Separation device intelligent optimization method based on digital twinborn and AI model

Abstract

The invention belongs to the technical field of separation device optimization, and particularly relates to a separation device intelligent optimization method based on digital twinning and AI models, which comprises the following specific steps: S1, data collection: firstly obtaining data related to material characteristics and quality detection, and then collecting operation parameters of a separation device in real time; s2, data processing: processing the acquired data so as to provide high-quality input for subsequent model training and analysis; s3, establishing a mechanism model: firstly establishing a relation model between operation parameters and performance indexes of the separation device, and then performing online optimization in combination with real-time data and working condition tracking conditions; and S4, working condition coverage: analyzing, judging and identifying the working condition of the separation device according to the production operation cycle and the working condition. According to the invention, through data processing, smooth processing, abnormal data cleaning, missing data filling and data correction setting operation can be carried out on the data, so that the data can be effectively purified.

Description

Technical Field

[0001] The present invention relates to the technical field of separation device optimization, and specifically to an intelligent optimization method for a separation device based on digital twins and AI models. Background Art

[0002] In modern industrial production, separation devices, as key equipment, are widely used in many fields such as petrochemicals, food processing, and environmental protection. Their operating efficiency and stability directly affect the benefits and quality of the entire production process. With the acceleration of industrial intelligence, traditional separation device optimization methods have the following problems:

[0003] 1. Data processing challenges: Separation equipment generates massive amounts of data during operation, often subject to high noise, numerous outliers, and missing data. For example, in distillation separation units in the petrochemical industry, sensors may generate erroneous data due to factors such as equipment vibration and environmental interference. Without effective processing, this can severely impact the accuracy of subsequent model analysis and optimization decisions.

[0004] 2. Poor adaptability to operating conditions: The operating parameters and performance of separation devices vary significantly across production cycles and operating conditions. Traditional optimization methods struggle to quickly and accurately analyze and assess operating condition changes, making it difficult to adjust operating parameters in a timely manner. For example, in the food processing industry, when producing different types of products, the material characteristics and throughput of the separation device change. Traditional methods are unable to achieve adaptive optimization, resulting in poor product separation and low production efficiency.

[0005] 3. Insufficient model accuracy: Existing mechanistic models struggle to fully describe the complex operational processes of separation devices, making it difficult to accurately predict device performance and optimize operating parameters. For example, in the environmental protection industry, wastewater treatment and separation devices are complex and variable in composition, making traditional models unable to accurately simulate the separation process. This results in unstable treatment results and makes it difficult to meet emission standards.

[0006] 4. Lack of real-time and intelligence: Traditional optimization methods are unable to obtain real-time device operating data and perform online optimization, making it difficult to achieve dynamic management of separation devices. For example, in chemical companies with continuous production, if a device operating abnormality occurs, traditional methods cannot detect and address it in a timely manner, which may lead to production interruption and huge economic losses.

[0007] Based on the above, an intelligent optimization method for a separation device based on digital twins and AI models is invented. Summary of the Invention

[0008] To solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions:

[0009] A method for intelligent optimization of a separation device based on digital twins and AI models includes the following specific steps:

[0010] S1, data collection: first obtain data related to material characteristics and quality inspection, and then collect the operating parameters of the separation device in real time;

[0011] S2, data processing: Process the acquired data to provide high-quality input for subsequent model training and analysis;

[0012] S3, Mechanism model: First, establish the relationship model between the separation device operating parameters and performance indicators, and then combine real-time data and operating condition tracking to perform online optimization;

[0013] S4, working condition coverage: identify the working condition of the separation device based on the production operation cycle and working condition analysis;

[0014] S5, Data Expansion: First, improve the generalization ability of the intelligent model through data sample expansion technology, then conduct in-depth analysis of the separation device operation data to promptly identify potential problems;

[0015] S6, Digital Twin: Build a digital twin of the separation device to predict the device's operating performance, diagnose faults, and make optimal decisions.

[0016] As a preferred solution of the intelligent optimization method for a separation device based on digital twin and AI model described in the present invention, the specific steps of S1 are as follows:

[0017] S11: The on-site separation device can obtain data related to material characteristics and quality testing by connecting to the laboratory information management system LIMS;

[0018] S12, collecting the operating parameters of the separation device in real time based on the SCADA / DCS control system, and being able to transmit the collected parameter data to a real-time database for storage;

[0019] S13, the data in the real-time database and LIMS system can be transmitted to the standard database after being sorted, so that the standard database can provide the data to the intelligent optimization system, thereby providing comprehensive and accurate data support for subsequent analysis and optimization.

[0020] As a preferred solution of the intelligent optimization method for a separation device based on digital twin and AI model described in the present invention, the specific steps of S2 are as follows:

[0021] S21, removes noise from the data through data smoothing to make the data smoother and more continuous;

[0022] S22, identify and eliminate erroneous or unreasonable data through abnormal data cleaning;

[0023] S23, supplement missing values ​​by missing data filling;

[0024] S24, through data correction and adjustment, calibrate the data according to actual working conditions and standards to ensure the accuracy and reliability of the data.

[0025] As a preferred solution of the intelligent optimization method for a separation device based on digital twin and AI model described in the present invention, the specific steps of S3 are as follows:

[0026] S31, learning and analyzing the processed data based on the intelligent model to explore the inherent laws in the operation process of the separation device and establish a relationship model between the operation parameters and performance indicators of the separation device;

[0027] S32, based on the analysis results of the intelligent model, enables the intelligent optimization system to combine real-time data and working condition tracking to perform online optimization and provide optimization decision suggestions for human-computer interaction;

[0028] S33 provides operators with an intuitive operating interface based on human-computer interaction, including performance monitoring, optimization design, economic evaluation, comprehensive reports, and constraint configuration functions, so that operators can understand the operating status of the device in real time and make optimization designs and decisions.

[0029] As a preferred solution of the intelligent optimization method for a separation device based on digital twin and AI model described in the present invention, the specific steps of S4 are as follows:

[0030] S41: Analysis of production operation cycle;

[0031] S42, based on the analysis results and combined with the working condition analysis and judgment algorithm, accurately identifies the working condition of the separation device and provides working condition information to the intelligent optimization system so that the system can adjust the optimization strategy according to different working conditions to ensure that the separation device can maintain efficient operation under various working conditions.

[0032] As a preferred solution of the intelligent optimization method for a separation device based on digital twin and AI model described in the present invention, the specific steps of S5 are as follows:

[0033] S51: Improving the generalization capability of intelligent models through data sample expansion technology, wherein the data sample expansion technology includes data enhancement algorithms to increase the diversity and quantity of data;

[0034] S52: Use data analysis and diagnostic tools to conduct in-depth analysis of the operating data of the separation device, promptly identify potential problems, and make optimization suggestions to achieve continuous improvement and optimization of the separation device.

[0035] As a preferred solution of the intelligent optimization method for a separation device based on digital twin and AI model described in the present invention, the specific steps of S6 are as follows:

[0036] S61: Based on the processed data and the established intelligent model, a digital twin of the separation device is constructed. The digital twin can map the operating status of the physical separation device in real time. Through data interaction and comparative analysis with the actual device, it can predict the device's operating performance, diagnose faults, and make optimization decisions.

[0037] S62: Operators can visually observe the operation of the device through the digital twin, identify potential problems in advance and take measures to improve the operational reliability and efficiency of the separation device.

[0038] Compared with existing technologies:

[0039] 1. Addressing data processing challenges: The present invention can perform data smoothing, abnormal data cleaning, missing data filling, and data correction and adjustment operations through data processing, thereby effectively purifying the data. Taking the distillation separation device in the petrochemical industry as an example, when processing erroneous data generated by sensor vibration and environmental interference, the data processing flow of the present invention can greatly improve data quality, provide a reliable basis for subsequent intelligent model analysis and optimized decision-making, and avoid decision-making errors caused by data problems.

[0040] 2. Regarding poor adaptability to working conditions: The present invention can accurately identify the working conditions of the separation device based on the production operation cycle and working condition analysis through working condition coverage. For example, in the food processing industry, when changes in the types of products produced cause changes in material properties, processing volume and other working conditions, the operating parameters can be quickly adjusted to achieve adaptive optimization, significantly improve product separation effects and production efficiency, and ensure the efficiency and stability of the production process.

[0041] 3. Regarding the lack of model accuracy: The present invention uses an intelligent model to study and analyze a large amount of processed data, deeply explore the operating rules of the device, and establish an accurate relationship model between operating parameters and performance indicators. It can accurately simulate the separation process, stabilize the treatment effect, and help meet emission standards.

[0042] 4. Addressing the lack of real-time and intelligence: This invention leverages real-time data acquisition and online optimization capabilities, combined with digital twin technology, to map the operating status of the separation device in real time, enabling dynamic management of the separation device. In chemical companies with continuous production, any operational anomalies can be quickly identified and optimized, effectively avoiding production interruptions, reducing economic losses, and significantly improving the reliability and intelligence of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the overall framework of the present invention. DETAILED DESCRIPTION

[0044] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0045] The present invention provides a separation device intelligent optimization method based on digital twin and AI model, please refer to Figure 1 , including the following specific steps:

[0046] S1, data collection: first obtain data related to material characteristics and quality inspection, and then collect the operating parameters of the separation device in real time;

[0047] The specific steps of S1 are as follows:

[0048] S11: The on-site separation device can obtain data related to material characteristics and quality testing by connecting to the laboratory information management system LIMS;

[0049] S12, collecting the operating parameters of the separation device in real time based on the SCADA / DCS control system, and being able to transmit the collected parameter data to a real-time database for storage;

[0050] S13, after being sorted, the data in the real-time database and LIMS system can be transmitted to the standard database, so that the standard database can provide the data to the intelligent optimization system, thereby providing comprehensive and accurate data support for subsequent analysis and optimization;

[0051] S2, data processing: Process the acquired data to provide high-quality input for subsequent model training and analysis;

[0052] The specific steps of S2 are as follows:

[0053] S21, removes noise from the data through data smoothing to make the data smoother and more continuous;

[0054] S22, identify and eliminate erroneous or unreasonable data through abnormal data cleaning;

[0055] S23, supplement missing values ​​by missing data filling;

[0056] S24, calibrate the data according to actual working conditions and standards through data correction and setting to ensure the accuracy and reliability of the data;

[0057] S3, Mechanism model: First, establish the relationship model between the separation device operating parameters and performance indicators, and then combine real-time data and operating condition tracking to perform online optimization;

[0058] The specific steps of S3 are as follows:

[0059] S31, learning and analyzing the processed data based on the intelligent model to explore the inherent laws in the operation process of the separation device and establish a relationship model between the operation parameters and performance indicators of the separation device;

[0060] S32, based on the analysis results of the intelligent model, enables the intelligent optimization system to combine real-time data and working condition tracking to perform online optimization and provide optimization decision suggestions for human-computer interaction;

[0061] S33, based on human-computer interaction, provides operators with an intuitive operation interface, including performance monitoring, optimization design, economic evaluation, comprehensive reporting, and constraint configuration functions, so that operators can understand the operating status of the device in real time and make optimization designs and decisions;

[0062] S4, working condition coverage: identify the working condition of the separation device based on the production operation cycle and working condition analysis;

[0063] The specific steps of S4 are as follows:

[0064] S41: Analysis of production operation cycle;

[0065] S42, based on the analysis results and combined with the working condition analysis and judgment algorithm, accurately identifies the working condition of the separation device and provides working condition information to the intelligent optimization system so that the system can adjust the optimization strategy according to different working conditions to ensure that the separation device can maintain efficient operation under various working conditions;

[0066] S5, Data Expansion: First, improve the generalization ability of the intelligent model through data sample expansion technology, then conduct in-depth analysis of the separation device operation data to promptly identify potential problems;

[0067] The specific steps of S5 are as follows:

[0068] S51: Improving the generalization capability of intelligent models through data sample expansion technology, wherein the data sample expansion technology includes data enhancement algorithms to increase the diversity and quantity of data;

[0069] S52: Use data analysis and diagnostic tools to conduct in-depth analysis of the separation unit's operating data, identify potential problems promptly, and propose optimization suggestions to achieve continuous improvement and optimization of the separation unit;

[0070] S6, Digital Twin: Build a digital twin of the separation device to predict the device's operating performance, diagnose faults, and make optimal decisions;

[0071] The specific steps of S6 are as follows:

[0072] S61: Based on the processed data and the established intelligent model, a digital twin of the separation device is constructed. The digital twin can map the operating status of the physical separation device in real time. Through data interaction and comparative analysis with the actual device, it can predict the device's operating performance, diagnose faults, and make optimization decisions.

[0073] S62: Operators can visually observe the operation of the device through the digital twin, identify potential problems in advance and take measures to improve the operational reliability and efficiency of the separation device.

[0074] Although the present invention has been described above with reference to embodiments, various modifications may be made thereto and equivalent components may be substituted without departing from the scope of the present invention. In particular, as long as there are no structural conflicts, the various features of the embodiments disclosed herein may be combined with each other in any manner, and the omission of an exhaustive description of such combinations in this specification is solely for the sake of space and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. An intelligent optimization method for separation devices based on digital twins and AI models, characterized in that: The specific steps are as follows: S1, data collection: first obtain data related to material characteristics and quality inspection, and then collect the operating parameters of the separation device in real time; S2, data processing: Process the acquired data to provide high-quality input for subsequent model training and analysis; S3, Mechanism model: First, establish the relationship model between the separation device operating parameters and performance indicators, and then combine real-time data and operating condition tracking to perform online optimization; S4, working condition coverage: identify the working condition of the separation device based on the production operation cycle and working condition analysis; S5, Data Expansion: First, improve the generalization ability of the intelligent model through data sample expansion technology, then conduct in-depth analysis of the separation device operation data to promptly identify potential problems; S6, Digital Twin: Build a digital twin of the separation device to predict the device's operating performance, diagnose faults, and make optimal decisions.

2. The intelligent optimization method for a separation device based on digital twins and AI models according to claim 1 is characterized in that: The specific steps of S1 are as follows: S11: The on-site separation device can obtain data related to material characteristics and quality testing by connecting to the laboratory information management system LIMS; S12, collecting the operating parameters of the separation device in real time based on the SCADA / DCS control system, and being able to transmit the collected parameter data to a real-time database for storage; S13, the data in the real-time database and LIMS system can be transmitted to the standard database after being sorted, so that the standard database can provide the data to the intelligent optimization system, thereby providing comprehensive and accurate data support for subsequent analysis and optimization.

3. The intelligent optimization method for a separation device based on digital twins and AI models according to claim 1 is characterized in that: The specific steps of S2 are as follows: S21, removes noise from the data through data smoothing to make the data smoother and more continuous; S22, identify and eliminate erroneous or unreasonable data through abnormal data cleaning; S23, supplement missing values ​​by missing data filling; S24, through data correction and adjustment, calibrate the data according to actual working conditions and standards to ensure the accuracy and reliability of the data.

4. The intelligent optimization method for a separation device based on digital twins and AI models according to claim 1 is characterized in that: The specific steps of S3 are as follows: S31, learning and analyzing the processed data based on the intelligent model to explore the inherent laws in the operation process of the separation device and establish a relationship model between the operation parameters and performance indicators of the separation device; S32, based on the analysis results of the intelligent model, enables the intelligent optimization system to combine real-time data and working condition tracking to perform online optimization and provide optimization decision suggestions for human-computer interaction; S33 provides operators with an intuitive operating interface based on human-computer interaction, including performance monitoring, optimization design, economic evaluation, comprehensive reports, and constraint configuration functions, so that operators can understand the operating status of the device in real time and make optimization designs and decisions.

5. The intelligent optimization method for separation device based on digital twin and AI model according to claim 1 is characterized in that: The specific steps of S4 are as follows: S41: Analysis of production operation cycle; S42, based on the analysis results and combined with the working condition analysis and judgment algorithm, accurately identifies the working condition of the separation device and provides working condition information to the intelligent optimization system so that the system can adjust the optimization strategy according to different working conditions to ensure that the separation device can maintain efficient operation under various working conditions.

6. The intelligent optimization method for separation device based on digital twin and AI model according to claim 1 is characterized in that: The specific steps of S5 are as follows: S51: Improving the generalization capability of intelligent models through data sample expansion technology, wherein the data sample expansion technology includes data enhancement algorithms to increase the diversity and quantity of data; S52: Use data analysis and diagnostic tools to conduct in-depth analysis of the operating data of the separation device, promptly identify potential problems, and make optimization suggestions to achieve continuous improvement and optimization of the separation device.

7. The intelligent optimization method for separation device based on digital twin and AI model according to claim 1 is characterized in that: The specific steps of S6 are as follows: S61: Based on the processed data and the established intelligent model, a digital twin of the separation device is constructed. The digital twin can map the operating status of the physical separation device in real time. Through data interaction and comparative analysis with the actual device, it can predict the device's operating performance, diagnose faults, and make optimization decisions. S62: Operators can visually observe the operation of the device through the digital twin, identify potential problems in advance and take measures to improve the operational reliability and efficiency of the separation device.

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