JP-7857225-B2 - Prediction method, prediction device

JP7857225B2JP 7857225 B2JP7857225 B2JP 7857225B2JP-7857225-B2

Inventors

野田敦裕
堀江一司
中谷香織

Assignees

株式会社日本触媒

Dates

Publication Date: 20260512
Application Date: 20211012
Priority Date: 20201013

Claims (9)

A method for predicting the physical properties of resin powder, The resin powder is either a water-absorbent resin powder or an intermediate product generated in the manufacturing process for producing the water-absorbent resin powder. A near-infrared measurement data acquisition step is to acquire near-infrared measurement data showing the near-infrared absorption spectrum of the resin powder, The prediction step includes inputting at least one of the near-infrared measurement data and one or more processing data generated based on the near-infrared measurement data into a prediction model to output prediction information related to the physical properties of the resin powder, The aforementioned prediction information is (1) The mass-average particle size (gel D50) of the water-containing gel which is the intermediate product, (2) The absorption ratio (CRC) of the resin powder under no pressure, (3) The absorption ratio (AAP) of the resin powder under pressure, (4) The saline flow induction property (SFC) of the resin powder, (5) Water absorption time (Vortex) of the resin powder, (6) The mass-average particle size (D50) of the resin powder, (7) The amount of monomers remaining in the resin powder, (8) Water absorption rate (FSR) of the resin powder, (9) Water absorption ratio (FSC) of the resin powder when suspended under no pressure, (10) Flow rate of the resin powder, (11) The bulk density of the resin powder, (12) Amount of water-soluble component of the resin powder (Ext), (13) The absorption ratio of the water-containing gel under no pressure (gel CRC), and (14) Amount of water-soluble components in the water-containing gel of the resin powder before drying (gel Ext), Includes at least one of the following: Prediction method.
The aforementioned predictive model is generated by machine learning using at least one of the following as training data: (1) a combination of near-infrared measurement data including near-infrared absorption spectra of multiple previously manufactured resin powders with known physical properties and physical property information of the final product associated with said near-infrared measurement data; and (2) a combination of near-infrared measurement data including near-infrared absorption spectra of multiple previously produced intermediate products with known physical properties generated in the manufacturing process for each manufactured resin powder and physical property information of the intermediate products associated with said near-infrared measurement data. The prediction method according to claim 1.
The aforementioned prediction model is generated using either linear regression or nonlinear regression. The prediction method according to claim 2.
The aforementioned prediction model was generated using either principal component regression or partial least squares regression. The prediction method according to claim 2 or 3.
This includes a preprocessing step for generating the aforementioned processing data, In the aforementioned preprocessing step, one or more of the following are performed: outlier removal, averaging, wavelength range selection, and differentiation. The prediction method according to any one of claims 1 to 4.
The manufacturing process for the resin powder includes a polymerization step and a drying step. The near-infrared absorption spectrum is measured before the polymerization step, between the polymerization step and the drying step, and after the drying step. Based on the prediction information output in the prediction step, one or more manufacturing devices used in the resin powder manufacturing process are controlled. The prediction method according to any one of claims 1 to 5.
A predictive device for predicting the physical properties of resin powder, The resin powder is either a water-absorbent resin powder or an intermediate product generated in the manufacturing process for producing the water-absorbent resin powder. The system comprises: a measurement data acquisition unit that acquires near-infrared measurement data showing the near-infrared absorption spectrum measured for the resin powder; and a prediction unit that inputs at least one of the near-infrared measurement data and one or more processing data generated based on the near-infrared measurement data into a prediction model and outputs prediction information related to the physical properties of the resin powder. The aforementioned prediction information is (1) The mass-average particle size (gel D50) of the water-containing gel which is the intermediate product, (2) The absorption ratio (CRC) of the resin powder under no pressure, (3) The absorption ratio (AAP) of the resin powder under pressure, (4) The saline flow induction property (SFC) of the resin powder, (5) Water absorption time (Vortex) of the resin powder, (6) The mass-average particle size (D50) of the resin powder, (7) The amount of monomers remaining in the resin powder, (8) Water absorption rate (FSR) of the resin powder, (9) Water absorption ratio (FSC) of the resin powder when suspended under no pressure, (10) Flow rate of the resin powder, (11) The bulk density of the resin powder, (12) Amount of water-soluble component of the resin powder (Ext), (13) The absorption ratio of the water-containing gel under no pressure (gel CRC), and (14) Amount of water-soluble components in the water-containing gel of the resin powder before drying (gel Ext), Includes at least one of the following: Prediction device.
A method for producing resin powder, comprising a polymerization step and a drying step, A method for producing resin powder, wherein the manufacturing conditions of one or more of the manufacturing steps of the resin powder are controlled based on prediction information obtained by the prediction method described in any one of claims 1 to 6.
A measurement method for measuring the near-infrared absorption spectrum of a resin powder used in the prediction method described in any one of claims 1 to 5, The steps include irradiating the resin powder with near-infrared light, The step includes calculating the near-infrared absorption spectrum of the resin powder from a measurement value obtained by measuring at least one of the reflected light and transmitted light from the resin powder, The resin powder is either a water-absorbent resin powder or an intermediate product generated in the manufacturing process for producing the water-absorbent resin powder. Measurement method.

Description

This disclosure relates to a prediction method and apparatus for predicting the physical properties of water-absorbent resin powder. Superabsorbent polymers (SAP) are resins that are both water-swellable and water-insoluble. SAP is often in powder (or granular) form. Known properties of superabsorbent polymers include water absorption ratio (CRC), water absorption ratio under load (AAP), water absorption rate, and SFC (saline flow induction). Because the required physical properties and their ranges differ depending on the application, specifically the type and composition of the sanitary material used, a wide variety of SAPs exhibiting diverse physical properties are required depending on the final product form. To confirm the physical properties of SAP powder, it is necessary to apply different measurement methods for each property measurement item, and each measurement requires a predetermined amount of time. Because it is difficult to grasp the physical property values of SAP at each stage of the manufacturing process in real time, there is a risk of producing products that do not meet specifications. In other words, there is a risk of a decrease in the yield during SAP manufacturing. Patent Document 1 discloses a method for predicting the physical properties of a water-absorbent resin using a specific Raman spectrum. International Publication No. 2020/109601 This is a block diagram showing an example of the configuration of a prediction system equipped with a prediction device according to Embodiment 1 of this disclosure.This is a functional block diagram showing an example of the main components of a prediction device.This is a flowchart showing the processing flow performed by the prediction device.This is a functional block diagram showing an example of the main components of a prediction device that generates a prediction model.This diagram shows the data structure of near-infrared measurement data.This is a diagram showing the data structure of physical property information.This flowchart shows the processing flow performed by a predictive machine that executes machine learning.Block diagram showing an example of a prediction system in Embodiment 2 of this disclosure.This table shows the correspondence between the MAC address acquired by the prediction device according to Embodiment 2 of this disclosure and the near-infrared spectrophotometer.This graph shows the correlation between measured and predicted values for Gel D50.This graph shows the correlation between measured and predicted CRC values.This graph shows the correlation between the measured and predicted values of AAP.This graph shows the correlation between the measured and predicted values of SFC.This graph shows the correlation between the measured and predicted values for D50.This graph shows the correlation between measured and predicted water content (solids content). [Embodiment 1] The embodiments of this disclosure will be described in detail below. (Configuration of prediction system 1000) First, the configuration of a prediction system 1000, which includes a prediction device 100 according to one embodiment of the present disclosure, will be described with reference to Figure 1. Figure 1 is a block diagram showing an example of the configuration of the prediction system 1000. The prediction system 1000 comprises a prediction device 100, a near-infrared spectrophotometer 3, and an external device 4. The prediction device 100 includes a CPU 1 and memory 2. As shown in Figure 1, the prediction device 100 may be connected to the near-infrared spectrophotometer 3 and the external device 4 in a communication-enabled manner. Communication between the prediction device 100 and the near-infrared spectrophotometer 3 may be via short-range wireless communication, wired connection, or communication via a network such as the Internet. Alternatively, communication between the prediction device 100 and the near-infrared spectrophotometer 3 may be directly connected via a connector such as a USB terminal. Communication between the prediction device 100 and the external device 4 is the same as communication between the prediction device 100 and the near-infrared spectrophotometer 3. Figure 1 shows a case where there is one near-infrared spectrophotometer 3 and one external device 4, both connected to the prediction device 100 in a communicative manner, but it is not limited to this. There may be one or more near-infrared spectrophotometers 3 and external devices 4, each connected to the prediction device 100 in a communicative manner. The prediction device 100 inputs at least one of the following into its prediction model: near-infrared measurement data showing the near-infrared absorption spectrum acquired from the near-infrared spectrophotometer 3, and one or more processing data generated based on the near-infrared measurement data. It then outputs prediction information related to the physical properties of the resin powder. In this specifi