阅读说明：本技术 一种高纯度单甘酯的提纯方法及装置 (Method and device for purifying high-purity monoglyceride ) 是由 孙敬章 于 2020-12-31 设计创作，主要内容包括：本发明公开了一种高纯度单甘酯的提纯方法及装置,通过获得单甘酯含量信息；获得第一预设蒸馏含量；将所述单甘酯含量信息、所述第一预设蒸馏含量输入第一训练模型,获得第一蒸馏参数信息,判断第一蒸馏参数信息是否满足第一预定条件,当满足时获得预设产品单甘酯含量；将预设产品单甘酯含量、第一预设蒸馏含量输入第二训练模型,获得第二蒸馏参数信息；判断第二蒸馏参数信息是否满足第二预定阈值；当满足时获得第一执行指令。解决了现有技术中单甘酯提纯过程比较统一,缺乏对提纯过程中参数的动态调整,存在单甘酯含量的控制水平不高的技术问题。达到按照预设的单甘酯含量要求动态设定具体的参数,以确保提纯效果的技术效果。(The invention discloses a method and a device for purifying high-purity monoglyceride, which are used for obtaining the content information of the monoglyceride; obtaining a first preset distillation content; inputting the monoglyceride content information and the first preset distillation content into a first training model to obtain first distillation parameter information, judging whether the first distillation parameter information meets a first preset condition, and if so, obtaining the monoglyceride content of a preset product; inputting the preset monoglyceride content and the first preset distillation content of the product into a second training model to obtain second distillation parameter information; determining whether the second distillation parameter information satisfies a second predetermined threshold; the first execution instruction is obtained when satisfied. The method solves the technical problems that in the prior art, the monoglyceride purification process is uniform, the dynamic adjustment of parameters in the purification process is lacked, and the control level of monoglyceride content is not high. The technical effect of dynamically setting specific parameters according to the preset monoglyceride content requirement so as to ensure the purification effect is achieved.)

1. A method for purifying high purity monoglyceride, wherein the method comprises:

obtaining the content information of monoglyceride;

obtaining a first preset distillation content;

inputting the monoglyceride content information and the first preset distillation content into a first training model, wherein the first training model is obtained by training multiple groups of training data, and each group of the multiple groups of training data comprises: the monoglyceride content information, the first preset distillation content and identification information for identifying the process parameters of the glycerol removing procedure matched with the preset monoglyceride content;

obtaining a first output result of the first training model, wherein the first output result comprises first distillation parameter information, and the first distillation information is a distillation setting parameter value in a glycerol removal distillation process;

judging whether the first distillation parameter information meets a first preset condition or not, and if so, obtaining the monoglyceride content of a preset product;

inputting the monoglyceride content and the first preset distillation content of the preset product into a second training model, wherein the second training model is obtained by training multiple groups of training data, and each group of the multiple groups of training data comprises: the preset product monoglyceride content, the first preset distillation content and identification information identifying a three-stage molecular distillation process parameter matched with the preset product monoglyceride content;

obtaining a second output result of the second training model, wherein the second output result comprises second distillation parameter information, and the second distillation parameter information is a distillation setting parameter value in a three-stage molecular distillation process;

determining whether the second distillation parameter information satisfies a second predetermined threshold;

obtaining a first execution instruction when the second distillation parameter information satisfies the second predetermined threshold.

2. The method of claim 1, wherein prior to obtaining the first predetermined distillation content, the method comprises:

obtaining a first catalyst predicted value according to the monoglyceride content information;

obtaining a neutralizer value according to the first catalyst predicted value;

obtaining a first neutralization instruction based on the neutralizer value, the first neutralization instruction for neutralizing the monoglyceride according to the neutralizer data.

3. The method of claim 1, wherein said determining whether the first distillation parameter information satisfies a first predetermined condition comprises:

when the first distillation parameter information does not meet the first preset condition, obtaining a first standard parameter, wherein the first standard parameter is an adjustable range of parameters in a glycerol removal distillation process;

obtaining a first parameter difference value according to the first distillation parameter information and the first standard parameter;

judging whether the first parameter difference value meets a third preset condition or not;

and when the first standard parameter is met, obtaining a first adjusting instruction, wherein the first adjusting instruction is used for adjusting the first distillation parameter information according to the first standard parameter.

4. The method of claim 1, wherein said determining whether the second distillation parameter information satisfies a second predetermined threshold comprises:

when the second distillation parameter information does not meet the second preset threshold value, obtaining a parameter adjustment range of the three-stage molecular distillation process;

obtaining a second parameter difference value according to the second distillation parameter information and the parameter adjustment range of the three-stage molecular distillation process;

judging whether the second parameter difference value meets a fourth preset condition or not;

when the first adjustment instruction is met, obtaining a second adjustment instruction, wherein the second adjustment instruction is used for adjusting the second distillation parameter information according to the parameter adjustment range of the three-stage molecular distillation process;

obtaining third distillation information according to the second adjustment instruction, wherein the third distillation information is a parameter of the adjusted three-stage molecular distillation process;

and obtaining the first execution instruction according to the third distillation information.

5. The method of claim 4, wherein said determining whether the second parameter difference satisfies a fourth predetermined condition comprises:

when the second parameter difference value does not meet the fourth preset condition, adjusting according to the parameter adjustment range of the three-stage molecular distillation process and the second distillation parameter information to obtain a first recommended parameter;

obtaining the monoglyceride content of a predicted product according to the first recommended parameter;

obtaining a first content difference value according to the predicted monoglyceride content of the product and the preset monoglyceride content of the product;

when the first content difference value meets a fifth preset condition, obtaining a third adjusting instruction, wherein the third adjusting instruction is used for adjusting the second distillation parameter information into the first recommended parameter;

and obtaining the first execution instruction according to the first recommended parameter.

6. The method of claim 5, wherein the method further comprises:

and when the first content difference does not meet the fifth preset condition, obtaining a second execution instruction according to the first content difference, wherein the second execution instruction is to add a third distillation process after the first execution instruction, and the third distillation process is to perform distillation operation again on the monoglyceride product obtained after the first execution instruction.

7. The method of claim 1, wherein said inputting said monoglyceride content information, said first predetermined distillation content, and into a first training model comprises:

obtaining first training data and second training data in a plurality of groups of training data of the first training model until Nth training data, wherein N is a natural number greater than 1;

generating a first verification code according to the first training data, wherein the first verification code corresponds to the first training data one to one;

generating a second verification code according to the second training data and the first verification code, and generating an Nth verification code according to the Nth training data and the N-1 th verification code by analogy;

all training data and verification codes are copied and stored on M electronic devices, wherein M is a natural number larger than 1.

8. An apparatus for purifying high-purity monoglyceride, wherein the apparatus comprises:

a first obtaining unit for obtaining monoglyceride content information;

a second obtaining unit for obtaining a first preset distillation content;

a first input unit, configured to input the monoglyceride content information and the first preset distillation content into a first training model, where the first training model is obtained by training multiple sets of training data, and each set of the multiple sets of training data includes: the monoglyceride content information, the first preset distillation content and identification information for identifying the process parameters of the glycerol removing procedure matched with the preset monoglyceride content;

a third obtaining unit, configured to obtain a first output result of the first training model, where the first output result includes first distillation parameter information, and the first distillation information is a distillation setting parameter value in a glycerol removal distillation process;

the first judgment unit is used for judging whether the first distillation parameter information meets a first preset condition or not, and when the first distillation parameter information meets the first preset condition, the content of monoglyceride of a preset product is obtained;

a first input unit, configured to input the preset monoglyceride content and the first preset distillation content into a second training model, where the second training model is obtained by training multiple sets of training data, and each set of the multiple sets of training data includes: the preset product monoglyceride content, the first preset distillation content and identification information identifying a three-stage molecular distillation process parameter matched with the preset product monoglyceride content;

a fourth obtaining unit, configured to obtain a second output result of the second training model, where the second output result includes second distillation parameter information, and the second distillation parameter information is a distillation setting parameter value in a three-stage molecular distillation process;

a second determination unit for determining whether the second distillation parameter information satisfies a second predetermined threshold;

a fifth obtaining unit for obtaining a first execution instruction when the second distillation parameter information satisfies the second predetermined threshold.

9. An apparatus for purifying high purity monoglyceride, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any one of claims 1 to 7 are performed when the program is executed by the processor.

Technical Field

The invention relates to the related field of monoglyceride purification, in particular to a method and a device for purifying high-purity monoglyceride.

Background

Most of the monoglycerides marketed in recent years are raw products which have not been distilled. It is actually a mixture of monoglycerides, diglycerides and small amounts of triglycerides. Because the diglyceride has only one hydroxyl group in the molecule, the hydrophilicity is too weak, the emulsifying capacity is low, and the triglyceride, namely the grease, has no emulsifying capacity at all, so that the monoglyceride is really used for emulsification in the product. Because of the relatively low monoglyceride content (below 50%) in such products, the range of application is greatly limited. In contrast, distilled high purity monoglyceride (content over 90%) has better properties and can meet various requirements. With the continuous progress of distillation technology, the yield of the monoglyceride will increase year by year.

However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:

in the prior art, the monoglyceride purification process is relatively uniform, the dynamic adjustment of parameters in the purification process is lacked, and the technical problem of low control level of monoglyceride content exists.

Disclosure of Invention

The embodiment of the application provides a method and a device for purifying high-purity monoglyceride, and solves the technical problems that in the prior art, the monoglyceride purification process is uniform, the dynamic adjustment of parameters in the purification process is lacked, and the control level of monoglyceride content is not high. The method has the advantages that specific parameters are dynamically set according to preset monoglyceride content requirements so as to ensure the purification effect, meanwhile, in order to improve the accuracy of parameter setting, a neural network model is added for system processing, the control process with accurate control and high automation degree is realized, meanwhile, the method has the technical effects of flexibly setting monoglyceride content requirements and wide application range.

In view of the above problems, the embodiments of the present application provide a method and an apparatus for purifying high-purity monoglyceride.

In a first aspect, the present embodiments provide a method for purifying high-purity monoglyceride, the method including: obtaining the content information of monoglyceride; obtaining a first preset distillation content; inputting the monoglyceride content information and the first preset distillation content into a first training model, wherein the first training model is obtained by training multiple groups of training data, and each group of the multiple groups of training data comprises: the monoglyceride content information, the first preset distillation content and identification information for identifying the process parameters of the glycerol removing procedure matched with the preset monoglyceride content; obtaining a first output result of the first training model, wherein the first output result comprises first distillation parameter information, and the first distillation information is a distillation setting parameter value in a glycerol removal distillation process; judging whether the first distillation parameter information meets a first preset condition or not, and if so, obtaining the monoglyceride content of a preset product; inputting the monoglyceride content and the first preset distillation content of the preset product into a second training model, wherein the second training model is obtained by training multiple groups of training data, and each group of the multiple groups of training data comprises: the preset product monoglyceride content, the first preset distillation content and identification information identifying a three-stage molecular distillation process parameter matched with the preset product monoglyceride content; obtaining a second output result of the second training model, wherein the second output result comprises second distillation parameter information, and the second distillation parameter information is a distillation setting parameter value in a three-stage molecular distillation process; determining whether the second distillation parameter information satisfies a second predetermined threshold; obtaining a first execution instruction when the second distillation parameter information satisfies the second predetermined threshold.

In another aspect, the present application also provides an apparatus for purifying high-purity monoglyceride, the apparatus comprising:

a first obtaining unit for obtaining monoglyceride content information;

a second obtaining unit for obtaining a first preset distillation content;

a first input unit, configured to input the monoglyceride content information and the first preset distillation content into a first training model, where the first training model is obtained by training multiple sets of training data, and each set of the multiple sets of training data includes: the monoglyceride content information, the first preset distillation content and identification information for identifying the process parameters of the glycerol removing procedure matched with the preset monoglyceride content;

a third obtaining unit, configured to obtain a first output result of the first training model, where the first output result includes first distillation parameter information, and the first distillation information is a distillation setting parameter value in a glycerol removal distillation process;

the first judgment unit is used for judging whether the first distillation parameter information meets a first preset condition or not, and when the first distillation parameter information meets the first preset condition, the content of monoglyceride of a preset product is obtained;

a first input unit, configured to input the preset monoglyceride content and the first preset distillation content into a second training model, where the second training model is obtained by training multiple sets of training data, and each set of the multiple sets of training data includes: the preset product monoglyceride content, the first preset distillation content and identification information identifying a three-stage molecular distillation process parameter matched with the preset product monoglyceride content;

a fourth obtaining unit, configured to obtain a second output result of the second training model, where the second output result includes second distillation parameter information, and the second distillation parameter information is a distillation setting parameter value in a three-stage molecular distillation process;

a second determination unit for determining whether the second distillation parameter information satisfies a second predetermined threshold;

a fifth obtaining unit for obtaining a first execution instruction when the second distillation parameter information satisfies the second predetermined threshold.

In a third aspect, the present invention provides an apparatus for purifying high purity monoglyceride, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to perform the steps of the method of the first aspect.

One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

the embodiment of the application provides a method and a device for purifying high-purity monoglyceride, which are used for obtaining the content information of the monoglyceride; obtaining a first preset distillation content; inputting the monoglyceride content information and the first preset distillation content into a first training model, wherein the first training model is obtained by training multiple groups of training data, and each group of the multiple groups of training data comprises: the monoglyceride content information, the first preset distillation content and identification information for identifying the process parameters of the glycerol removing procedure matched with the preset monoglyceride content; obtaining a first output result of the first training model, wherein the first output result comprises first distillation parameter information, and the first distillation information is a distillation setting parameter value in a glycerol removal distillation process; judging whether the first distillation parameter information meets a first preset condition or not, and if so, obtaining the monoglyceride content of a preset product; inputting the monoglyceride content and the first preset distillation content of the preset product into a second training model, wherein the second training model is obtained by training multiple groups of training data, and each group of the multiple groups of training data comprises: the preset product monoglyceride content, the first preset distillation content and identification information identifying a three-stage molecular distillation process parameter matched with the preset product monoglyceride content; obtaining a second output result of the second training model, wherein the second output result comprises second distillation parameter information, and the second distillation parameter information is a distillation setting parameter value in a three-stage molecular distillation process; determining whether the second distillation parameter information satisfies a second predetermined threshold; obtaining a first execution instruction when the second distillation parameter information satisfies the second predetermined threshold. The method has the advantages that specific parameters are dynamically set according to preset monoglyceride content requirements so as to ensure the purification effect, meanwhile, in order to improve the accuracy of parameter setting, a neural network model is added for system processing, the control process with accurate control and high automation degree is realized, meanwhile, the method has the technical effects of flexibly setting monoglyceride content requirements and wide application range. Therefore, the technical problems that the purification process of the monoglyceride is uniform, the dynamic adjustment of parameters in the purification process is lacked, and the control level of the monoglyceride content is not high in the prior art are solved.

The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.

Drawings

FIG. 1 is a schematic flow diagram of a process for purifying high purity monoglyceride according to an embodiment of the present invention;

FIG. 2 is a schematic diagram showing the structure of an apparatus for purifying high-purity monoglyceride according to an embodiment of the present invention;

fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.

Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a first input unit 13, a third obtaining unit 14, a first judging unit 15, a second input unit 16, a fourth obtaining unit 17, a second judging unit 18, a fifth obtaining unit 19, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 306.

Detailed Description

The embodiment of the application provides a method and a device for purifying high-purity monoglyceride, and solves the technical problems that in the prior art, the monoglyceride purification process is uniform, the dynamic adjustment of parameters in the purification process is lacked, and the control level of monoglyceride content is not high. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.

Summary of the application

The content of primary monoglyceride in the market is relatively low (below 50 percent), so the application range of the monoglyceride is greatly limited. In contrast, distilled high purity monoglyceride (content over 90%) has better properties and can meet various requirements. With the continuous progress of distillation technology, the yield of the monoglyceride will increase year by year. However, in the prior art, the monoglyceride purification process is relatively uniform, the dynamic adjustment of parameters in the purification process is lacked, and the technical problem that the control level of monoglyceride content is not high exists.

In view of the above technical problems, the technical solution provided by the present application has the following general idea:

obtaining the content information of monoglyceride; obtaining a first preset distillation content; inputting the monoglyceride content information and the first preset distillation content into a first training model, wherein the first training model is obtained by training multiple groups of training data, and each group of the multiple groups of training data comprises: the monoglyceride content information, the first preset distillation content and identification information for identifying the process parameters of the glycerol removing procedure matched with the preset monoglyceride content; obtaining a first output result of the first training model, wherein the first output result comprises first distillation parameter information, and the first distillation information is a distillation setting parameter value in a glycerol removal distillation process; judging whether the first distillation parameter information meets a first preset condition or not, and if so, obtaining the monoglyceride content of a preset product; inputting the monoglyceride content and the first preset distillation content of the preset product into a second training model, wherein the second training model is obtained by training multiple groups of training data, and each group of the multiple groups of training data comprises: the preset product monoglyceride content, the first preset distillation content and identification information identifying a three-stage molecular distillation process parameter matched with the preset product monoglyceride content; obtaining a second output result of the second training model, wherein the second output result comprises second distillation parameter information, and the second distillation parameter information is a distillation setting parameter value in a three-stage molecular distillation process; determining whether the second distillation parameter information satisfies a second predetermined threshold; obtaining a first execution instruction when the second distillation parameter information satisfies the second predetermined threshold. The method has the advantages that specific parameters are dynamically set according to preset monoglyceride content requirements so as to ensure the purification effect, meanwhile, in order to improve the accuracy of parameter setting, a neural network model is added for system processing, the control process with accurate control and high automation degree is realized, meanwhile, the method has the technical effects of flexibly setting monoglyceride content requirements and wide application range.

Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.

Example one

As shown in fig. 1, the present application provides a method for purifying high-purity monoglyceride, which includes:

step S100: and acquiring the content information of monoglyceride.

Specifically, the monoglyceride content information is the content data of primary monoglyceride to be purified, the content of the primary monoglyceride is usually 35-50% and cannot exceed 60% at most, and the most common content of industrial monoglyceride is 35-40%.

Step S200: a first predetermined distillation content is obtained.

Specifically, the first predetermined distillation content is a predetermined monoglyceride content standard after the first distillation treatment, and is usually subjected to a first distillation treatment, i.e., a glycerol removal treatment, which is performed in a thin film evaporator, wherein a gaseous product, water and a part of glycerin are separated from a mixture (degassed), and then glycerin, free fatty acids and a part of short carbon chain monoglyceride are distilled under high vacuum (established by a multistage steam vacuum pump). After the treatment of the step, the separated and distilled product contains about 60% of monoglyceride, and the first preset distillation content can be specifically set according to the requirement of the product content and different processes, and the set value is usually 60%. Different preset distillation contents can be set according to different monoglyceride content information.

Step S300: inputting the monoglyceride content information and the first preset distillation content into a first training model, wherein the first training model is obtained by training multiple groups of training data, and each group of the multiple groups of training data comprises: the monoglyceride content information, the first preset distillation content and the identification information identifying the process parameters of the glycerol removing process matched with the preset monoglyceride content.

Step S400: obtaining a first output result of the first training model, wherein the first output result comprises first distillation parameter information, and the first distillation information is a distillation setting parameter value in a glycerol-removing distillation process.

Specifically, parameters in the procedure are set according to the monoglyceride content information and the first preset distillation content, in order to improve the reliability of the set parameters and ensure that the monoglyceride content obtained by primary separation and distillation meets the requirements of set processing, a Neural network model is added for data processing, the first training model is a Neural network model in machine learning, and a Neural Network (NN) is a complex Neural network system formed by widely interconnecting a large number of simple processing units (called neurons), reflects many basic characteristics of human brain functions, and is a highly complex nonlinear dynamic learning system. Neural network models are described based on mathematical models of neurons. Artificial Neural Networks (Artificial Neural Networks) are a description of the first-order properties of the human brain system. Briefly, it is a mathematical model. And inputting the monoglyceride content information and the first preset distillation content into a neural network model through training of a large amount of training data, and outputting the process parameter information of the glycerol removal process matched with the input requirement.

Furthermore, the training process is essentially a supervised learning process, each group of supervised data comprises the monoglyceride content information, the first preset distillation content and identification information for identifying the process parameters of the glycerol removal process matched with the preset monoglyceride content, the monoglyceride content information and the first preset distillation content are input into a neural network model, the neural network model carries out continuous self-correction and adjustment according to the identification information for identifying the process parameters of the glycerol removal process matched with the preset monoglyceride content, and the next group of data supervised learning is finished until the obtained output result is consistent with the identification information; and when the output information of the neural network model reaches the preset accuracy rate/reaches the convergence state, finishing the supervised learning process. Through the supervised learning of the neural network model, the neural network model can process the input information more accurately, so that more accurate and suitable process parameters of the glycerol removal process are obtained, and then more accurate regulation and control guidance can be carried out on purification treatment of monoglyceride, so that the effect of preliminary distillation purification is ensured through controlling the process parameters of the glycerol removal process, and the technical effect that monoglyceride purification meets the preset requirement is ensured. Typically the operating conditions for this step are: the residual pressure is 0.013-0.039 kPa (0.1-0.3 mmHg), the temperature is about 160 ℃, and specific parameter setting is carried out according to the specific principle of processing and purifying monoglyceride in the embodiment of the application so as to ensure that the corresponding content requirement is met.

Step S500: and judging whether the first distillation parameter information meets a first preset condition, and if so, obtaining the monoglyceride content of a preset product.

Specifically, whether the first distillation parameter information is in accordance with the parameter adjustment range of the treatment process is judged, namely whether the adjustment interval of the equipment in the step is not met, and if the adjustment interval is met, the first distillation parameter is determined.

Step S600: inputting the monoglyceride content and the first preset distillation content of the preset product into a second training model, wherein the second training model is obtained by training multiple groups of training data, and each group of the multiple groups of training data comprises: the preset product monoglyceride content, the first preset distillation content and identification information for identifying a three-stage molecular distillation process parameter matched with the preset product monoglyceride content.

Step S700: and obtaining a second output result of the second training model, wherein the second output result comprises second distillation parameter information, and the second distillation parameter information is a distillation setting parameter value in the three-stage molecular distillation process.

Specifically, when the first distillation parameter information is confirmed, monoglyceride meeting the preset distillation content is correspondingly produced, namely the monoglyceride content after preliminary separation and distillation is the first preset distillation content, in order to obtain high-purity monoglyceride, the monoglyceride cannot meet the requirement only through preliminary distillation, secondary distillation and purification are required to be carried out on a three-stage molecular distillation device of a thin film evaporator, and the parameter of the process directly influences the content of a monoglyceride product. In the embodiment of the application, the purification process parameters are set according to the target content set in the process step, namely the preset monoglyceride content of a product, and the content of the raw material for purification in the step, namely the first preset distillation content, in order to improve the reliability of the set parameters and ensure the purification effect of monoglyceride, a Neural network model is added for data processing, a second training model is a Neural network model in machine learning, a Neural Network (NN) is a complex Neural network system formed by widely connecting a large number of simple processing units (called neurons), reflects many basic characteristics of human brain functions, and is a highly complex nonlinear dynamical learning system. Neural network models are described based on mathematical models of neurons. Artificial Neural Networks (Artificial Neural Networks) are a description of the first-order properties of the human brain system. Briefly, it is a mathematical model. And inputting the preset monoglyceride content and the first preset distillation content of the product into a neural network model through training of a large amount of training data, and outputting second distillation parameter information matched with the input requirement.

More specifically, the training process is a supervised learning process, each group of supervised data includes preset product monoglyceride content, the first preset distillation content and identification information identifying a three-stage molecular distillation process parameter matched with the preset product monoglyceride content, the preset product monoglyceride content and the first preset distillation content are input into a neural network model, the neural network model performs continuous self-correction and adjustment according to the identification information for identifying the three-stage molecular distillation process parameter matched with the preset product monoglyceride content, and the next group of data supervised learning is finished until an obtained output result is consistent with the identification information; and when the output information of the neural network model reaches the preset accuracy rate/reaches the convergence state, finishing the supervised learning process. Through supervised learning of the neural network model, the neural network model can process the input information more accurately, so that more accurate and suitable distillation process parameters can be obtained, more accurate regulation and control guidance can be performed on the three-stage molecular distillation process of monoglyceride purification, and the technical effect of ensuring that the monoglyceride purified by distillation meets the set content requirement through controlling the distillation purification processing process parameters and further ensuring the purity of monoglyceride generated is achieved.

Step S800: determining whether the second distillation parameter information satisfies a second predetermined threshold.

Step S900: obtaining a first execution instruction when the second distillation parameter information satisfies the second predetermined threshold.

Specifically, whether the obtained second distillation parameter information meets the regulation requirement of equipment in the process or not is judged, a corresponding second preset threshold value can be specifically set according to the model of the equipment and data in the history generation process, the conventional data in the step is that the temperature range is 205-210 ℃, the residual pressure is 0.0013-0.0039 kPa (0.01-0.03 mmHg), but different parameter settings are corresponding to raw materials and equipment indexes with different monoglyceride contents and the regulation range, different parameter settings directly influence the distillation purification effect, if any, the parameters, the equipment and the raw materials can reach 95 percent after being treated, and if any, the parameters and the raw materials can reach 85 percent, so that the parameter settings are closely related to ensure that the monoglyceride product content meets the set requirement, and when the second distillation parameter information meets the second preset threshold value, the requirements of the equipment are met, meanwhile, the parameter can be verified according to historical data, and when the parameter is satisfied, the parameter information is determined and corresponding purification processing is carried out according to the parameter. The method has the advantages that specific parameters are dynamically set according to preset monoglyceride content requirements so as to ensure the purification effect, meanwhile, in order to improve the accuracy of parameter setting, a neural network model is added for system processing, the control process with accurate control and high automation degree is realized, meanwhile, the method has the technical effects of flexibly setting monoglyceride content requirements and wide application range. Therefore, the technical problems that the purification process of the monoglyceride is uniform, the dynamic adjustment of parameters in the purification process is lacked, and the control level of the monoglyceride content is not high in the prior art are solved.

Further, before obtaining the first predetermined distillation content, embodiments of the present application include:

step S1010: obtaining a first catalyst predicted value according to the monoglyceride content information;

step S1020: obtaining a neutralizer value according to the first catalyst predicted value;

step S1030: obtaining a first neutralization instruction based on the neutralizer value, the first neutralization instruction for neutralizing the monoglyceride according to the neutralizer data.

Specifically, before the preliminary separation distillation treatment, a catalyst neutralization process is also provided, and in the embodiment of the application, the catalyst is removed to the maximum extent by performing systematic analysis on the content data of the monoglyceride, i.e. the content of the raw material to be purified, for example, the corresponding component content in unit weight, determining the content value of the catalyst therefrom, and adding a neutralizing agent according to the content of the catalyst to ensure the neutralization effect. The specific calculation process can be used for carrying out component chemical experimental analysis and also can be used for carrying out predictive analysis on components through a neural network model.

Further, after determining whether the first distillation parameter information satisfies a first predetermined condition, an embodiment of the present application includes:

step S1110: when the first distillation parameter information does not meet the first preset condition, obtaining a first standard parameter, wherein the first standard parameter is an adjustable range of parameters in a glycerol removal distillation process;

step S1120: obtaining a first parameter difference value according to the first distillation parameter information and the first standard parameter;

step S1130: judging whether the first parameter difference value meets a third preset condition or not;

step S1140: and when the first standard parameter is met, obtaining a first adjusting instruction, wherein the first adjusting instruction is used for adjusting the first distillation parameter information according to the first standard parameter.

Specifically, when the first distillation parameter information does not meet the first preset condition, that is, does not meet the adjustable range of the equipment and process in the process, and exceeds the parameter set value, the first distillation parameter information needs to be correspondingly adjusted, the adjustment range is adjusted according to the corresponding process requirement and the equipment requirement, if the temperature set value in the first distillation parameter information exceeds the set range of the thin film evaporator under high vacuum, the corresponding judgment is carried out according to the difference value between the first distillation parameter information and the first standard parameter and the corresponding monoglyceride content, whether the purification requirement can be met through the adjustment parameter, if yes, the third preset condition is met, and then the first adjustment instruction is obtained to adjust the first distillation parameter information.

Further, after determining whether the second distillation parameter information satisfies a second predetermined threshold, embodiments of the present application include:

step 1210: when the second distillation parameter information does not meet the second preset threshold value, obtaining a parameter adjustment range of the three-stage molecular distillation process;

step S1220: obtaining a second parameter difference value according to the second distillation parameter information and the parameter adjustment range of the three-stage molecular distillation process;

step S1230: judging whether the second parameter difference value meets a fourth preset condition or not;

step S1240: when the first adjustment instruction is met, obtaining a second adjustment instruction, wherein the second adjustment instruction is used for adjusting the second distillation parameter information according to the parameter adjustment range of the three-stage molecular distillation process;

step S1250: obtaining third distillation information according to the second adjustment instruction, wherein the third distillation information is a parameter of the adjusted three-stage molecular distillation process;

step S1260: and obtaining the first execution instruction according to the third distillation information.

Specifically, the feasibility judgment is carried out on the second distillation parameter information in the same way, whether the parameter setting requirement in the three-stage molecular distillation process of the thin film evaporator is met or not is judged, the adjustment range of equipment is included, if the parameter setting requirement is not met, the second distillation parameter information and the standard parameter range in the process are compared and analyzed, whether the adjustment requirement is met or not is judged, if the exceeding range is not large, the purification requirement can be achieved through corresponding parameter adjustment, namely the fourth preset condition is met, at the moment, a second adjustment instruction is obtained, the second distillation parameter information is adjusted, so that the parameter has feasibility, the requirements of the equipment and the actual processing process can be met, the requirement of the product content index can also be met, and the technical effect of dynamically adjusting the parameter to meet different processing and purification requirements is achieved.

Further, after determining whether the second parameter difference satisfies a fourth predetermined condition, an embodiment of the present application includes:

step 1310: when the second parameter difference value does not meet the fourth preset condition, adjusting according to the parameter adjustment range of the three-stage molecular distillation process and the second distillation parameter information to obtain a first recommended parameter;

step S1320: obtaining the monoglyceride content of a predicted product according to the first recommended parameter;

step S1330: obtaining a first content difference value according to the predicted monoglyceride content of the product and the preset monoglyceride content of the product;

step S1340: when the first content difference value meets a fifth preset condition, obtaining a third adjusting instruction, wherein the third adjusting instruction is used for adjusting the second distillation parameter information into the first recommended parameter;

step S1350: and obtaining the first execution instruction according to the first recommended parameter.

Specifically, when the second parameter difference value is not in the adjustable range, namely the fourth preset condition is not met, adjusting the second distillation parameter information according to the parameter adjustment range of the three-stage molecular distillation process to obtain a first recommended parameter; predicting the monoglyceride content of the product according to the first recommended parameter, judging the difference value between the predicted monoglyceride content of the product and the preset monoglyceride content of the product, if the difference value is small and accords with the fluctuation range of the product purity, determining the first recommended parameter as a corresponding processing and purification index, and performing purification operation of a three-stage molecular distillation process. The purification parameters are dynamically adjusted according to the content of the monoglyceride, so that the technical effect of processing and purifying effects is ensured.

Further, the embodiment of the present application further includes: and when the first content difference does not meet the fifth preset condition, obtaining a second execution instruction according to the first content difference, wherein the second execution instruction is to add a third distillation process after the first execution instruction, and the third distillation process is to perform distillation operation again on the monoglyceride product obtained after the first execution instruction.

Specifically, when the first content difference exceeds the adjustment range, that is, the fifth predetermined condition is not met, a secondary distillation process is set according to the purity of the monoglyceride, the monoglyceride produced after the first execution instruction is purified, the purification requirement is met through the adjustment of the unaligned parameters and processes, the content of the monoglyceride is increased, the purification requirements of different monoglyceride contents are met, and the production of the monoglyceride with higher purity is realized.

Further, after the information on the content of monoglyceride and the first preset distillation content are input into the first training model, step S300 in this embodiment of the present application further includes:

step S310: obtaining first training data and second training data in a plurality of groups of training data of the first training model until Nth training data, wherein N is a natural number greater than 1;

step S320: generating a first verification code according to the first training data, wherein the first verification code corresponds to the first training data one to one;

step S330: generating a second verification code according to the second training data and the first verification code, and generating an Nth verification code according to the Nth training data and the N-1 th verification code by analogy;

step S340: all training data and verification codes are copied and stored on M electronic devices, wherein M is a natural number larger than 1.

In particular, the blockchain technique, also referred to as a distributed ledger technique, is an emerging technique in which several computing devices participate in "accounting" together, and maintain a complete distributed database together. The blockchain technology has been widely used in many fields due to its characteristics of decentralization, transparency, participation of each computing device in database records, and rapid data synchronization between computing devices. Generating a first verification code according to the first training data, wherein the first verification code corresponds to the first training data one to one; generating a second verification code according to the second training data and the first verification code, wherein the second verification code corresponds to the second training data one to one; by analogy, generating an Nth verification code according to the Nth training data and the Nth-1 verification code, wherein N is a natural number larger than 1, respectively copying and storing all the training data and the verification code on M devices, wherein the first training data and the first verification code are stored on one device as a first storage unit, the second training data and the second verification code are stored on one device as a second storage unit, the Nth training data and the Nth verification code are stored on one device as an Nth storage unit, when the training data need to be called, after each subsequent node receives the data stored by the previous node, the data are checked and stored through a common identification mechanism, each storage unit is connected in series through a hash function, so that the screening condition is not easy to lose and destroy, and the training data are encrypted through the logic of a block chain, the safety of the training data is guaranteed, the accuracy of the first training model obtained through training of the training data is further guaranteed, and a foundation is tamped for obtaining more accurate first distillation parameter information subsequently.

Example two

Based on the same inventive concept as that of the purification method of high purity monoglyceride in the previous embodiment, the present invention also provides an apparatus for purifying high purity monoglyceride, as shown in fig. 2, the apparatus comprising:

a first obtaining unit 11, wherein the first obtaining unit 11 is used for obtaining the content information of monoglyceride;

a second obtaining unit 12, said second obtaining unit 12 being configured to obtain a first preset distillation content;

a first input unit 13, where the first input unit 13 is configured to input the monoglyceride content information and the first preset distillation content into a first training model, where the first training model is obtained through training of multiple sets of training data, and each of the multiple sets of training data includes: the monoglyceride content information, the first preset distillation content and identification information for identifying the process parameters of the glycerol removing procedure matched with the preset monoglyceride content;

a third obtaining unit 14, where the third obtaining unit 14 is configured to obtain a first output result of the first training model, where the first output result includes first distillation parameter information, and the first distillation information is a distillation setting parameter value in a glycerol removal distillation process;

the first judging unit 15 is used for judging whether the first distillation parameter information meets a first preset condition, and when the first distillation parameter information meets the first preset condition, the monoglyceride content of a preset product is obtained;

a first input unit 16, where the first input unit 16 is configured to input the preset monoglyceride content and the first preset distillation content into a second training model, where the second training model is obtained by training multiple sets of training data, and each of the multiple sets of training data includes: the preset product monoglyceride content, the first preset distillation content and identification information identifying a three-stage molecular distillation process parameter matched with the preset product monoglyceride content;

a fourth obtaining unit 17, where the fourth obtaining unit 17 is configured to obtain a second output result of the second training model, where the second output result includes second distillation parameter information, and the second distillation parameter information is a distillation setting parameter value in a three-stage molecular distillation process;

a second determination unit 18, the second determination unit 18 being configured to determine whether the second distillation parameter information satisfies a second predetermined threshold;

a fifth obtaining unit 19, said fifth obtaining unit 19 being configured to obtain a first execution instruction when said second distillation parameter information satisfies said second predetermined threshold.

Further, the apparatus further comprises:

a sixth obtaining unit, configured to obtain a first catalyst prediction value according to the monoglyceride content information;

a seventh obtaining unit, configured to obtain a neutralizer value according to the first catalyst prediction value;

an eighth obtaining unit, configured to obtain a first neutralization instruction according to the neutralizer value, where the first neutralization instruction is configured to neutralize the monoglyceride according to the neutralizer data.

Further, the apparatus further comprises:

a ninth obtaining unit, configured to obtain a first standard parameter when the first distillation parameter information does not satisfy the first predetermined condition, where the first standard parameter is an adjustable parameter range in a glycerol removal distillation process;

a tenth obtaining unit for obtaining a first parameter difference value according to the first distillation parameter information and the first standard parameter;

a third judging unit, configured to judge whether the first parameter difference satisfies a third predetermined condition;

an eleventh obtaining unit configured to obtain, when satisfied, a first adjustment instruction for adjusting the first distillation parameter information according to the first standard parameter.

Further, the apparatus further comprises:

a twelfth obtaining unit for obtaining a parameter adjustment range of the three-stage molecular distillation process when the second distillation parameter information does not satisfy the second predetermined threshold;

a thirteenth obtaining unit, configured to obtain a second parameter difference according to the second distillation parameter information and a parameter adjustment range of the three-stage molecular distillation process;

a fourth judging unit, configured to judge whether the second parameter difference satisfies a fourth predetermined condition;

a fourteenth obtaining unit, configured to, when satisfied, obtain a second adjustment instruction, where the second adjustment instruction is configured to adjust the second distillation parameter information according to a parameter adjustment range of the three-stage molecular distillation process;

a fifteenth obtaining unit, configured to obtain third distillation information according to the second adjustment instruction, where the third distillation information is a parameter of the adjusted three-stage molecular distillation process;

a sixteenth obtaining unit configured to obtain the first execution instruction according to the third distillation information.

Further, the apparatus further comprises:

a seventeenth obtaining unit, configured to, when the second parameter difference does not satisfy the fourth predetermined condition, perform adjustment according to a parameter adjustment range of the three-stage molecular distillation process and the second distillation parameter information, to obtain a first recommended parameter;

an eighteenth obtaining unit, configured to obtain a predicted monoglyceride content of the product according to the first recommended parameter;

a nineteenth obtaining unit, configured to obtain a first content difference according to the predicted monoglyceride content of the product and the preset monoglyceride content of the product;

a twentieth obtaining unit, configured to obtain a third adjustment instruction when the first content difference value satisfies a fifth predetermined condition, the third adjustment instruction being configured to adjust the second distillation parameter information to the first recommended parameter;

a twenty-first obtaining unit, configured to obtain the first execution instruction according to the first recommended parameter.

Further, the apparatus further comprises:

a twenty-second obtaining unit, configured to, when the first content difference does not satisfy the fifth predetermined condition, obtain a second execution instruction according to the first content difference, where the second execution instruction is to add a third distillation process after the first execution instruction, and the third distillation process is to perform a second distillation operation on the monoglyceride product obtained after the first execution instruction.

Further, the apparatus further comprises:

a twenty-third obtaining unit, configured to obtain first training data and second training data in multiple sets of training data of the first training model until nth training data, where N is a natural number greater than 1;

a first generating unit, configured to generate a first verification code according to the first training data, where the first verification code corresponds to the first training data one to one;

a second generating unit, configured to generate a second verification code according to the second training data and the first verification code, and generate an nth verification code according to the nth training data and the nth-1 verification code by analogy;

the first storage unit is used for copying and storing all training data and verification codes on M pieces of electronic equipment, wherein M is a natural number greater than 1.

Various modifications and specific examples of a method for purifying high purity monoglyceride in the first embodiment of fig. 1 described above are also applicable to a method for purifying high purity monoglyceride in this embodiment, and the method for implementing a method for purifying high purity monoglyceride in this embodiment will be apparent to those skilled in the art from the foregoing detailed description of a method for purifying high purity monoglyceride, and therefore, for the sake of brevity of description, will not be described in detail herein.

Exemplary electronic device

The electronic device of the embodiment of the present application is described below with reference to fig. 3.

Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.

Based on the inventive concept of a method for purifying high-purity monoglyceride as in the preceding embodiment, the present invention also provides a device for purifying high-purity monoglyceride, on which a computer program is stored, which program, when executed by a processor, implements the steps of any one of the aforementioned methods for purifying high-purity monoglyceride.

Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 306 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium.

The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.

One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

the embodiment of the application provides a method and a device for purifying high-purity monoglyceride, which are used for obtaining the content information of the monoglyceride; obtaining a first preset distillation content; inputting the monoglyceride content information and the first preset distillation content into a first training model, wherein the first training model is obtained by training multiple groups of training data, and each group of the multiple groups of training data comprises: the monoglyceride content information, the first preset distillation content and identification information for identifying the process parameters of the glycerol removing procedure matched with the preset monoglyceride content; obtaining a first output result of the first training model, wherein the first output result comprises first distillation parameter information, and the first distillation information is a distillation setting parameter value in a glycerol removal distillation process; judging whether the first distillation parameter information meets a first preset condition or not, and if so, obtaining the monoglyceride content of a preset product; inputting the monoglyceride content and the first preset distillation content of the preset product into a second training model, wherein the second training model is obtained by training multiple groups of training data, and each group of the multiple groups of training data comprises: the preset product monoglyceride content, the first preset distillation content and identification information identifying a three-stage molecular distillation process parameter matched with the preset product monoglyceride content; obtaining a second output result of the second training model, wherein the second output result comprises second distillation parameter information, and the second distillation parameter information is a distillation setting parameter value in a three-stage molecular distillation process; determining whether the second distillation parameter information satisfies a second predetermined threshold; obtaining a first execution instruction when the second distillation parameter information satisfies the second predetermined threshold. The method has the advantages that specific parameters are dynamically set according to preset monoglyceride content requirements so as to ensure the purification effect, meanwhile, in order to improve the accuracy of parameter setting, a neural network model is added for system processing, the control process with accurate control and high automation degree is realized, meanwhile, the method has the technical effects of flexibly setting monoglyceride content requirements and wide application range. Therefore, the technical problems that the purification process of the monoglyceride is uniform, the dynamic adjustment of parameters in the purification process is lacked, and the control level of the monoglyceride content is not high in the prior art are solved.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.