US-20260127490-A1 - METHOD FOR CREATING MACHINE LEARNING MODEL FOR ESTIMATING STATE OF FLUID IN TANK, AND METHOD FOR ESTIMATING STATE OF FLUID IN TANK USING MACHINE LEARNING MODEL

US20260127490A1US 20260127490 A1US20260127490 A1US 20260127490A1US-20260127490-A1

Abstract

[Problem] To provide a method for creating a machine learning model that allows for a quick and accurate estimation of a state of a fluid in a tank. [Solution] The method for creating a machine learning model for estimating a state of a fluid in a tank, wherein the fluid contains a plurality of substances, the method comprising: creating a plurality of parameter sets, each set comprising tank information including at least a shape and size of the tank, operating conditions of the tank, and substance information including at least an amount, and one or more physical properties of each of the plurality of substances, wherein the plurality of parameter sets are created by varying the parameters of the operating conditions and the substance information; performing a numerical fluid dynamics analysis based on the plurality of parameter sets to provide a plurality of calculation results based on fluid information including distributions of physical quantities related to the fluid and/or an amount of each of the plurality of substances; and creating the machine learning model by a machine learning operation which involves training the machine learning model with teaching data including the plurality of parameter sets and the corresponding plurality of calculation results.

Inventors

Takehito YASUI
Yuzuru Ito
Junko KITAMOTO
Satoshi Sugiyama

Assignees

CHIYODA CORPORATION

Dates

Publication Date: 20260507
Application Date: 20231018
Priority Date: 20221026

Claims (13)

1 . A method for creating a machine learning model for estimating a state of a fluid in a tank, wherein the fluid contains a plurality of substances, the method comprising: creating a plurality of parameter sets, each set comprising tank information including at least a shape and size of the tank, operating conditions of the tank, and substance information including at least an amount, and one or more physical properties of each of the plurality of substances, wherein the plurality of parameter sets are created by varying the parameters of the operating conditions and the substance information; performing a numerical fluid dynamics analysis based on the plurality of parameter sets to provide a plurality of calculation results as fluid information, wherein the fluid information includes distributions of physical quantities related to the fluid and/or an amount of each of the plurality of substances; and creating the machine learning model by a machine learning operation which involves training the machine learning model with teaching data including the plurality of parameter sets and the corresponding plurality of calculation results.
2 . A method for creating a machine learning model for estimating a state of a fluid in a tank, wherein the fluid contains a plurality of substances, the method comprising: creating a plurality of parameter sets, each set comprising tank information including at least a shape and size of the tank, operating conditions of the tank, and substance information including at least an amount, and one or more physical properties of each of the plurality of substances, wherein the plurality of parameter sets are created by varying the parameters of the tank information, the operating conditions, and the substance information; performing a numerical fluid dynamics analysis based on the plurality of parameter sets to provide a plurality of calculation results as fluid information, wherein the fluid information includes distributions of a physical quantities related to the fluid and/or an amount of each of the plurality of substances; and creating the machine learning model by a machine learning operation which involves training the machine learning model with teaching data including the plurality of parameter sets and the corresponding plurality of calculation results.
3 . The method as claimed in claim 1 , wherein the plurality of substances comprise cells or microorganisms, and wherein the fluid information includes the distributions and/or the total number of the cells or microorganisms.
4 . The method as claimed in claim 1 , wherein the step of creating the machine learning model comprising: extracting first fluid information related to the state of the fluid at a first position in the tank from the plurality of calculation results, and creating the teaching data by using: the tank information, the operating conditions of the tank, the one or more physical properties of each of the plurality of substances, and the extracted first fluid information as explanatory variables; and the fluid information including the distributions of the physical quantities related to the fluid and/or the amount of each of the plurality of substances at each position in the tank as objective variables.
5 . A method for estimating the state of the fluid in the tank using the machine learning model created by the method as claimed in claim 1 , the method comprising: inputting first fluid information related to the state of the fluid at a first position in the tank, the operating conditions, and the substance information to the machine learning model, thereby providing the fluid information as output data of the machine learning model.
6 . A method for estimating the state of the fluid in the tank using the machine learning model created by the method as claimed in claim 1 , the method comprising: inputting first fluid information related to the state of the fluid at a first position in the tank, second fluid information related to the state of the fluid at a second position in the tank, the operating conditions, and the substance information to the machine learning model, thereby providing the fluid information as output data of the machine learning model.
7 . A method for estimating the state of the fluid in the tank using the machine learning model created by the method as claimed in claim 2 , the method comprising: inputting first fluid information related to the state of the fluid at a first position in the tank, the tank information, the operating conditions, and the substance information to the machine learning model, thereby providing the fluid information as output data of the machine learning model.
8 . The method as claimed in claim 5 , wherein the plurality of substances comprise cells or microorganisms, and wherein the fluid information includes the distributions and/or the total number of the cells or microorganisms.
9 . The method as claimed in claim 8 , wherein the first fluid information is a number of the cells or microorganisms at the first position.
10 . The method as claimed in claim 1 , wherein the numerical fluid dynamics analysis is a first numerical fluid dynamics analysis, the method comprising: performing a second numerical fluid dynamics analysis based on the plurality of parameter sets and a further parameter set related to an additional substance to be put into the tank after the tank starts operation; calculating an additional substance diffusion time, which is a time required for the additional substance to diffuse to every position in the tank, based on results of the second numerical fluid dynamics analysis; and creating the machine learning model by a machine learning operation which involves training the machine learning model with teaching data including the plurality of parameter sets and the further parameter set related to the additional substance, and the additional substance diffusion time.
11 . A method for estimating the state of the fluid and the additional substance diffusion time required for the additional substance to diffuse in the tank using the machine learning model created by the method as claimed in claim 10 , the method comprising: inputting first fluid information related to the state of the fluid at a first position in the tank, the operating conditions, and the substance information to the machine learning model, thereby providing the fluid information as output data of the machine learning model; and inputting first fluid information related to the state of the fluid at a first position in the tank, the operating conditions, the substance information, and information related to the additional substance to the machine learning model, thereby providing the substance information and the additional substance diffusion time as output data of the machine learning model.
12 . The method as claimed in claim 5 , wherein the substance information includes an amount, a physical quantity, a put-in position, and a put-in rate of an additional substance to be put into the tank at a predetermined put-in time.
13 . The method as claimed in claim 12 , further comprising: inputting a plurality of sets of input data to the machine learning model, thereby providing a plurality of sets of the fluid information as output data of the machine learning model, wherein the plurality of sets of input data are created by varying the put-in time at which the additional substance is put into the tank; calculating additional substance diffusion times, each being a time required for the additional substance to diffuse in the tank, for the plurality of sets of the fluid information; and determining a put-in time which minimizes the additional substance diffusion time based on the additional substance diffusion times for the plurality of sets of the fluid information.

Description

TECHNICAL FIELD The present invention relates to a method for creating a machine learning model for estimating a state of a fluid in a tank and a method for estimating a state of a fluid in a tank using a machine learning model. BACKGROUND ART Patent Document 1 discloses a cell culture apparatus comprising a culture tank, a stirring blade in the culture tank, a drive unit that allows the stirring blade to rotate, and a control unit that controls the drive unit. The cell culture apparatus performs fluid analysis by using variables that include a density of a substance contained in a culture fluid, a viscosity of the culture fluid, a shape of the culture tank, the shape of the stirring blade, a condition of a wall surface of the culture tank, and the number of rotations of the stirring blade, to thereby calculate a shear stress distribution within the culture tank, and then controls the drive device such that the shear stress distribution is within a predetermined range. PRIOR ART DOCUMENT(S) Patent Document(s) Patent Document 1: JP 2014-124139A SUMMARY OF THE INVENTION Task to be Accomplished by the Invention However, the prior art technology involves a problem that the fluid analysis requires a lot of time for numerical calculations, making it difficult to use calculation results in controlling the cell culture apparatus on a real-time basis, and in improving the culture conditions. Moreover, cells in a culture medium proliferate exponentially, causing the viscosity of the culture medium to increase rapidly in a short period of time. As a result, the long period of time used for fluid analysis tends to cause a problem that calculation results do not match a current state of the culture fluid. The present invention has been made in view of the problems of the prior art, and a primary object of the present invention is to provide a method for creating an estimation model (in particular, machine learning model) that allows for a quick and accurate estimation of a state of a fluid in a tank. Another object of the present invention is to provide a method for estimating a state of a fluid in a tank by using such an estimation model. Means to Accomplish the Task As a solution to the above-described tasks to be accomplished, a first aspect of the present invention provides a method for creating a machine learning model for estimating a state of a fluid in a tank, wherein the fluid contains a plurality of substances, the method comprising: creating a plurality of parameter sets, each set comprising tank information including at least a shape and size of the tank, operating conditions of the tank, and substance information including at least an amount, and one or more physical properties of each of the plurality of substances, wherein the plurality of parameter sets are created by varying the parameters of the operating conditions and the substance information; performing a numerical fluid dynamics analysis based on the plurality of parameter sets to provide a plurality of calculation results as fluid information, wherein the fluid information includes distributions of physical quantities related to the fluid and/or an amount of each of the plurality of substances; and creating the machine learning model by a machine learning operation which involves training the machine learning model with teaching data including the plurality of parameter sets and the corresponding plurality of calculation results. In this configuration, a machine learning model is created using calculation results of a numerical fluid dynamics analysis, which allows for an accurate estimation of a state of a fluid in a tank through the use of the machine learning model. Calculations with the use of the machine learning model require a shorter time than those with the numerical fluid dynamics analysis, which allows for a quick estimation of the state of the fluid in the tank. In addition, the tank information used in the numerical fluid dynamics analysis is the same as that for the actual tank, which allows for reduction of input data required for calculations with the use of the machine learning model. A second aspect of the present invention provides a method for creating a machine learning model for estimating a state of a fluid in a tank, wherein the fluid contains a plurality of substances, the method comprising: creating a plurality of parameter sets, each set comprising tank information including at least a shape and size of the tank, operating conditions of the tank, and substance information including at least an amount, and one or more physical properties of each of the plurality of substances, wherein the plurality of parameter sets are created by varying the parameters of the tank information, the operating conditions, and the substance information; performing a numerical fluid dynamics analysis based on the plurality of parameter sets to provide a plurality of calculation results as fluid information, wherein the fluid information incl