schematic view of an identification of a thermal image of a leak gas from a respiratory valve;
FIG. 5 is a schematic diagram of the identification of a corrected computer simulation model for a thermal imaging graph of leakage gas of a certain breathing valve;
FIG. 6 is a manually labeled graph of leakage for a thermal imaging map;
FIG. 7 is a plot of leakage from the corrected computer simulation model to the thermal imaging map;
FIG. 8 is a schematic diagram of the variation of the extra-high pressure steam flow PFR701 and the pressure PIC 174;
FIG. 9 is a schematic diagram of the operating state index trend of the GB1201 compressor process;
FIG. 10 is a schematic diagram of the index trend of the operating state of the BA111 cracking furnace process;
FIG. 11 is a schematic diagram of BA111 cracking furnace abnormality monitoring associated measuring points;
FIG. 12 is a schematic diagram of the ultra-high pressure steam flow of BA111 cracking furnace;
FIG. 13 is a schematic representation of BA111 furnace drum level.
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.
The reasonable computer simulation model can visually display the chemical process flow, and if the chemical process flow can be corrected in the process of simulating the chemical process flow by the computer simulation model, the usability of the computer simulation model in the technical field of chemical industry can be effectively improved.
Example one
The embodiment of the invention provides a correction system of a computer simulation model for chemical process abnormity identification, which comprises a processing module, wherein the processing module executes steps S110 to S120 in FIG. 1 to realize correction of the computer simulation model.
In step S110, the accuracy of the initial computer simulation model is determined from operating condition data associated with the chemical process.
The data of the working conditions of the chemical process can be from historical working condition data in the production process of a factory, or can be working condition data obtained by artificially modifying and verifying simulation data obtained by a computer simulation model, and the working condition data is collected and sorted to be used as a test set for determining the accuracy of the computer simulation model.
To facilitate the ability to modify the computer model during its use, test set data used to verify the accuracy of the computer simulation model may be stored in the data platform. The data platform can be pre-stored for the pre-known working condition data, the real-time acquired working condition data can be stored into the corresponding position of the data platform while being acquired, and the data can be stored after the corresponding label is added through manual examination and judgment.
Optionally, the data platform may include an expert knowledge database, a chemical process actual condition database, a simulation model simulation condition database, a physical model database, and a pre-stored data database.
The expert knowledge database can pre-store typical fault condition data, diagnosis results, analysis conclusions and the like of professionals on the typical fault conditions, and the typical fault condition data can be used as negative samples to correct a computer simulation model.
The actual working condition data of the chemical process can be historical working condition data of the chemical process, and can also be data generated in real time.
The computer simulation model can simulate the chemical process, so that the output simulation result can be stored in a simulation working condition database of the simulation model, and the operations such as later review, sorting, analysis, statistics and the like are facilitated.
Considering that the actual production requirements in a chemical plant are large, the general equipment is large in size, so that a physical model can be set for a chemical process, the physical model is used for an actual micro equipment to model the flow of the chemical process, working condition data of the physical model can be obtained, the working condition data obtained by the physical model can also be used for constructing and correcting a computer simulation model, and the part of data can be stored in a database of the physical model.
The test set comprises working condition data including input data and output data corresponding to the input data, the input data are input into the computer simulation model, an output result corresponding to the input data can be obtained, and the accuracy of the computer simulation model can be determined by comparing the output result of the computer simulation model with the output data corresponding to the input data in the test set.
Optionally, the working condition data obtained by the computer simulation model, the working condition data obtained by the physical model, or the actual working condition data may be labeled, and then the labeled data is stored in the data platform, and in the process of correcting the computer simulation model later, the labeled data may also be used as a new training set to train the computer simulation model, so as to optimize the computer simulation model.
In this embodiment, the manner for determining the accuracy of the computer simulation model may be any manner known in the art.
In step S120, the computer simulation model is optimized to obtain a corrected computer simulation model when the accuracy of the computer simulation model is not expected.
The type of the obtained output result can be different according to the different process working conditions and the different input data simulated by the computer simulation model. For example, the output result may be only 0 or 1, or the output result may be specific data, percentage, or the like, and the accuracy of the computer simulation model may be obtained by knowing the actual result corresponding to the input data and the result of the computer simulation model.
The expectation may be a preset value or the like set according to actual requirements.
Considering that the establishment of the computer simulation model needs to be based on the mathematical model, the computer simulation model can be corrected by selecting any one of the two aspects under the condition that the accuracy of the computer simulation model is not expected.
Optionally, the computer simulation model may be modified by using the labeled data in the working condition data for determining the accuracy of the computer simulation model as a new training set, and the modified computer simulation model may be directly obtained.
Optionally, the mathematical model used for forming the computer simulation model may be modified, including but not limited to adjustment of a hyper-parameter, selection of an activation function, further training of the mathematical model using the labeled data in the working condition data for determining the accuracy of the computer simulation model as a new training set, and the like, and the computer simulation model may be reconstructed based on the mathematical model under the condition that the modified mathematical model is obtained, which is the modified computer simulation model.
The technical scheme provided by the embodiment of the invention provides a method for correcting the computer simulation model by using the working condition data related to the chemical process on the basis of the established computer simulation model, so that the reliability of the computer simulation model and the accuracy of the identification result can be improved.
Example two
Considering that the computer simulation model is built by a mathematical model related to the working conditions of the chemical process, in the process of building the computer simulation model based on the mathematical model, errors in building the computer simulation model due to misoperation and the like may also occur, so that the processing module of the correction system of the computer simulation model for identifying the chemical process abnormality can correct the computer simulation model by the following method after the computer simulation model is built:
acquiring the output result of the mathematical model and the output result of the computer simulation model aiming at the same test set (the data is derived from the known working condition data aiming at the chemical process);
comparing the output result of the mathematical model with the output result of the computer simulation model;
if the output result of the mathematical model is consistent with the output result of the computer simulation model, no problem is shown, and if the output result of the mathematical model is not completely consistent with the output result of the computer simulation model, an error is shown in the process of constructing the computer simulation model, and the error needs to be corrected by taking the mathematical model as a basis to solve the error.
Alternatively, the mathematical model mentioned in the embodiment of the present invention may be any existing mathematical model, for example, it may be a CNN model, an R-CNN model, a CNN-DAE model, or an SSLN model.
According to the technical scheme provided by the embodiment of the invention, in the process of constructing the computer simulation model by the mathematical model, the computer simulation model is corrected based on the mathematical model, so that the problem that the subsequent identification result of the computer simulation model is unreliable due to misoperation in the step of constructing the computer simulation model by the mathematical model is avoided.
EXAMPLE III
The technical solution provided by the embodiment of the present invention is explained by taking the computer simulation model as an abnormal condition recognition model as an example, a modified flow chart is shown in fig. 2, and specific modification steps are shown as follows.
(1) Classifying and sorting actual abnormal working conditions which appear or possibly appear in a certain chemical process production device, and abstracting concrete abnormal working conditions into a conceptual model;
(2) carrying out mathematical modeling aiming at the proposed concept model to obtain a mathematical model, and optionally establishing a physical model aiming at the concept model in order to realize data comparison;
(3) for a physical model, an experiment platform needs to be built first, then a specific experiment scheme is designed, and various abnormal working conditions are set on the experiment platform, so that symptoms and evolution data of various abnormal working conditions can be obtained;
(4) on the basis of the constructed mathematical model, the mathematical model is converted into a computer simulation model, in order to ensure the correctness of the computer simulation model, in the process of constructing the computer simulation model (including the process of computer simulation and model simulation), the unpaired comparison test and modification of the computer simulation model and the mathematical model are required to be perfected so as to obtain a preliminarily perfected computer simulation model (a complete computer simulation model of a chemical process is required to be constructed by a plurality of mathematical models respectively aiming at different working conditions);
(5) and comparing the simulation result of the computer simulation model with the experimental result of the physical model to verify whether the computer simulation model can correctly identify the abnormal working condition, if the comparison result of the computer simulation model and the experimental result of the physical model can reach the expectation, putting the computer simulation model into use, otherwise, revising the computer simulation model or the mathematical model for constructing the computer simulation model until the expectation is reached.
Example four
The embodiment of the invention also provides a correction system provided with a data platform, and the structure diagram of the correction system is shown in fig. 3.
The control system DCS of the physical simulation model is accessed to the data platform through the OPC interface, the industrial data acquisition system is accessed to the data platform through the API interface, the computer simulation model is accessed to the data platform through the API interface, the output results of the industrial data acquisition system and the computer simulation model can be written into corresponding databases in the data platform in real time, the written data format can be any, for example, the data in the video format can also be stored in the data platform, and in addition, the data related to the working conditions of the chemical process, such as abnormal working condition data, diagnostic analysis and the like, can be stored in advance according to requirements.
According to actual abnormal working conditions appearing in the industrial device, historical data stored in the data platform are screened out, input data of the computer simulation model are adjusted, and therefore the abnormal recognition result of the computer simulation model aiming at the input data is determined.
And identifying the result output by the computer simulation model, marking a corresponding label, and taking the labeled data as a training set to train the mathematical model. The method comprises the steps of selecting a proper mathematical model by using an abnormal recognition algorithm scheduler according to different types of the mathematical model, wherein the mathematical model can be any one of deep learning network models such as a CNN model, an R-CNN model, a CNN-DAE model or an SSLN model.
And finally, displaying the abnormal recognition result of the computer simulation model.
The correction system provided by the fourth embodiment of the invention takes the data platform as a data exchange core, and realizes the functions of simulation, abnormal working condition setting, industrial/simulation data acquisition and storage and the like in the chemical process production process. The data of the computer simulation model can be used for correcting the mathematical model, abnormal working conditions are artificially set on the simulation process of the computer simulation model, the algorithm adopted by the abnormality identification is preliminarily tested, and the computer simulation model and the mathematical model can be corrected by using the operation data of the chemical process production process in combination with the expert knowledge of field operation.
EXAMPLE five
Now, taking thermal imaging of leaked gas of a certain breather valve as an example, an abnormal recognition rate of a preliminarily established fast RCNN model (one of R-CNN models) is tested, and as shown in fig. 4, the leakage recognition accuracy rate is 97%. For automatic identification, there is a certain false positive of this accuracy, which is not sufficient to justify the occurrence of a leak here.
And further correcting the hyper-parameters of the mathematical model through a data check set, and retraining to obtain a new Faster RCNN model. The result shows that the accuracy rate of leakage identification can reach 99%, and the occurrence rate of false alarm is further reduced, as shown in fig. 5.
EXAMPLE six
A computer simulation model is obtained by performing mathematical model training on data when a radiant section furnace pipe in a certain chemical production device of a certain chemical plant is blocked. When new blockage exists, the highest recognition accuracy rate of the computer simulation model is only 60.23%, and the generalization capability of the computer simulation model is poor.
During the correction of the computer simulation model, it was found that the initial mathematical model did not take into account the effect of the cross-section pressure on the model results. Thus, adding the cross-section pressure to the model as one of the variables enriches the choice of model variables. The retraining result shows that the accuracy rate of the abnormal recognition can be improved to 80.1%.
And further continuously correcting the hyper-parameters of the mathematical model and correcting the noise input of the mathematical model on the basis of the model test set, wherein the accuracy rate of the corresponding computer simulation model abnormity identification reaches 90.1 percent finally.
After continuous correction, the abnormity identification accuracy of the model is obviously improved, and the effect of model correction is achieved.
EXAMPLE seven
As shown in fig. 6, the right-side leaking gas range is clearly visible to the human eye, and the leaking gas label is manually given.
FIG. 7 is a screenshot of an infrared thermal imaging video effect detected based on a fast RCNN model computer simulation model. As shown in the figure, the computer simulation model based on the fast RCNN model can not only identify the gas range marked on the right side by hand and with obvious leakage, but also identify the gas range not marked on the left side, and the left side is trace gas leakage, which reaches the limit of the leakage identification capability of naked eyes. Therefore, the computer simulation model provided by the embodiment of the invention has better recognition effect.
Example eight
According to the technical scheme provided by the embodiment of the invention, a semi-supervised ladder network (SSLN) based computer simulation model is established for 12 cracking furnaces and 5 compressors of a certain 70 ten thousand ton/year ethylene cracking device. In order to facilitate model validation, the output result of the model is converted into a process running state index, which ranges from 0 to 100. Wherein 98-100 represent that the monitored subject is completely normal.
At a certain day of 20:25, the external ultrahigh-pressure Steam (SS) of the ethylene cracking device is interrupted, the pressure and the flow of an ultrahigh-pressure Steam pipe network are greatly fluctuated, and the power Steam of a cracking gas compressor is insufficient. From 20:29, the drum pressure, the compressor extraction amount, the ultrahigh steam pressure and the like of a plurality of cracking furnaces are reduced, and then the running states of the plurality of compressors fluctuate.
The specific situation is as follows:
(1) the external flow rate of the ultrahigh pressure steam PFR701 of the ethylene cracking device is rapidly reduced to 0 from 155.6 tons/hour on the same day, and is rapidly increased to the full scale after 20:29 and is rapidly reduced to 0 ton/hour; the external high-pressure steam PFR701 with the flow rate of 20:41 is initially in a certain amount, and the flow rate of the external high-pressure steam PFR701 with the flow rate of 20:54 is gradually stabilized and is maintained for about 128 tons/hour. During this period, the ultrahigh pressure steam pressure is reduced to 7.4MPa at the lowest. The trend of the super high pressure steam flow PFR701 and the variation of the pressure PIC174 are shown in fig. 8.
(2) On the same day, the rotating speed and the suction pressure of the compressor GB-1201 start to fluctuate at 20:25, the rotating speed of the compressor GB-1201 at 20:35 is reduced to 6513r/min from 6558r/min, and the rotating speed of the compressor GB-51 at 20:51 is reduced to 6311 r/min; GB-1201 suction pressure rises gradually from 56KPa to 97 KPa.
(3)20:41, opening a PC1201 valve on the top of a section of suction tank FA-1201 of a compressor GB-1201 to an open flame-off torch, wherein the opening degree of the valve position is up to 28.8%; and (3) opening a PC1122 valve on the top of the 20:42 water scrubber DA-1103 by using a flare torch, wherein the opening degree of the valve position is as large as 37.4%. PC1201 flare duration 33min, PC1122 flare duration 26 min.
Starting from about 20:30 on the same day, the computer simulation model monitors that each cracking furnace and each compressor have abnormal states, which shows that the whole ethylene cracking process has serious abnormality, and the specific conditions are shown in table 1.
TABLE 1
In the aspect of a compressor, for example, GB1201, the monitoring conditions of computer simulation software are shown in fig. 9.
Monitoring results show that the GB1201 compressor process running state index falls off in a cliff type on the same day at 20:29, and the degree of the index reduction is consistent with the change of key parameters of the actual process.
Meanwhile, for the cracking furnace, such as the BA111 process running state index, the cracking furnace also falls off in a cliff manner, and the situation monitored by computer simulation is shown in fig. 10.
FIG. 11 shows abnormal parameters monitored by the model, namely FI11128PV (SS flow) is low and TICA11128PV (SS temperature) is low.
After the pressure of the ultrahigh-pressure steam pipe network is reduced, the pressure of a steam drum of the cracking furnace fluctuates along with the pressure, and the ultrahigh-pressure steam generating system of the cracking furnace shakes to greatly influence the steam drum of the cracking furnace. Taking BA111 as an example, the vaporization amount of the heated boiler feed water in the waste boiler increases suddenly, so the drum liquid level of BA111 also increases rapidly, as shown in fig. 12 and 13.
In summary, the computer simulation model modified according to the embodiment of the present invention can monitor the abnormality of the external ultrahigh pressure steam interruption of the ethylene cracking apparatus in time, and the monitoring result matches with the change of the key parameter of the actual process, and the performance of abnormality identification reaches the expected target, and can be put into production for use. In the subsequent use process, the computer simulation model can be further corrected so as to gradually improve the computer simulation model.
Example nine
The embodiment of the invention also provides a correction method of the computer simulation model for chemical process anomaly identification, which comprises the following steps: determining the accuracy of the initial computer simulation model according to the working condition data related to the chemical process; under the condition that the accuracy does not reach the expectation, correcting the computer simulation model according to the working condition data to obtain a corrected computer simulation model; or correcting the mathematical model used for forming the initial computer simulation model according to the working condition data to obtain a corrected mathematical model, and determining the corrected computer simulation model constructed by the corrected mathematical model.
Optionally, the operating condition data related to the chemical process may be stored in a data platform, where the data platform includes an expert knowledge database, a chemical process actual operating condition database, a simulation model simulation operating condition database, a physical model database, and a pre-stored data database.
The data of the data platform can be stored in real time from actual production data, and can be stored in advance.
Optionally, the working condition data obtained by the computer simulation model, the working condition data obtained by the physical model, or the actual working condition data may be labeled, and then the labeled data is stored in the data platform, and in the process of correcting the computer simulation model later, the labeled data may also be used as a new training set to train the computer simulation model, so as to optimize the computer simulation model.
Example ten
In some alternative embodiments, the initial computer simulation model is created from a first computer simulation model, which in turn is created from a mathematical model associated with the operating conditions of the chemical process, so that the computer simulation model may also be initially validated based on the mathematical model in determining the initial computer simulation model. Specifically, an output result of the mathematical model and an output result of the first computer simulation model are obtained; comparing the output of the mathematical model with the output of the first computer simulation model; under the condition that the output result of the mathematical model is inconsistent with the output result of the first computer simulation model, correcting the first computer simulation model by taking the mathematical model as a basis to obtain the initial computer simulation model; and taking the first computer simulation model as the initial computer simulation model under the condition that the output result of the mathematical model is consistent with the output result of the first computer simulation model.
For specific details and benefits of the method for correcting a computer simulation model for chemical process anomaly identification provided in the above embodiment of the present invention, reference may be made to the above description of the correction system for a computer simulation model for chemical process anomaly identification provided in the present invention, and details are not described herein again.
Accordingly, an embodiment of the present invention further provides a machine-readable storage medium, which stores instructions for causing a machine to execute the above method for correcting a computer simulation model for chemical process anomaly identification.
Although the embodiments of the present invention have been described in detail with reference to the accompanying drawings, the embodiments of the present invention are not limited to the details of the above embodiments, and various simple modifications can be made to the technical solutions of the embodiments of the present invention within the technical idea of the embodiments of the present invention, and the simple modifications all belong to the protection scope of the embodiments of the present invention.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, the embodiments of the present invention do not describe every possible combination.
Those skilled in the art will understand that all or part of the steps in the method according to the above embodiments may be implemented by a program, which is stored in a storage medium and includes several instructions to enable a single chip, a chip, or a processor (processor) to execute all or part of the steps in the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In addition, any combination of various different implementation manners of the embodiments of the present invention is also possible, and the embodiments of the present invention should be considered as disclosed in the embodiments of the present invention as long as the combination does not depart from the spirit of the embodiments of the present invention.
Claims (13)
1. A rework system for a computer simulation model for chemical process anomaly identification, the rework system comprising a processing module for performing the following operations:
determining the accuracy of the initial computer simulation model according to the working condition data related to the chemical process; and
in case the accuracy is not as expected, the following steps are performed:
correcting the initial computer simulation model according to the working condition data to obtain a corrected computer simulation model; or
And correcting the mathematical model used for forming the initial computer simulation model according to the working condition data to obtain a corrected mathematical model, and determining the corrected computer simulation model constructed by the corrected mathematical model.
2. The rework system of claim 1, wherein the initial computer simulation model is determined from a first computer simulation model established from a mathematical model related to an operating condition of the chemical process, the processing module determining the initial computer simulation model according to the following steps:
acquiring an output result of the mathematical model and an output result of the first computer simulation model;
comparing the output of the mathematical model with the output of the first computer simulation model;
under the condition that the output result of the mathematical model is inconsistent with the output result of the first computer simulation model, correcting the first computer simulation model by taking the mathematical model as a basis to obtain the initial computer simulation model; and
and taking the first computer simulation model as the initial computer simulation model under the condition that the output result of the mathematical model is consistent with the output result of the first computer simulation model.
3. The correction system according to claim 1, wherein the processing module is further configured to:
storing simulation data of the computer model with the label and/or chemical process working condition data with the label to a data platform; and
and correcting the mathematical model and/or correcting the computer simulation model constructed by the mathematical model by using the simulation data of the computer model with the label and/or the chemical process working condition data with the label.
4. The correction system according to claim 1, wherein the operating condition data related to the chemical process is derived from actual production data and/or simulation data of a physical model.
5. The rework system of claim 1, wherein the operating condition data associated with the chemical process is stored on a data platform that includes an expert knowledge database, a database of actual operating conditions for the chemical process, a database of simulated operating conditions for the simulation model, a database of physical models, and a database of pre-stored data.
6. The correction system according to claim 1, characterized in that the type of the mathematical model is one or more of the following: CNN model, R-CNN model, CNN-DAE model, and SSLN model.
7. A method of modifying a computer simulation model for chemical process anomaly identification, the method comprising:
determining the accuracy of the initial computer simulation model according to the working condition data related to the chemical process; and
under the condition that the accuracy does not reach the expectation, correcting the computer simulation model according to the working condition data to obtain a corrected computer simulation model; or correcting the mathematical model used for forming the initial computer simulation model according to the working condition data to obtain a corrected mathematical model, and determining the corrected computer simulation model constructed by the corrected mathematical model.
8. The method of modifying of claim 7 wherein said initial computer simulation model is determined from a first computer simulation model created from a mathematical model relating to the operating conditions of said chemical process, said modifying further determining said initial computer simulation model by:
acquiring an output result of the mathematical model and an output result of the first computer simulation model;
comparing the output of the mathematical model with the output of the first computer simulation model; and
under the condition that the output result of the mathematical model is inconsistent with the output result of the first computer simulation model, correcting the first computer simulation model by taking the mathematical model as a basis to obtain the initial computer simulation model; and
and taking the first computer simulation model as the initial computer simulation model under the condition that the output result of the mathematical model is consistent with the output result of the first computer simulation model.
9. The correction method according to claim 7, characterized in that the correction method further comprises:
storing simulation data of the computer model with the label and/or chemical process working condition data with the label to a data platform; and
and correcting the mathematical model and/or correcting the computer simulation model formed by the mathematical model by using the simulation data of the computer model with the label and/or the chemical process working condition data with the label.
10. The correction method according to claim 6, wherein the operating condition data related to the chemical process are derived from actual production data and/or simulation data of a physical model.
11. The method of modifying of claim 7, wherein the operating condition data associated with the chemical process is stored on a data platform comprising an expert knowledge database, a database of actual operating conditions of the chemical process, a database of simulated operating conditions of the simulation model, a database of physical model, and a database of pre-stored data.
12. Correction method according to claim 7, characterized in that the type of the mathematical model is one or more of the following: CNN model, R-CNN model, CNN-DAE model, and SSLN model.
13. A machine-readable storage medium having stored thereon instructions for causing a machine to perform a method of modifying a computer simulation model for chemical process anomaly identification according to any one of claims 7-12.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116933703A (en) * | 2023-07-17 | 2023-10-24 | 合芯科技(苏州)有限公司 | Integrated circuit verification method, system and device based on multiple platforms and storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104196506A (en) * | 2014-08-01 | 2014-12-10 | 中国石油大学(北京) | Injection and production parameter joint debugging method, device and system for SAGD single well set |
| CN104657586A (en) * | 2014-12-31 | 2015-05-27 | 重庆科技学院 | Abnormal working condition detection-based high-sulfur natural gas purification process modeling optimization method |
| CN105005210A (en) * | 2015-07-07 | 2015-10-28 | 奇瑞汽车股份有限公司 | Mechanical-electrical integration simulation system and use method thereof |
| CN105160604A (en) * | 2015-09-29 | 2015-12-16 | 广东电网有限责任公司电力科学研究院 | Power grid equipment state pre-warning platform based on abnormal operation condition identification |
| CN206130547U (en) * | 2016-07-07 | 2017-04-26 | 北京信息科技大学 | Gas transmission pipeline leak testing system under multiplex condition |
| CN107505837A (en) * | 2017-07-07 | 2017-12-22 | 浙江大学 | A kind of semi-supervised neural network model and the soft-measuring modeling method based on the model |
| CN108469745A (en) * | 2018-03-05 | 2018-08-31 | 中国神华能源股份有限公司 | Operating condition in-circuit emulation method and on-line simulation system for gas-fired station |
| CN110363231A (en) * | 2019-06-27 | 2019-10-22 | 平安科技（深圳）有限公司 | Abnormality recognition method, device and storage medium based on semi-supervised deep learning |
| CN110976830A (en) * | 2019-12-06 | 2020-04-10 | 北京科技大学 | Control method for casting defects of aluminum alloy gear shifting hub |
-
2020
- 2020-05-25 CN CN202010450949.7A patent/CN113722973A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104196506A (en) * | 2014-08-01 | 2014-12-10 | 中国石油大学(北京) | Injection and production parameter joint debugging method, device and system for SAGD single well set |
| CN104657586A (en) * | 2014-12-31 | 2015-05-27 | 重庆科技学院 | Abnormal working condition detection-based high-sulfur natural gas purification process modeling optimization method |
| CN105005210A (en) * | 2015-07-07 | 2015-10-28 | 奇瑞汽车股份有限公司 | Mechanical-electrical integration simulation system and use method thereof |
| CN105160604A (en) * | 2015-09-29 | 2015-12-16 | 广东电网有限责任公司电力科学研究院 | Power grid equipment state pre-warning platform based on abnormal operation condition identification |
| CN206130547U (en) * | 2016-07-07 | 2017-04-26 | 北京信息科技大学 | Gas transmission pipeline leak testing system under multiplex condition |
| CN107505837A (en) * | 2017-07-07 | 2017-12-22 | 浙江大学 | A kind of semi-supervised neural network model and the soft-measuring modeling method based on the model |
| CN108469745A (en) * | 2018-03-05 | 2018-08-31 | 中国神华能源股份有限公司 | Operating condition in-circuit emulation method and on-line simulation system for gas-fired station |
| CN110363231A (en) * | 2019-06-27 | 2019-10-22 | 平安科技（深圳）有限公司 | Abnormality recognition method, device and storage medium based on semi-supervised deep learning |
| CN110976830A (en) * | 2019-12-06 | 2020-04-10 | 北京科技大学 | Control method for casting defects of aluminum alloy gear shifting hub |
Non-Patent Citations (4)
| Title |
|---|
| 中国食品药品检定研究院组织: "《药用辅料和药品包装材料检验技术》", 30 November 2019, 中国医药科技出版社, pages: 260 - 264 * |
| 徐瑞华: "《运输与物流***仿真》", 28 February 2009, 同济大学出版社, pages: 69 - 71 * |
| 梁静国: "《管理***仿真》", 31 December 1993, 哈尔滨船舶工程学院出版社, pages: 245 - 247 * |
| 董鸿晔等: "《药学计算导论》", 30 September 2013, 中国铁道出版社, pages: 55 - 57 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116933703A (en) * | 2023-07-17 | 2023-10-24 | 合芯科技(苏州)有限公司 | Integrated circuit verification method, system and device based on multiple platforms and storage medium |
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