WO2022118554A1 - Estimation system, estimation method, and program - Google Patents

Abstract

An estimation system for estimating the molecular weight of a polymer that is present as a solid component in an aqueous slurry, the estimation system having: a storage unit for storing relevant information indicating a correspondence among a solvent in which the polymer is dissolved, each of at least one item of solute concentration information relating to the solute concentration of a polymer solution in which the polymer is dissolved as a solute and viscosity information indicating the viscosity of the polymer solution, and molecular weight information indicating the molecular weight of the polymer; and an estimation unit for estimating the molecular weight information of the polymer on the basis of the relevant information and input data including the solute concentration information and the viscosity information.

Description

Estimating system, estimation method, and program

The present invention relates to a method for estimating the molecular weight of a polymer, and is particularly suitable for rapidly grasping the molecular weight of a biodegradable polymer existing as a solid content of an aqueous suspension, and is useful for a system for feeding back measurement results to process control. Regarding molecular weight estimation systems, estimation methods, and programs.
This application claims priority based on Japanese Patent Application No. 2020-202074 filed in Japan on December 4, 2020, the contents of which are incorporated herein by reference.

Various plastics, mainly polyolefins, have high corrosion resistance and are used in a wide range of fields, mainly in the container and packaging field, from the viewpoint of weight reduction and energy saving. Various molding resins and molded products have been developed according to the contents, applications, and molding methods, and have become indispensable materials in modern life.

On the other hand, many plastics are mass-produced, mass-consumed, and mass-discarded due to the difficulty of reuse and hygiene concerns, causing problems associated with landfill and incineration. In addition, it has been pointed out that the plastic pellets that have flowed out into the ocean remain in the environment because of their high corrosion resistance and are not easily decomposed by microorganisms, which has an impact on the ecosystem. Furthermore, it has been pointed out that in recent years, microplastics, which have been disintegrated and atomized by ultraviolet rays, adsorb harmful compounds in seawater, and when marine organisms ingest them, harmful substances are taken into the food chain. In order to realize a circulating society on a global scale, biodegradable plastics, which are decomposed into water and carbon dioxide by the action of microorganisms, are attracting attention.

Biodegradable plastics are completely biodegraded by microorganisms in the soil and water and are taken up by the carbon cycle process in nature, so they are aggressive as environmentally friendly plastic materials with little adverse effect on the ecosystem. It is desired to be used. As a typical biodegradable plastic, plant-derived biodegradable plastics such as polyhydroxyalkanoate (hereinafter, also referred to as “PHA”) are attracting attention. PHA is an aliphatic polyester (thermoplastic polyester) that is produced by microorganisms using natural organic acids and fats and oils derived from plants as a carbon source and accumulated in cells as an energy storage substance. As a specific PHA, there is poly (3-hydroxy alkanoate) (hereinafter, also referred to as “P3HA”). P3HA is a thermoplastic polyester produced and accumulated as an energy storage substance in the cells of many microbial species, and is a material that can undergo biodegradation not only in soil but also in seawater.

PHA produced by microorganisms is water-insoluble and usually accumulates in microbial cells as granules. Therefore, in order to use PHA as a plastic, a step of separating and taking out PHA from inside microbial cells is necessary. be. Known methods for separating and purifying PHA from microbial cells include a method of extracting PHA from microbial cells using an organic solvent in which PHA is soluble, and a method of disrupting or solubilizing cell constituents other than PHA to remove them. As a result, a method for obtaining PHA or the like is used.

For example, as in Patent Document 1 and Patent Document 2 below, many methods for separating and purifying PHA using extraction with an organic solvent have been reported in early research. In these reports, a halogen compound such as chloroform is used as the organic solvent having the highest solubility of PHA, but when PHA is dissolved in the solvent, the viscosity of the solution becomes very high and it is difficult to handle. Therefore, in order to extract PHA, it is necessary to treat the polymer under extremely thin conditions of about 2 to 3%, and a very large amount of solvent is required. In addition, in order to crystallize PHA from the solvent layer with a high recovery rate, a large amount of a PHA-poor solvent such as methanol or hexane, which is 4 to 5 times the amount of the above solvent, is separately required. Therefore, large-scale equipment is required for industrial production. Further, since the amount of the solvent used is enormous, there is a problem that the recovery cost of the solvent and the cost of the lost solvent are high.

In Patent Document 3 below, as a method for separating and purifying PHA, an alkali is added to an aqueous suspension, and the suspension is ejected from a minute opening under pressurized and heated conditions to perform separation and purification by fluid shearing force. How to do it has been reported.
In Patent Document 4 below, as a method for separating and purifying PHA, alkali depolymerization and physical crushing are performed while controlling the hydrolysis of PHA by continuously adding an alkali to an aqueous suspension and controlling the pH. Purification methods have been reported.
In these reports, since the molecular weight of PHA obtained by microbial production is relatively low, it is required to obtain high-purity PHA while suppressing the depolymerization reaction by performing separation and purification in a short time. , Does not include reports on feedback control to obtain arbitrary molecular weight or rapid molecular weight confirmation.

The following Patent Documents 5 and 6 report a method for producing a high molecular weight PHA using a microorganism capable of producing a high molecular weight PHA, which can obtain an industrially useful plastic molded body.

Japanese Unexamined Patent Publication No. 2-69187 Japanese Unexamined Patent Publication No. 7-79788 Japanese Unexamined Patent Publication No. 7-31489 International Publication No. 2003/091444 International Publication No. 2012/102371 International Publication No. 2014/06253

However, in the method using a viscometer or GPC alone as in the above patent document, it takes time to measure the molecular weight of the polymer (including pretreatment). In particular, in the pretreatment step of the polymer existing as the solid content of the aqueous suspension, it takes time to dry, weigh, and extract the solvent of the polymer containing water. Therefore, it takes time from the start of the pretreatment to the completion of the measurement, and it is difficult to give quick feedback because of the process control. Therefore, in the depolymerization step of adjusting the molecular weight from the high molecular weight to an arbitrary molecular weight, it is desired to shorten the time required for measuring the molecular weight of the polymer.

In view of the above problems, an object of the present invention is an estimation that the pretreatment step required for measuring the molecular weight of a polymer can be greatly simplified and the time required for measuring the molecular weight of a polymer can be shortened. To provide systems, estimation methods, and programs.

In order to solve the above-mentioned problems, the estimation system according to one aspect of the present invention is an estimation system that estimates the molecular weight of a polymer existing as a solid content of an aqueous slurry, and comprises a solvent that dissolves the polymer and the polymer. Stores at least one solute concentration information regarding the solute concentration of the polymer solution dissolved as a solute and related information indicating the correspondence between each of the viscosity information indicating the viscosity of the polymer solution and the molecular weight information indicating the molecular weight of the polymer. It has a storage unit, an estimation unit that estimates the molecular weight information of the polymer based on the input data including the solute concentration information and the viscosity information, and the related information.

The estimation method according to one aspect of the present invention is an estimation method for estimating the molecular weight of a polymer existing as a solid content of an aqueous slurry, in which a storage unit dissolves a solvent that dissolves the polymer and the polymer as a solute. To store and estimate at least one solute concentration information regarding the solute concentration of the polymer solution and the relevant information indicating the correspondence between each of the viscosity information indicating the viscosity of the polymer solution and the molecular weight information indicating the molecular weight of the polymer. The unit includes estimating the molecular weight information of the polymer based on the input data including the solute concentration information and the viscosity information and the related information.

The program according to one aspect of the present invention is a program for estimating the molecular weight of a polymer existing as a solid content of an aqueous slurry, in which a computer is used to dissolve a solvent for dissolving the polymer and the polymer as a solute. A storage unit that stores at least one solute concentration information regarding the solute concentration of the polymer solution and the viscosity information indicating the viscosity of the polymer solution, and related information indicating the correspondence between the molecular weight information indicating the molecular weight of the polymer, and the above-mentioned storage unit. It functions as an estimation unit for estimating the molecular weight information of the polymer based on the input data including the solute concentration information and the viscosity information and the related information.

According to the present invention, the pretreatment step required for measuring the molecular weight of the polymer can be greatly simplified, and the time required for measuring the molecular weight of the polymer existing as the solid content of the aqueous slurry can be shortened. ..

It is a block diagram which shows an example of the structure of the estimation system which concerns on 1st Embodiment. It is a block diagram which shows an example of the structure of the estimation apparatus which concerns on each embodiment. It is a flowchart which shows the flow of the regression model generation processing in the estimation system which concerns on 1st Embodiment. It is a flowchart which shows the flow of the molecular weight estimation process in the estimation system which concerns on 1st Embodiment. It is a block diagram which shows an example of the structure of the estimation system which concerns on 2nd Embodiment. It is a flowchart which shows the flow of the regression model generation processing in the estimation system which concerns on 2nd Embodiment. It is a flowchart which shows the flow of the molecular weight estimation process in the estimation system which concerns on 2nd Embodiment.

Hereinafter, each embodiment of the present invention will be described in detail with reference to the drawings.

<< 1. First Embodiment >>
The first embodiment will be described with reference to FIGS. 1 to 4.
In the first embodiment, the solubility of water in the solvent is sufficiently negligible (the influence of the water content in the polymer does not significantly affect the solution viscosity and the solution density), and the two components of the solvent and the polymer are used. An example in which molecular weight information is estimated based on one solute concentration information and viscosity information will be described in a system that can be regarded.

The method for acquiring solute concentration information in the first embodiment is not limited to the method using density as long as the influence of water can be sufficiently ignored. For example, a method for measuring the refractive index, the speed of sound using ultrasonic waves, the attenuation rate, and the like may be adopted. Even if the effect of water cannot be ignored, the effect of water contained in the polymer has a significant effect on the solution viscosity by using spectroscopic methods in the infrared, near-infrared, and ultraviolet visible regions. As long as it is not given, solute concentration information in the three-component system of solvent, water and polymer can be obtained without being affected by water.

<1-1. Configuration of estimation system>
The configuration of the estimation system according to the first embodiment will be described with reference to FIG. 1. FIG. 1 is a block diagram showing an example of the configuration of the estimation system 1 according to the first embodiment.

The estimation system 1 according to the first embodiment is a system for estimating the molecular weight information of the polymer. In the first embodiment, an example in which the estimation target of the molecular weight information is poly (3-hydroxybutyrate-co-3-hydroxyhexanoate) (hereinafter, also referred to as "PHBH") will be described. PHBH is a Kaneka biodegradable polymer (registered trademark).
The estimation target of the molecular weight information is not limited to PHBH. The estimation target is not limited to PHBH as long as it is a polymer or the like existing as the solid content of the aqueous slurry. The polymer present as the solid content of the aqueous slurry is, for example, a polymer produced by culture. Specifically, the polymer produced by culture is polyhydroxyalkanoate (hereinafter, also referred to as “PHA”). More specifically, PHA is a microbially produced poly (3-hydroxyalkanoate) (hereinafter, also referred to as "P3HA"). The molecular weight information of the polymer estimated by the estimation system 1 includes, for example, the ultimate viscosity, the viscosity average molecular weight, the weight average molecular weight, and the like, but is not particularly limited.

The estimation system 1 estimates the molecular weight (molecular weight information) of PHBH based on the solution density (an example of solute concentration information) and the viscosity (viscosity information) of the polymer solution. The polymer solution is a solution in which PHBH is dissolved as a solute. Solute concentration information is information regarding the solute concentration of the polymer solution. The solute concentration information is used to calculate the solute concentration.
The estimation system 1 uses a regression model (an example of related information) for estimating the molecular weight. The regression model of the first embodiment is a model showing the correspondence between the solvent for dissolving PHBH, the solution density of the polymer solution, the viscosity, and the molecular weight. The estimation system 1 obtains the solution density and viscosity of the polymer solution depending on the solvent. The estimation system 1 inputs the acquired solution density and viscosity to the regression model. Thereby, the estimation system 1 can estimate the molecular weight output from the regression model as the molecular weight of PHBH.

As shown in FIG. 1, the estimation system 1 includes a density meter 10, a viscometer 20, and an estimation device 30-1.

(1) Density meter 10
The density meter 10 is a device for measuring the density of the polymer solution. The measurement method of the densitometer according to the first embodiment is a vibration type. The measurement method of the density meter is not limited to the vibration method.
As shown in FIG. 1, the density meter 10 has a density measuring unit 11 and a communication unit 12.

The density measuring unit 11 has a function of measuring the density of the polymer solution. The polymer solution after the pretreatment is input to the density meter 10. The density measuring unit 11 measures the density of the input polymer solution. After measuring the density, the density measuring unit 11 inputs the density information indicating the measured density to the communication unit 12. The polymer solution used to measure the density is input from the densitometer 10 to the viscometer 20.

The communication unit 12 has a function of transmitting and receiving various information. For example, the communication unit 12 transmits the density information to the estimation device 30-1.
The communication unit 12 may use either a wired or wireless communication method.

(2) Viscometer 20
The viscometer 20 is a device for measuring the viscosity of the polymer solution. The measuring method of the viscometer 20 according to the first embodiment is a falling ball type. The measuring method of the viscometer is not limited to the falling ball method. For example, the measuring method of the viscometer may be an EMS (Electro Magnetically Spinning) type, a rotary type, a thin tube type, a vibration type, or the like.
As shown in FIG. 1, the viscometer 20 has a viscosity measuring unit 21 and a communication unit 22.

The viscosity measuring unit 21 has a function of measuring the viscosity of the polymer solution. A polymer solution whose density has been measured by the density meter 10 is input to the viscometer 20. The viscosity measuring unit 21 measures the viscosity of the input polymer solution. After measuring the viscosity, the viscosity measuring unit 21 inputs the viscosity information indicating the measured viscosity to the communication unit 22. The polymer solution used to measure the viscosity is discarded as waste liquid.

The communication unit 22 has a function of transmitting and receiving various information. For example, the communication unit 22 transmits the viscosity information to the estimation device 30-1.
The communication unit 22 may use either a wired or wireless communication method.

When measuring the molecular weight by gel permeation chromatography (GPC), extraction of the solvent to be measured, drying of the solvent, etc. are performed as pretreatment. In GPC, not only the pretreatment takes time, but also the measurement of molecular weight takes time. On the other hand, in the estimation system 1 according to the first embodiment, the pretreatment can be simplified by the solvent extraction method by using the solution density and viscosity of the polymer solution measured by the densitometer 10 and the viscometer 20. .. As a result, the estimation system 1 can reduce the time required for estimating the molecular weight. For example, in the GPC, it took about 2 hours from the pretreatment to the completion of the measurement, but in the estimation system 1, it can be shortened to 30 minutes or less.

(3) Estimator 30-1
The estimation device 30-1 is a device for estimating the molecular weight of the polymer. Examples of the estimation device 30-1 include a PC (Personal Computer), a server device, a smartphone, a tablet terminal, and the like.

<1-2. Estimator configuration>
The configuration of the estimation device 30-1 according to the first embodiment will be described with reference to FIG. 2. FIG. 2 is a block diagram showing an example of the configuration of the estimation device 30 according to each embodiment. In FIG. 2, "-1" is added to the reference numeral of the configuration of the estimation device 30-1 according to the first embodiment. As shown in FIG. 2, the estimation device 30-1 has a communication unit 31-1, a control unit 32-1, a storage unit 33-1 and an output unit 34-1.

(1) Communication unit 31-1
The communication unit 31-1 has a function of transmitting and receiving various information. For example, the communication unit 31-1 receives density information from the density meter 10. Further, the communication unit 31-1 receives viscosity information from the viscometer 20. The communication unit 31-1 inputs the received information to the control unit 32-1.
The communication unit 31-1 may use either a wired or wireless communication method.

(2) Control unit 32-1
The control unit 32-1 has a function of controlling the overall operation of the estimation device 30-1. This function is realized, for example, by causing a CPU (Central Processing Unit) provided as hardware in the estimation device 30-1 to execute a program.

As shown in FIG. 2, the control unit 32-1 has a model generation unit 320-1, an estimation unit 321-1, and an output processing unit 322-1.

(2-1) Model generator 320-1
The model generation unit 320-1 has a function of generating a regression model 330-1. The regression model 330-1 is a model showing the correspondence between the solvent that dissolves PHBH, the solution density and viscosity of the polymer solution, and the molecular weight of the polymer. The model generation unit 320-1 stores the generated regression model 330-1 in the storage unit 33-1.

The model generation unit 320-1 of the first embodiment generates a regression model 330-1 by statistical analysis.
For example, the model generator 320-1 generates a regression model 330-1 based on each of the solution densities and viscosities of the polymer solution and the known molecular weights corresponding to the solution densities and viscosities. Specifically, the model generation unit 320-1 calculates the solute concentration from the solution density, and generates a model for calculating the molecular weight based on the solute concentration and the viscosity.

There is a correlation between the solution density of the polymer solution and the solute concentration. The model generation unit 320-1 generates an equation showing the correlation from the solution density and the solute concentration of the polymer solution obtained in advance. If the solution density of the polymer solution is known from the formula, the solute concentration of the polymer solution can be calculated.
In the case of a two-component system as in the first embodiment, each component concentration can be expressed by one variable (for example, W _x ). For example, if the concentration of one of the two components is W _x %, the concentration of the other component is (1-W _x )%.
The correlation between the solution density of the polymer solution and the component concentration W _x % is shown by the following mathematical formula (1). In addition, Constant. Is a constant.
Solution density = Const. + A x W _x (1)
The value of a in the formula (1) can be calculated by learning using a plurality of data. If the value of a in the formula (1) is known, the component concentration W _x % can be retrospectively predicted from the density of the polymer solution measured by the density measuring unit 11 using the formula (1). Further, if the component concentration W _x % is known, another component concentration (1-W _x )% can also be calculated. Therefore, in the two-component system, if the solution density of the polymer solution is known, the solute concentration of the polymer solution can be calculated. That is, the solute concentration of the polymer solution can be calculated based on only one measured value.
The one measured value used for calculating the solute concentration is not limited to the measured value of the density, and may be the measured value of the speed of sound.

The model generation unit 320-1 calculates the molecular weight using the calculated solute concentration and the viscosity obtained in advance. Assuming that the reduced viscosity is η _redo , the specific viscosity is η _sp , the solute concentration is c, and the intrinsic viscosity (extreme viscosity) is [η], the reduced viscosity η _redo is expressed by the following formula (2).
η _red = η _sp / c = [η] + k ₂ c + k ₃ c ² + ... (2)

Assuming that the viscosity of the polymer solution is η and the viscosity of the solvent is η ₀ , the specific viscosity η _sp is expressed by the following mathematical formula (3).
η _sp = (η-η ₀ ) / η ₀ (3)

Assuming that the molecular weight is M, the intrinsic viscosity [η] is expressed by the following mathematical formula (4) (Mark-Howwink-Sakurada formula). In addition, K and α are constants.
[Η] = KM ^α (4)

The model generation unit 320-1 can calculate the intrinsic viscosity [η] from the mathematical formulas (2) and (3). The model generation unit 320-1 can calculate the molecular weight M from the calculated intrinsic viscosity [η] and the mathematical formula (4).

The model generation unit 320-1 generates an equation showing the correlation between the solution density and viscosity obtained in advance and the molecular weight as the regression model 330-1 based on the calculation result of the molecular weight. The correlation is shown, for example, by a linear regression model.

(2-2) Presumption unit 321-1
The estimation unit 321-1 estimates the molecular weight of the polymer solution using the regression model 330-1. The estimation unit 321-1 can easily estimate the molecular weight by using the regression model 330-1 generated in advance by the model generation unit 320-1.

The estimation unit 321-1 estimates the molecular weight of PHBH based on the input data including the solution density (one solute concentration information) and the viscosity, and the regression model 330-1 stored in the storage unit 33-1. ..
The estimation unit 321-1 inputs the density information measured by the density meter 10 and the viscosity information measured by the viscometer 20 into the regression model 330-1. The regression model 330-1 to which the density information and the viscosity information are input outputs the molecular weight information of the polymer solution to be estimated. The estimation unit 321-1 estimates the molecular weight indicated by the molecular weight information as the molecular weight to be estimated.

(2-3) Output processing unit 322-1
The output processing unit 322-1 has a function of controlling the output of molecular weight information. For example, the output processing unit 322-1 inputs the molecular weight estimated by the estimation unit 321-1 to the output unit 34-1 as molecular weight information and displays it. This allows the user to confirm the molecular weight of the estimation target.

(3) Storage unit 33-1
The storage unit 33-1 has a function of storing various types of information. The storage unit 33-1 is a storage medium, for example, an HDD (Hard Disk Drive), a flash memory, an EEPROM (Electrically Erasable Programmable Read Only Memory), a RAM (Random Access read / maid) It is composed of any combination of these storage media. The storage unit 33-1 can use, for example, a non-volatile memory.
As shown in FIG. 2, the storage unit 33-1 stores the regression model 330-1.

(4) Output unit 34-1
The output unit 34-1 has a function of outputting various information. The output unit 34-1 is realized by a display device such as a display included in the estimation device 30-1. The output unit 34-1 displays the molecular weight information input from the output processing unit 322-1.

<1-3. Processing flow>
A processing flow in the estimation system 1 according to the first embodiment will be described with reference to FIGS. 3 and 4. FIG. 3 is a flowchart showing the flow of the regression model generation process in the estimation system 1 according to the first embodiment. FIG. 4 is a flowchart showing the flow of the molecular weight estimation process in the estimation system 1 according to the first embodiment.

(1) Regression model generation process As shown in FIG. 3, first, the estimation device 30-1 calculates the solute concentration from the solution density of the polymer solution (S100).

Next, the estimation device 30-1 calculates the molecular weight of the polymer solution from the viscosity of the polymer solution and the calculated solute concentration (S102).

Next, the estimation device 30-1 generates a regression model 330-1 based on the solution density, viscosity, and molecular weight (S104).

Finally, the estimation device 30-1 stores the generated regression model 330-1 in the storage unit 33-1 (S106).

(2) Molecular Weight Estimating Process As shown in FIG. 4, first, the densitometer 10 acquires the solution density of the polymer solution to be estimated (S200).

Next, the viscometer 20 acquires the viscosity of the polymer solution to be estimated (S202).

Next, the estimation device 30-1 inputs the solution density and viscosity into the regression model 330-1 (S204).

Finally, the estimation device 30-1 estimates the molecular weight output by the regression model 330-1 as the molecular weight to be estimated (S206).

As described above, the estimation system 1 according to the first embodiment is a system for estimating the molecular weight of the polymer existing as the solid content of the aqueous slurry.
The storage unit 33-1 of the estimation system 1 stores a regression model 330-1 showing the correspondence between the solvent for dissolving the polymer, each of the polymer solution density and the viscosity of the polymer solution, and the molecular weight information indicating the molecular weight of the polymer. ..
The estimation unit 321-1 of the estimation system 1 estimates the molecular weight of the polymer based on the input data including the solution density and the viscosity and the regression model 330-1.

With this configuration, in the estimation system 1 according to the first embodiment, when the solution density and viscosity of the polymer solution to be estimated are input to the regression model 330-1, the molecular weight of the polymer is output. As a result, the time required for measuring the molecular weight can be shortened. Further, the solution density and the viscosity of the polymer solution can be obtained by the densitometer 10 and the viscometer 20, respectively. Therefore, in the estimation system 1, the pretreatment can be simplified by the solvent extraction method. As a result, the time required for preprocessing can be shortened.

Therefore, the estimation system 1 according to the first embodiment can shorten the time required for measuring the molecular weight of the polymer.

<< 2. Second embodiment >>
In the above-described embodiment, an example in which molecular weight information is estimated based on one solute concentration information (solution density) and viscosity information in a two-component system of a solvent and a polymer has been described, but the present invention is not limited to this example. For example, in a three-component system, molecular weight information may be estimated based on two solute concentration information and viscosity information. Specifically, in the second embodiment, the solubility of water in the solvent is in a non-negligible range (which has a significant effect on the solution density), and the two solute concentration information in the solvent, polymer, and water three-component system. An example in which the molecular weight information is estimated based on the viscosity information will be described.
Hereinafter, the second embodiment will be described with reference to FIGS. 5 to 7, but the description overlapping with the first embodiment will be omitted.

<2-1. Configuration of estimation system>
The configuration of the estimation system according to the second embodiment will be described with reference to FIG. FIG. 5 is a block diagram showing an example of the configuration of the estimation system 2 according to the second embodiment.

The estimation system 2 according to the second embodiment is a system for estimating the molecular weight of the polymer as in the estimation system 1. In the estimation system 2, two solute concentration information is used to measure the molecular weight in the three-component system. One of the two solute concentration information is the solution density as in the estimation system 1. The other of the two solute concentration information is sound velocity information (an example of solute concentration information) indicating the speed of sound (sound velocity) propagating in the polymer solution. That is, the estimation system 2 according to the second embodiment estimates the molecular weight of PHBH based on the solution density, sound velocity (sound velocity information), and viscosity (viscosity information) of the polymer solution.
In the estimation system 2 according to the second embodiment, by using the speed of sound, the drying step, which is a pretreatment step for measuring the molecular weight, can be omitted in the manufacturing step of the culture polymer sampled in a water-containing state, and the pretreatment can be performed. It is possible to reduce the time required for. As a result, the estimation system 2 can shorten the measurement time for confirming the molecular weight of the polymer.

The method for acquiring the solute concentration information in the second embodiment is not limited to the method based on the combination of the solution density and the sound velocity information. For example, it is also possible to use density, refractive index, sound velocity and attenuation using ultrasonic waves, spectroscopic methods in the infrared / near-infrared / ultraviolet-visible region, and the like. By adopting any combination of these, solute concentration information in the three-component system of the solvent, water and the polymer can be obtained.

Since the estimation target according to the second embodiment is the same as the estimation target according to the first embodiment described above, duplicate description will be omitted.

The estimation system 2 uses a regression model for estimating the molecular weight. The regression model of the second embodiment is a model showing the correspondence between the solvent for dissolving PHBH, the solution density of the polymer solution, the speed of sound, the viscosity, and the molecular weight. The estimation system 2 can estimate the molecular weight of PHBH by a regression model by acquiring the solution density, sound velocity, and viscosity of the polymer solution according to the solvent.

As shown in FIG. 5, the estimation system 1 includes a density meter 10, a sound velocity meter 40, a viscometer 20, and an estimation device 30-2.

(1) Density meter 10
Since the density meter 10 according to the second embodiment is the same device as the density meter 10 according to the first embodiment described above, overlapping description will be omitted. However, the difference is that the polymer solution used for measuring the density is input from the density meter 10 to the sound velocity meter 40.

(2) Sound velocity meter 40
The sound velocity meter 40 is a device for measuring the speed of sound in a polymer solution.
As shown in FIG. 5, the sound velocity meter 40 has a sound velocity measuring unit 41 and a communication unit 42.

The sound velocity measuring unit 41 has a function of measuring the sound velocity in the polymer solution. The polymer solution whose density has been measured by the density meter 10 is input to the sound velocity meter 40. The sound velocity measuring unit 41 measures the sound velocity in the input polymer solution. After measuring the sound velocity, the sound velocity measuring unit 41 inputs sound velocity information indicating the measured sound velocity to the communication unit 42. The polymer solution used for measuring the speed of sound is input from the sound velocity meter 40 to the viscometer 20.

The communication unit 42 has a function of transmitting and receiving various information. For example, the communication unit 42 transmits the sound velocity information to the estimation device 30-2.
The communication unit 42 may use either a wired or wireless communication method.

(3) Viscometer 20
Since the viscometer 20 according to the second embodiment is the same device as the viscometer 20 according to the first embodiment described above, overlapping description will be omitted. However, the difference is that the polymer solution is input from the sonicometer 40.

(4) Estimator 30-2
The estimation device 30-2 is a device for estimating the molecular weight of the polymer. Examples of the estimation device 30-2 include a PC (Personal Computer), a server device, a smartphone, a tablet terminal, and the like.

<2-2. Estimator configuration>
The configuration of the estimation device 30-2 according to the second embodiment will be described with reference to FIG. 2. FIG. 2 is a block diagram showing an example of the configuration of the estimation device 30 according to each embodiment. In FIG. 2, "-2" is added to the reference numeral of the configuration of the estimation device 30-2 according to the second embodiment. As shown in FIG. 2, the estimation device 30-2 has a communication unit 31-2, a control unit 32-2, a storage unit 33-2, and an output unit 34-2.

(1) Communication unit 31-2
Since the function of the communication unit 31-2 according to the second embodiment is the same as the function of the communication unit 31-1 according to the first embodiment described above, duplicate description will be omitted. However, the difference is that the communication unit 31-2 receives the sound velocity information from the sound velocity meter 40.

(2) Control unit 32-2
The control unit 32-2 has a function of controlling the overall operation of the estimation device 30-2. This function is realized, for example, by causing a CPU (Central Processing Unit) provided as hardware in the estimation device 30-2 to execute a program.

As shown in FIG. 2, the control unit 32-2 has a model generation unit 320-2, an estimation unit 321-2, and an output processing unit 322-2.

(2-1) Model generator 320-2
Since the function of the model generation unit 320-2 according to the second embodiment is the same as the function of the model generation unit 320-1 according to the first embodiment described above, duplicate description will be omitted. However, the difference is that the model generator 320-2 calculates the solute concentration from the solution density and the speed of sound. The model generation unit 320-2 generates a regression model 330-2.

There is a correlation between the solution density of polymer solutions and the speed of sound and solute concentration. The model generation unit 320-2 generates an equation showing the correlation from the solution density, the speed of sound, and the solute concentration of the polymer solution obtained in advance. From this formula, if the solution density and sound velocity of the polymer solution are known, the solute concentration of the polymer solution can be calculated.
In the case of a three-component system as in the second embodiment, each component concentration can be expressed by at least two variables (for example, W _x and W _y ). For example, if the concentration of the first component of the three components is W _x % and the concentration of the second component is W _y %, the concentration of the third component is (1-W _x −W _y )%.
The correlation between the solution density of the polymer solution, the component concentration W _x %, and the component concentration W _y % is shown by the following mathematical formula (5). In addition, Constant. Is a constant.
Solution density = Const. + A x W _x + b x W _y ... (5)
Further, the correlation between the speed of sound, the component concentration W _x %, and the component concentration W _y % is shown by the following mathematical formula (6). In addition, Constant. Is a constant.
Speed of sound = Const. + C x W _x + d x W _y ... (6)
The values of a and b in the formula (5) and the values of c and d in the formula (6) can be calculated by learning using a plurality of data. If the values of a and b in the formula (5) and the values of c and d in the formula (6) are known, the density and sound velocity of the polymer solution measured by the density measuring unit 11 using the formulas (5) and (6). The component concentration W _x % and the component concentration W _y % can be recursively predicted from the speed of sound in the polymer solution measured by the measuring unit 41. Further, if the component concentration W _x % and the component concentration W _y % are known, the third component concentration (1-W _x −W _y )% can also be calculated. Therefore, in the three-component system, if the solution density of the polymer solution and the speed of sound in the polymer solution are known, the solute concentration of the polymer solution can be calculated. That is, the solute concentration of the polymer solution can be calculated based on the two measured values.
The two measured values used for calculating the solute concentration are not limited to the measured values of density and sound velocity as long as they are a plurality of measured values having different degrees of influence of each component concentration.

(2-2) Presumption unit 321-2
Since the function of the estimation unit 321-2 according to the second embodiment is the same as the function of the estimation unit 321-1 according to the first embodiment described above, duplicate description will be omitted. However, the difference is that the estimation unit 321-2 inputs input data including the solution density, the speed of sound (two solute concentration information), and the viscosity to the regression model 330-2.

(2-3) Output processing unit 322-2
Since the function of the output processing unit 322-2 according to the second embodiment is the same as the function of the output processing unit 322-1 according to the first embodiment described above, duplicate description will be omitted.

(3) Storage unit 33-2
Since the function of the storage unit 33-2 according to the second embodiment is the same as the function of the storage unit 33-1 according to the first embodiment described above, duplicate description will be omitted.

(4) Output unit 34-2
Since the function of the output unit 34-2 according to the second embodiment is the same as the function of the output unit 34-1 according to the first embodiment described above, duplicate description will be omitted.

<2-3. Processing flow>
A processing flow in the estimation system 2 according to the second embodiment will be described with reference to FIGS. 6 and 7. FIG. 6 is a flowchart showing the flow of the regression model generation process in the estimation system 2 according to the second embodiment. FIG. 7 is a flowchart showing the flow of the molecular weight estimation process in the estimation system 2 according to the second embodiment.

(1) Regression model generation process As shown in FIG. 6, first, the estimation device 30-2 calculates the solute concentration from the solution density and the speed of sound of the polymer solution (S300).

Next, the estimation device 30-2 calculates the molecular weight of the polymer solution from the viscosity of the polymer solution and the calculated solute concentration (S302).

Next, the estimation device 30-2 generates a regression model 330-2 based on the solution density, viscosity, sound velocity, and molecular weight (S304).

Finally, the estimation device 30-2 stores the generated regression model 330-2 in the storage unit 33-2 (S306).

(2) Molecular Weight Estimating Process As shown in FIG. 7, first, the densitometer 10 acquires the solution density of the polymer solution to be estimated (S400).

Next, the sound velocity meter 40 acquires the sound velocity in the polymer solution to be estimated (S402).

Next, the viscometer 20 acquires the viscosity of the polymer solution to be estimated (S404).

Next, the estimation device 30-2 inputs the solution density, sound velocity, and viscosity into the regression model 330-2 (S406).

Finally, the estimation device 30-2 estimates the molecular weight output by the regression model 330-2 as the molecular weight to be estimated (S408).

As described above, the estimation system 2 according to the second embodiment is a system for estimating the molecular weight of the polymer existing as the solid content of the aqueous slurry.
The storage unit 33-2 of the estimation system 2 corresponds to each of the solvent for dissolving the polymer, the solution density of the polymer solution, the sound velocity in the polymer solution, and the viscosity of the polymer solution, and the molecular weight information indicating the molecular weight of the polymer. The regression model 330-2 shown is stored.
The estimation unit 321-2 of the estimation system 2 estimates the molecular weight of the polymer based on the input data including the solution density, the speed of sound, and the viscosity, and the regression model 330-2.

With this configuration, in the estimation system 2 according to the second embodiment, when the solution density, sound velocity, and viscosity of the polymer solution to be estimated are input to the regression model 330-2 in the three-component system, the molecular weight of the polymer is output. Will be done. As a result, the time required for measuring the molecular weight can be shortened. Further, the solution density, sound velocity, and viscosity of the polymer solution can be obtained by the density meter 10, the sound velocity meter 40, and the viscometer 20, respectively. Therefore, in the estimation system 2, the pretreatment can be simplified by the solvent extraction method. As a result, the time required for preprocessing can be shortened.

Therefore, the estimation system 2 according to the second embodiment can shorten the time required for measuring the molecular weight of the polymer.

<< 3. Modification example >>
The embodiment of the present invention has been described above. Subsequently, a modified example of the embodiment of the present invention will be described. In addition, each modification described below may be applied to the embodiment of the present invention alone, or may be applied to the embodiment of the present invention in combination. Further, each modification may be applied in place of the configuration described in the embodiment of the present invention, or may be additionally applied to the configuration described in each embodiment of the present invention.

(1) First Modification Example In each of the above-described embodiments, an example in which the model generation unit 320 generates a regression model by a statistical method has been described, but the present invention is not limited to this example. The model generation unit 320 may generate a regression model 330 (trained model) by machine learning. Examples of machine learning methods include SVR (support vector regression), random forest, deep learning by neural network, and the like.

The model generation unit 320 generates a trained model by, for example, supervised learning. In supervised learning, a learning model is trained using a learning data set. The data set is a set of learning data that is input at the time of learning and teacher data that shows the correct answer of the data output based on the input data.

In the case of the first embodiment, the training data is the solution density and viscosity of the polymer solution. The teacher data is the molecular weight of the polymer. The model generation unit 320-1 according to the first embodiment generates a trained model in which the correspondence between the solution density, the viscosity, and the molecular weight is learned by using the training data and the teacher data.
The estimation unit 321-1 according to the first embodiment obtains the molecular weight of the polymer solution as an output by inputting the density measured by the densitometer 10 and the viscosity measured by the viscometer 20 into the trained model. be able to.

In the second embodiment, the training data are the solution density and viscosity of the polymer solution and the speed of sound in the polymer solution. The model generation unit 320-2 according to the second embodiment generates a trained model in which the correspondence between the solution density, the viscosity, the speed of sound, and the molecular weight is learned by using the training data and the teacher data.
The estimation unit 321-2 according to the second embodiment inputs the density measured by the density meter 10, the viscosity measured by the viscosity meter 20, and the sound velocity measured by the sound velocity meter 40 into the trained model. , The molecular weight of the polymer solution can be obtained as an output.

(2) Second Modified Example In the above-described embodiment, an example in which the regression model 330 is generated by the estimation device 30 has been described, but the present invention is not limited to this example. The regression model 330 may be generated by an external device (eg, a server device) different from the estimation device 30. In this case, the estimation device 30 stores the regression model 330 generated by the external device in the storage unit 33, and uses the regression model 330.

The modification of the embodiment of the present invention has been described above. The estimation system 1 in the above-described embodiment may be realized by a computer. In that case, a program for realizing this function may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read by a computer system and executed. The term "computer system" as used herein includes hardware such as an OS and peripheral devices. Further, the "computer-readable recording medium" refers to a portable medium such as a flexible disk, a magneto-optical disk, a ROM, or a CD-ROM, and a storage device such as a hard disk built in a computer system. Further, a "computer-readable recording medium" is a communication line for transmitting a program via a network such as the Internet or a communication line such as a telephone line, and dynamically holds the program for a short period of time. It may also include a program that holds a program for a certain period of time, such as a volatile memory inside a computer system that is a server or a client in that case. Further, the above program may be for realizing a part of the above-mentioned functions, and may be further realized for realizing the above-mentioned functions in combination with a program already recorded in the computer system. It may be realized by using a programmable logic device such as FPGA (Field Programmable Gate Array).

Although the embodiments of the present invention have been described in detail with reference to the drawings, the specific configuration is not limited to the above, and various design changes and the like can be made without departing from the gist of the present invention. It is possible to do.

Hereinafter, the present invention will be described in more detail by way of examples, but the present invention is not limited to these examples.

[Measurement and regression model construction]
The measurement and evaluation in the above-described embodiment were performed by the following methods.

(Preparation of PHA aqueous suspension)
The cells were cultured by the method described in International Publication No. 2014/65253 to obtain a cell culture solution containing cells containing PHA. The obtained bacterial cell culture solution was heated and stirred at an internal temperature of 70 ° C. for 8 hours, and then sterilized.
Lysozyme (Fujifilm) is an enzyme that decomposes sugar chains (peptidoglycan) in the cell wall after adjusting the solid content concentration to 18% by adding IW (industrial water) to the inactivated culture solution obtained above. Wako Pure Chemical Industries, Ltd.) was added, and the mixture was kept at 50 ° C. for 2 hours. Then, alcalase (manufactured by Novozymes), which is a proteolytic enzyme, was added, and then 30% sodium hydroxide was added at 50 ° C., and the mixture was kept for 2 hours while adjusting the pH to 8.5.
For the enzyme-treated liquid obtained above, adjust the pH to 11.6 ± 0.2 with 30% sodium hydroxide, set the internal temperature to 50 ± 2 ° C, and carry out the alkaline depolymerization reaction of PHA. After 14 hours, a PHA aqueous suspension was obtained. Further, by obtaining a sample having an arbitrary reaction time, PHA aqueous suspensions having different molecular weights were obtained.

(Measurement of reduced viscosity, extreme viscosity, density, speed of sound, construction of regression model)
The PHA aqueous suspension obtained by alkaline depolymerization was centrifuged, the supernatant was removed, and the suspension was washed with ethanol. The solid content after washing was dried in a vacuum dryer for 15 minutes to obtain PHA powder.
Dissolve 1 mg to 10 mg of PHA powder in 1 ml of chloroform to prepare at least three-level PHA solutions with known concentrations and reduced viscosities in the range of 0.05 to 1, and measure the viscosities of those solutions with a falling ball viscometer. It was measured using (manufactured by Anton Pearl). According to JIS-K7367, the reduced viscosity of the PHA solution and the ultimate viscosity of PHA were calculated by the least squares method as the limit values in the infinite dilution of the reduced viscosity. In addition, the PHA solution prepared for viscosity measurement and the PHA solution adjusted by adding an arbitrary amount of water are distributed to a vibration type density / sound velocity meter (manufactured by Anton Pearl Co., Ltd.) to measure the density and sound velocity, and the density and sound velocity are measured. A regression model for predicting PHA density using the speed of sound as an explanatory variable was constructed.

(Measurement of weight average molecular weight, construction of regression model)
After dissolving 5 mg of PHA powder in 5 ml of chloroform, the insoluble material was removed by filtration. The molecular weight of this solution was measured using a GPC system manufactured by Shimadzu Corporation equipped with "Chromatography K805L (300 x 8 mm, two connected)" (manufactured by Showa Denko KK) using chloroform as a mobile phase. As a molecular weight standard sample, a polystyrene standard product manufactured by Showa Denko KK (molecular weight 6 levels: 6,870,000, 2,000,000, 696,000, 129,000, 19,900, 3,090) was used. , A calibration curve was created and the molecular weight in terms of PS was calculated. We also constructed a regression model for weight average molecular weight prediction using information on viscosity and concentration as explanatory variables.

[Example 1]
The PHA aqueous suspension obtained by alkaline depolymerization was centrifuged, the supernatant was removed, and the suspension was washed with ethanol. The solid content after washing was dried in a vacuum dryer for 15 minutes to obtain PHA powder.
5 mg of PHA powder was dissolved in 5 ml of chloroform, measured using a falling ball viscometer and a vibration type density system (manufactured by Anton Pearl Co., Ltd.), and the predicted molecular weight was calculated by a regression prediction model.

[Example 2]
After centrifuging the PHA aqueous suspension obtained by alkaline depolymerization, the supernatant was removed and washed with pure water to obtain a PHA powder containing water.
5 mg of PHA powder was dissolved in 5 ml of chloroform, measured using a falling ball viscometer and a vibration type density system (manufactured by Anton Pearl Co., Ltd.), and the predicted molecular weight was calculated by a regression prediction model.

1, 2, estimation system 10 Density meter 11 Density measurement unit 12 Communication unit 20 Viscometer 21 Viscosity measurement unit 22 Communication unit 30-1, 30-2 Estimator 31-1, 31-2 Communication unit 32-1, 32-2 Control unit 33-1, 33-2 Storage unit 34-1, 34-2 Output unit 40 Viscometer 41 Sound velocity measurement unit 42 Communication unit 320-1, 320-2 Model generation unit 321-1,321-2 Estimating unit 322 -1,322-2 Output processing unit 330-1,330-2 Regression model

Claims (11)

1. An estimation system that estimates the molecular weight of a polymer that exists as the solid content of an aqueous slurry.
   Each of the solvent for dissolving the polymer, at least one solute concentration information regarding the solute concentration of the polymer solution in which the polymer is dissolved as a solute, and the viscosity information indicating the viscosity of the polymer solution, and the molecular weight indicating the molecular weight of the polymer. A storage unit that stores related information indicating the correspondence with the information,
   An estimation unit that estimates the molecular weight information of the polymer based on the input data including the solute concentration information and the viscosity information and the related information.
   Has an estimation system.
2. The related information indicates the correspondence between the two solute concentration information, the viscosity information, and the molecular weight information.
   The estimation unit estimates the molecular weight information of the polymer based on the input data including the two solute concentration information and the viscosity information and the related information.
   The estimation system according to claim 1.
3. One of the solute concentration information is sound velocity information indicating the velocity of sound propagating in the polymer solution.
   The solute concentration is calculated from the solute concentration information including at least the sound velocity information.
   The estimation system according to claim 2.
4. The related information indicates the correspondence between the solute concentration information, the viscosity information, and the molecular weight information.
   The estimation unit estimates the molecular weight information of the polymer based on the input data including the solute concentration information and the viscosity information and the related information.
   The estimation system according to any one of claims 1 to 3.
5. One of the solute concentration information is the solution density of the polymer solution.
   The solute concentration is calculated from the solute concentration information including at least the solution density.
   The estimation system according to any one of claims 1 to 4.
6. A model generator that generates a regression model that estimates the molecular weight of the polymer solution based on each of the solute concentration information and the viscosity information, and the known molecular weight information corresponding to the solute concentration information and the viscosity information.
   Further prepare
   The estimation unit estimates the molecular weight of the polymer by using the regression model as the related information.
   The estimation system according to any one of claims 1 to 5.
7. The model generation unit generates the regression model by machine learning.
   The estimation system according to claim 6.
8. The polymer is a culture-produced polymer.
   The estimation system according to any one of claims 1 to 7.
9. The polymer is P3HA (poly (3-hydroxy alkanoate)),
   The estimation system according to any one of claims 1 to 8.
10. It is an estimation method for estimating the molecular weight of a polymer existing as a solid content of an aqueous slurry.
    The storage unit contains at least one solute concentration information regarding the solvent for dissolving the polymer, the solute concentration of the polymer solution in which the polymer is dissolved as a solute, and viscosity information indicating the viscosity of the polymer solution, and each of the viscosity information of the polymer. To store related information indicating the correspondence with the molecular weight information indicating the molecular weight, and
    The estimation unit estimates the molecular weight information of the polymer based on the input data including the solute concentration information and the viscosity information and the related information.
    Estimating method, including.
11. A program for estimating the molecular weight of a polymer present as the solid content of an aqueous slurry.
    Computer,
    Each of the solvent for dissolving the polymer, at least one solute concentration information regarding the solute concentration of the polymer solution in which the polymer is dissolved as a solute, and the viscosity information indicating the viscosity of the polymer solution, and the molecular weight indicating the molecular weight of the polymer. A storage unit that stores related information indicating the correspondence with the information,
    An estimation unit that estimates the molecular weight information of the polymer based on the input data including the solute concentration information and the viscosity information and the related information.
    A program that functions as.

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