Characterization system and characterization method

Abstract

This system provides a property evaluation system that appropriately evaluates and manages the properties of resins and resin products, while minimizing the time and burden required to build a system and learning model for evaluating properties such as viscosity, thermal properties, and molecular weight of resins and resin products. [Solution] A characterization system 3 predicts the viscosity, thermal properties, and molecular weight of a resin based on the fluorescence properties of the resin, comprising a computing device that executes a program and a storage device that stores the program, wherein the storage device stores a database of the fluorescence properties of each of a plurality of products mainly composed of a first resin, and measured values ​​of any of the viscosity, thermal properties, and molecular weight corresponding to the fluorescence properties, and the computing device receives the fluorescence properties of the product to be predicted, mainly composed of the first resin, as input, predicts any of the viscosity, thermal properties, and molecular weight of the product to be predicted based on the fluorescence properties of the product to be predicted and the database, and outputs the viscosity, thermal properties, or molecular weight of the product to be predicted.

Description

Technical Field

[0001] The present invention relates to a characteristic evaluation system for evaluating the characteristics of a resin material based on a fluorescence fingerprint, and a characteristic evaluation method.

Background Art

[0002] With the transition from a linear economy society to a circular economy society, the quality control of resin materials and resin products is also shifting from management only during manufacturing or use to management in the life cycle including recycling. The quality control of resin materials and resin products, specifically, for example, is the quality control of the molecular weight of resin materials, viscosity such as solution viscosity and intrinsic viscosity, phase change, glass transition temperature, crystallization temperature, melting temperature, etc. However, in the case of assuming recycling, in particular, it is the quality control of viscosity, thermal properties, and molecular weight. Note that viscosity is a characteristic that contributes to molding and processing, and thermal properties are characteristics that contribute not only to mechanical properties such as shape and appearance but also to optical properties. The polydispersity related to the molecular weight and its molecular weight distribution is a physical property index indicating the deterioration state and the regeneration state in recycling.

[0003] These characteristics are generally evaluated by destructive analysis, but in recent years, non-destructive analysis for characteristic evaluation is also becoming widespread. For example, in the abstract of Patent Document 1, it is described that "a measurement step of irradiating a resin composition in which TiO2 particles are dispersed in a base material mainly composed of silicone rubber with a laser and measuring a Raman spectrum, and a determination step of determining the concentration of the TiO2 particles in the resin composition based on the intensity of the fluorescence spectrum in the Raman spectrum are included, and a quality control method for a resin composition is provided." In paragraph 0073, it is described that "furthermore, the quality control method for a resin composition according to the above embodiment, the quality control method for a cable or a tube, the determination device and the inspection system used for the quality control method for a cable or a tube, etc. can also be applied to material development using materials informatics (MI) that analyzes data by utilizing machine learning and artificial intelligence (AI), etc." Thus, in this document, the quality of the resin composition is non-destructively analyzed based on the intensity of the fluorescence spectrum by utilizing artificial intelligence and the like. [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2023-044083 [Overview of the project] [Problems that the invention aims to solve]

[0005] Even if the type of resin is the same, differences in application and other factors can lead to variations in composition and manufacturing methods for each product, resulting in differences in the resin's viscosity and thermal properties. Furthermore, during recycling, the resin's structure changes from its original state due to environmental factors, time, and stress during use and storage. Therefore, even with the same type of resin, the resin's structure can be highly diverse.

[0006] Therefore, when measuring viscosity, thermal properties, or molecular weight using the informatics analysis characterization method described in Patent Document 1, the microstructure of resin materials or resin products can be diverse and unclear due to the addition of additives to the main component material through recycling, etc. This may necessitate multiple analyses and large amounts of training data to accommodate the diverse microstructures of resins. Consequently, implementing the characterization method described in Patent Document 1 presents a problem in that it requires an enormous amount of work time for data acquisition analysis and evaluation, as well as system construction and learning model construction for data analysis.

[0007] This invention has been made in view of the circumstances described above, and aims to provide a property evaluation system and a property evaluation method that can appropriately evaluate and manage the properties of resins and resin products, while reducing the time and burden required to construct a system and learning model for evaluating the viscosity, thermal properties, and molecular weight properties of resins and resin products. [Means for solving the problem]

[0008] To solve the above problems, the characterization system of the present invention comprises a computing device that executes a program and a storage device that stores the program, wherein the storage device stores a database of actual values ​​of the fluorescence properties of each of a plurality of products mainly composed of a first resin and the viscosity, thermal properties, or molecular weight corresponding to the fluorescence properties, and the computing device receives the fluorescence properties of a product to be predicted, mainly composed of the first resin, as input, predicts the viscosity, thermal properties, or molecular weight of the product to be predicted based on the fluorescence properties of the product to be predicted and the database, and outputs the viscosity, thermal properties, or molecular weight of the product to be predicted. [Effects of the Invention]

[0009] According to the characterization system and characterization method of the present invention, it is possible to appropriately evaluate and manage the properties of resin materials and resin products while reducing the time and burden of constructing a system and learning model for evaluating properties such as viscosity, thermal properties, and molecular weight of resins and resin products. [Brief explanation of the drawing]

[0010] [Figure 1A] Functional block diagram of the evaluation system in Example 1. [Figure 1B] Functional block diagram of the evaluation system for a modified example of Example 1. [Figure 2] A functional block diagram illustrating an example of the spectrofluorometer in Example 1. [Figure 3] An example of fluorescent fingerprint data for a resin material. [Figure 4A] Flowchart of the predictive model creation process using the evaluation system in Example 1. [Figure 4B] Flowchart of the evaluation process using the evaluation system of Example 1. [Figure 5] An example of a modeling database for Example 1. [Figure 6] An example of the results of the multiple regression analysis performed in Example 1. [Figure 7] An example of a predictive model database for Example 1. [Figure 8] An example of the analysis database of Example 1. [Figure 9] An example of the characteristic prediction database of Example 1. [Figure 10] Relationship between the measured value and the predicted value of the intrinsic viscosity in the first characteristic evaluation of Example 1. [Figure 11A] Relationship between the measured value and the predicted value of the glass transition temperature in the second characteristic evaluation of Example 1. [Figure 11B] Relationship between the measured value of the fluorescence intensity of the fluorescence wavelength at a predetermined excitation wavelength and the measured value of the glass transition temperature in the second characteristic evaluation of Example 1. [Figure 12] Relationship between the measured value and the predicted value of the crystallization peak temperature in the third characteristic evaluation of Example 1. [Figure 13] Relationship between the measured value and the predicted value of the melting point in the third characteristic evaluation of Example 1. [Figure 14] Relationship between the measured value and the predicted value of the glass transition temperature in the fourth characteristic evaluation of Example 1. [Figure 15] Graph showing the wavelengths of the measurement targets of the spectral intensity in the fifth characteristic evaluation of Example 1. [Figure 16] Relationship between the measured value and the predicted value of the intrinsic viscosity in the fifth characteristic evaluation of Example 1. [Figure 17] Graph showing the wavelengths of the measurement targets of the spectral intensity in the sixth characteristic evaluation of Example 1. [Figure 18] Relationship between the measured value and the predicted value of the intrinsic viscosity in the sixth characteristic evaluation of Example 1. [Figure 19A] Relationship between the measured value and the predicted value of the weight average molecular weight in the seventh characteristic evaluation of Example 1. [Figure 19B] Relationship between the measured value and the predicted value of the number average molecular weight in the seventh characteristic evaluation of Example 1. [Figure 19C] Relationship between the measured value and the predicted value of the polydispersity in the seventh characteristic evaluation of Example 1. [Figure 20] An example of the display video on the display unit of Example 2. [Figure 21] An example of the display video on the display unit of Example 2. [Figure 22] An example of the display video on the display unit of Example 2. [Figure 23A]Schematic diagram of the fluorescence characteristic measurement system in Example 2. [Figure 23B] Schematic diagram of the fluorescence characteristic measurement system in Example 2. [Figure 23C] Schematic diagram of the fluorescence characteristic measurement system in Example 2. [Figure 23D] Schematic diagram of the fluorescence characteristic measurement system in Example 2. [Figure 24] A perspective view showing an example of the shape of the second resin sample from Example 3. [Figure 25A] Schematic diagram of the fluorescence characteristic measurement system in Example 3. [Figure 25B] Schematic diagram of the fluorescence characteristic measurement system in Example 3. [Figure 26] Perspective view of the evaluation system in Example 4. [Figure 27] Perspective view of the evaluation system in Example 5. [Figure 28] Relationship between measured viscosity (IV) and measured weight-average molecular weight (Mw) and number-average molecular weight (Mn) in Example 1. [Modes for carrying out the invention]

[0011] In recent years, businesses have been increasingly required to engage in resource recycling, such as resin materials and resin products. Resin materials are generally easy to process and mold, are lightweight, and can be given functions through compounding, making them suitable for a wide variety of products. Typically, resin products with the desired shape, optical properties (such as transparency), and mechanical properties (such as strength) are molded by heating and melting the resin material, followed by cooling and solidifying it.

[0012] Here, we will describe the resin used as a raw material in this embodiment. The resin in this embodiment is a thermoplastic resin that absorbs ultraviolet and infrared light and emits light (fluorescence) upon excitation, making it suitable for recycling. The fluorescence emission of the resin is affected by (1) the molecular structure of the resin, (2) additives and their residues during the synthesis process, (3) environmental factors such as light, heat, water, and gas atmosphere during manufacturing, use, and storage, and (4) changes in the structural composition (e.g., molecular weight, functional groups, conjugation, oxidation, decomposition, polymerization, addition, and content) caused by factors from manufacturing, use, and recycling, such as acidic and basic cleaning agents, organic solvents, and volatile organic compounds (VOCs). Therefore, by observing the fluorescence emission of the resin, it is possible to infer the molecular structure of the resin.

[0013] In this embodiment, the fluorescent luminescent resin is, for example, polyethylene (PE), polypropylene (PP), polystyrene (PS), acrylonitrile-butadiene-styrene copolymer (ABS), polyvinyl alcohol (PVA), polyvinyl chloride (PVC), polyvinylidene chloride (PVDC), polyacrylonitrile (PAN), acrylic resin (PMMA), phenolic resin (PF), epoxy resin (EP), polycarbonate (PC), polyethylene terephthalate (PET), polybutylene terephthalate (PBT), polyethylene naphthalate (PEN), unsaturated polyester These include telamine resin (UP), polyamide (PA), polyacetal (POM), polyphenylene sulfide (PPS), polyether ether ketone (PEEK), silicone resin (SI), melamine resin (MF), urea resin (UF), polyurethane resin (PUR), ethylene vinyl acetate copolymer (EVA), polylactic acid (PLA), polybutylene succinate (PBS), polybutylene adipate terephthalate resin (PBAT), resinous starch, polyhydroxybutyrate (PHB), and 3-hydroxybutyrate-co-3-hydroxyhexanoate polymer (PHBH). Furthermore, the fluorescent luminescent resin of this embodiment may be composed of a modified resin of the above resin, a mixture of the above resin, or a compatible material such as polycarbonate and acrylonitrile-butadiene-styrene copolymer synthetic resin, with functional groups such as acid anhydride groups (maleic acid anhydride, itaconic acid anhydride, etc.), ester groups, epoxy groups, acrylates, vinyl groups (styrene, butadiene, etc.), benzene rings, azo groups, amino groups, amide groups, carboxyl groups, carbonyl groups, hydroxyl groups, etc., which affect the absorption and excitation of ultraviolet light in the wavelength range of 200-800 nm to visible light, as well as the intensity and wavelength shift of fluorescence emission due to electron donation and electron withdrawal, being copolymerized or attached to the terminals or side chains of the basic structure.

[0014] In the embodiments described later, it is preferable to apply the above-mentioned general-purpose resins, such as polyethylene, polyolefins like polypropylene, polyesters like polyethylene terephthalate and polycarbonate, polystyrene, acrylonitrile-butadiene-styrene copolymer synthetic resins, polyvinyl chloride, polyamide (nylon), polyacrylonitrile, acrylic resins, polyurethane resins, and ethylene vinyl acetate copolymers, to which a wide variety of types are available depending on performance and application. In particular, it is preferable to apply the above-mentioned general-purpose resins, such as polyethylene, polypropylene, polyester terephthalate resins, polybutylene succinate, polybutylene adipate terephthalate resins, resinous starch, polyhydroxybutyrate, and 3-hydroxybutyrate-co-3-hydroxyhexanoate polymers, to which a wide variety of types are available depending on performance and application.

[0015] In the field of materials technology, such as resins, materials informatics, which utilizes data on material properties and characteristics along with information science techniques such as machine learning, is beginning to gain popularity for material design, development, and quality control. When materials informatics (hereinafter also referred to as MI) is used in a data-driven manner, which involves large amounts of data, a large amount of data and a systematically compiled database (hereinafter also referred to as DB) are required.

[0016] To address the lifecycle recycling of resin materials and products, it is usually necessary to collect a wide variety of data regarding their structural integrity, provenance, and processing during manufacturing, use, and storage. It is known that variations in physical properties and characteristics occur throughout the lifecycle recycling process.

[0017] Possible methods for constructing a database include using publicly available databases that compile characteristic and physical property data, or collecting data through literature. However, while publicly available databases and literature exist for virgin resin materials, there are almost no similar publicly available databases or literature that include the recycling process.

[0018] Another method for building a database is to actually analyze and collect characteristic and physical property data. However, as mentioned above, the characteristics and physical properties of resin materials and resin products vary depending on the type, composition, and origin. Furthermore, building a database based on measurements requires tasks such as sample preparation and measurement. In addition, there is a need for efficient collection of analytical data and expansion by increasing the types of analysis to accommodate various applications and purposes.

[0019] Therefore, the embodiments described later apply fluorescence property measurement, a non-destructive and simple analysis method, to measure fluorescence fingerprints, which contain a large amount of information useful in analysis, and realize an evaluation system that can obtain the viscosity, thermal properties, and molecular weight of resin materials and resin products through informatics analysis. [Examples]

[0020] First, the evaluation system 100 according to Example 1 of the present invention will be explained using Figures 1A to 19.

[0021] Figure 1A is a block diagram of the evaluation system 100 according to this embodiment. As shown in the figure, the evaluation system 100 is a system comprising an input device 1, an output device 2, a characteristic evaluation system 3, a management server 4, and a network 5 that relays data communication between each device. The details of each device will be described in order below.

[0022] (Input device 1) The input device 1 includes a fluorescence properties measurement unit 11, a viscosity measurement unit 12, a thermal properties measurement unit 13, a molecular weight measurement unit 14, a reading input unit 15, a communication unit 16, and a control unit 17 that controls these units.

[0023] ((Fluorescence characteristic measurement unit 11)) The fluorescence characteristic measurement unit 11 is a functional unit that measures the spectral intensity due to the fluorescence response of resin materials and resin products to excitation light, and acquires fluorescence characteristic data such as the spectral intensity of fluorescence at each excitation light wavelength, and fluorescence fingerprints that represent it in three dimensions. For example, it is a spectrofluorometer.

[0024] Here, an example of the configuration of the fluorescence characteristic measurement unit 11 (spectrofluorometer) will be explained using Figure 2. The spectrofluorometer illustrated here consists of a photometer unit 110, a data processing unit 111, and an interface unit 112.

[0025] In the photometer unit 110, continuous light from the light source a1 is spectrally separated as excitation light by the excitation-side spectrometer a2, and then irradiated onto the measurement sample a7 placed in the sample placement unit a6 via the beam splitter a3 and excitation-side filter a4. At this time, the amount of some of the excitation light separated by the beam splitter a3 is measured by the monitor detector a8, and the fluctuations of the light source a1 are corrected. The fluorescence emitted from the measurement sample a7 passes through the fluorescence-side filter a9, is spectrally separated into monochromatic light by the fluorescence-side spectrometer a10, and the monochromatic light is detected by the detector a11. The output of the detector a11 is then converted into a digital signal by the A / D converter b1, which is taken in as signal intensity by the processing unit b2, and the measurement result is output to the monitor c1. In the figure, c2 is the user-operated interface, and M is a motor controlled by the processing unit b2 when changing the characteristics of the excitation-side spectrometer a2, excitation-side filter a4, fluorescence-side filter a9, and fluorescence-side spectrometer a10.

[0026] The method for obtaining a fluorescence fingerprint (3D fluorescence spectrum) is as follows: First, the fluorescence spectrum is measured with the excitation wavelength fixed. Once the fluorescence spectrum scanning is complete, the fluorescence wavelength is returned to the starting wavelength, the excitation wavelength is driven by a predetermined wavelength interval, and the fluorescence spectrum at the next excitation wavelength is measured. Next, the obtained fluorescence fingerprint is stored in three dimensions: excitation wavelength, fluorescence wavelength, and fluorescence intensity. This process is repeated until the excitation wavelength reaches the final wavelength, thereby obtaining a 3D fluorescence spectrum. Furthermore, the obtained fluorescence fingerprint is plotted as a contour map or bird's-eye view by connecting lines at the same fluorescence intensities. The excitation and fluorescence wavelengths that form peaks in the contour lines represent the optimal excitation wavelength and characteristic fluorescence wavelength of the sample, allowing for a clear visual representation of the fluorescence characteristics of the excitation and fluorescence wavelengths within the measurement range of the sample. A significant advantage of the fluorescence fingerprint is that it allows for obtaining a wealth of information, such as the number of fluorescent components in the sample and their identification. In this way, the spectral intensity distribution in a predetermined wavelength range, which represents the fluorescence characteristics, is obtained.

[0027] When measuring excitation spectra, fluorescence spectra, and fluorescence fingerprints in the fluorescence characteristic measurement unit 11 (spectrofluorometer), the generation of higher-order light, other than the desired fluorescence, becomes a problem. In the case of excitation spectra, a cut filter corresponding to a fixed fluorescence wavelength is selected and inserted between the measurement sample and the fluorescence spectrometer to obtain the excitation spectrum, thereby suppressing higher-order light. In the case of fluorescence spectra, a cut filter corresponding to a fixed excitation wavelength is selected and inserted between the measurement sample and the fluorescence spectrometer to obtain the fluorescence spectrum, thereby suppressing higher-order light. In this embodiment, a single cut filter is selected and inserted to suppress higher-order light in both excitation spectra and fluorescence spectra during measurement.

[0028] Figure 3 shows an example of fluorescence fingerprint data for a resin material. This Figure 3 shows fluorescence intensity as the fluorescence fingerprint, with the excitation light wavelength (vertical axis) in the range of 250 to 600 nm and the fluorescence wavelength (horizontal axis) in the range of 200 to 700 nm. For example, it shows that when a resin material is irradiated with excitation light of approximately 350 nm, strong fluorescence is generated in the range of 400 to 500 nm.

[0029] ((Viscosity measurement part 12)) The viscosity measurement unit 12 performs processing and solution preparation, such as creating a solution for measuring resin materials. For example, if the resin material is PET, a mixed solvent prepared by mixing phenol and 1,1,2,2,-tetrachloroethane in a specified mass ratio (e.g., 50:50) is used as the measurement solvent, in accordance with JIS K 7367-5. For measurement, an Ubbelohde viscometer is used to measure the intrinsic viscosity under temperature control (e.g., maintained at 25°C), and then the viscosity measurement data is output.

[0030] ((Thermal properties measurement unit 13)) The thermal properties measurement unit 13 measures the glass transition temperature, crystallization and melting peak temperatures of the resin material through thermal analysis and outputs the thermal properties measurement data. The thermal analysis measurement includes differential scanning calorimetry, differential thermal analysis, and functions that also include thermogravimetric analysis, as well as a function for evaluating dynamic viscoelasticity.

[0031] ((Molecular weight measurement section 14) The molecular weight measurement unit 14 performs processing and solution preparation for the production of measurement solutions for resin materials. During measurement, it measures the molecular weight distribution, weight-average molecular weight, number-average molecular weight, and polydispersity, and outputs the molecular weight measurement data. The molecular weight measurement uses a high-performance liquid chromatography (HPLC) and includes an evaluation function for size exclusion chromatography (SEC) measurements.

[0032] ((Reading input section 15)) The reading input unit 15 optically reads the RFID tag via wireless communication the shape of the resin material or resin product, location information such as the installation position and the position for evaluating the fluorescence properties, the assigned product name, unique identification information (hereinafter also referred to as ID), and the 2D or 3D code storing that information. If the product name and unique identification information are not assigned, or if additional or updated supplementary information is to be entered directly by the user via an input terminal, the information is acquired by the evaluation system 100. For optical reading of the shape, location information, product name, unique identification information, and 2D or 3D codes, any laser scanning using a CMOS image sensor, CCD camera, or galvanometer mirror may be used. In this embodiment, data measured from the fluorescence properties measurement unit 11, viscosity measurement unit 12, thermal properties measurement unit 13, and molecular weight measurement unit 14 was received from the input device, but this measurement data may also be received as input from the reading input unit 15. Furthermore, items to be predicted regarding the resin being measured may also be accepted. The items to be predicted are thermal properties, viscosity, or molecular weight.

[0033] ((Communications Section 16)) The communication unit 16 is a functional unit that relays communication between the fluorescence property measurement unit 11, viscosity measurement unit 12, thermal property measurement unit 13, molecular weight measurement unit, or reading input unit 15 and the network 5.

[0034] ((Control Unit 17)) The control unit 17 is a functional unit that controls various parts within the device. Specifically, the control unit 17 is realized by a processing unit such as a CPU executing a predetermined program.

[0035] (Output device 2) As shown in Figure 1A, the output device 2 includes a display unit 21, an output unit 22, a marking unit 23, a communication unit 24, and a control unit 25.

[0036] The display unit 21 is a functional unit that displays evaluation data obtained from measurement and analysis of resin materials and resin products, as well as supplementary information such as their history. Specifically, it may be a liquid crystal display, a touch panel, etc. These may also be output via a speaker or printer.

[0037] The output unit 22 is a functional unit that outputs the same information displayed on the display unit 21 to the outside.

[0038] The marking unit 23 is a functional unit that applies product name, unique identification information, and management-related supplementary information to resin materials or their packaging materials by printing, engraving, etc., as characters, 2D codes, 3D codes, etc., and also issues and attaches RFID tags, and if an RFID tag is already attached, it applies and updates each piece of information via wireless communication. For marking in the marking unit 23, any of the following can be used: an inkjet printer, thermal printer, laser marker, engraver, stamper, pen, or the application of labels or stickers printed using these methods.

[0039] The communication unit 24 is a functional unit that relays communication between the display unit 21, the output unit 22, or the marking unit 23 and the network 5.

[0040] The control unit 25 is a functional unit that controls various parts within the device. Specifically, the control unit 25 is realized by an arithmetic unit such as a CPU executing a predetermined program.

[0041] (Characteristic evaluation system 3) As shown in Figure 1A, the characteristic evaluation system 3 comprises a storage device 31, an arithmetic unit 32, a communication unit 33, and a control unit 34.

[0042] The storage device 31 stores various databases (hereinafter also referred to as DBs) used for characterizing resin materials. The databases stored here include a prediction model database 31a used for analyzing viscosity, temperature characteristics, and molecular weight; a modeling database 31b used for creating prediction models; an analysis database 31c related to measurement data acquired by the input device 1; a characteristic prediction database 31d related to analysis data of viscosity, temperature characteristics, and molecular weight; and a unique identification information database 31e obtained from a management server or similar source, containing supplementary information such as the history of the material being evaluated, including its manufacture and use, unique identification number, and the status of analysis processing and measurement during the evaluation.

[0043] The prediction model database 31a may store prediction models generated for each item to be predicted. Alternatively, it may store prediction models for each product of the data used to generate the prediction model.

[0044] The computing unit 32 includes an analysis condition processor 32a for selecting analysis conditions for measurement in the input device 1, a scaling processor 32b for performing correction processing of measurement data, a predictor variable processor 32c for selecting and correcting variables in predictor model creation, a predictor model creator 32d for creating predictor models from fluorescence characteristic measurement data using regression analysis of viscosity and thermal properties, a predictor calculator 32e for analyzing viscosity and thermal properties from fluorescence characteristic measurement data using the predictor model, and a data integrator 32f for selecting and integrating various data from the storage device 31 for analysis processing, data output, and display. In this embodiment, the multivariate analysis software "3D SpectAlyZe" from Dynacom Co., Ltd. is used as the scaling processor 32b and the predictor model creator 32d.

[0045] The communication unit 33 is a functional unit that relays communication between the storage device 31 or the arithmetic unit 32 and the network 5.

[0046] The control unit 34 is a functional unit that controls various parts within the device. Specifically, the control unit 34 is realized by an arithmetic unit such as a CPU executing a predetermined program.

[0047] (Management Server 4) The management server 4 collects the various information mentioned above from each device via the communication units 16, 24, and 33 of each device and the network 5, and directs and manages the execution of tasks related to the issuance and assignment of unique identification information for management purposes, production, measurement, storage, transport, disposal, and secondary use of the resin materials and their products being evaluated, as well as monitoring of environmental conditions and compliance with laws and regulations, and input / output and processing such as collection, reporting, provision, and disclosure of various data.

[0048] (Modified version of Example 1) In the evaluation system 100 in Figure 1A, the input device 1, output device 2, and characteristic evaluation system 3 are separate devices. However, a configuration like that shown in Figure 1B is also possible, where the functions corresponding to input device 1 and output device 2 in Figure 1A are incorporated into the characteristic evaluation system 3. As is evident from a comparison of Figures 1A and 1B, the input section 35 in Figure 1B corresponds to the input device 1 in Figure 1A, and the output section 36 in Figure 1B corresponds to the output device 2 in Figure 1A.

[0049] (Operation of the evaluation system in this embodiment) Figures 4A and 4B are flowcharts showing the operation of the evaluation system 100.

[0050] ((Flowchart for creating a predictive model)) First, the process of creating a predictive model using the evaluation system 100 in this embodiment will be explained using the flowchart in Figure 4A.

[0051] In step S1, the reading input unit 15 of the input device 1 acquires information on the material and product of sample resins (hereinafter referred to as "first resin R1") used for model creation, which have different histories such as manufacturing, use, and storage, as well as fluorescence properties, viscosity, and thermal properties. This information includes the ID, product name, form, and location of the first resin R1.

[0052] In step S2, the fluorescence property measurement unit 11, viscosity measurement unit 12, and thermal property measurement unit 13 of the input device 1 measure fluorescence properties, viscosity, thermal properties, and molecular weight according to predetermined measurement conditions formulated by the analysis condition processor 32a of the calculation device 32, based on supplementary information about the first resin R1 in the management server 4 or the unique identification information database 31e, to acquire model creation data, and store the acquired model creation data in the modeling database 31b.

[0053] In this embodiment, non-destructive analysis, specifically fluorescence properties, is measured first, followed by destructive analysis of viscosity, thermal properties, and molecular weight using a sample obtained from the same first resin R1. During the measurement, the form and location of the first resin R1 for model creation (for molded bodies such as beverage bottles, containers, and casings, this includes whether the surface is exposed to light and ambient light, etc.), resin type, manufacturing information, legal and regulatory information such as recycling and environmental considerations, supplementary information such as a unique identification ID, and measurement information regarding the equipment, environment, and operator of the measurement are also acquired and stored in the modeling database 31b. Furthermore, information indicating that it has been stored in the modeling database 31b may be added to the first resin R1 and its packaging material by the marking unit 23, or by attaching an RFID tag, or if an RFID tag is already attached, the information may be added and updated via wireless communication. Additionally, from a security standpoint, such as limiting disclosure and preventing tampering, the public key may be deployed to a limited number of stakeholders, and encrypted information may be added.

[0054] Figure 5 shows an example of the modeling database 31b. In this example, each information item obtained in step S2 is stored in a tabular format.

[0055] In step S3, the scaling processor 32b performs scaling operations such as standardization, normalization, and smoothing on the model creation data stored in the modeling database 31b.

[0056] In step S4, the predictive model generator 32d creates a predictive model using multiple model creation data after scaling, performing regression analysis on fluorescence properties, viscosity, temperature properties, and molecular weight. Methods for creating predictive models using regression analysis include simple regression (calibration curve) on fluorescence intensity, scattering intensity, and absorption intensity at excitation wavelengths specified for each sample, or multiple regression, ridge regression, Lasso regression, PLS regression, elastic net, support vector regression, decision tree, random forest, and gradient boosting decision tree based on spectral intensity data such as fluorescence, scattering, and absorption at multiple excitation wavelengths. In this embodiment, multiple regression, Lasso regression, and PLS regression were used for fluorescence fingerprints, which have a large amount of data on fluorescence properties. Figure 6 shows an example of the results of the multiple regression analysis performed in this step.

[0057] In step S5, the prediction model generator 32d stores the prediction model created in step S4 in the prediction model database 31a. Figure 7 shows an example of the prediction model database 31a in this embodiment. As illustrated here, the prediction model database 31a stores information such as the ID of the created prediction model, the creation date, the creator, the type of first resin R1 and the sample, the form and location at the time of measurement, the type of target characteristic of the prediction model and its evaluation method, the characteristic range, the excitation of the fluorescence characteristic, the fluorescence wavelength range, the wavelength interval, the type of regression model and the number of data records used to create the prediction model, accuracy information such as the coefficient of determination of the prediction model, and the data file of the prediction model.

[0058] ((Evaluation process flowchart)) Next, using the flowchart in Figure 4B, we will explain the process of evaluating the material of the resin to be evaluated (hereinafter referred to as "second resin R2") and the viscosity and thermal properties of the molded product based on the measurement data of the fluorescence properties.

[0059] In step S11, first, the reading input unit 15 of the input device 1 acquires information regarding the product name or ID, form, and location assigned to the second resin R2. Next, the analysis condition processor 32a of the characterization system 3 queries the management server 4 or the unique identification information database 31e to create information on measurement conditions such as the specified wavelength range, light intensity, measurement site, and environmental conditions such as temperature.

[0060] In step S12, the fluorescence property measurement unit 11 of the input device 1 measures the fluorescence properties of the second resin R2 based on the measurement conditions created in step S11 and acquires the measurement data.

[0061] In step S13, the data integrator 32f of the characterization system 3 integrates the fluorescence characteristic data measured in step S12 with supplementary information about the second resin R2 obtained from the management server 4 and the unique identification information database 31e, based on the ID, including the actual measurement date, the person who performed the measurement, the measurement device, the measurement wavelength, the wavelength interval, and the data file name of the device output, and stores it in the analysis database 31c. Figure 8 shows an example of the analysis database 31c. In this example, each information item from step S13 is stored in a tabular format.

[0062] In step S14, the scaling processor 32b of the characterization system 3 refers to the information on the type of resin material, wavelength range, and measurement site stored in step S13, and selects a prediction model suitable for evaluating the viscosity, temperature characteristics, and molecular weight of the second resin R2 from the prediction model database 31a. Here, a prediction model generated based on the same resin type as the main component of the second resin R2 may be selected. Prediction accuracy can be improved by using a prediction model of the same resin type as the main component of the second resin R2. Then, each measurement data of the second resin R2 is scaled according to the scaling processing method of the selected prediction model.

[0063] In step S15, the predictive calculator 32e of the characterization system 3 calculates predicted values ​​for viscosity, temperature characteristics, and molecular weight from the measured fluorescence characteristics data of the second resin R2 by performing regression analysis using the predictive model selected in step S14.

[0064] In step S16, the data integrator 32f stores the predicted values ​​of viscosity, temperature characteristics, and molecular weight calculated in step S15, along with the information of the prediction model used, into the characteristic prediction database 31d. Figure 9 shows an example of the characteristic prediction database 31d. In this example, each information item from step S16 is stored in a tabular format.

[0065] In step S17, the data integrator 32f converts the viscosity, temperature characteristics, and molecular weight prediction data stored in the characteristic prediction database 31d into a format suitable for secondary use, such as reports like material inspection certificates, dashboard displays for manufacturing equipment and process control, and then integrates it with related data from each database. The communication unit 33 then outputs the information regarding the second resin R2 stored in the characteristic prediction database 31d to the output device 2 and the management server 4.

[0066] Furthermore, information indicating that the fluorescence properties of the second resin R2 have been stored in the analysis database 31c, or information indicating that the data has been stored in the property prediction database 31d, may be attached to the second resin R2 and its packaging material using a marking device, or an RFID tag containing information selected from the information about the second resin R2 stored in the property prediction DB may be attached, or if an RFID tag is already attached, the information may be attached and updated via wireless communication. This allows the evaluation status and evaluation information to be shared with stakeholders related to the second resin R2. From a security standpoint, such as limiting access and preventing tampering, the public key may be deployed and encrypted information attached to limited stakeholders.

[0067] Furthermore, each database can be arranged not only in the tabular format shown, but also as a graph, where each task, from the production and evaluation of resin materials to related disposal and reuse, is represented as a node according to its flow, and the nodes are connected by edges, with information stored in the nodes and edges. In addition, the ID of each database is also stored in a unique identification information database for management purposes such as querying, updating, and modifying the data in each database.

[0068] The following describes a specific example of the characteristic evaluation process using the flowchart in Figure 4B.

[0069] (First characteristic evaluation) Figure 10 is a graph showing the results of the first characteristic evaluation in this embodiment, illustrating the relationship between the measured intrinsic viscosity and the predicted value obtained from the flow in Figure 4B when the second resin R2 is PET.

[0070] In this example, 80 types of secondary resin products, such as beverage bottles, packaging materials, and film / sheet product base materials, which have various structural properties, were exposed to light including ultraviolet, visible, and infrared rays, similar to sunlight, using a carbon arc lamp, and placed in a high-temperature (≧60°C) and high-humidity (≧85%RH) environment.

[0071] For these 80 products, fluorescence fingerprints were measured using the fluorescence characteristic measurement unit 11 (spectrofluorometer). Following Figure 4B, the excitation wavelength was 250-600 nm, and the wavelength range of spectral intensity due to fluorescence, scattering, and absorption was 250-700 nm. For each product, scaling was performed by standardization using the spectral intensity of the fluorescence fingerprint. Figure 10 shows the difference between the intrinsic viscosity calculated using a prediction model based on Lasso regression and the measured viscosity.

[0072] In this characterization evaluation, the fluorescence properties of the exposed surface of the second resin R2 were measured. The measured intrinsic viscosity ranged from 0.46 to 0.99 dL / g, and the solid line in the figure indicates that the calculated predicted value and the measured value match. Regarding the accuracy of the prediction, it is generally said that there is a correlation if the coefficient of determination is 0.49 or higher, and furthermore, if it is 0.6 or higher, it is considered an effective prediction, and if it is 0.8 or higher, it is considered that the prediction is highly accurate. The coefficient of determination for the prediction in this characterization evaluation was 0.88, indicating that highly accurate predicted values ​​were obtained.

[0073] (Second characteristic evaluation) Figure 11A is a graph showing the results of the second characteristic evaluation in this embodiment, illustrating the relationship between the measured glass transition temperature and the predicted value obtained using the flow chart in Figure 4B when the second resin R2 is PET.

[0074] In this example, 144 types of secondary resin products, such as beverage bottles, packaging materials for containers and packaging, and film / sheet product substrates, were exposed to light including ultraviolet, visible, and infrared rays, similar to sunlight, using carbon arc lamps and xenon lamps, and placed in a high-temperature (60-150°C) and high-humidity (85-90%RH) environment.

[0075] For these 144 products, fluorescence fingerprints were measured using the fluorescence characteristic measurement unit 11 (spectrofluorometer). Following Figure 4B, the excitation wavelength was 250-550 nm, and the wavelength range of spectral intensity due to fluorescence, scattering, and absorption was 250-700 nm. For each product, scaling was performed by standardization using the spectral intensity of the fluorescence fingerprint. Figure 11A shows the difference between the glass transition temperature calculated using a Lasso regression prediction model and the measured temperature.

[0076] In this characterization evaluation, the fluorescence properties of the exposed surface of the second resin R2 were also measured. The measured range of the glass transition temperature was 71.4 to 79.0°C, and the coefficient of determination in the prediction was 0.85. This indicates that highly accurate prediction values ​​were obtained through this evaluation.

[0077] Figure 11B is a graph showing the relationship between the fluorescence intensity at a specified excitation wavelength in PET and the glass transition temperature of PET. As can be seen in Figure 11B, there is a correlation between fluorescence intensity and glass transition temperature.

[0078] (Third characteristic evaluation) Figure 12 is a graph showing the results of the third property evaluation in this embodiment, illustrating the relationship between the measured crystallization peak temperature and the predicted value obtained using the flow chart in Figure 4B when the second resin R2 is PET. Figure 13 is a graph showing another result of the third property evaluation, illustrating the relationship between the measured melting point and the predicted value obtained using the flow chart in Figure 4B when the second resin R2 is PET.

[0079] In this characterization evaluation, the same 144 products as in the second characterization evaluation were used, but the samples were prepared as plate-like flakes cut to a size of 5 cm or less, and then the characterization was performed. As shown in Figure 4B, measurement data of the fluorescence fingerprint of the samples was obtained for excitation wavelengths of 250 to 600 nm and spectral intensity wavelengths of 250 to 800 nm for fluorescence and scattering / absorption. For each sample, scaling was performed by standardization using the spectral intensity of the fluorescence fingerprint. Figure 12 shows the difference between the crystallization peak temperature calculated using a predictive model based on PLS regression and the measured temperature, and Figure 13 shows the difference between the melting point temperature and the measured temperature.

[0080] The measured temperature range for the crystallization peak temperature was 129.8–158.3°C, and the coefficient of determination for prediction was 0.83. The measured temperature range for the melting point temperature was 245.1–254.6°C, and the coefficient of determination for prediction was 0.84. Using the same fluorescence fingerprint measurement data, we were able to simultaneously obtain predicted values ​​for multiple temperature characteristics with a coefficient of determination > 0.8.

[0081] (Fourth characteristic evaluation) Figure 14 is a graph showing the results of the fourth characteristic evaluation in this embodiment, illustrating the relationship between the measured glass transition temperature and the predicted value obtained using the flow chart in Figure 4B when the second resin R2 is PC, a polyester resin similar to PET.

[0082] In this example, the second resin R2 is in the form of granular pellets. During fluorescence property measurements in this example, the measurement system is covered to block or reduce ambient light, such as sunlight or artificial lighting, which modulates intensity and wavelength, thereby minimizing the influence of external light. Furthermore, the pellets are filled into a container that is transparent only in the wavelength range to be measured, so that the property evaluation is performed in an environment where only the excitation light, fluorescence, and scattered light necessary for evaluation are observed. Suitable container materials in this example include quartz, alkali-free glass, and borosilicate glass, and in this property evaluation, a quartz glass container is used in particular. The irradiation surface of the container may be curved or flat.

[0083] Figure 14 plots the difference between the glass transition temperature calculated using a Lasso regression prediction model and the measured temperature, following scaling by standardization using the spectral intensity of the fluorescence fingerprint for each product, with excitation wavelengths ranging from 250 to 600 nm and spectral intensity wavelengths for fluorescence, scattering, and absorption ranging from 250 to 800 nm, as shown in Figure 4B. The measured temperature range for the crystallization peak temperature was 137 to 150°C, and the coefficient of determination in the prediction was 0.9. This indicates that highly accurate predicted values ​​were obtained through this evaluation.

[0084] (Evaluation of the fifth characteristic) Figure 15 is a graph showing the wavelengths of the spectral intensity measured in the fifth characteristic evaluation of this embodiment. Figure 16 is a graph showing the results of the fifth characteristic evaluation, illustrating the relationship between the measured intrinsic viscosity and the predicted value obtained using the flow chart in Figure 4B when the second resin R2 is PET.

[0085] In this example, for evaluations of the same type as the first characteristic evaluation, a predictive model using Lasso regression, created for film and sheet products, is calculated using the spectral intensity of the second resin R2 at the wavelengths shown in Figure 15. In the figure, dark plots represent positive correlations, and light plots represent negative correlations. The wavelengths were selected as the wavelengths contributing to the Lasso regression in the creation of the predictive model. Note that wavelengths may also be selected using principal component analysis (PCA), filtering, or wrapping methods.

[0086] In this example, 34 types of secondary resin products were classified into categories such as packaging materials for beverage bottles and containers, and film / sheet product substrates. These were then exposed to light including ultraviolet, visible, and infrared rays, similar to sunlight, using carbon arc lamps, and placed in a high-temperature (≧60°C) and high-humidity (≧85%RH) environment.

[0087] For these 34 products, fluorescence characteristic data was acquired and scaled according to the flow chart in Figure 4B for excitation wavelengths of 290-320, 380, and 420 nm, which are effective fluorescence fingerprint wavelengths for prediction, and for spectral intensity at specific wavelengths of 400-450 and 480-550 nm. In other words, fluorescence characteristic data at wavelengths that have a low contribution to prediction and may become noise were not used in the prediction. In this example, instead of a spectrofluorometer, separate devices were used for the excitation light source and the measurement of spectral intensity. A deuterium lamp was used as the excitation light source, and a spectrometer using a silicon photodiode array with sensitivity to each wavelength was used for measuring spectral intensity. A silicon photodiode array spectrometer does not require the driving of each element as is done when measuring spectral intensity using conventional prisms or diffraction gratings, and spectral intensity can be measured in real time. Note that the excitation light source may be a combination of a high-pressure mercury lamp, xenon lamp, metahalal light lamp, or LED element, as well as a tungsten lamp or semiconductor laser.

[0088] Figure 16 plots the difference between the intrinsic viscosity calculated using a Lasso regression prediction model created for film and sheet products and the measured viscosity, following the scaling process by standardization using the spectral intensity of the fluorescence fingerprint for each product, with excitation wavelengths ranging from 280 to 450 nm and spectral intensity wavelengths for fluorescence, scattering, and absorption ranging from 350 to 550 nm, as shown in Figure 4B.

[0089] In this characterization evaluation, the measured intrinsic viscosity range of the second resin R2 was 0.46 to 0.77 dL / g, and the solid line in the figure indicates that the calculated predicted value and the measured value match. The coefficient of determination in the prediction was 0.98, which was higher than in the first characterization evaluation. This indicates that highly accurate predicted values ​​were obtained using fluorescence characteristic data at specific wavelengths and the fluorescence intensity distribution, which is the fluorescence fingerprint, in this evaluation. Furthermore, reducing the fluorescence characteristic data reduces the computational load in measurement and prediction.

[0090] (6th characteristic evaluation) Figure 17 is a graph showing the wavelengths of the spectral intensity measured in the sixth characteristic evaluation of this embodiment. Figure 18 is a graph showing the results of the sixth characteristic evaluation, illustrating the relationship between the measured intrinsic viscosity and the predicted value obtained using the flow chart in Figure 4B when the second resin R2 is PET.

[0091] In this example, for the same type of evaluation as the second characteristic evaluation, the prediction model is calculated using the spectral intensity of the second resin R2 at the wavelength shown in Figure 17, as is done with Lasso regression, which was created for film and sheet products, just as with the fifth characteristic evaluation. In the figure, dark plots represent positive correlations, and light plots represent negative correlations.

[0092] In this example, 61 types of secondary resin products were classified into categories such as packaging materials for beverage bottles and containers, and film / sheet product base materials. These were then exposed to light including ultraviolet, visible, and infrared rays, similar to sunlight, using carbon arc lamps, and placed in a high-temperature (60-150°C) and high-humidity (85-90%RH) environment.

[0093] For these 61 products, fluorescence fingerprints were measured according to the flow chart in Figure 4B. The excitation wavelength was set to 250-450 nm, and the spectral intensity wavelength range was 250-650 nm. For each product, scaling was performed by standardization using the spectral intensity of the fluorescence fingerprint. Figure 18 plots the difference between the calculated glass transition temperature and the measured temperature, using a Lasso regression prediction model created for film and sheet products. In this characterization, the fluorescence properties of the exposed surface were also measured. The measured glass transition temperature range was 71.4-79.0°C, and the coefficient of determination in the prediction was 0.92, higher than in the second characterization. This indicates that by classifying the resin products in this evaluation and using the prediction model for the classified resin products, highly accurate prediction values ​​could be obtained.

[0094] (7th characteristic evaluation) Figure 19 is a graph showing the results of the seventh characteristic evaluation in this embodiment, illustrating the relationship between the weight-average molecular weight, number-average molecular weight, polydispersity (calculated by dividing the measured weight-average molecular weight by the measured number-average molecular weight) and the predicted value obtained using the flow chart in Figure 4B, when the second resin R2 is PET.

[0095] In this example, the second resin R2 was prepared as plate-like flakes cut to a size of 5 cm or less, and then the characterization was performed. As shown in Figure 4B, fluorescence fingerprint measurement data of the samples was obtained for excitation wavelengths of 250 to 600 nm and spectral intensity wavelength ranges of 250 to 800 nm for fluorescence and scattering / absorption. For each sample, scaling was performed by standardization using the spectral intensity of the fluorescence fingerprint. Figures 19A and 19B plot the weight-average molecular weight and number-average molecular weight calculated using a Lasso regression prediction model, and the difference from the measured values, while Figure 19C plots the difference from the measured values ​​of polydispersity.

[0096] The coefficient of determination for the predicted molecular weight was 0.93 for both the weight-average molecular weight and the number-average molecular weight, while the coefficient of determination for polydispersity was 0.88. In other words, this evaluation allowed us to obtain predicted values ​​for molecular weight, including weight-average molecular weight, number-average molecular weight, and polydispersity.

[0097] Based on the above, by using the property evaluation system and evaluation method of the present invention, it is possible to measure the fluorescence fingerprint of non-destructive fluorescence properties by irradiating with a wavelength range of 200 to 800 nm for excitation, and the wavelength range related to received fluorescence and scattering absorption of 200 to 900 nm. Using the measurement data, it is possible to obtain highly accurate predictive data for intrinsic viscosity, glass transition temperature, crystallization peak temperature, melting point, weight-average molecular weight, number-average molecular weight, and polydispersity related to the molecular weight distribution (weight-average molecular weight divided by the number-average molecular weight), which are also related to molecular weight. Furthermore, it is possible to easily perform quality control on resin materials and resin products such as polyester resins such as PET and PC.

[0098] One reason why predictive data can be obtained in this invention is that fluorescent resins contain functional groups in their structure that absorb excitation light and contribute to excitation, energy transfer, and fluorescence emission, respectively. For example, in the case of the polyester resin fluorescent resin mentioned in this embodiment, the ester bonds included in the basic structure contribute to fluorescence emission, and furthermore, in the case of polyethylene terephthalate and polycarbonate, conjugated systems such as benzene rings also contribute to fluorescence emission and are thought to affect properties such as fluorescence intensity and wavelength. Therefore, the molecular weight of the fluorescent resin can be calculated based on the fluorescence properties due to the ester bonds included in the basic structure of the fluorescent resin. Molecular weight is correlated with the viscosity and thermal properties of the fluorescent resin. This is shown in Figure 28, which illustrates the relationship between viscosity (IV), weight-average molecular weight (Mw), and number-average molecular weight (Mn) measured using a sample degraded in a weathering test. The correlation can also be seen from Figure 28. From this, it is possible to calculate the molecular weight, viscosity, and thermal properties of the fluorescent resin from its fluorescence properties. In addition, by calculating the molecular weight, thermal properties, and viscosity from the fluorescence properties, it is possible to predict the properties without damaging the product. For this reason, if the polyester resin has ester bonds, the molecular weight, thermal properties, and viscosity can be calculated from the fluorescence properties. Furthermore, it can also be applied to polycarbonates (PC), polyethylene terephthalate (PET), polybutylene terephthalate (PBT), polyethylene naphthalate (PEN), unsaturated polyester resins (UP), acrylic resins (PMMA), polyurethane resins (PUR), ethylene vinyl acetate copolymers (EVA), polylactic acid (PLA), polybutylene succinate (PBS), polybutylene adipate terephthalate resins (PBAT), resinous starch, polyhydroxybutyrate (PHB), and 3-hydroxybutyrate-co-3-hydroxyhexanoate polymers (PHBH). Furthermore, it is also suitable for calculating molecular weight, viscosity, and thermal properties of modified resins that contain ester groups in polyethylene (PE), polypropylene (PP), polystyrene (PS), acrylonitrile-butadiene-styrene copolymer synthetic resin (ABS), polyvinyl alcohol (PVA), polyvinyl chloride (PVC), polyvinylidene chloride (PVDC), polyacrylonitrile (PAN), phenolic resin (PF), epoxy resin (EP), polyamide (PA), polyacetal (POM), polyphenylene sulfide (PPS), polyetheretherketone (PEEK), silicone resin (SI), melamine resin (MF), and urea resin (UF), which do not have ester bonds in their basic structure, as well as modifying groups that contribute to fluorescence emission through absorption and excitation of ultraviolet to visible light in the wavelength range of 200-800 nm as described above. [Examples]

[0099] Next, the evaluation system 100 according to Embodiment 2 of the present invention will be described using Figures 20 to 23D. Note that common points with Embodiment 1 will be omitted from the explanation.

[0100] (Example of output video) In the evaluation system 100 of this embodiment, examples of images displayed on the display unit 21 (liquid crystal display) of the output device 2, showing the characteristic data of the second resin R2 stored in the analysis database 31c and the characteristic prediction database 31d, are shown in Figures 20, 21, and 22, respectively.

[0101] ((First video example)) The example video in Figure 20 shows a screen where multiple pieces of information can be viewed simultaneously by displaying the selected second resin R2 and its associated measurement information (Sample Information section), the acquisition of measurement conditions (Measurement section), the measurement results of the fluorescence properties (Result section), and the predicted viscosity and thermal properties results from the prediction model (Analysis section). Furthermore, it is possible to perform operations such as adding information (Import file button), changing the analysis model, and updating and saving data (Export file button) all on the same screen.

[0102] ((Second video example)) The example video in Figure 21 shows a screen displaying the fluorescence properties of multiple items with the same ID, such as the same manufacturing lot of the second resin R2, measured continuously. For the second resin R2 with the selected ID, the changes in viscosity and thermal properties over time, as well as the progress towards predetermined values ​​(Monitoring), and a graph showing the changes (Trend Graph) are displayed.

[0103] Here, using Figures 23A to 23D, we will explain the outline of the measurement system of this embodiment, which enables continuous measurement of the fluorescence properties of a large number of second resins R2. As shown in each figure, the samples 50 of the second resin R2 are placed on the transport device 51 and transported in the transport direction. Then, the fluorescence properties of the transported samples 50 are sequentially measured using the fluorescence property measurement unit 11, which consists of an excitation light source 11a and a photodetector element 11b. In each figure, the fluorescence properties are measured for each sample 50, but multiple small samples such as pellets or flakes may be placed on the transport device, or they may be filled into transparent containers with the excitation light wavelength and fluorescence wavelength to be measured, such as quartz, and measured together. Furthermore, the irradiation diameter (spot) size of the excitation light may be adjusted to be the same size as the sample in order to measure each sample, or the irradiation diameter size may be increased so that measurement data can be obtained by measuring multiple small samples at once and averaging the results. In that case, the irradiation diameter size information will be stored in the analysis database 31c.

[0104] In the measurement system shown in Figure 23A, the excitation light source 11a and the photodetector 11b of the fluorescence characteristic measurement unit 11 are alternately positioned opposite the upper surface of the sample 50 being transported on the transport device 51. The upper surface of the sample 50 is highly reflective in the measurement wavelength range of excitation light and fluorescence. The excitation light is irradiated from above, and the fluorescence in the upward direction is measured by the photodetector 11b. To prevent ambient light transmitted from the top or bottom of the transport device 51 from entering the photodetector, light shields or the like may be installed above the fluorescence characteristic measurement unit 11 and below the transport device.

[0105] In the measurement system shown in Figure 23B, a semi-cylindrical or hemispherical light-receiving element 11b is positioned above the excitation light source 11a in the depth direction. This configuration allows for efficient measurement of fluorescence from the sample 50 with Lambertsian light distribution while suppressing the influence of ambient light.

[0106] In the measurement system shown in Figure 23C, the transport device 51 is transparent in the measurement wavelength range of excitation light and fluorescence (transmittance ≥ 10%, aperture ratio ≥ 10%, such as a mesh), with the excitation light source 11a positioned above the transport device 51 and the light-receiving detection element 11b positioned below. When ultraviolet light is irradiated onto the sample 50 on the transport device 51 from the excitation light source 11a, the transmission and absorption characteristics, including surface reflection and scattering of the sample due to the fluorescence transmitted through the transport device 51 and the non-fluorescence of the sample 50, can be evaluated simultaneously.

[0107] In the measurement system shown in Figure 23D, the transport device 51 is transparent, similar to that in Figure 23C, with the excitation light source 11a located at the bottom of the transport device 51 and the light-receiving detection element 11b located at the top. The same effects and advantages as in Figure 23C can be obtained with this measurement system as well.

[0108] Furthermore, it is desirable to irradiate the transport device 51 with excitation light before placing the sample 50 in the transport device 51 shown in Figures 23A to 23D, and measure the fluorescence characteristics of the transport device 51 itself, or the transmitted and reflected light, so that the condition of the transport device 51, such as contamination or deterioration due to operation, can be managed. In addition, even during the measurement of fluorescence characteristics, the fluorescence, transmitted and reflected light from the non-placed portion of the transport device 51 due to excitation light can be measured, and the intensity of each light can be corrected, thereby obtaining stable measurement data.

[0109] On the screen shown in Figure 21, similar to Figure 20, supplementary information (Information) regarding the preparation, history, and measurement conditions of the selected second resin R2, as well as the changes in viscosity and thermal properties over time for each sample, can be displayed in the form of a table (Monitoring) and a graph (Trend Graph), and output processing such as saving (Export file button) can be performed. Furthermore, if measurements and evaluation of viscosity and thermal properties are performed continuously after the preparation of the second resin R2 sample, fluorescence analysis data useful for quality control, such as changes in each property over time, can be created, and it can also display whether the intrinsic viscosity value (0.8-1.0 dL / g for bottles, 0.6-1.0 dL / g for films and sheets, 0.4-0.7 dL / g for fibers), which is one of the indicators for selecting the application of PET, is within the specified range or if a deviation has occurred (Alert in the table).

[0110] ((Third video example)) The video example in Figure 22 shows the operating status of a system consisting of a management server 4 for collecting supplementary information on the second resin R2, information on manufacturing and inspection, operational management and status monitoring of manufacturing and inspection, manufacturing equipment and transport equipment for the second resin R2, inspection equipment, characterization equipment, and power supply equipment that supplies power to these. The system displays integrated information on the second resin R2 manufactured by the system, including manufacturing data output from the manufacturing equipment for each ID of the second resin R2, output data from the transport equipment, inspection data output from the inspection equipment, characterization data output from the characterization equipment, and information on legal compliance collected on the management server. It also displays a graph showing the changes and trends over time for inspection and characterization for each ID.

[0111] The inspection device includes a foreign matter measurement unit that detects unintentionally mixed-in foreign matter such as glass, dust and other inorganic materials contained in the second resin R2, rubber and resins other than the second resin; a shape measurement unit that measures the shape and size of the sample; a color tone measurement unit that measures chromaticity (Lab, RGB, etc.); an appearance measurement unit that measures gloss and turbidity (haze); a moisture measurement unit that measures water content; a specific gravity measurement unit that measures specific gravity (density) from weight and volume; and RoHS (Restriction of Hazardous Materials). It consists of a regulated substance measurement unit that measures regulated substances such as per- and per-fluorine compounds, substances subject to design guidelines for products covered by the Substances Directive, REACH, Food Sanitation Law and other restrictions on the use of hazardous substances, and per- and per-packaging recycling laws; a molecular structure measurement unit that analyzes carbonyl index, acid value, hydroxyl value, molecular weight and monomers, oligomers, modified products, etc., and measures the ratio of recycled materials and biomass materials; an oxidation reaction measurement unit that measures oxidation reactivity such as oxidation induction time (OIT), oxidation onset temperature (IOT), chemiluminescence, and free radicals; and a mechanical properties measurement unit that measures tensile strength, tensile yield stress, tensile modulus, tensile fracture strain, flexural strength, flexural modulus, Charpy impact, Izod impact, drop impact, Duro hardness, pencil hardness, Rockwell hardness, and nanoindentation.

[0112] Measurement methods for the foreign matter measurement section include visual inspection using limit samples, CCD cameras, transmission X-ray devices, metal detectors, fluorescence X-ray devices, near-infrared discriminators, and hyperspectral cameras. Measurement methods for the shape measurement section include calipers, micrometers, optical step gauges, optical film thickness gauges, and stylus-type length measuring devices. Measurement methods for the color tone measurement section include visual inspection using color samples, spectrophotometers, CCD cameras, and hyperspectral cameras. For the appearance measurement section, visual inspection for unevenness, bumps, and scratches due to defects in molding processes, CCD cameras, haze meters, and gloss meters are used. Measurement methods for the moisture content measurement section include electronic balances for measuring the weight of the sample before and after drying in a drying oven, heating and drying type moisture meters, thermogravimetric analyzers (TG) and simultaneous differential thermal analysis (DTA) and differential scanning calorimetry (DSC) analyzers, Karl Fischer moisture meters, electrical resistance moisture meters, capacitance moisture meters, infrared moisture meters, and microwave moisture meters. Examples of specific gravity measurement equipment include electronic balances for calculating specific gravity from shape and size measurement data from the shape measurement equipment, electronic balances capable of wet weighing in liquids, and dry densimeters. Examples of regulated substance measurement equipment include fluorescent X-rays, radio frequency argon plasma emission spectrometers (ICP-AES), radio frequency argon plasma mass spectrometers (ICP / MS), gas chromatography-mass spectrometers (GC / MS), and liquid chromatography-mass spectrometers (LC / MS). Examples of molecular structure measurement equipment include Fourier transform infrared spectroscopy (FT-IR), near-infrared spectrometers (NIR), Raman spectrometers, nuclear magnetic resonance spectrometers (NMR), high-performance liquid chromatography (HPLC), photoacoustic spectroscopy (PAS), direct ionization analyzers (DART-MS), and titrators. Examples of oxidation reaction measurement equipment include DTA, DSC, chemiluminescence analyzers, and electron spin resonance spectroscopy. These analytical instruments used in the inspection equipment can also acquire measurement data that is valid in other measurement units, and measurement units may be omitted or measurement data may be shared.

[0113] As shown in Figure 22, it is possible to manage operations while monitoring the operating status of various equipment in the manufacturing and inspection of the second resin R2, and checking the trends of inspection and characteristic evaluation data, while also monitoring the quality of the second resin R2 and identifying signs of abnormalities. [Examples]

[0114] Next, the evaluation system 100 according to Embodiment 3 of the present invention will be described using Figures 24 to 25B. Note that common points with the above embodiments will be omitted from the explanation.

[0115] If the shape of the sample of the second resin R2 is a planar sample 50a such as a film or sheet as illustrated in Figure 24, or a linear sample 50b such as a thread, fiber, or strand, the sample 50 may be moved by pulling it, such as by winding up the end of the sample 50, and the fluorescence characteristics of the sample 50 may be continuously measured without using the transport device 51. A schematic diagram of the fluorescence characteristic measurement unit 11 corresponding to such measurement is shown in Figures 25A and 25B.

[0116] In the measurement system shown in Figure 25A, the excitation light source 11a and the corresponding photodetector 11b are arranged so as to be nonlinear across the sample 50, and are shielded by a light-shielding section 52 to suppress the influence of ambient light. In order to shorten the measurement time of the sample 50, the excitation light source 11a and the photodetector 11b may be arranged in an array, with the excitation wavelength selected from Figure 17 of the sixth characteristic evaluation and the fluorescence wavelength being sensitive to the excitation light source and fluorescence wavelength.

[0117] On the other hand, in the measurement system shown in Figure 25B, the light-receiving detection elements 11b are positioned at two locations: one perpendicular to the tangent to the incident light source of the integrating sphere 53 with a light-shielding plate, and another at a position other than the incident light source where the incident light is blocked and does not directly enter the sphere. This allows for the simultaneous measurement of turbidity (haze) due to transmission of the sample 50.

[0118] According to the configuration shown in Figure 25A or Figure 25B, the fluorescence characteristics of the sample 50 can be measured without using a transport device 51 as shown in Figures 23A to 23D, thus avoiding degradation of the transport device 51 due to ultraviolet light and contamination of the sample via the transport device 51. [Examples]

[0119] Next, an evaluation system 100 according to Embodiment 4 of the present invention will be described using Figure 26. Note that common points with the above embodiment will be omitted for brevity. Samples 50 of the second resin R2 are dispersed on a transport device 51 illustrated in Figure 26, and each sample is transported sequentially from the back to the front. Position information of each sample 50 on the transport device 51 is acquired by the reading input unit 15.

[0120] The fluorescence characteristic measurement unit 11 identifies the position where the sample 50 passes through the fluorescence characteristic measurement unit 11 as an evaluation position, based on the sample's position information acquired by the reading input unit 15 and the sample's transport speed in the transport device 51, and selectively measures the sample 50 on the transport device 51.

[0121] To acquire fluorescence characteristic data of selective samples 50, the fluorescence characteristic measurement unit 11 has multiple measurement arrays 54, each consisting of an excitation light source and a photodetector, arranged in directions perpendicular to orthogonal to the sample transport direction. This allows the evaluation position on the transport device to be divided into multiple sections, and the fluorescence characteristics of the sample 50 can be measured using the corresponding measurement array 54. Alternatively, multiple photodetectors may be arranged, and the upper surface of the transport device on an axis perpendicular to the transport direction may be illuminated from a single excitation light source via a light guide plate, a diffuser plate, and a branch in the light guide, and the fluorescence from the passing sample 50 may be measured by the photodetector. Alternatively, the excitation light may be parallel light, and only the sample 50 at the evaluation position may be selectively illuminated using a galvanometer mirror to acquire fluorescence characteristic data of the sample 50.

[0122] According to the configuration shown in Figure 26, even when multiple samples 50 are transported to the fluorescence characteristic measurement unit, fluorescence characteristic data can be acquired for each sample 50, enabling individual evaluation and detailed quality control for each sample 50. Furthermore, the samples 50 may be sorted based on the viscosity, thermal properties, and molecular weight values ​​predicted using the acquired fluorescence characteristic data. Specifically, samples 50 that fall below pre-set thresholds for viscosity, thermal properties, and molecular weight can be removed from the transport route using a robot or the like, or the collection position can be changed for each threshold. In addition, if the resin type of the product contained in the sample 50 is known in advance, the transport position may be changed according to the resin type. In this case, a characterization system with a prediction model learned using products containing the same resin type as the sample 50 may be placed at each transport position, and fluorescence characteristic data and predictions of viscosity, thermal properties, and molecular weight can be performed for each resin type. [Examples]

[0123] Next, the evaluation system 100 according to Embodiment 4 of the present invention will be explained using Figure 27. Note that common points with the above embodiment will be omitted from the explanation.

[0124] Figure 27 is a perspective view illustrating the specific arrangement of the fluorescence characteristic measurement unit 11 and reading input unit 15 of the input device 1, and the marking unit 23 of the output device 2 in the evaluation system 100 of this embodiment.

[0125] As shown in the figure, multiple columnar samples 50 are placed on the transport device 51, and each sample is transported sequentially from the back to the front. In addition, the marking unit 23, the reading input unit 15, and the fluorescence characteristic measurement unit 11 are installed facing the samples 50 on the transport device 51, from the upstream side to the downstream side.

[0126] The marking unit 23 is positioned to face the top surface of the sample 50, and is used to assign an ID code or attach an RFID tag to the top surface of a sample 50 that has not been issued an ID or has not been assigned an ID code, for the purpose of managing the sample 50 for measurement, storage, and transport.

[0127] The reading input unit 15 is positioned to face the top surface of the sample 50 and reads the ID code or RFID on the top surface of the sample 50.

[0128] The fluorescence characteristic measurement unit 11 is positioned to face the side of the sample 50 and measures the fluorescence characteristics of the sample 50 whose ID has been read. [Explanation of symbols]

[0129] 100 Evaluation System 1 Input device 11. Fluorescence characteristic measurement section 11a Excitation light source 11b Light-receiving element 12 Viscosity measurement section 13 Thermal properties measurement section 14 Molecular weight measurement section 15. Reading input section 16 Communications Department 17 Control Unit 2 Output device 21 Display section 22 Output section 23 Marking section 24 Communications Department 25 Control Unit 3. Characterization System 31 Storage device 32 Arithmetic unit 33 Communications Department 34 Control Unit 35 Input section 36 Output section 4. Management Server 50 samples 51 Conveying device 52 Light-shielding part 53 Integrating sphere with light-shielding plate 54 Measurement Array

Claims

1. It comprises an arithmetic unit for executing a program and a memory device for storing the program, The memory device stores a database of the fluorescence properties of each of several products mainly composed of resin, and a database of measured values ​​of viscosity, thermal properties, or molecular weight corresponding to the fluorescence properties, or a predictive model learned based on the database. The calculation device receives the fluorescence properties of the product to be predicted, which mainly consists of the resin, as input. Based on the fluorescence properties of the product to be predicted and the database or prediction model, the viscosity, thermal properties, or molecular weight of the product to be predicted is predicted. Outputting the viscosity, thermal properties, or molecular weight of the product to be predicted. A characteristic evaluation system characterized by the following:

2. In the characteristic evaluation system of claim 1, A characteristic evaluation system characterized in that the viscosity is intrinsic viscosity.

3. In the characteristic evaluation system of claim 1, A property evaluation system characterized in that the thermal property is one of the glass transition temperature, crystallization temperature, or melting point.

4. In the characteristic evaluation system of claim 1, A characterization system characterized in that the molecular weight is one of the weight-average molecular weight, number-average molecular weight, or polydispersity.

5. In the characteristic evaluation system of claim 1, A characterization system characterized in that the fluorescence characteristics are any of the three-dimensional data of excitation wavelength, fluorescence wavelength, and fluorescence intensity.

6. In the characteristic evaluation system of claim 1, A characterization system characterized in that the wavelength range of the excitation light for the fluorescence properties is 250 to 600 nm, and the wavelength range of the spectral intensities for fluorescence and scattering / absorption is 200 to 700 nm.

7. In the characteristic evaluation system of claim 6, A characterization system characterized in that the wavelength range of the excitation light for the fluorescence properties is 250 to 450 nm, and the wavelength range of the spectral intensities for fluorescence and scattering / absorption is 250 to 600 nm.

8. In the characteristic evaluation system of claim 1, The aforementioned computing device is a characterization system characterized by generating a predictive model based on a database of fluorescence properties for each of a plurality of products mainly composed of resin, and measured values ​​of viscosity, thermal properties, or molecular weight corresponding to the fluorescence properties.

9. In the characteristic evaluation system of claim 8, The aforementioned prediction model is characterized by being created using one of the following methods: simple regression, multiple regression, Lasso regression, PLS regression, ridge regression, support vector regression, elastic network, decision tree, random forest, or gradient boosting decision tree.

10. In the characteristic evaluation system of claim 2, A characteristic evaluation system characterized in that the intrinsic viscosity range is 0.46 to 0.99 dL / g.

11. In the characteristic evaluation system of claim 1, A characterization system characterized in that the form of the product is one of a bottle, container, film, sheet, pellet, flake, or strand.

12. In the characteristic evaluation system of claim 1, The property evaluation system is characterized in that the resin is a polyester resin.

13. In the characteristic evaluation system of claim 1, The resin is characterized by having ester bonds in the property evaluation system.

14. In the characteristic evaluation system of claim 12, The property evaluation system is characterized in that the resin is one of the following: polyethylene terephthalate, polycarbonate, polybutylene terephthalate, polyethylene naphthalate, polylactic acid, or a copolymer synthetic resin of polycarbonate and acrylonitrile butadiene styrene.

15. In the characteristic evaluation system of claim 8, The storage device has a predictive model for each product, with the first resin as the main component. The calculation device receives the fluorescence properties and product information of the product to be predicted, which mainly consists of the resin, as input. Based on the aforementioned product information, select a predictive model to use for the prediction. Based on the selected prediction model and the fluorescence properties of the product to be predicted, the viscosity, thermal properties, and molecular weight of the product to be predicted are predicted. A characteristic evaluation system characterized by the following:

16. The characteristic evaluation system of claim 1 is installed in accordance with the transport position of the product to be predicted, The storage device stores a predictive model learned based on a plurality of products containing the same main component as the product transported to the transport position, The calculation device acquires the fluorescence properties of the product that has been transported to the transport position. Based on the fluorescence properties and the database, the viscosity, thermal properties, or molecular weight of the product transported to the transport position is predicted. A characteristic evaluation system characterized by the following:

17. It comprises an arithmetic unit for executing a program and a memory device for storing the program, The aforementioned computing device accepts as input a database of fluorescence properties for each of several products mainly composed of resin, and measured values ​​of viscosity, thermal properties, or molecular weight corresponding to the fluorescence properties. The system learns and generates a predictive model by taking the aforementioned fluorescence properties as input and the viscosity, thermal properties, or molecular weight as output. A characteristic evaluation system characterized by the following:

18. A property evaluation method for predicting the viscosity and thermal properties of a resin based on its fluorescence properties, A fluorescence property measurement step for measuring the fluorescence properties of a resin, A viscosity measurement step for measuring the viscosity of the resin, A thermal properties measurement step for measuring the thermal properties of a resin, A molecular weight measurement step for measuring the molecular weight of the resin, A predictive model creation step involves creating a predictive model that takes the first fluorescence property measured from the first resin as input and outputs either the first viscosity, first thermal property, or first molecular weight. A prediction calculation step that predicts either the second thermal properties, second thermal properties, or second molecular weight relating to the second resin based on the second fluorescence properties measured from the second resin and the prediction model, A method for evaluating characteristics, characterized by comprising the following:

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