JP2002340792A - Method for measuring density of polymer material - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method for measuring a density of a polymer material which is more simple and speedier than conventional method of measuring a mid-infrared spectrum, a nuclear magnetic resonance(NMR) spectrum and the like, and a method for recycling a polymer material waste which utilizes the density measuring method. SOLUTION: A near infrared spectrum in a near infrared wavelength region is measured for the polymer material whose density is to be measured. A data analysis method (neutral network, a main component analysis method or the like) in chemometrics is applied to the near infrared spectral data, and the density of the polymer material is measured on the basis of information obtained by the data analysis method. The polymer material waste to be recycled is detected with the use of the density measuring method in a method for recycling the polymer material.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for measuring the density of polymer materials such as plastics, fibers, rubbers, paints and adhesives based on data of near-infrared spectra, and a method for measuring the density of the polymer materials. The present invention relates to a method for recycling polymer material waste using a measurement method.

[0002]

2. Description of the Related Art Recycling of waste plastics has made great progress technically in recent years, and various fields such as containers and packaging, home appliances, automobiles, agricultural supplies, and construction wastes have been undertaken. Under such circumstances, the demand for recycling has become increasingly sophisticated year by year. Have been. The present inventors have proposed a method for solving these problems in Japanese Patent Application No. 2000-275693 “Grade identification method for polymer materials”. Further, it is expected that it will be required to separate the resin by density, not by grade, in order to maintain the quality of the recycled product.

Conventionally, as a method of measuring the density of a polymer material at a laboratory level, JISK 7112 describes:
As a method for measuring the density and specific gravity of plastic-non-foamed plastic, four types of methods, namely, an underwater displacement method, a pycnometer method, a floating / sedimentation method, and a density gradient tube method are specified. In addition, analytical methods for destructive / non-destructive measurement such as infrared spectrum, X-ray and neutral ray diffraction, differential scanning calorimetry (DSC), and solid-state NMR measurement are used. However, although these methods can measure the density of a polymer material, they lack the quickness of measurement and density measurement, and have reached a practical level that can be used at the time of actual recycling at present. There was nothing.

[0004]

Among the above-mentioned methods for measuring the density of a polymer material, a promising method that is closest to the practical level is:
This is considered to be a method using infrared rays. When the polymer material is irradiated while changing the wavelength of the infrared light, the ratio of infrared light absorbed or reflected by the polymer material changes depending on the wavelength. When the absorption or reflectance of the infrared ray is measured while changing the wavelength of the irradiation light, an infrared spectrum is obtained. Since this infrared spectrum changes depending on the type and density of the monomer used for the polymer material,
A polymer material can be measured from a difference in infrared spectrum.

The infrared spectrum is a near-infrared spectrum (wavelength range: 1.0 to
2.5 μm), mid-infrared spectrum (wavelength range: 2.5
２５25 μm), far-infrared spectrum (wavelength range: 25-1)
00 μm). Among these, the mid-infrared rays have different absorption rates depending on the density of the polymer material,
The density of the polymer material can be accurately measured based on the mid-infrared spectrum. However, in this mid-infrared wavelength range, the light absorption by the polymer material is quite high, so it is necessary to perform a transmission method measurement after performing a pretreatment to reduce the thickness of the sample (object to be measured). . Therefore, the method for measuring the infrared spectrum in the mid-infrared wavelength range is a destructive inspection, and requires a complicated work of pre-processing the sample. I couldn't say it.

The problem of measuring the density of a polymer material by a simple and quick method is that plastic, fiber, rubber,
This can also occur when all polymer materials such as paints and adhesives are efficiently recycled.

SUMMARY OF THE INVENTION Accordingly, an object of the present invention is to provide a method for measuring the density of a polymer material which is simpler and faster than the conventional methods for measuring the density in view of the above problems.

[0008]

Means for Solving the Problems Accordingly, the present inventors have:
To achieve the above objectives, as a result of intensive studies, the density of the polymer material can be quickly measured by applying a data analysis method in chemometrics to near-infrared spectral data in the near-infrared wavelength range. Focusing on its usefulness in the polymer material recycling business, the present inventors have arrived at the present invention based on those findings.

That is, the invention of claim 1 measures a near-infrared spectrum in a near-infrared wavelength range of a polymer material to be subjected to density measurement, and applies a data analysis method in chemometrics to data of the near-infrared spectrum. The present invention also relates to a method for measuring the density of a polymer material, comprising measuring the density of the polymer material based on information obtained by the data analysis method.

Here, the above-mentioned "chemometrics" means to apply a mathematical method or a statistical method to plan and select an optimal procedure and an optimal experimental plan, and to maximize the amount of chemical information obtained from chemical data. (For example, see Tetsuro Aishima, "Chemometrics-New Analytical Chemistry", Maruzen, p. 1). As the above “data analysis method in chemometrics”, in addition to neural networks, principal component analysis, PLS (Part
ial Least Squares Regression), PCR (Principa
l Components Regression method, hierarchical cluster analysis method, SIMCA (Soft Independent Modeling of Class)
Analogy) method, KNN (K-Nearest Neighbours) method, etc. (for example, Yoshikatsu Miyashita and Shinichi Sasaki, "Chemometrics Chemical Pattern Recognition and Multivariate Analysis")
See Kyoritsu Publishing).

In the method for measuring the density of a polymer material according to the first aspect, near-infrared rays (wavelength range: 1.0 to 2.0) including wavelengths having different light absorptivity and reflectance depending on the density of the polymer material.
5 μm), it is possible to obtain near-infrared spectrum data including information on the density of the polymer material. In addition, unlike the mid-infrared ray (wavelength range: 2.5 to 25 μm), the near-infrared ray is not excessively absorbed by the polymer material and becomes transmitted light or reflected light having an appropriate intensity. In addition, it is not necessary to perform pretreatment on the polymer material to be measured, and it is possible to obtain data of a near-infrared spectrum having a sufficient intensity.

Then, by applying a data analysis technique in chemometrics to the near-infrared spectrum data, information on the density of the polymer material is extracted from the near-infrared spectrum data. The density of the polymer material is measured based on the information obtained by this data analysis technique. Since data processing using a computer can be easily applied to the data analysis method in chemometrics, simple and quick density measurement can be performed.

According to a second aspect of the present invention, in the method for measuring the density of a polymer material according to the first aspect, the data analysis method in the chemometrics includes sharpening of a peak, correction of a baseline slope, It is composed of pre-processing for performing processing including normalization, and data processing by a neural network for near-infrared spectrum data that has been subjected to the pre-processing.
Here, the "neural network" is a mathematical model that mimics information processing performed by the brain, and is a network that performs data processing performed by replacing data processing performed by nerve cells with processing elements called units and interconnecting them. It is. For data processing of the near-infrared spectrum, in particular, a hierarchical perceptron model was adopted,
A neural network having a three-layer structure including an input layer, an intermediate layer, and an output layer, and which is learned by an error back propagation method, is preferable.

According to a second aspect of the present invention, the near-infrared spectrum data is subjected to pre-processing including second-order differentiation processing and normalization processing, and is performed between a plurality of high-density polymer materials to be measured. To reduce the variation of the data and increase the accuracy of the final density measurement. The pre-processed near-infrared spectrum data is input to a neural network, and density prediction is performed based on the data output from the neural network. In the case of the analysis method using the neural network, the accuracy of the density measurement is improved by optimizing the parameters of the neural network. Further, even when the near-infrared spectrum data has strong nonlinearity, the density of the polymer material can be accurately measured.

Further, according to the second aspect of the present invention, there is provided a method for measuring the density of a polymer material using near-infrared light, comprising the following steps (1) to (6); Irradiating (2)
(3) receiving the diffuse reflected light obtained in the above step (1), and (3) preprocessing the near infrared spectrum data obtained in the above light receiving step by averaging, peak sharpening and normalizing the data. (4) subjecting the pre-processed near-infrared spectrum data obtained in the above step (3) to analysis processing by a neural network, (5) applying the near-infrared spectrum data to the output layer of the neural network in the analysis processing. A step of learning by inputting at least one value relating to the density of the polymer material as teacher data; and (6) a step of calculating a predicted density value of the unknown polymer material in the step (5). A method for measuring the density of a polymeric material is provided.

According to a third aspect of the present invention, a spectroscopic measuring apparatus using an acousto-optic element as a means for measuring a near-infrared spectrum used in the method for measuring the density of a polymer material according to the present invention is specified. According to the fourth aspect of the present invention, the near-infrared spectrum has a wavelength range of 1.0 to 2.5 μm. The density of the polymer material can be quickly and accurately measured in such a wavelength range, but more preferably 1.1 to 2.2 μm
It is preferable to use the wavelength range of By using this wavelength range, the scan width of near-infrared rays can be reduced without lowering the density measurement accuracy of the polymer material, so that the density measurement can be performed more quickly.

According to a fourth aspect of the present invention, there is provided a method of recycling a high polymer material waste, wherein the high polymer material waste is determined using the method of measuring the density of the high polymer material according to the first aspect. You. The method of recycling polymer materials usually includes the steps of identification, classification, decomposition, regeneration, quality inspection, optimization, etc. of the waste. The density measurement method of the present invention uses the steps of identification, classification, quality inspection, etc. Can be used in

[0018]

DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment in which the present invention is applied to a density measuring method for measuring the density of polyethylene will be described below.

Polyethylene is mass-produced as one of general-purpose plastics which is a polymer material. Polyethylene is light (specific gravity: 0.91 to 0.97), transparent to translucent, and cold resistant (usable temperature range: -60 to 80).
° C) is good, the electrical insulation is particularly excellent, the withstand voltage is large, and the high frequency characteristics are good. It does not absorb or permeate water, but has the property of passing air. In addition, polyethylene
Excellent in water resistance, oil resistance, acid resistance (excluding concentrated nitric acid), alkali resistance, and organic solvent resistance (excluding constant temperature), and low cost. Although polyethylene is inferior in strength, rigidity, surface hardness, and heat resistance, it has properties exceeding these properties and is therefore used in large quantities. At the same time, it is true that they are being mass-consumed and discarded.

On the other hand, with the full enforcement of the Containers and Packaging Recycling Law and the Home Appliance Recycling Law, the need to recycle plastic has been pressing. Under such circumstances, there is a pressing need to recycle polyethylene that has been consumed or discarded in large quantities. However, polyethylene having a different density depending on the application is used. The reason why polyethylenes having different densities are present is that, depending on the method of producing polyethylene, branching may occur, and the degree of crystallinity may decrease accordingly. This decrease in crystallinity leads to a decrease in rigidity and an improvement in transparency. As described above, appearance characteristics such as rigidity (flexibility) and transparency of polyethylene greatly vary depending on crystallinity. Density is used as a guide to crystallinity. Since polyethylenes having different densities have different characteristics, as described above, it is necessary to separate polyethylene more finely for each density when recycling.

In this embodiment, in order to measure the density of the polyethylene, the near-infrared spectrum of the polyethylene is measured, and a data analysis method in chemometrics is applied to the near-infrared spectrum data, and the data is obtained by this data analysis method. A density measurement method for measuring the density of polyethylene based on the information obtained is used.

Here, the wavelength in the near-infrared region is used because of the engineering characteristics of polyethylene. CH
The bands of the first, second, third, etc. of the stretching vibration are all like tablets, but the longer wavelength side is due to the methylene group and the shorter wavelength side is due to the methyl group. It is. As the order of the overtones increases to 1, 2, and 3, the intensity decreases, but the band resolving power improves. Therefore, in the identification of a compound, the near-infrared region corresponding to the higher-order vibration is extremely effective. Another advantage of using this near-infrared region is that the spectrum is relatively simple because only vibrations with large anharmonic constants remain in this region and those with small anharmonic constants are not observed. There are advantages.

[0023]

EXAMPLE A polymer material sample having a density of 0.898-0.
23 kinds of 960 commercially available polyethylene pellets and powders were prepared. For the measurement of the near infrared spectrum of polyethylene, a near infrared spectrum measuring device (trade name: PlaScan) manufactured by Opto Giken was used. This near-infrared spectrum measuring apparatus uses an acousto-optic modulation filter (AOTF:
Acousto-Optical Tunable Filter) spectroscopy is used, and the diffuse reflection spectrum in the near-infrared region (1.0 to 2.5 μm) can be quickly and highly resolved (0.0005 μm).
This is a spectrometer that can be measured in m). Using this near-infrared spectrum measuring apparatus, first, a ceramic sample serving as a standard sample has a wavelength range of 1200 to 1200 μm in 1200.
The intensity of the diffuse reflected light at the point was measured after 10 integrations. Next, for a plurality of polyethylenes for density measurement,
The intensity of the diffuse reflected light at 1200 points in the same wavelength region of 1.1 to 2.2 μm was measured with 20 integrations. Then, for each measurement wavelength point, the logarithm of the relative reflectance, which is the ratio of the reflected light intensity of the polyethylene to the reflected light intensity of the ceramic, is calculated as the absorbance, whereby the near-infrared spectrum in the wavelength region of 1.1 to 2.2 μm is calculated. Data was obtained. The near-infrared absorption spectrum was measured five times each.

Next, the data of averaging, peak sharpening, and normalization for noise removal before performing data processing such as a neural network on the near infrared spectrum measurement data indicating the relationship between the wavelength and the absorbance. As processing, the following pre-processing of data (1) to (4) was performed. (1) Standardization was performed so that the minimum value was set to 0 and the maximum value was set to 1 for 1200 measured spectral data in the wavelength region of 1.1 to 2.2 μm. (See FIG. 1 (a).) (2) The normalized spectrum at 1200 points was averaged every 10 points to create data at 120 points. (See FIG. 1 (b).) (3) A second derivative spectrum was calculated using the data at 120 points. (See FIG. 1 (c).) (4) Normalization was performed again so that the maximum value of the absolute value was 1. (See FIG. 1 (d).)

The reason why the data averaging process is performed is to remove noise. The reason why the second derivative processing is performed is to sharpen the peak and correct the inclination of the baseline. Preprocessing of this type of data includes:
MSC (Multiplicative Scatter Correction)
Although there are methods and the like, in the present embodiment, a simple 2
The second derivative method was mainly used.

In the present embodiment, a neural network employing a hierarchical perceptron model is used as one of the data analysis methods in chemometrics for analyzing the near infrared spectrum data. Data processing by the neural network used software (trade name: NEUROSIM / L) manufactured by Fujitsu Limited installed on a personal computer.

This neural network has an input layer,
The intermediate layer and the output layer each have a three-layer structure of one layer, and an error back propagation method is used for learning of the neural network. The near-infrared spectrum (second derivative spectrum) data of each pretreated polyethylene is input to the input layer of this neural network, and the minimum value (0.898) and the maximum value of the sample density are input to the output layer. Learning was performed by inputting values (0.960) normalized to -0.8 and 0.8 as teacher data. In the present embodiment, the input layer is 1
20 units, 5 units for the intermediate layer and 1 unit for the output layer were set. As learning constants, ε = 5.0, α =
The convergence condition was set to 0.1 using 0.1.

To determine the ability of the neural network to measure polyethylene density, four out of five measurements were performed.
The network was learned by using the data of the first round, and the unlearned one data was input to the network after the convergence, the measured value of the density was obtained, and compared with the actually measured value. As a result, as shown in FIG. 1, the measured density value agreed with the measured actual density value within an average error of 0.003, and it was found that the density of polyethylene could be quickly measured by this method.

In the above embodiment, the case where the density of polyethylene is measured has been described. However, the present invention is also applicable to the case where the density of another polymer material such as polystyrene or polypropylene is measured. .

[0030]

According to the first to fourth aspects of the present invention, it is not necessary to preprocess a polymer material before measuring a near infrared spectrum, and data processing using a computer can be easily applied. There is an excellent effect that the density of the polymer material can be measured more easily and quickly than the measuring method.

In particular, according to the invention of claim 2, it becomes possible to determine the same kind of polymer material for each density.
Also, compared to the data analysis method using a linear model,
Even when the nonlinearity of near infrared spectrum data is strong, there is an excellent effect that the density of the polymer material can be measured more accurately. In particular, according to the invention of claim 3, the measurement of the near-infrared spectrum is performed by the acousto-optic modulation filter AO.
By using the TF spectroscopy, there is an excellent effect that the density of the polymer material can be determined with higher accuracy.

In particular, according to the fourth aspect of the present invention, the wavelength range of near-infrared spectrum measurement and data processing can be limited, so that there is an excellent effect that the density of polyethylene can be measured more easily and quickly. According to the invention of claim 5, there is an excellent effect that the classification and quality evaluation of the polymer material can be performed accurately and quickly in recycling the polymer material waste.

[Brief description of the drawings]

FIG. 1 is a near-infrared spectrum graph (a) of polyethylene in a wavelength range of 1.1 to 2.2 μm;
A graph in which the maximum value is normalized to be 1. (B) FIG.
A graph of data obtained by averaging the data of (a) for every 10 points and compressing the data to 120 points. (C) A graph of data obtained by performing a second-order differentiation process using the data of FIG. (D) FIG.
7C is a graph of data re-normalized so that the maximum absolute value of the data in FIG.

FIG. 2 is a graph showing the relationship between measured density values of commercially available polyethylene and measured density values according to examples of the present invention.

 ──────────────────────────────────────────────────続 き Continued on the front page (72) Inventor Kazutoshi Tanabe 1-1 1-1 Higashi 1-chome, Tsukuba City, Ibaraki Prefecture Within the National Institute of Advanced Industrial Science and Technology Tsukuba Center (72) Inventor Takatoshi Matsumoto 1-1-1 Higashi 1-chome, Tsukuba City, Ibaraki Prefecture 1 Tsukuba Center, National Institute of Advanced Industrial Science and Technology (72) Inventor Wako Saeki 150 Futamicho, Takaoka, Toyama Pref., Central Research Laboratory of Toyama Prefectural Industrial Technology Center (72) Toshio Amano, Nihonbashi Honmachizo, Chuo-ku, Tokyo No. 1-6 Opto Giken Co., Ltd. F term (reference) 2G059 AA03 AA05 BB08 BB15 EE01 EE02 EE12 HH01 HH06 JJ01 JJ18 MM03 NN01

Claims (5)

[Claims] 1. A near-infrared spectrum in a near-infrared wavelength range is measured for a polymer material to be subjected to density measurement, and a data analysis method in chemometrics is applied to the near-infrared spectrum data. A method for measuring the density of a polymer material, comprising measuring the density of the polymer material based on the acquired information. 2. The method for measuring the density of a polymer material according to claim 1, wherein the data analysis method in the chemometrics includes sharpening of a peak, correction of a baseline slope, and normalization of the near infrared spectrum data. And a data processing by a neural network of near-infrared spectrum data subjected to the pre-processing. 3. The method for measuring the density of a polymer material according to claim 1, wherein the near-infrared spectrum is measured by an acousto-optic modulation filter AOTF spectroscopy. A method for measuring the density of molecular materials. 4. The method for measuring the density of a polymer material according to claim 1, wherein the wavelength range of the near-infrared spectrum is 1.0 to 2.5 μm. A method for measuring the density of a polymer material. 5. A method for recycling polymer material waste, comprising determining the polymer material waste using the method for measuring polymer material density according to claim 1.