Temperature Control Method, Device, Equipment, Medium and Program Product for Aldehyde Separation Tower Based on Industrial Internet

Abstract

The present application provides a temperature control method, device, equipment, medium and program product for an aldehyde separation tower based on industrial Internet, which relates to the field of industrial automation technology. The method includes: obtaining the parameter values of multiple operating parameters of the aldehyde separation tower, and determining a predicted separation effect value of the aldehyde separation tower according to the parameter values of the multiple operating parameters; according to the predicted separation effect value, with the goal of minimizing the predicted proportion value of non-separated components in the overhead material and the predicted proportion value of separated components in the bottom material, using an optimization algorithm to determine the optimal tray temperature of the aldehyde separation tower. This method is applied in the industrial Internet, realizing the precise control and optimization of the temperature control parameters of the aldehyde separation tower in full automation, improving the accuracy and stability of the aldehyde separation process, and further enhancing the product quality and production efficiency.

Description

Technical Field

[0001] This application relates to the field of industrial automation technology, and in particular, to a temperature control method, device, equipment, medium, and program product for an aldehyde separation column based on the industrial Internet. Background Art

[0002] The separation of aldehyde mixtures (such as butyraldehyde and valeraldehyde) plays an important role in chemical production; by taking advantage of the differences in physical and chemical properties of aldehyde compounds, specific technical means can be used to effectively separate them.

[0003] In chemical separation technology, an aldehyde separation column is a commonly used device, and its design and operating parameters are crucial for the separation effect, especially the temperature setting; in traditional separation operations, the adjustment of temperature parameters mainly relies on the experience and intuition of operators.

[0004] However, it is difficult to ensure that the aldehyde separation column always separates at a suitable temperature by manually adjusting the temperature parameters, resulting in poor accuracy of the separation effect. Summary of the Invention

[0005] Embodiments of this application provide a temperature control method, device, equipment, medium, and program product for an aldehyde separation column based on the industrial Internet, so as to achieve precise control and optimization of the temperature control parameters of the aldehyde separation column, thereby improving the accuracy and stability of the separation process.

[0006] In a first aspect, embodiments of this application provide a temperature control method for an aldehyde separation column based on the industrial Internet, including:

[0007] Obtain the parameter values of multiple operating parameters of the aldehyde separation column, where the operating parameters include the tray temperature of the aldehyde separation column and one or more other operating parameters that affect the aldehyde separation effect except the tray temperature;

[0008] Determine a predicted separation effect value of the aldehyde separation column according to the parameter values of the multiple operating parameters, where the predicted separation effect value includes predicted proportion values of each component in the overhead material of the aldehyde separation column and predicted proportion values of each component in the bottom material;

[0009] According to the predicted separation effect value, with the goal of minimizing the predicted proportion value of non-separated components in the overhead material and the predicted proportion value of separated components in the bottom material, use an optimization algorithm to determine the optimal tray temperature of the aldehyde separation column.

[0010] In a possible implementation manner, the determining the predicted separation effect value of the aldehyde separation column according to the parameter values of the multiple operating parameters includes:

[0011] Input the parameter values of the multiple operating parameters into a pre-trained prediction model to obtain the predicted separation effect value output by the prediction model, where the prediction model is trained based on the historical parameter values of the operating parameters of the aldehyde separation column and the true historical separation effect values corresponding to the historical parameter values.

[0012] In a possible implementation manner, before inputting the parameter values of the multiple operating parameters into the pre-trained prediction model, the method further includes:

[0013] Determine the corresponding prediction model according to the composition components of the aldehyde mixture in the aldehyde separation column, where the prediction model is trained based on the composition components of the aldehyde mixture, the historical parameter values of the operating parameters of the aldehyde separation column, and the true historical separation effect values corresponding to the historical parameter values.

[0014] In a possible implementation manner, based on the predicted separation effect value, with the goal of minimizing the predicted proportion of non-separated components in the top product and the predicted proportion of separated components in the bottom product, an optimization algorithm is used to determine the optimal tray temperature of the aldehyde separation column, including:

[0015] Under the condition that the other operating parameters remain unchanged, use the particle swarm optimization algorithm to iteratively adjust the tray temperature, and determine the adjusted predicted separation effect value according to the adjusted tray temperature and the other operating parameters, and determine the tray temperature that minimizes the predicted proportion of non-separated components in the top product and the predicted proportion of separated components in the bottom product as the optimal tray temperature.

[0016] In a possible implementation manner, the temperature control method of the aldehyde separation column based on the industrial Internet further includes:

[0017] Control the aldehyde separation column to operate according to the optimal tray temperature and monitor the true separation effect value of the aldehyde separation column;

[0018] When the true separation effect value exceeds the preset range, output an alarm message and, in response to a user operation, adjust the tray temperature of the aldehyde separation column.

[0019] In a possible implementation manner, the obtaining of the multiple operating parameters of the aldehyde separation column includes:

[0020] Within a preset time period, collect the numerical values of each operating parameter according to the collection frequency of each operating parameter of the aldehyde separation column;

[0021] For each operating parameter, determine the average value of the numerical values collected within the preset time period as the parameter value of the operating parameter.

[0022] In a second aspect, an embodiment of the present application provides a temperature control device for an aldehyde separation column based on the industrial Internet, including:

[0023] An acquisition module, configured to acquire parameter values of a plurality of operating parameters of the aldehyde separation column, where the operating parameters include the tray temperature of the aldehyde separation column and one or more other operating parameters that affect the aldehyde separation effect except the tray temperature.

[0024] A determination module, configured to determine a separation effect prediction value of the aldehyde separation column according to the parameter values of the plurality of operating parameters, where the separation effect prediction value includes a predicted proportion value of each component in the overhead material of the aldehyde separation column and a predicted proportion value of each component in the bottom material.

[0025] The determination module is further configured to, according to the separation effect prediction value, with the goal of minimizing the predicted proportion value of the non-separated component in the overhead material and the predicted proportion value of the separated component in the bottom material, use an optimization algorithm to determine the optimal tray temperature of the aldehyde separation column.

[0026] Optionally, the temperature control device for the aldehyde separation column based on the industrial Internet further includes: a processing module.

[0027] The processing module is configured to input the parameter values of the plurality of operating parameters into a pre-trained prediction model to obtain the separation effect prediction value output by the prediction model, where the prediction model is trained based on historical parameter values of the operating parameters of the aldehyde separation column and historical separation effect true values corresponding to the historical parameter values.

[0028] Optionally, the determination module is further configured to determine the corresponding prediction model according to the composition components of the aldehyde mixture in the aldehyde separation column, where the prediction model is trained based on the composition components of the aldehyde mixture, historical parameter values of the operating parameters of the aldehyde separation column, and historical separation effect true values corresponding to the historical parameter values.

[0029] Optionally, the processing module is further configured to iteratively adjust the tray temperature using a particle swarm optimization algorithm under the condition that the other operating parameters remain unchanged.

[0030] The determination module is further configured to determine an adjusted separation effect prediction value according to the adjusted tray temperature and the other operating parameters, and determine the tray temperature that minimizes the predicted proportion value of the non-separated component in the overhead material and the predicted proportion value of the separated component in the bottom material as the optimal tray temperature.

[0031] Optionally, the processing module is further configured to control the aldehyde separation column to operate according to the optimal tray temperature and monitor the true value of the separation effect of the aldehyde separation column.

[0032] The processing module is further configured to output an alarm message when the true value of the separation effect exceeds a preset range, and adjust the tray temperature of the aldehyde separation column in response to a user operation.

[0033] Optionally, the acquisition module is further configured to collect the values of the operating parameters at the acquisition frequency of each of the operating parameters of the aldehyde separation column within a preset time period.

[0034] The determination module is further configured to determine, for each of the operating parameters, the average value of the values collected within the preset time period as the parameter value of the operating parameter.

[0035] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory and a processor;

[0036] The memory stores computer-executable instructions;

[0037] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the first aspect and / or various possible implementation manners of the first aspect as described above.

[0038] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the first aspect and / or various possible implementation manners of the first aspect as described above.

[0039] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, and when the computer program is executed by a processor, it implements the first aspect and / or various possible implementation manners of the first aspect as described above.

[0040] The temperature control method, device, equipment, medium and program product of an aldehyde separation tower based on industrial Internet provided by the embodiments of the present application collect the values of each operating parameter according to the collection frequency of each operating parameter of the aldehyde separation tower within a preset time period; for each operating parameter, determine the average value of the values collected within the preset time period as the parameter value of the operating parameter; input the parameter values of multiple operating parameters into a pre-trained prediction model to obtain a separation effect prediction value output by the prediction model; under the condition that other operating parameters remain unchanged, use the particle swarm optimization algorithm to iteratively adjust the tray temperature, and determine the adjusted separation effect prediction value according to the adjusted tray temperature and other operating parameters, and determine the tray temperature that minimizes the predicted value of the proportion of non-separated components in the top material and the predicted value of the proportion of separated components in the bottom material as the optimal tray temperature; control the aldehyde separation tower to operate according to the optimal tray temperature, and monitor the true value of the separation effect of the aldehyde separation tower; in the case that the true value of the separation effect exceeds the preset range, output an alarm message, and in response to a user operation, adjust the tray temperature of the aldehyde separation tower. This method is applied in the industrial Internet, realizing the precise control and optimization of the temperature control parameters of the aldehyde separation tower in a fully automated manner, enabling the temperature control parameters to automatically adapt to changes in different production conditions and raw material characteristics, improving the accuracy and stability of the aldehyde separation process, and further enhancing the product quality and production efficiency, providing a more efficient and reliable solution for chemical production. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present application and used together with the specification to explain the principles of the present application.

[0042] Figure 1 is a flowchart of the temperature control method of the aldehyde separation tower based on industrial Internet provided by the present application Figure 1 ;

[0043] Figure 2 is a flowchart of the temperature control method of the aldehyde separation tower based on industrial Internet provided by the present application Figure 2 ;

[0044] Figure 3 is a schematic structural diagram of the temperature control device of the aldehyde separation tower based on industrial Internet provided by the present application;

[0045] Figure 4 is a schematic structural diagram of the electronic device provided by the present application.

[0046] Through the above accompanying drawings, specific embodiments of the present application have been shown, and there will be more detailed descriptions hereinafter. These drawings and text descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. Detailed implementation manners

[0047] To make the objectives, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below with reference to the accompanying drawings in this application. Apparently, the described embodiments are some but not all of the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without making creative efforts shall fall within the scope of protection of this application.

[0048] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned accompanying drawings are used to distinguish similar objects and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data may be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order different from those illustrated or described herein.

[0049] In the embodiments of this application, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in this application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0050] Exemplary embodiments will be described in detail herein, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with this application. On the contrary, they are merely examples of devices and methods consistent with some aspects of this application as detailed in the appended claims.

[0051] First, the nouns involved in this application are explained:

[0052] DCS: Distributed Control System, a distributed control system, which refers to a technical solution in which multiple controllers coordinate with each other to achieve automated control of the system. DCS is usually applied to fields such as industrial production, manufacturing and processing, including industries such as chemical industry, pharmaceuticals, food, pulp and paper.

[0053] LIMS: Laboratory Information Management System; it is a software system that can comprehensively manage information such as laboratory processes, data, instruments, personnel, documents and reports.

[0054] Particle Swarm Optimization Model: Particle Swarm Optimization, abbreviated as PSO model, is an optimization algorithm based on swarm intelligence that simulates the behavior of particle swarms in nature to find the optimal solution in the problem space. In the PSO model, each particle represents a potential solution, and it adjusts its velocity and position based on its individual historical best position and the global best position to find the optimal solution.

[0055] The separation of aldehyde mixtures (such as butyraldehyde and valeraldehyde) plays an important role in chemical production. Among them, since aldehyde compounds usually have different physical and chemical properties, such as boiling point, solubility, etc., therefore, the differences in different physical and chemical properties enable them to be effectively separated through specific separation techniques.

[0056] In chemical separation technology, an aldehyde separation tower is a commonly used device. It utilizes the distribution difference of substances at different heights in the tower to separate different aldehydes in the mixture through distillation. The design and operating parameters of the aldehyde separation tower have a crucial impact on the separation effect. Among them, temperature setting is a core control parameter.

[0057] In the operation of traditional aldehyde separation towers, the adjustment of temperature control parameters mainly relies on the experience and intuition of operators. However, there are differences in the experience and intuition of different operators, which may lead to different separation effects under the same production conditions, and the manual adjustment of temperature control parameters cannot quickly respond to changes in the production process, thus affecting the separation effect of the aldehyde separation tower.

[0058] The temperature control method of the aldehyde separation tower based on the industrial Internet provided by this application, based on historical DCS data and LIMS data, learns the mapping relationship between DCS parameters affecting the separation effect and the separation effect through a deep learning model, so as to realize the prediction of the aldehyde separation effect during the real-time separation process. Based on the real-time prediction effect, the temperature control parameters of the aldehyde separation tower are inversely controlled, so as to achieve precise control and optimization of the temperature control parameters of the aldehyde separation tower in full automation, and improve the accuracy and stability of the separation process. This method is applied in the industrial Internet, solving the technical problems that the adjustment of temperature control parameters of traditional aldehyde separation towers depends on manual experience, resulting in poor accuracy of separation effects and insufficient stability during the separation process.

[0059] The following uses specific embodiments to elaborate in detail on the technical solution of this application and how the technical solution of this application solves the above technical problems. These several specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this application will be described below in conjunction with the drawings.

[0060] Figure 1Flow schematic of the temperature control method for the aldehyde separation tower based on the industrial Internet provided by this application Figure 1 The execution subject of this embodiment can be, for example, a central data platform equipped with a temperature control module. As Figure 1 shown, the temperature control method for the aldehyde separation tower based on the industrial Internet provided by this embodiment includes:

[0061] S101: Obtain the parameter values of multiple operating parameters of the aldehyde separation tower.

[0062] Among them, the operating parameters are used to indicate the variables or conditions that need to be monitored and adjusted during the operation and control of the aldehyde separation tower. The operating parameters include the tray temperature of the aldehyde separation tower and one or more other operating parameters that affect the aldehyde separation effect other than the tray temperature.

[0063] In the embodiment of this application, a variety of detection devices are arranged in the aldehyde separation tower to detect various operating parameters and states in the aldehyde separation tower to ensure the smooth progress of the separation process and the quality of the product; the detection devices include: temperature sensors, pressure sensors, flow meters, and liquid level gauges.

[0064] A DCS system is also arranged in the aldehyde separation tower, and the DCS system is respectively communicatively connected to a variety of detection devices, and can obtain the real-time detection data of the detection devices in real time, so as to realize the real-time detection of various operating parameters in the aldehyde separation tower. In addition, the DCS system can also control or adjust the corresponding operating parameters.

[0065] Exemplarily, the operating parameters that can be obtained by the DCS system of the aldehyde separation tower include: feed flow rate, feed temperature, steam flow rate, E120 condensate tank liquid level, aldehyde separation tower top pressure, aldehyde separation tower top reflux flow rate, aldehyde separation tower top reflux material temperature, aldehyde separation tower top reflux tank liquid level, aldehyde separation tower bottom liquid level, aldehyde separation tower sensitive point temperature, tray temperature, tower pressure; through the detection devices arranged at the corresponding positions, the specific parameter values of the corresponding operating parameters can be obtained, specifically: feed flow rate - 500 kg / h, feed temperature - 60 °C, steam flow rate - 200 kg / h, E120 condensate tank liquid level - 75%, aldehyde separation tower top pressure - 1.2 bar, aldehyde separation tower top reflux flow rate - 150 kg / h, aldehyde separation tower top reflux material temperature - 40 °C, aldehyde separation tower top reflux tank liquid level - 60%, aldehyde separation tower bottom liquid level - 50%, aldehyde separation tower sensitive point temperature - 85 °C, tower pressure - 1.5 bar, where the tray temperature includes: the first tray temperature - 90 °C, the second tray temperature - 88 °C, the third tray temperature - 86 °C.

[0066] It should be noted that an aldehyde separation column usually consists of multiple trays. The setting of multiple trays can effectively promote the contact between gas and liquid, thereby realizing the separation of the components of the aldehyde mixture, effectively improving the separation efficiency, and ensuring the purity and quality of the product. Since the bottom of the column is usually heated to provide the necessary energy for evaporation, while the top of the column is the condensation area, there is usually a temperature gradient from the bottom to the top of the aldehyde separation column, with a higher temperature at the bottom and a lower temperature at the top.

[0067] S102: Determine the predicted separation effect value of the aldehyde separation column according to the parameter values of multiple operating parameters.

[0068] S103: Based on the predicted separation effect value, with the goal of minimizing the predicted proportion value of non-separated components in the overhead product and the predicted proportion value of separated components in the bottom product of the aldehyde separation column, use an optimization algorithm to determine the optimal tray temperature of the aldehyde separation column.

[0069] Among them, the predicted separation effect value includes the predicted proportion values of each component in the overhead product of the aldehyde separation column and the predicted proportion values of each component in the bottom product.

[0070] In the embodiments of the present application, the DCS system set in the aldehyde separation column can obtain the corresponding operating parameters in real time. Therefore, the parameter values corresponding to the operating parameters can also be called DCS data. An LIMS system is also set in the aldehyde separation column. The LIMS system is used to detect and store the separation effect of the corresponding compound when the aldehyde separation column separates the compound with different operating parameters. Among them, the separation effect of the aldehyde separation column can be determined by the components of different compounds at the top and bottom of the column. For example, the separation effect of the aldehyde separation column can be reflected by the proportion value of C5 aldehyde at the top of the column and the proportion values of heavy components and butyraldehyde at the bottom of the column. That is to say, the LIMS system can determine the actual separation effect of the aldehyde separation column for that time by detecting the proportion value of C5 aldehyde at the top of the column and the proportion values of heavy components and butyraldehyde at the bottom of the column.

[0071] It can be understood that the temperature control module stores the mapping relationship between multiple operating parameters and the separation effect, and this mapping relationship is determined according to the historical operating parameters and the corresponding historical separation effects. During the actual operation of the aldehyde separation column, due to the time delay between the DCS system and the LIMS system, in order to ensure the separation effect of the aldehyde separation column, the central data platform obtains the operating parameters in real time and automatically adjusts the tray temperature according to the mapping relationship to maintain or improve the separation efficiency of the aldehyde separation column.

[0072] Based on the parameter values of multiple operating parameters currently obtained, call the mapping relationship stored in the temperature control module, use this mapping relationship to determine the separation effect corresponding to the current operating parameters, and determine this separation effect as the predicted value of the separation effect of the aldehyde separation column; according to the currently determined predicted value of the separation effect, with the goal of minimizing the predicted proportion of non-separated components in the top product and the predicted proportion of separated components in the bottom product, use an optimization algorithm to calculate the tray temperature that the aldehyde separation column can adopt under the current operating conditions when minimizing the non-separated components in the top product and minimizing the separated components in the bottom product, and determine this tray temperature as the optimal tray temperature.

[0073] In some embodiments, a proportion threshold is stored in the temperature control module, and this proportion threshold is used to measure the separation effect of the aldehyde separation column. If the proportion value of non-separated components in the top product and the proportion value of separated components in the bottom product are less than this proportion threshold, it indicates that under the current operating parameters, the separation effect of the aldehyde separation column reaches the expected effect and there is no need to adjust the tray temperature; if the proportion value of non-separated components in the top product and / or the proportion value of separated components in the bottom product is not less than this proportion threshold, it indicates that under the current operating parameters, the separation effect of the aldehyde separation column does not reach the expected effect. At this time, it is necessary to recalculate the tray temperature corresponding to the current operating parameters and control the aldehyde separation column to perform compound separation at the corresponding tray temperature.

[0074] Exemplarily, based on the parameter values of multiple operating parameters currently obtained, call the mapping relationship stored in the temperature control module, use this mapping relationship to determine the separation effect corresponding to the current operating parameters, and determine this separation effect as the predicted value of the separation effect of the aldehyde separation column. This predicted value of the separation effect can be: "The proportion of C5 aldehyde in the top product is 7%, the proportion of butyraldehyde in the top product is 93%, the proportion of heavy components in the bottom product is 6%, the proportion of butyraldehyde in the bottom product is 6%, and the proportion of valeraldehyde in the bottom product is 88%"; if the proportion threshold is 5%, and the proportion of C5 aldehyde in the top product, the proportion of heavy components in the bottom product, and the proportion of butyraldehyde in the bottom product are all higher than this proportion threshold, at this time, it is necessary to adjust the tray temperature to achieve the best separation effect; according to the currently determined predicted value of the separation effect, with the goal function of minimizing the predicted proportion of non-separated components in the top product and the predicted proportion of separated components in the bottom product, use the particle swarm optimization model to calculate the tray temperature that the aldehyde separation column can adopt under this operating condition when the proportion of C5 aldehyde in the top product, the proportion of heavy components in the bottom product, and the proportion of butyraldehyde in the bottom product are all lower than this proportion threshold, and determine this tray temperature as the optimal tray temperature.

[0075] The temperature control method for an aldehyde separation column based on the industrial Internet provided by the embodiments of the present application obtains the parameter values of multiple operating parameters of the aldehyde separation column, and determines the predicted separation effect value of the aldehyde separation column according to the parameter values of the multiple operating parameters; according to the predicted separation effect value, with the goal of minimizing the predicted proportion of non-separated components in the overhead material and the predicted proportion of separated components in the bottom material, an optimization algorithm is used to determine the optimal tray temperature of the aldehyde separation column. This method is applied in the industrial Internet, realizing the precise control and optimization of the temperature control parameters of the aldehyde separation column in a fully automated manner, improving the accuracy and stability of the aldehyde separation process, and thus enhancing the product quality and production efficiency.

[0076] Figure 2 is a schematic flow chart of the temperature control method for an aldehyde separation column based on the industrial Internet provided by the present application Figure 2 , as Figure 2 shown, on the basis of the Figure 1 embodiment, the temperature control method for an aldehyde separation column based on the industrial Internet is described in detail. The method includes:

[0077] S201: Within a preset time period, collect the numerical values of each operating parameter according to the collection frequency of each operating parameter of the aldehyde separation column.

[0078] S202: For each operating parameter, determine the average value of the numerical values collected within the preset time period as the parameter value of the operating parameter.

[0079] Among them, the preset time period can be, for example, 10 min, and the collection frequency can be, for example, 10 s / time.

[0080] It can be understood that there are differences in the collection frequencies of different types of operating parameters in the DCS system, and there are also differences in the collection frequencies of the DCS system and the LIMS system for obtaining data, and the data collection frequency of the LIMS system is lower than that of the DCS system; in order to facilitate the real-time prediction of the separation effect of the aldehyde separation column, therefore, it is necessary to preprocess the obtained DCS data, that is, the specific data of the operating parameters of the aldehyde separation column, to unify the time formats of the DCS data and the LIMS data; in addition, this preprocessing also includes data cleaning, format conversion, outlier detection, and data correction.

[0081] Within a preset time period, collect the specific numerical values of each operating parameter according to the collection frequency of each operating parameter of the aldehyde separation column; for each operating parameter, calculate the average value of the numerical values collected within the preset time period, and determine the average value of the corresponding operating parameter as the parameter value of the corresponding operating parameter.

[0082] Exemplarily, the DCS system obtains the specific values of multiple operating parameters within 10 minutes according to the acquisition frequency of each operating parameter of the aldehyde separation column; the central data platform obtains the data information transmitted by the DCS system, and preprocesses the time format of the multiple DCS data obtained within 10 minutes, that is, for different operating parameters, calculates the average value of the multiple values obtained within 10 minutes respectively, and determines the average value as the parameter value of the corresponding operating parameter; specifically, it can be: the DCS system obtains the temperature of the reflux material at the top of the aldehyde separation column at a frequency of 10 s / time, and obtains 60 specific values of the temperature of the reflux material at the top of the aldehyde separation column within 10 minutes. The central data platform obtains these 60 DCS data and calculates the average value. The obtained calculation result is 40 °C. At this time, the calculation result is determined as the parameter value of the temperature of the reflux material at the top of the aldehyde separation column.

[0083] S203: Input the parameter values of multiple operating parameters into a pre-trained prediction model to obtain the separation effect prediction value output by the prediction model.

[0084] Among them, the prediction model is used to predict the separation effect of the aldehyde separation column under real-time operating conditions.

[0085] Exemplarily, if the parameter values of the currently obtained operating parameters include: feed flow rate - 500 kg / h, feed temperature - 60 °C, steam flow rate - 200 kg / h, E120 condensate tank liquid level - 75%, aldehyde separation column top pressure - 1.2 bar, aldehyde separation column top reflux flow rate - 150 kg / h, aldehyde separation column top reflux material temperature - 40 °C, aldehyde separation column top reflux tank liquid level - 60%, aldehyde separation column bottom liquid level - 50%, aldehyde separation column sensitive point temperature - 85 °C, tower pressure - 1.5 bar, among which the tray temperatures include: the first tray temperature - 90 °C, the second tray temperature - 88 °C, the third tray temperature - 86 °C; input the parameter values of multiple operating parameters into the prediction model to obtain the separation effect prediction value corresponding to the multiple operating parameters. The GIA separation effect prediction value includes: "the proportion of C5 aldehydes at the top of the tower is 7%, the proportion of heavy components at the bottom of the tower is 6%, and the proportion of butyraldehyde at the bottom of the tower is 6%".

[0086] In some embodiments, before inputting the parameter values of multiple operating parameters into a pre-trained prediction model, the corresponding prediction model is determined according to the composition of the aldehyde mixture in the aldehyde separation column.

[0087] It is understandable that the prediction model is trained based on the composition of the aldehyde mixture, the historical parameter values of the operating parameters of the aldehyde separation column, and the true historical separation effect values corresponding to the historical parameter values. Since different aldehyde mixtures may have significant differences in chemical properties and separation behaviors, in order to improve the accuracy of the prediction model, corresponding prediction models are trained separately for the separation of different aldehyde mixtures, which can more accurately capture and reflect the unique behaviors and characteristics of each aldehyde mixture, thereby improving the prediction accuracy and the optimization effect of the separation process.

[0088] Exemplarily, based on historical DCS data and historical LIMS data, using a machine learning model to learn the correlation between the parameters affecting the separation effect and the separation effect, that is, using a machine learning model to establish the correlation between multiple input data (parameters affecting the separation effect of the aldehyde mixture) and output data (parameters characterizing the separation effect). Optionally, the parameters affecting the separation effect of the aldehyde mixture include: feed flow rate, feed temperature, steam flow rate, liquid level of the E120 condensate tank, top pressure of the aldehyde separation column, top reflux flow rate of the aldehyde separation column, temperature of the top reflux material of the aldehyde separation column, liquid level of the top reflux drum of the aldehyde separation column, bottom liquid level of the aldehyde separation column, sensitive point temperature of the aldehyde separation column, tray temperature, tower pressure; the parameters characterizing the separation effect include: the proportion of C5 aldehydes in the top product after the separation of the aldehyde mixture, the proportion of heavy components at the bottom, and the proportion of butyraldehyde at the bottom. The trained machine learning model is used as the prediction model for the corresponding aldehyde mixture. Among them, the machine learning models that can be selected and used can be, for example, a linear regression model and catboost.

[0089] S204: Under the condition that other operating parameters remain unchanged, use the particle swarm optimization algorithm to iteratively adjust the tray temperature, and determine the predicted separation effect value after adjustment based on the adjusted tray temperature and other operating parameters. The tray temperature that minimizes the predicted proportion of non-separated components in the top product and the predicted proportion of separated components in the bottom product is determined as the optimal tray temperature.

[0090] It can be understood that the aldehyde separation column utilizes the different physical and chemical properties of different aldehyde compounds to separate the components of the aldehyde mixture by controlling the change of the tray temperature. For example, the main components in the current aldehyde mixture include butyraldehyde and valeraldehyde, and the boiling points of butyraldehyde and valeraldehyde are different. When the mixture is heated, due to the difference in boiling points, butyraldehyde will start to evaporate earlier than valeraldehyde, thus achieving the separation of butyraldehyde and valeraldehyde. After separating the aldehyde mixture, the top of the aldehyde separation column mainly contains butyraldehyde, and the bottom of the aldehyde separation column mainly contains valeraldehyde. Since the aldehyde mixture is not completely separated, the butyraldehyde in the top of the column is an aldehyde mixture containing C5 aldehyde, and the valeraldehyde in the bottom of the column is an aldehyde mixture containing butyraldehyde and heavy components. In the actual separation process, in order to ensure the best separation effect, it is necessary to ensure that the proportion prediction value of the non-separated components in the butyraldehyde at the top of the column is the lowest, and the proportion prediction value of the separated components (butyraldehyde and heavy components) in the valeraldehyde at the bottom of the column is the lowest. That is, it is necessary to ensure that the proportion prediction value of the non-separated components in the top material and the proportion prediction value of the separated components in the bottom material are the smallest.

[0091] Exemplarily, under the condition that other operating parameters remain unchanged, the particle swarm optimization algorithm is used to iteratively adjust the tray temperature. Randomly initialize the tray temperature of each particle (i.e., the position of each particle), and initialize the velocity of each particle to obtain multiple adjusted tray temperatures. Then, according to the multiple adjusted tray temperatures and other operating parameters, re-determine the predicted value of the adjusted separation effect. Compare the new predicted value of the separation effect with the preset range respectively, screen out the new predicted value of the separation effect that does not exceed the preset range, and then determine the tray temperature that will minimize the proportion prediction value of the non-separated components in the top material and the proportion prediction value of the separated components in the bottom material from them, and determine this tray temperature as the optimal tray temperature.

[0092] In some embodiments, when screening out the new predicted value of the separation effect that does not exceed the preset range, the suitable tray temperature can be determined according to the actual operating environment and operating conditions of the current aldehyde separation column, and this tray temperature is determined as the optimal tray temperature.

[0093] S205: Control the aldehyde separation column to operate at the optimal tray temperature and monitor the true value of the separation effect of the aldehyde separation column.

[0094] S206: In the case that the true value of the separation effect exceeds the preset range, output an alarm message and adjust the tray temperature of the aldehyde separation column in response to the user operation.

[0095] It can be understood that the separation effect includes the proportion values of each component in the top material and the bottom material of the aldehyde separation column, and there are differences in the preset ranges for the separated components and non-separated components at the top and the bottom. Among them, the preset range corresponding to the separated components can be, for example, 5% - 6%.

[0096] In the embodiments of the present application, the true separation effect value can be determined through the data information obtained by the LIMS system. The central data platform can obtain in real time the proportion of non-separated components in the overhead material detected by the LIMS system and the proportion of separated components in the bottom material, so as to determine the actual separation effect of the current aldehyde separation column, and further obtain whether the optimal tray temperature determined by the prediction model and the particle swarm optimization algorithm is accurate.

[0097] Control the aldehyde separation column to operate at the optimal tray temperature, monitor the true separation effect value of the aldehyde separation column, and determine in real time whether the true separation effect value exceeds the preset range; if the true separation effect value does not exceed the preset range, it indicates that the current separation effect meets the expected effect. At this time, control the aldehyde separation column to continue operating; in the case where the true separation effect value exceeds the preset range, the actual separation effect of the current aldehyde separation column does not reach the expected effect. At this time, output an alarm message and, in response to the user's operation, readjust the tray temperature of the aldehyde separation column.

[0098] It can be understood that when the central data platform generates an alarm message, it can be determined that the tray temperature adopted by the current aldehyde separation column is not the optimal tray temperature, and the actual separation effect of the aldehyde separation column does not reach the expected effect, that is, the proportion of non-separated components in the overhead material and the proportion of separated components in the bottom material. At this time, there is an error in the prediction result of the prediction model, and the prediction model needs to be re-optimized.

[0099] Exemplarily, if the currently determined optimal tray temperature is 65°C, then control the aldehyde separation column to operate at 65°C and monitor the true separation effect value of the aldehyde separation column in real time; if the currently obtained true separation effect value is: "the proportion of C5 aldehyde in the overhead is 7%, the proportion of heavy components in the bottom is 7%, and the proportion of butyraldehyde in the bottom is 8%", that is, the proportion of C5 aldehyde in the overhead, the proportion of heavy components in the bottom, and the proportion of butyraldehyde in the bottom all exceed the preset range. At this time, the central data platform generates and outputs an alarm message; after the aldehyde separation column issues an alarm, the user can perform manual intervention, and the aldehyde separation column responds to the user's operation, adjusts the corresponding tray temperature, and continues to separate the aldehyde mixture.

[0100] The temperature control method for an aldehyde separation tower based on industrial Internet provided by the embodiments of the present application includes: collecting the values of various operating parameters of the aldehyde separation tower at the collection frequency of each operating parameter within a preset time period; for each operating parameter, determining the average value of the collected values within the preset time period as the parameter value of the operating parameter; inputting the parameter values of multiple operating parameters into a pre-trained prediction model to obtain a separation effect prediction value output by the prediction model; when other operating parameters remain unchanged, using the particle swarm optimization algorithm to iteratively adjust the tray temperature, and determining the adjusted separation effect prediction value according to the adjusted tray temperature and other operating parameters, and determining the tray temperature that minimizes the predicted proportion of non-separated components in the overhead material and the predicted proportion of separated components in the bottom material of the aldehyde separation tower as the optimal tray temperature; controlling the aldehyde separation tower to operate according to the optimal tray temperature, and monitoring the true value of the separation effect of the aldehyde separation tower; when the true value of the separation effect exceeds the preset range, outputting an alarm message, and in response to a user operation, adjusting the tray temperature of the aldehyde separation tower. This method is applied in industrial Internet, realizing the precise control and optimization of the temperature control parameters of the aldehyde separation tower in a fully automated manner, enabling the temperature control parameters to automatically adapt to changes in different production conditions and raw material characteristics, improving the accuracy and stability of the aldehyde separation process, and further enhancing the product quality and production efficiency, providing a more efficient and reliable solution for chemical production.

[0101] Figure 3 FIG. is a schematic structural diagram of a temperature control device for an aldehyde separation tower based on industrial Internet provided by the present application, as Figure 3 shown, the temperature control device 30 for an aldehyde separation tower based on industrial Internet provided by this embodiment includes:

[0102] An acquisition module 301, configured to acquire the parameter values of multiple operating parameters of the aldehyde separation tower, where the operating parameters include the tray temperature of the aldehyde separation tower and one or more other operating parameters that affect the aldehyde separation effect except the tray temperature.

[0103] A determination module 302, configured to determine a separation effect prediction value of the aldehyde separation tower according to the parameter values of the multiple operating parameters, where the separation effect prediction value includes the predicted proportion of each component in the overhead material of the aldehyde separation tower and the predicted proportion of each component in the bottom material.

[0104] The determination module 302 is further configured to, with the goal of minimizing the predicted proportion of non-separated components in the overhead material and the predicted proportion of separated components in the bottom material according to the separation effect prediction value, use an optimization algorithm to determine the optimal tray temperature of the aldehyde separation tower.

[0105] Optionally, the temperature control device for an aldehyde separation tower based on industrial Internet further includes: a processing module 303.

[0106] The processing module 303 is configured to input the parameter values of the multiple operating parameters into a pre-trained prediction model to obtain the separation effect prediction value output by the prediction model, where the prediction model is trained based on the historical parameter values of the operating parameters of the aldehyde separation column and the historical separation effect true values corresponding to the historical parameter values.

[0107] Optionally, the determining module 302 is further configured to determine the corresponding prediction model according to the composition components of the aldehyde mixture in the aldehyde separation column, where the prediction model is trained based on the composition components of the aldehyde mixture, the historical parameter values of the operating parameters of the aldehyde separation column, and the historical separation effect true values corresponding to the historical parameter values.

[0108] Optionally, the processing module 303 is further configured to iteratively adjust the tray temperature by using a particle swarm optimization algorithm under the condition that the other operating parameters remain unchanged.

[0109] The determining module 302 is further configured to determine the adjusted separation effect prediction value according to the adjusted tray temperature and the other operating parameters, and determine the tray temperature that minimizes the predicted value of the proportion of the non-separated components in the overhead material and the predicted value of the proportion of the separated components in the bottom material as the optimal tray temperature.

[0110] Optionally, the processing module 303 is further configured to control the aldehyde separation column to operate at the optimal tray temperature and monitor the true value of the separation effect of the aldehyde separation column.

[0111] The processing module 303 is further configured to output an alarm message when the true value of the separation effect exceeds a preset range, and adjust the tray temperature of the aldehyde separation column in response to a user operation.

[0112] Optionally, the obtaining module 301 is further configured to collect the numerical values of the operating parameters at the acquisition frequency of each operating parameter of the aldehyde separation column within a preset time period.

[0113] The determining module 302 is further configured to determine the average value of the numerical values collected within the preset time period as the parameter value of each operating parameter for each operating parameter.

[0114] The temperature control device of the aldehyde separation column based on the industrial Internet provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effect are similar, which will not be elaborated here in this embodiment.

[0115] Figure 4 It is a schematic structural diagram of an electronic device provided in this application. As Figure 4As shown in the figure, the electronic device 40 provided in this embodiment includes: at least one processor 401 and a memory 402. Optionally, the device 40 further includes a communication component 403. Among them, the processor 401, the memory 402, and the communication component 403 are connected through a bus 404.

[0116] In the specific implementation process, at least one processor 401 executes the computer-executable instructions stored in the memory 402, so that at least one processor 401 executes the above-mentioned method.

[0117] For the specific implementation process of the processor 401, reference can be made to the above method embodiment, and its implementation principle and technical effect are similar, so they will not be elaborated here in this embodiment.

[0118] In the above embodiment, it should be understood that the processor may be a central processing unit (English: Central Processing Unit, abbreviated as: CPU), and may also be other general-purpose processors, digital signal processors (English: Digital Signal Processor, abbreviated as: DSP), application-specific integrated circuits (English: Application Specific Integrated Circuit, abbreviated as: ASIC), etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the invention can be directly embodied as being executed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0119] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0120] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0121] This application also provides a computer program product, including a computer program, which implements the above-mentioned method when executed by a processor.

[0122] The present application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0123] The above-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk or an optical disk. The readable storage medium can be any available medium accessible by a general-purpose or special-purpose computer.

[0124] An exemplary readable storage medium is coupled to the processor so that the processor can read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in a device.

[0125] The division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed among each other can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0126] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0127] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0128] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, and other various media that can store program codes.

[0129] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When this program is executed, it executes the steps including the above method embodiments; and the aforementioned storage medium includes: ROMs, RAMs, magnetic disks, or optical discs, and other various media that can store program codes.

[0130] Finally, it should be noted that: After considering the specification and practicing the invention disclosed herein, those skilled in the art will easily think of other implementation manners of the present invention. The present invention is intended to cover any variations, uses, or adaptive changes of the present invention. These variations, uses, or adaptive changes follow the general principles of the present invention and include the common general knowledge or conventional technical means in the technical field not disclosed in the present invention. It is not limited to the exact structures already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A temperature control method for an aldehyde separation tower based on the industrial Internet, characterized in that: The method comprises: Acquiring parameter values ​​of a plurality of operating parameters of the aldehyde separation tower, wherein the operating parameters include a tray temperature of the aldehyde separation tower and one or more other operating parameters affecting the aldehyde separation effect except the tray temperature; Determining a corresponding prediction model according to the composition of the aldehyde mixture in the aldehyde separation tower, wherein the prediction model is trained based on the composition of the aldehyde mixture, historical parameter values ​​of operating parameters of the aldehyde separation tower, and historical separation effect true values ​​corresponding to the historical parameter values; Inputting the parameter values ​​of the plurality of operating parameters into a pre-trained prediction model to obtain a separation effect prediction value of the aldehyde separation tower, wherein the separation effect prediction value includes a proportion prediction value of each component in the tower top material and a proportion prediction value of each component in the tower bottom material of the aldehyde separation tower; According to the separation effect prediction value, an optimization algorithm is used to determine the optimal tray temperature of the aldehyde separation tower with the goal of minimizing the predicted value of the proportion of non-to-be-separated components in the tower top material and the predicted value of the proportion of to-be-separated components in the tower bottom material; The method of determining the optimal tray temperature of the aldehyde separation tower by using an optimization algorithm according to the predicted value of the separation effect with the goal of minimizing the predicted value of the proportion of the non-to-be-separated components in the tower top material and the predicted value of the proportion of the to-be-separated components in the tower bottom material comprises: When the other operating parameters remain unchanged, the particle swarm optimization algorithm is used to iteratively adjust the tower plate temperature, and the adjusted separation effect prediction value is determined based on the adjusted tower plate temperature and the other operating parameters. The tower plate temperature that minimizes the predicted value of the proportion of non-to-be-separated components in the top material and the predicted value of the proportion of components to be separated in the bottom material is determined as the optimal tower plate temperature.

2. The method according to claim 1, characterized in that Also includes: Controlling the aldehyde separation tower to operate according to the optimal tray temperature, and monitoring the actual value of the separation effect of the aldehyde separation tower; When the actual value of the separation effect exceeds a preset range, an alarm message is output, and in response to user operation, the tray temperature of the aldehyde separation tower is adjusted.

3. The method according to claim 1, characterized in that The obtaining of a plurality of operating parameters of the aldehyde separation tower comprises: Within a preset time period, collecting the values ​​of the operating parameters of the aldehyde separation tower according to the collection frequency of the operating parameters; For each of the operating parameters, an average value of the values ​​collected within the preset time period is determined as the parameter value of the operating parameter.

4. A temperature control device for an aldehyde separation tower based on the industrial Internet, characterized in that: include: An acquisition module, used for acquiring parameter values ​​of a plurality of operating parameters of the aldehyde separation tower, wherein the operating parameters include a tray temperature of the aldehyde separation tower and one or more other operating parameters affecting the aldehyde separation effect except the tray temperature; A determination module, configured to determine a predicted value of a separation effect of the aldehyde separation tower according to the parameter values ​​of the plurality of operating parameters, wherein the predicted value of the separation effect includes a predicted value of a proportion of each component in a tower top material and a predicted value of a proportion of each component in a tower bottom material of the aldehyde separation tower; The determination module is further configured to determine the optimal tray temperature of the aldehyde separation tower using an optimization algorithm based on the separation effect prediction value, with the goal of minimizing the predicted value of the proportion of non-to-be-separated components in the tower top material and the predicted value of the proportion of to-be-separated components in the tower bottom material; A processing module, used for inputting the parameter values ​​of the plurality of operating parameters into a pre-trained prediction model to obtain the separation effect prediction value output by the prediction model, wherein the prediction model is trained based on the historical parameter values ​​of the operating parameters of the aldehyde separation tower and the historical separation effect true values ​​corresponding to the historical parameter values; The determination module is further used to determine the corresponding prediction model according to the composition of the aldehyde mixture in the aldehyde separation tower, wherein the prediction model is trained based on the composition of the aldehyde mixture, the historical parameter values ​​of the operating parameters of the aldehyde separation tower, and the historical separation effect true values ​​corresponding to the historical parameter values; The processing module is also used to iteratively adjust the tower plate temperature by using a particle swarm optimization algorithm when the other operating parameters remain unchanged, and determine the adjusted separation effect prediction value based on the adjusted tower plate temperature and the other operating parameters, and determine the tower plate temperature that minimizes the predicted value of the proportion of non-to-be-separated components in the top material and the predicted value of the proportion of components to be separated in the bottom material as the optimal tower plate temperature.

5. An electronic device, characterized in that: include: Memory; processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 3 when executed by a processor.

7. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 3 when being executed by a processor.

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