WO2023206724A1 - Système de commande de dedressement et procédé de commande pour la préparation de difluorométhane de qualité électronique - Google Patents

Système de commande de dedressement et procédé de commande pour la préparation de difluorométhane de qualité électronique Download PDF

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WO2023206724A1
WO2023206724A1 PCT/CN2022/097770 CN2022097770W WO2023206724A1 WO 2023206724 A1 WO2023206724 A1 WO 2023206724A1 CN 2022097770 W CN2022097770 W CN 2022097770W WO 2023206724 A1 WO2023206724 A1 WO 2023206724A1
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feature
feature map
distillation
predetermined time
tower
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PCT/CN2022/097770
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Chinese (zh)
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张朝春
林百志
刘志强
李卫国
黄华华
邱玲
赖甜华
廖耀东
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福建德尔科技股份有限公司
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D3/00—Distillation or related exchange processes in which liquids are contacted with gaseous media, e.g. stripping
    • B01D3/009—Distillation or related exchange processes in which liquids are contacted with gaseous media, e.g. stripping in combination with chemical reactions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D3/00—Distillation or related exchange processes in which liquids are contacted with gaseous media, e.g. stripping
    • B01D3/14—Fractional distillation or use of a fractionation or rectification column
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01D—SEPARATION
    • B01D3/00—Distillation or related exchange processes in which liquids are contacted with gaseous media, e.g. stripping
    • B01D3/42—Regulation; Control
    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07C—ACYCLIC OR CARBOCYCLIC COMPOUNDS
    • C07C17/00—Preparation of halogenated hydrocarbons
    • C07C17/093—Preparation of halogenated hydrocarbons by replacement by halogens
    • C07C17/20—Preparation of halogenated hydrocarbons by replacement by halogens of halogen atoms by other halogen atoms
    • C07C17/202—Preparation of halogenated hydrocarbons by replacement by halogens of halogen atoms by other halogen atoms two or more compounds being involved in the reaction
    • C07C17/206—Preparation of halogenated hydrocarbons by replacement by halogens of halogen atoms by other halogen atoms two or more compounds being involved in the reaction the other compound being HX
    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07C—ACYCLIC OR CARBOCYCLIC COMPOUNDS
    • C07C17/00—Preparation of halogenated hydrocarbons
    • C07C17/38—Separation; Purification; Stabilisation; Use of additives
    • C07C17/383—Separation; Purification; Stabilisation; Use of additives by distillation

Definitions

  • the present invention relates to the field of intelligent manufacturing, and more specifically, to a distillation control system for the preparation of electronic-grade difluoromethane and a control method thereof.
  • Difluoromethane is a Freon substitute with good thermodynamic properties.
  • the ODP of R32 is 0 and the GWP value is very low.
  • the azeotropes and near-azeotropic mixtures formed by R32 and other components are regarded as the most potential substitutes for R22.
  • the liquid phase fluorination method is widely used in the production of R32.
  • the reaction mixture also contains the intermediate product R31, R23, R22, R40, R21, R143a, R50 generated by other side reactions, and entrained A small amount of raw materials R30 and HF.
  • the purity of electronic-grade difluoromethane is as high as 99.9999%, which places higher requirements on the existing difluoromethane preparation process.
  • Existing manufacturers mainly improve the purity of difluoromethane through two technical routes.
  • One technical direction is to change the preparation principle of difluoromethane (essentially a chemical direction), and the other technical direction is to optimize the purification process of difluoromethane ( Essentially a physical direction), but whether it is a chemical direction or a physical direction, it ultimately requires optimization of the purification process (especially the distillation process) to prepare electronic-grade difluoromethane that meets the purity requirements.
  • deep learning and neural networks have been widely used in computer vision, natural language processing, speech signal processing and other fields.
  • deep learning and neural networks have also shown that they are close to or even beyond human performance in areas such as image classification, object detection, semantic segmentation, and text translation.
  • Embodiments of the present application provide a rectification control system for the preparation of electronic-grade difluoromethane and a control method thereof, wherein the rectification control system for the preparation of electronic-grade difluoromethane includes a reactor and a reflux tower. , degassing tower, distillation tower and controller.
  • An artificial intelligence-based parameter control algorithm is deployed in the controller to dynamically adjust the distillation control parameters of the distillation tower based on the global situation of the distillation control system. In this way, from the perspective of optimal control To improve the purification accuracy of electronic grade difluoromethane.
  • a distillation control system for electronic-grade difluoromethane preparation which includes:
  • Reflux tower used to receive the first generated mixed gas containing difluoromethane and separate the hydrogen fluoride, the difluoromethane and the monofluorochloromethane from the generated mixed gas containing difluoromethane. To obtain the second generated mixture;
  • a degassing tower configured to receive the second product mixture and remove trifluoromethane and methane in the second product mixture to obtain a third product mixture
  • a rectification tower configured to receive the third generated mixed gas and perform rectification on the third generated mixed gas to obtain a distillation product, where the distillation product is electronic grade difluoromethane with a purity of greater than or equal to 99.9999%; as well as
  • a controller for dynamically controlling the temperature and pressure of the rectification tower based on global parameters of the rectification control system where the global parameters of the rectification control system include the pressure of the reflux tower, the reflux tower temperature, the pressure of the degassing tower, the temperature of the degassing tower, the pressure of the rectification tower and the temperature of the rectification tower.
  • the controller is used for:
  • the multiple control parameters include: the pressure of the reflux tower, the temperature of the reflux tower, the pressure of the degassing tower, the The temperature of the degassing tower, the pressure of the rectification tower and the temperature of the rectification tower;
  • the first feature vectors at each predetermined time point are two-dimensionally arranged into a feature matrix and then passed through a second convolutional neural network to obtain a second feature map;
  • the responsiveness estimate between the first feature map and the second feature map is calculated using normalization based on the characterization information relationship between the local and the global to obtain a responsive feature map, wherein the using is based on the local and global
  • the normalization of the characterization information relationship between them is to divide the logarithmic function value of the sum of the eigenvalues of each position in the first feature map and one by the sum of the eigenvalues of all positions in the second feature map. the value of the logarithmic function summed with one;
  • the response feature map is passed through a classifier to obtain a classification result.
  • the classification result is used to indicate that the pressure of the rectification tower should be increased or decreased, and the temperature of the rectification tower should be increased or decreased. Small.
  • the controller is further used to: use the first convolutional neural network using a three-dimensional convolution kernel to calculate the The gas chromatograms of the distillation products at multiple predetermined time points are encoded to obtain the first characteristic diagram;
  • H j , W j and R j represent the length, width and height of the three-dimensional convolution kernel respectively
  • m represents the number of the (i-1)th layer feature map
  • b ij is the bias
  • f represents the activation function.
  • the controller includes:
  • An embedding conversion unit configured to use the embedding layer of the encoder model containing the context of the embedding layer to respectively convert a plurality of control parameters at each of the predetermined time points into input vectors to obtain a sequence of parameter input vectors;
  • a context encoding unit configured to perform global-based context semantic encoding on the sequence of parameter input vectors using the converter of the encoder model containing the context of the embedded layer to obtain the plurality of feature vectors;
  • a cascading unit is used to cascade multiple feature vectors to obtain a first feature vector corresponding to each predetermined time point.
  • the controller is further used for:
  • the input data is respectively subjected to convolution processing, pooling processing along the channel dimension and activation processing in the forward pass of the layer to obtain the final result of the second convolutional neural network.
  • One layer generates the second feature map, wherein the input of the first layer of the second convolutional neural network is the feature matrix.
  • the controller is further used for:
  • the controller is further used to: use the classifier to process the responsiveness feature map with the following formula to generate a classification result ;
  • the formula is: softmax ⁇ (W n ,B n ):...:(W 1 ,B 1 )
  • a control method which includes:
  • the multiple control parameters include: the pressure of the reflux tower, the temperature of the reflux tower, the pressure of the degassing tower, the temperature of the degassing tower, The pressure of the distillation column and the temperature of the distillation column;
  • the first feature vectors at each predetermined time point are two-dimensionally arranged into a feature matrix and then passed through a second convolutional neural network to obtain a second feature map;
  • the responsiveness estimate between the first feature map and the second feature map is calculated using normalization based on the characterization information relationship between the local and the global to obtain a responsive feature map, wherein the using is based on the local and global
  • the normalization of the characterization information relationship between them is to divide the logarithmic function value of the sum of the eigenvalues of each position in the first feature map and one by the sum of the eigenvalues of all positions in the second feature map. the value of the logarithmic function summed with one;
  • the response feature map is passed through a classifier to obtain a classification result.
  • the classification result is used to indicate that the pressure of the rectification tower should be increased or decreased, and the temperature of the rectification tower should be increased or decreased. Small.
  • multiple control parameters at each of the predetermined time points are passed through a context encoder including an embedding layer to obtain multiple feature vectors, And concatenate multiple feature vectors to obtain the first feature vector corresponding to each predetermined time point, including:
  • Multiple feature vectors are concatenated to obtain a first feature vector corresponding to each predetermined time point.
  • the distillation control system for the preparation of electronic-grade difluoromethane includes a reactor and a reflux tower. , a degassing tower, a distillation tower and a controller, wherein an artificial intelligence-based parameter control algorithm is deployed in the controller to dynamically adjust the distillation rate of the distillation tower based on the global situation of the distillation control system. Distillation control parameters, in this way, improve the purification accuracy of electronic grade difluoromethane from the perspective of optimized control.
  • Figure 1 is a schematic block diagram of a distillation control system for the preparation of electronic-grade difluoromethane according to an embodiment of the present application.
  • FIG. 2 illustrates a block diagram of a controller in the distillation control system for electronic-grade difluoromethane preparation according to an embodiment of the present application.
  • Figure 3 is a flow chart of a control method of a distillation control system for electronic grade difluoromethane preparation according to an embodiment of the present application.
  • Figure 4 is a schematic structural diagram of a control method of a distillation control system for electronic-grade difluoromethane preparation according to an embodiment of the present application.
  • difluoromethane (R32) is a thermodynamically good alternative to Freon.
  • the ODP of R32 is 0 and the GWP value is very low.
  • the azeotropes and near-azeotropic mixtures formed by R32 and other components are regarded as the most potential substitutes for R22.
  • the liquid phase fluorination method is widely used in the production of R32.
  • the reaction mixture also contains the intermediate product R31, R23, R22, R40, R21, R143a, R50 generated by other side reactions, and entrained A small amount of raw materials R30 and HF.
  • the purity of electronic-grade difluoromethane is as high as 99.9999%, which places higher requirements on the existing difluoromethane preparation process.
  • Existing manufacturers mainly improve the purity of difluoromethane through two technical routes.
  • One technical direction is to change the preparation principle of difluoromethane (essentially a chemical direction), and the other technical direction is to optimize the purification process of difluoromethane ( Essentially a physical direction), but whether it is a chemical direction or a physical direction, it ultimately requires optimization of the purification process (especially the distillation process) to prepare electronic-grade difluoromethane that meets the purity requirements.
  • deep learning and neural networks have been widely used in computer vision, natural language processing, speech signal processing and other fields.
  • deep learning and neural networks have also shown that they are close to or even beyond human performance in areas such as image classification, object detection, semantic segmentation, and text translation.
  • multiple control parameters of the rectification system at multiple predetermined time points are first obtained.
  • the multiple control parameters include: the pressure of the reflux tower, the pressure of the reflux tower temperature, the pressure of the degassing tower, the temperature of the degassing tower, the pressure of the rectification tower and the temperature of the rectification tower, and at the same time, obtain the distillation products at the multiple predetermined time points gas chromatogram.
  • the gas chromatograms of the distillation products at the plurality of predetermined time points are passed through a first convolutional neural network using a three-dimensional convolution kernel to obtain a first feature map.
  • the first feature map using a three-dimensional convolution kernel is A convolutional neural network can effectively extract the dynamic characteristics of the distillation products.
  • multiple control parameters at each predetermined time point are passed through a context encoder including an embedding layer to obtain multiple feature vectors, and the multiple feature vectors are concatenated to obtain the first feature corresponding to each predetermined time point. vector.
  • the context encoder can perform global encoding on each parameter based on context semantics to extract high-dimensional latent features of each parameter and global high-dimensional latent features between various parameters.
  • the first feature vectors at each predetermined time point are two-dimensionally arranged into a feature matrix and then passed through the second convolutional neural network to obtain the second feature map. That is, the second convolutional neural network is used to extract each Implicit associations between parameters between time points.
  • the control result of the desired control parameters can be obtained.
  • the gas chromatography feature expressed by the first feature map F 1 is a local three-dimensional correlation of the three-dimensional convolution kernel Based on feature extraction, it focuses more on local feature expression, so it easily leads to low dependence on global responsiveness when calculating responsiveness. Based on this, it is calculated using a normalized expression based on the representation information relationship between the part and the whole, specifically:
  • the aggregation of the responsiveness of the feature locally equivalent to the feature as a whole is achieved, thereby improving the responsiveness of the feature map for the first feature map F1 pair
  • the global dependence of the expected responsiveness of the second feature map F 2 thereby improves the accuracy of the final classification.
  • this application proposes a distillation control system for the preparation of electronic-grade difluoromethane, which includes: a reactor for receiving difluoromethane and hydrogen fluoride, wherein the difluoromethane and the hydrogen fluoride are in A reaction occurs under the catalysis of a catalyst to generate a first mixed gas containing difluoromethane, and the catalyst is loaded in the reactor; a reflux tower is used to receive the first mixed gas containing difluoromethane.
  • a degassing tower for receiving the third The second product mixture is used to remove trifluoromethane and methane in the second product mixture to obtain the third product mixture; a rectification tower is used to receive the third product mixture and mix the third product mixture.
  • the gas is rectified to obtain a distillation product, which is electronic grade difluoromethane with a purity of greater than or equal to 99.9999%; and, a controller, used to: obtain multiple data of the rectification system at multiple predetermined time points.
  • control parameters include: the pressure of the reflux tower, the temperature of the reflux tower, the pressure of the degassing tower, the temperature of the degassing tower, the pressure of the rectification tower and The temperature of the distillation tower; obtaining the gas chromatograms of the distillation products at the multiple predetermined time points; passing the gas chromatograms of the distillation products at the multiple predetermined time points through using a three-dimensional convolution kernel
  • the first convolutional neural network obtains the first feature map; passing multiple control parameters at each predetermined time point through a context encoder including an embedding layer to obtain multiple feature vectors, and concatenating the multiple feature vectors to Obtain the first feature vector corresponding to each predetermined time point; arrange the first feature vectors at each predetermined time point in two dimensions into a feature matrix and then use the second convolutional neural network to obtain the second feature map; use local-based and the normalization of the characterization information relationship between the whole to calculate the responsiveness estimate between the first feature map and the second feature map to obtain the responsive
  • Figure 2 illustrates a flow chart of a distillation control system for electronic grade difluoromethane preparation according to an embodiment of the present application.
  • the distillation control system 200 for the preparation of electronic grade difluoromethane according to the embodiment of the present application includes: a reactor 210, a reflux tower 220, a degassing tower 230, a distillation tower 240 and a controller 250 .
  • the reactor 210 is used to receive dichloromethane and hydrogen fluoride, wherein the dichloromethane and the hydrogen fluoride react under the catalytic action of a catalyst to generate a first mixed gas containing difluoromethane,
  • the catalyst is packed in the reactor. That is, the reactor 210 is a place where a crude product of difluoromethane is generated through a chemical reaction.
  • Zhang Yanhong the difluoromethane is produced using a gas-phase fluorination process, and its chemical reaction process includes:
  • the difluoromethane can also be produced using other principles, which is not limited by this application.
  • the reflux tower 220 is configured to receive the first generated mixed gas containing difluoromethane and separate the hydrogen fluoride, the difluoromethane and the monofluoride from the generated mixed gas containing difluoromethane. Monochloromethane to obtain the second mixed gas. That is to say, after the first generated mixed gas containing difluoromethane output from the reactor 210 is input to the reflux tower 220, the reflux tower 220 sequentially separates HF through its acid gas separation system. and HCl.
  • the degassing tower 230 is configured to receive the second product mixture and remove trifluoromethane and methane in the second product mixture to obtain a third product mixture. Specifically, considering that trifluoromethane and methane have relatively low boiling points, after the second generated mixed gas enters the degassing tower 230, the degassing tower can use the third Different components in the secondary gas mixture have different boiling points to filter out low-boiling impurities trifluoromethane and methane (ie, R23 and R50).
  • the rectification tower 240 is configured to receive the third generated mixed gas and perform rectification on the third generated mixed gas to obtain a distillation product.
  • the distillation product is electronic grade II with a purity of greater than or equal to 99.9999%. Fluoromethane. That is to say, the distillation tower 240 is used to purify the third generated mixed gas to produce the electronic grade difluoromethane.
  • the reactor 210, the reflux tower 220, the degassing tower 230 and the rectification tower 240 can use any existing equipment to construct the rectification tower. Distillation control system. Compared with the traditional distillation control system, the inventor of the present application optimized the distillation and purification accuracy of monofluoromethane from the perspective of the control end.
  • the purity of electronic-grade difluoromethane is as high as 99.9999%, which places higher requirements on the existing difluoromethane preparation process.
  • Existing manufacturers mainly improve the purity of difluoromethane through two technical routes.
  • One technical direction is to change the preparation principle of difluoromethane (essentially a chemical direction), and the other technical direction is to optimize the purification process of difluoromethane ( Essentially a physical direction), but whether it is a chemical direction or a physical direction, it ultimately requires optimization of the purification process (especially the distillation process) to prepare electronic-grade difluoromethane that meets the purity requirements.
  • the distillation control system 200 for the preparation of electronic-grade difluoromethane further includes the controller 250, wherein a controller based on The artificial intelligence parameter control algorithm dynamically adjusts the distillation control parameters of the distillation tower based on the overall situation of the distillation control system. In this way, the efficiency of electronic grade difluoromethane is improved from the perspective of optimal control. Purified precision.
  • the controller 250 is configured to obtain multiple control parameters of the distillation system at multiple predetermined time points, where the multiple control parameters include: the parameters of the reflux tower. pressure, the temperature of the reflux tower, the pressure of the degassing tower, the temperature of the degassing tower, the pressure of the rectification tower and the temperature of the rectification tower; obtain the values of the multiple predetermined time points
  • the gas chromatogram of the distillation product passing the gas chromatogram of the distillation product at the plurality of predetermined time points through a first convolutional neural network using a three-dimensional convolution kernel to obtain a first feature map; passing each of the Multiple control parameters at a predetermined time point are passed through a context encoder including an embedding layer to obtain multiple feature vectors, and the multiple feature vectors are concatenated to obtain a first feature vector corresponding to each predetermined time point;
  • the first feature vector at the predetermined time point is two-dimensionally arranged into a feature matrix and then passed through the second convolution
  • Responsiveness estimation between a first feature map and a second feature map is used to obtain a responsive feature map, wherein the normalization based on the characterization information relationship between the local and the whole is used to calculate each feature map in the first feature map.
  • the logarithmic function value of the sum of the feature values of the positions and one is divided by the logarithmic function value of the sum of the feature values of all positions in the second feature map and the logarithmic function value of one; and, the responsiveness feature map is divided by
  • the classifier obtains a classification result, and the classification result is used to indicate that the pressure of the rectification tower should be increased or decreased, and the temperature of the rectification tower should be increased or decreased.
  • FIG. 2 illustrates a block diagram of a controller in the distillation control system for electronic-grade difluoromethane preparation according to an embodiment of the present application.
  • the controller 250 includes: a parameter acquisition unit 251, used to acquire multiple control parameters of the distillation system at multiple predetermined time points.
  • the multiple control parameters include: the reflux The pressure of the tower, the temperature of the reflux tower, the pressure of the degassing tower, the temperature of the degassing tower, the pressure of the rectification tower and the temperature of the rectification tower; the product data acquisition unit 252 uses In order to obtain the gas chromatograms of the distillation products at the plurality of predetermined time points; the convolution encoding unit 253 is used to convert the gas chromatograms of the distillation products at the plurality of predetermined time points by using a three-dimensional convolution kernel.
  • the first convolutional neural network to obtain the first feature map; the context encoding unit 254 is used to pass multiple control parameters of each predetermined time point through a context encoder including an embedding layer to obtain multiple feature vectors, and Multiple feature vectors are concatenated to obtain the first feature vector corresponding to each predetermined time point; the correlation pattern extraction unit 255 is used to two-dimensionally arrange the first feature vectors at each predetermined time point into a feature matrix and then pass The second convolutional neural network obtains the second feature map; the multi-receptive field normalization unit 256 is used to calculate the first feature map and the second feature map using normalization based on the representation information relationship between the local and the whole.
  • Responsiveness estimation between feature maps to obtain responsive feature maps wherein the normalization based on the characterization information relationship between the local and the whole is based on the feature value of each position in the first feature map and a The logarithmic function value of the sum divided by the logarithmic function value of the sum of the feature values of all positions in the second feature map and the logarithmic function value of the sum of one; and, the control result generation unit 257 is used to convert the responsive feature
  • the graph is passed through a classifier to obtain a classification result, which is used to indicate that the pressure of the rectification tower should be increased or decreased, and the temperature of the rectification tower should be increased or decreased.
  • multiple control parameters of the distillation control system and distillation product data are collected.
  • multiple control parameters of the distillation system at multiple predetermined time points can be obtained through multiple temperature and pressure sensors provided in the distillation system, and the multiple predetermined time points can be obtained through a gas chromatograph.
  • the gas chromatogram of the distillation product discharged from the distillation tower is obtained, that is, the global parameters of the distillation control system are obtained.
  • the gas chromatograms of the distillation products at the multiple predetermined time points are passed through the first step using a three-dimensional convolution kernel.
  • Convolutional neural network to obtain the first feature map. That is, in the technical solution of the present application, the gas chromatograms of the distillation products at multiple predetermined time points are further processed through the first convolutional neural network using a three-dimensional convolution kernel to extract the The local correlation features of the gas chromatogram of the distillation product in the time series dimension are used to obtain the first feature map.
  • the first convolutional neural network using a three-dimensional convolution kernel can effectively extract the dynamic change characteristics of the distillation product.
  • the process of passing the gas chromatograms of the distillation products at multiple predetermined time points through the first convolutional neural network using a three-dimensional convolution kernel to obtain the first feature map includes: : Use the following formula to pass the gas chromatograms of the distillation products at the plurality of predetermined time points through a first convolutional neural network using a three-dimensional convolution kernel to obtain a first feature map;
  • H j , W j and R j represent the length, width and height of the three-dimensional convolution kernel respectively
  • m represents the number of the (i-1)th layer feature map
  • b ij is the bias
  • f represents the activation function.
  • the context encoding unit 254 of the controller 250 is used to pass multiple control parameters of each predetermined time point through a context encoder including an embedding layer to obtain multiple features. vector, and concatenate multiple feature vectors to obtain a first feature vector corresponding to each predetermined time point.
  • a context encoder including an embedding layer to obtain multiple features. vector, and concatenate multiple feature vectors to obtain a first feature vector corresponding to each predetermined time point.
  • multiple control parameters at each predetermined time point are passed through a context encoder including an embedding layer to obtain multiple feature vectors, and the multiple feature vectors are concatenated to obtain the corresponding
  • the process of obtaining the first feature vector at each predetermined time point includes: first, using the embedding layer of the encoder model that includes the context of the embedding layer to respectively convert a plurality of control parameters at each of the predetermined time points into input vectors to Get a sequence of parameter input vectors. Then, global-based context semantic encoding is performed on the sequence of parameter input vectors using the transformer of the encoder model containing the context of the embedded layer to obtain the plurality of feature vectors. Finally, multiple feature vectors are concatenated to obtain a first feature vector corresponding to each predetermined time point.
  • the correlation pattern extraction unit 255 of the controller 250 is used to two-dimensionally arrange the first feature vectors of each predetermined time point into a feature matrix and then pass the second volume Accumulate the neural network to obtain the second feature map. That is to say, in the technical solution of the present application, after obtaining the first feature vector, the first feature vectors at each predetermined time point are two-dimensionally arranged into a feature matrix and then processed through the second convolutional neural network. Processing to extract implicit correlation features between parameters between the various time points, thereby obtaining a second feature map.
  • the input data is subjected to convolution processing, pooling processing along the channel dimension and activation processing in the forward pass of the layer through each layer of the second convolutional neural network to be processed by the The last layer of the second convolutional neural network generates the second feature map, wherein the input of the first layer of the second convolutional neural network is the feature matrix.
  • normalization based on the characterization information relationship between the local and the whole is used to calculate the first Responsiveness estimation between the feature map and the second feature map to obtain the responsive feature map, wherein the normalization based on the characterization information relationship between the local and the whole is based on each position in the first feature map
  • the logarithmic function value of the sum of the feature values and one is divided by the logarithmic function value of the sum of the feature values of all positions in the second feature map and one.
  • the first feature map and the second feature map can be further fused and passed through a classifier to obtain the desired control parameters. result.
  • the gas chromatography feature expressed by the first feature map F 1 is a local three-dimensional correlation of the three-dimensional convolution kernel Based on feature extraction, it focuses more on local feature expression, so it easily leads to low dependence on global responsiveness when calculating responsiveness. Therefore, in the technical solution of the present application, a normalized expression based on the characterization information relationship between the part and the whole is further used to calculate the responsiveness estimate between the first feature map and the second feature map to obtain the responsiveness.
  • Feature map a normalized expression based on the characterization information relationship between the part and the whole is further used to calculate the responsiveness estimate between the first feature map and the second feature map to obtain the responsiveness.
  • the responsiveness estimate between the first feature map and the second feature map is calculated using normalization based on the characterization information relationship between the part and the whole to obtain the responsiveness feature.
  • the process of mapping includes: using normalization based on the characterization information relationship between the local and the whole to calculate the responsiveness estimate between the first feature map and the second feature map using the following formula to obtain the responsive feature map;
  • control result generating unit 257 of the controller 250 is used to pass the responsive feature map through a classifier to obtain a classification result, and the classification result is used to represent the The pressure of the distillation column should be increased or decreased, and the temperature of the distillation column should be increased or decreased. That is to say, in the technical solution of the present application, after obtaining the response characteristic map, the response characteristic map is further passed through a classifier to obtain a value indicating whether the pressure of the distillation tower should be increased or decreased. Small, the classification result of the distillation tower temperature should be increased or should be reduced.
  • the classifier processes the responsiveness feature map with the following formula to generate a classification result, where the formula is: softmax ⁇ (W n ,B n ):...:( W 1 ,B 1 )
  • the distillation control system 200 for the preparation of electronic-grade difluoromethane based on the embodiment of the present application is clarified, which uses a first convolutional neural network using a three-dimensional convolution kernel to obtain all the data from multiple predetermined time points. Extract the dynamic change characteristics of the distillation product from the gas chromatogram of the distillation product, and use the context encoder to extract the high-dimensional implicit features of each control parameter at the multiple predetermined time points and the relationships between the parameters.
  • the global high-dimensional latent features further use a normalized expression based on the representation information relationship between the local and the whole to fuse the two feature information, so that by introducing the robustness around the minimization loss of the representation information to the responsiveness estimation, The aggregation of the local feature equivalent to the responsiveness of the entire feature is achieved, thereby improving the global dependence of the responsive feature map on the expected responsiveness of the first feature map to the second feature map. In turn, the accuracy of classification can be improved.
  • the controller 250 in the rectification control system 200 for the preparation of electronic grade difluoromethane according to the embodiment of the present application can be implemented in various terminal equipment, such as the rectification system for the preparation of electronic grade difluoromethane. Control algorithm server, etc.
  • the controller 250 according to the embodiment of the present application can be integrated into the terminal device as a software module and/or a hardware module.
  • the controller 250 may be a software module in the operating system of the terminal device, or may be an application program developed for the terminal device; of course, the controller 250 may also be a software module of the terminal device.
  • One of many hardware modules are examples of many hardware modules.
  • the controller 250 and the terminal device may also be separate devices, and the controller 250 may be connected to the terminal device through a wired and/or wireless network, and according to the agreed data format to transmit interactive information.
  • Figure 3 illustrates a flow chart of a control method of a distillation control system for electronic grade difluoromethane production.
  • the control method of the distillation control system for the preparation of electronic grade difluoromethane according to the embodiment of the present application includes the step of: S110, obtaining multiple controls of the distillation system at multiple predetermined time points.
  • the plurality of control parameters include: the pressure of the reflux tower, the temperature of the reflux tower, the pressure of the degassing tower, the temperature of the degassing tower, the pressure of the rectification tower and the The temperature of the distillation tower; S120, obtain the gas chromatograms of the distillation products at the multiple predetermined time points; S130, obtain the gas chromatograms of the distillation products at the multiple predetermined time points by using three-dimensional convolution The first convolutional neural network of the kernel is used to obtain the first feature map; S140, pass the multiple control parameters of each predetermined time point through the context encoder including the embedding layer to obtain multiple feature vectors, and convert the multiple feature vectors Perform cascade to obtain the first feature vector corresponding to each predetermined time point; S150, arrange the first feature vectors of each predetermined time point in two dimensions into a feature matrix and then use the second convolutional neural network to obtain the second feature vector.
  • Feature map use normalization based on the characterization information relationship between the local and the whole to calculate the responsiveness estimate between the first feature map and the second feature map to obtain the responsive feature map, wherein, the The normalization based on the characterization information relationship between the local and the whole is divided by the logarithmic function value of the sum of the feature value of each position in the first feature map and one by the logarithmic function value of all positions in the second feature map.
  • the logarithmic function value of the sum of feature values and the sum of one; and, S170 pass the responsiveness feature map through a classifier to obtain a classification result, which is used to indicate that the pressure of the distillation tower should be increased. should be increased or should be decreased, the temperature of the distillation tower should be increased or should be decreased.
  • FIG. 4 illustrates a schematic structural diagram of a control method of a distillation control system for electronic-grade difluoromethane preparation according to an embodiment of the present application.
  • the gas chromatograms of the distillation products obtained at the multiple predetermined time points are obtained (For example, P1 as shown in Figure 4)
  • a first feature map (for example, as shown in Figure 4) is obtained by using a first convolutional neural network (for example, CNN1 as shown in Figure 4) of a three-dimensional convolution kernel F1); S140, pass multiple control parameters (for example, P2 as shown in Figure 4) of each predetermined time point through a context encoder including an embedding layer (for example, as shown in Figure 4 E)
  • Obtain multiple feature vectors for example, VF1 as illustrated in Figure 4
  • concatenate the multiple feature vectors to obtain a first feature vector corresponding to each predetermined time point (for example,
  • steps S110 and S120 multiple control parameters of the rectification system at multiple predetermined time points are obtained.
  • the multiple control parameters include: the pressure of the reflux tower, the temperature of the reflux tower. , the pressure of the degassing tower, the temperature of the degassing tower, the pressure of the rectification tower and the temperature of the rectification tower, and obtain the gas phase of the rectification product at the multiple predetermined time points Chromatogram.
  • the control parameters of each part of the distillation system are often set based on predetermined values, and it is impossible to dynamically adjust and optimize based on the actual situation.
  • there is a correlation between the control parameters of each part of the distillation system and the global optimization cannot be achieved by considering the conditions of each part alone, that is, the purity of the difluoromethane finally obtained cannot meet the preset requirements.
  • a gas chromatograph is used to obtain the gas chromatograms of the distillation products discharged from the distillation tower at the plurality of predetermined time points, and the gas chromatograms of the distillation products discharged from the distillation tower at the plurality of predetermined time points are obtained, and the gas chromatograms are Temperature and pressure sensors acquire multiple control parameters of the distillation system at multiple predetermined time points.
  • the multiple control parameters include: the pressure of the reflux tower, the temperature of the reflux tower, the degassing The pressure of the tower, the temperature of the degassing tower, the pressure of the rectification tower and the temperature of the rectification tower.
  • step S130 the gas chromatograms of the distillation products at the plurality of predetermined time points are passed through a first convolutional neural network using a three-dimensional convolution kernel to obtain a first feature map. That is, in the technical solution of the present application, the gas chromatograms of the distillation products at multiple predetermined time points are further processed through the first convolutional neural network using a three-dimensional convolution kernel to extract the The local correlation features of the gas chromatogram of the distillation product in the time series dimension are used to obtain the first feature map.
  • the first convolutional neural network using a three-dimensional convolution kernel can effectively extract the dynamic change characteristics of the distillation product.
  • steps S140 and S150 multiple control parameters at each predetermined time point are passed through a context encoder including an embedding layer to obtain multiple feature vectors, and the multiple feature vectors are concatenated to obtain Corresponding to the first feature vectors at each predetermined time point, the first feature vectors at each predetermined time point are then two-dimensionally arranged into a feature matrix and then passed through the second convolutional neural network to obtain the second feature map. It should be understood that there is a correlation between the control parameters of each part in the distillation system, and global optimization cannot be achieved by solely considering the conditions of each part.
  • multiple control parameters at each of the predetermined time points are further globally encoded through a context encoder including an embedding layer, so as to extract the high-dimensional implicit features of each parameter and each item.
  • a context encoder including an embedding layer
  • global high-dimensional latent features between parameters thereby obtaining multiple feature vectors.
  • multiple feature vectors can be concatenated to obtain the first feature vector corresponding to each predetermined time point, thereby facilitating subsequent feature extraction.
  • the first feature vectors at each predetermined time point are two-dimensionally arranged into a feature matrix and then processed through a second convolutional neural network to extract the hidden characteristics between the parameters between each time point.
  • sexually related features to obtain the second feature map.
  • the input data is subjected to convolution processing, pooling processing along the channel dimension and activation processing in the forward pass of the layer through each layer of the second convolutional neural network to be processed by the The last layer of the second convolutional neural network generates the second feature map, wherein the input of the first layer of the second convolutional neural network is the feature matrix.
  • step S160 the responsiveness estimate between the first feature map and the second feature map is calculated using normalization based on the characterization information relationship between the part and the whole to obtain a responsive feature map
  • the normalization based on the characterization information relationship between the local part and the whole is divided by the logarithmic function value of the sum of the feature value of each position in the first feature map and one by the second feature map
  • the logarithmic function value of the sum of the eigenvalues at all locations in and the sum of one can be further fused and passed through a classifier to obtain the desired control parameters. result.
  • the gas chromatography feature expressed by the first feature map F 1 is a local three-dimensional correlation of the three-dimensional convolution kernel Based on feature extraction, it focuses more on local feature expression, so it easily leads to low dependence on global responsiveness when calculating responsiveness. Therefore, in the technical solution of the present application, a normalized expression based on the characterization information relationship between the part and the whole is further used to calculate the responsiveness estimate between the first feature map and the second feature map to obtain the responsiveness.
  • Feature map since the gas chromatography feature expressed by the first feature map F 1 is a local three-dimensional correlation of the three-dimensional convolution kernel Based on feature extraction, it focuses more on local feature expression, so it easily leads to low dependence on global responsiveness when calculating responsiveness. Therefore, in the technical solution of the present application, a normalized expression based on the characterization information relationship between the part and the whole is further used to calculate the responsiveness estimate between the first feature map and the second feature map to obtain the responsiveness. Feature map.
  • the responsiveness feature map is passed through a classifier to obtain a classification result.
  • the classification result is used to indicate that the pressure of the rectification tower should be increased or decreased.
  • the temperature of the tower should be increased or should be decreased. That is to say, in the technical solution of the present application, after obtaining the response characteristic map, the response characteristic map is further passed through a classifier to obtain a value indicating whether the pressure of the distillation tower should be increased or decreased. Small, the classification result of the distillation tower temperature should be increased or should be reduced.
  • the classifier processes the responsiveness feature map with the following formula to generate a classification result, where the formula is: softmax ⁇ (W n ,B n ):...:( W 1 ,B 1 )
  • the control method of the distillation control system for the preparation of electronic-grade difluoromethane based on the embodiments of the present application is clarified, which uses a first convolutional neural network using a three-dimensional convolution kernel from multiple predetermined time points. Extract the dynamic change characteristics of the distillation product from the gas chromatogram of the distillation product, and use the context encoder to extract the high-dimensional implicit features of various control parameters at the multiple predetermined time points and among the various parameters The global high-dimensional latent features between the two feature information are further used to fuse the two feature information using a normalized expression based on the representation information relationship between the local and the whole. In this way, the robustness of minimizing the loss around the representation information is introduced to the responsiveness estimation.

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Abstract

Un système de commande de redressement (200) et un procédé de commande pour la préparation de difluorométhane de qualité électronique. Le système de commande de redressement (200) pour la préparation de difluorométhane de qualité électronique comprend un réacteur (210), une tour de reflux (220), une tour de dégazage (230), une tour de redressement (240) et un dispositif de commande (250). Un algorithme de commande de paramètre basé sur l'intelligence artificielle est déployé dans le dispositif de commande (250), de façon à ajuster dynamiquement des paramètres de commande de redressement de la tour de redressement (240) sur la base de la situation globale du système de commande de redressement (200). De cette manière, la précision de purification du difluorométhane de qualité électronique est améliorée du point de vue de la commande optimisée.
PCT/CN2022/097770 2022-04-29 2022-06-09 Système de commande de dedressement et procédé de commande pour la préparation de difluorométhane de qualité électronique WO2023206724A1 (fr)

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