KR20230081417A - Methods for predicting quality of polymers - Google Patents

Abstract

Measuring the molecular weight distribution and the number distribution of short chain branches of the polymer of the present invention (S1), calculating the average value and deviation of the crystal generating unit of the polymer from the measured molecular weight distribution and the number distribution of short chain branches of the polymer It relates to a polymer quality prediction method comprising step (S2) and step (S3) of calculating a predicted value of withstand pressure from the average value and deviation of crystal forming units of the polymer.

Description

Polymer quality prediction method {METHODS FOR PREDICTING QUALITY OF POLYMERS}

The present invention relates to a method for predicting polymer quality, and more specifically, predicts the long-term withstand voltage characteristics of a polymer product before commercializing the polymer, and distributes an optimal short chain branch (SCB) that satisfies the long-term withstand voltage characteristics. It relates to a method for predicting the quality of polymers that can derive

The determination of the polymer is a major factor in determining mechanical properties such as crack stability and long-term withstand pressure. In particular, since the number, distribution and structure of short chain branches of a polymer control the distribution and content of crystalline and amorphous regions in the polymer, the determination of the polymer according to the number, distribution and structure of short chain branches in the polymer Modeling the distribution is important for predicting the quality of crystalline polymer products.

The crystal distribution of conventional polymers can be experimentally analyzed using CRYSTAF (Crystallization Analysis Fractionation), TREF (Temperature Rising Elution Fractionation), or DSC (Differential Scanning Calorimetry). CRYSTAF is a method of dissolving a polymer in a solvent and then measuring the crystal distribution of the polymer from the concentration of the non-crystallized polymer while cooling. TREF is a method of measuring the crystal distribution of the polymer according to the elution temperature of the polymer, and DSC is a method of measuring the polymer crystal distribution It is a method of measuring the crystal distribution of a polymer from the energy absorbed or emitted by a sample.

However, since the above methods can be measured after commercializing the crystalline polymer, the quality of the product cannot be predicted before commercializing the crystalline polymer, so it takes a lot of time and money to reach the desired product properties. there was

KR 10-2002-0016086 A

The present invention is to solve the above problems, by measuring the molecular weight distribution of the polymer and the number distribution of short chain branches, predicting the crystal distribution of the polymer, predicting the physical properties of the polymer, and It is intended to provide a method for deriving an optimal short chain branch distribution that satisfies .

(1) The present invention measures the molecular weight distribution of the polymer and the number distribution of short chain branches (S1), the average value and deviation of the crystal generating unit of the polymer from the measured molecular weight distribution of the polymer and the number distribution of short chain branches It provides a polymer quality prediction method comprising the step of calculating (S2) and the step of calculating the withstand pressure prediction value from the average value and deviation of the crystal forming units of the polymer (S3).

(2) The present invention provides a method for predicting polymer quality according to (1), further comprising the step (S4) of deriving molecular weight distribution and short chain branch distribution of the polymer from the predicted internal pressure value.

(3) The present invention provides a polymer quality prediction method according to (1) or (2) above, wherein the molecular weight distribution of the polymer in step (S1) is measured using gel permeation chromatography.

(4) In the present invention, in any one of (1) to (3), the number distribution of the short chain branches in step (S1) is measured by using infrared spectroscopy to determine the number of short chains per 1,000 carbon atoms of the main chain of the polymer. A method for predicting polymer quality by measuring the number of branches is provided.

(5) The present invention provides a method for predicting polymer quality according to any one of (1) to (4) above, wherein the withstand pressure prediction value (P) is calculated by Equation 1 below.

[Equation 1]

P = 11.0019 × m _ccu ^-0.08 × σ _ccu ^-0.15 + 3.8414

In Equation 1, m _ccu is the average value of the crystal forming units of the polymer, and σ _ccu is the deviation of the crystal forming units of the polymer.

(6) The present invention provides a method for predicting polymer quality according to any one of (1) to (5) above, wherein the predicted withstand voltage is greater than 10.2.

(7) The present invention provides a polymer quality prediction method according to any one of (1) to (6) above, wherein the average value of the crystal forming units of the polymer is calculated by Equation 2 below.

[Equation 2]

In Equation 2, m _ccu,i is the average value of the ith molecular weight crystal generating unit of the polymer, L _bb is the number of carbon atoms in the main chain, and n _br is the number of short chain branches.

(8) In the present invention, in any one of (1) to (7), when the number of short chain branches per 1,000 carbon atoms of the polymer is 20 or more, the average value of the crystal forming units of the polymer is expressed by Equation 3 below Provides a method for predicting polymer quality that is calculated.

[Equation 3]

In Equation 3 above,

m _ccu,i is the average value of the ith molecular weight crystal generating unit of the polymer,

M is the molecular weight of the polymer, M _br is the molecular weight of short chain branches, and SCB/C1000 is the number of short chain branches per 1,000 carbons.

(9) In the present invention, in any one of (1) to (8) above, when the number of short chain branches per 1,000 carbon atoms of the polymer is less than 20, the average value of the crystal forming units of the polymer is expressed by Equation 4 below Provides a method for predicting polymer quality that is calculated.

[Equation 4]

In Equation 4, m _ccu,i is the average value of the ith molecular weight crystal generating unit of the polymer,

M is the molecular weight of the polymer, M _br is the molecular weight of short chain branches, and SCB/C1000 is the number of short chain branches per 1,000 carbons.

(10) The present invention provides a polymer quality prediction method according to any one of (1) to (9) above, wherein the deviation of the crystal forming unit is calculated by Equation 5 below.

[Equation 5]

In Equation 5, σ _ccu,i is the ith molecular weight crystal generation unit deviation of the polymer,

m _ccu,i is the average value of the ith molecular weight crystal generating unit of the polymer, L _bb is the number of carbon atoms in the main chain, and n _br is the number of short chain branches.

(11) In the present invention, in any one of (1) to (10), the step (S2) is performed by using Equation 6 below from the average value and deviation of the crystal generating unit of the polymer. Provides a polymer quality prediction method comprising the step (S21) of deriving the distribution of.

[Equation 6]

In Equation 6, m _ccu,i is the average value of the ith molecular weight crystal generating unit of the polymer, σ _ccu,i is the deviation of the ith molecular weight crystal generating unit of the polymer,

The CCU is the value of the crystal generating unit of the entire polymer, f(CCU,i) is the probability that the crystal generating unit of the ith molecular weight of the polymer will generate a crystal, and P(CCU) is the probability distribution of the crystal generating unit of the entire polymer indicate

(12) In the present invention, in the above (11), the (S2) step is the crystal distribution of the polymer according to the temperature using Equation 7 from the distribution of the crystal generating units of the polymer derived in the (S21) step. It provides a polymer quality prediction method comprising the step (S22) of predicting.

[Equation 7]

In Equation 7, CCU is the value of the crystal generating units of the entire polymer,

T ₀ is the equilibrium temperature, T _C is the crystallization temperature of the polymer, α and β are α 10 to 22 determined through the crystal distribution of the polymer according to temperature, and β is -30 to -15.

(13) The present invention provides a polymer quality prediction method according to (2) above, wherein the molecular weight distribution and short chain branch distribution of the polymer are derived from Equation 8 below.

[Equation 8]

In Equation 8, M is the molecular weight of the polymer,

SCB/C1000 is the number of short chain branches per 1,000 carbons, a _o is -21.444, a ₁ is 13.409, a ₂ is -2.2117, and a ₃ is 0.1251.

(14) The present invention provides a polymer quality prediction method according to any one of (1) to (13) above, wherein the polymer is polyolefin.

(15) The present invention, in the above (14), The polyolefin is ethylene, propylene, 1-butene, 1-pentene, 1-hexene, 4-methyl-1-pentene, 1-octene, 1-decene, 1-dodecene, 1-tetradecene, 1-hexadecene, Provided is a method for predicting the quality of a polymer obtained by polymerizing at least one monomer selected from the group consisting of 1-octadecene and 1-eicosene.

The polymer quality prediction method of the present invention measures the molecular weight distribution and the number distribution of short chain branches of the polymer to model the crystal distribution of the polymer, so that the mechanical properties of the actual polymer product can be obtained without manufacturing the product. can predict In addition, by presenting an optimized polymer structure based on the predicted mechanical property values of the polymer product, it is possible to shorten the development process of the polymer product.

1 shows the molecular weight distribution and the number distribution of short chain branches of high-density polyethylene resin 184K.
2 is a graph showing the result of measurement of crystal distribution according to temperature of high-density polyethylene resin 184K and the result of crystal distribution according to temperature according to the prediction method of the present invention.
3 is a graph showing actually measured internal pressure values of 15 types of high-density polyethylene and a linear regression function of Equation 1.
4 (a) and (b) are high-density polyethylene (HDPE) samples 1 and 2 selected from 35 types of polymer molecular weight distribution and short chain branch number distribution graphs and crystal distribution graphs according to temperature, (c) and (d) is a graph of the molecular weight distribution of samples 3 and 4 selected from among the 24 types of linear low-density polyethylene (LLDPE), the number distribution graph of short chain branches, and the crystal distribution graph according to temperature.
5 is a graph showing the molecular weight distribution predicted for the sample name opt_pert, the distribution of the number of short chain branches, and the molecular weight distribution of the actual pilot product.

Hereinafter, the present invention will be described in more detail to aid understanding of the present invention. At this time, the terms or words used in this specification and claims should not be construed as being limited to ordinary or dictionary meanings, and the inventor appropriately defines the concept of terms in order to explain his/her invention in the best way. It should be interpreted as meaning and concept consistent with the technical idea of the present invention based on the principle that it can be done.

The method for predicting polymer quality of the present invention includes measuring the molecular weight distribution of the polymer and the number distribution of short chain branches (S1), and the average value of crystal generating units of the polymer from the measured molecular weight distribution of the polymer and the number distribution of short chain branches. and calculating a deviation (S2) and a polymer quality prediction method comprising calculating a predicted withstand pressure value from the average value and deviation of crystal forming units of the polymer (S3).

According to an embodiment of the present invention, the average value and deviation of the Critical Crystallizable Unit (hereinafter referred to as CCU) of the polymer are calculated from the measured molecular weight distribution and the number distribution of short chain branches of the polymer, and the crystallizable unit Using the average value and deviation of , it is possible to calculate the predictive value of the withstand voltage of the polymer, and predict the long-term withstand voltage characteristics of the polymer product from the predicted withstand voltage value.

On the other hand, when the ratio of low molecular weight polymer is increased to improve processability during pipe extrusion, there is a problem in that the pressure resistance of the pipe is relatively deteriorated. Accordingly, the present invention provides a parameter capable of deriving a predictive value of withstand pressure using an average value and deviation of a Critical Crystallizable Unit (CCU) of a polymer in order to simultaneously improve processability and withstand pressure. Through this, it is possible to accurately predict long-term withstand pressure characteristics of a polymer product to be manufactured using the polymer without manufacturing the product.

In the present specification, a crystal forming unit (CCU) of a polymer refers to a unit interval between short chain branches (hereinafter referred to as SCBs) located in the main chain of a polymer. Based on the Flory-Huggins theory in relation to the crystal generating unit of the polymer, the polymer may have both a crystalline region and an amorphous region, and the crystalline region may form a lamella structure similar to a sandwich shape. When a short chain branch (SCB) is created in the main chain by the introduction of a monomer, the short chain branch (SCB) is included in the lamellar crystal structure to hinder the growth of the smooth lamella structure of the main chain and an amorphous region contribute to At this time, the interval between the short chain branches (SCB) that can be crystals can be defined as a crystal production unit.

In addition, the predicted withstand pressure value in this specification corresponds to the withstand pressure value measured using the ISO9080 method for pipe products with a diameter of 110 mm to 200 mm made of high-density polyethylene having a density of 0.92 to 0.97 g / cm ³ and a crystallinity of 30 to 70%. It is a parameter that can predict the withstand pressure value and long-term withstand pressure characteristics of the pipe product through the crystal forming unit of the polymer before actually manufacturing it as a pipe product.

According to an embodiment of the present invention, the predicted internal pressure value P may be calculated by Equation 1 below.

[Equation 1]

P = 11.0019 × m _ccu ^-0.08 × σ _ccu ^-0.15 + 3.8414

In Equation 1, m _ccu is the average value of the crystal forming units of the polymer, and σ _ccu is the deviation of the crystal forming units of the polymer.

The predicted withstand pressure value is a parameter capable of predicting the withstand pressure value and long-term withstand pressure characteristics of a pipe product manufactured by processing a polymer as described above, and may be derived from Equation 1 above. Equation 1 above may represent a pressure resistance linear regression function from the average value of the crystal forming units of the polymer and the deviation of the crystal forming units of the polymer. More specifically, the average value and deviation of the crystal producing units may be derived by Equations 2 to 5 to be described below based on the crystal distribution modeling after modeling the distribution of the crystal producing units and the crystal distribution according to temperature.

According to an embodiment of the present invention, the polymer quality prediction method may further include deriving molecular weight distribution and short chain branch distribution of the polymer from the withstand pressure prediction value (S4). As a specific example, the step (S4) may be to derive the molecular weight distribution and short chain branch distribution of the polymer having an estimated withstand pressure value calculated in the step (S3) of greater than 10.2 MPa.

According to one embodiment of the present invention, the polymer quality prediction method can predict the long-term withstand pressure characteristics of the polymer pipe product through the predictive pressure value calculated in the step (S3). Large-diameter pipes of 110 mm to 200 mm need to be manufactured to have long-term stability from deformation or destruction by external pressure and excellent workability, and the long-term pressure resistance required for this purpose may be 13 MPa or more. According to one embodiment of the present invention, the predicted internal pressure value corresponding thereto may exceed 10.2 MPa. That is, according to the polymer quality prediction method, if the predicted withstand pressure value calculated in step (S3) exceeds 10.2 MPa, it may mean that the long-term withstand pressure characteristic value of the polymer pipe product corresponds to 13 MPa or more. Specifically, the predicted internal pressure may be 10.3 MPa or more, 10.5 MPa or more, 10.7 MPa or more, or 10.9 MPa or more, and may also be 15 MPa or less, 14.8 MPa or less, 14.6 MPa or less, or 14.4 MPa or less. When it has a value corresponding to the predicted internal pressure range, it can withstand without deformation for a long time in response to the external force exerted on the polymer pipe product at high temperature.

In addition, as will be described below, the predicted withstand voltage can be derived from the average value and the deviation of the crystal forming units calculated for a given polymer, and the predicted withstand voltage can be obtained for a desired long-term when a given polymer is manufactured into a pipe product. It is a reference point for determining whether or not it can have withstand pressure characteristics. Specifically, when the predicted withstand voltage of the arbitrary polymer exceeds 10.2 MPa, the molecular weight distribution and the number distribution of short chain branches of the arbitrary polymer can be derived by Equation 8 described below, If the predictive pressure resistance of the polymer of is 10.2 MPa or less, a new arbitrary polymer may be selected again as an arbitrary polymer for manufacturing a pipe product. After deriving the average value and the deviation of the crystal forming units for the newly selected arbitrary polymer, it is possible to determine whether the withstand pressure exceeds 10.2 MPa after deriving the predicted value.

According to one embodiment of the present invention, the average value and deviation of the crystal forming unit of the polymer may be calculated from the molecular weight of the polymer and the number distribution of short chain branches.

As described above, the crystal generating unit of the polymer means a unit interval that can be crystallized between the short chain branch (SCB) and the short chain branch, and the crystal generating unit of the polymer is the molecular weight of the polymer and the number of short chain branches. distribution is affected. The average value of crystal generating units for each molecular weight can be calculated through the number and distribution of short chain branches corresponding to each molecular weight. A standard deviation value of each crystal-producing unit may be calculated, and a probability distribution of the crystal-producing unit of the entire polymer may be calculated using a Gaussian distribution from the average value and deviation of the crystal-producing unit.

On the other hand, when the number of short chain branches per 1,000 carbons of the polymer main chain is 20 or more, the persistence length of the polymer increases, and accordingly the average value of the crystal forming unit of the polymer decreases. When the number of short chain branches per 1,000 carbons of the chain is less than 20, the polymer's persistence length decreases, and the average value of the crystal forming units of the polymer increases accordingly.

According to one embodiment of the present invention, the molecular weight distribution of the polymer in the step (S1) can be measured using gel permeation chromatography. In addition, the number distribution of the short chain branches in the step (S1) may be measured by measuring the number of short chain branches per 1,000 carbon atoms constituting the main chain of the polymer using infrared spectroscopy.

More specifically, for the molecular weight distribution of the polymer in the step (S1), a polymer to be analyzed is arbitrarily selected, and the log value of the molecular weight of the polymer measured using gel permeation chromatography (GPC) for the selected polymer is used as the x-axis. And, it can be derived by deriving the molecular weight distribution curve of the polymer using the molecular weight distribution for the logarithmic value as the y-axis. In addition, in the step (S1), the distribution of the number of short chain branches is determined by using infrared spectroscopy (FT-IR) for the selected polymer, with the logarithmic value of molecular weight as the x-axis, and each polymer main chain for the logarithmic value The number of short chain branches per 1,000 carbons (number of short chain branches per 1,000 carbons constituting the main chain, units/1,000C, branched chains with 2 to 7 carbon atoms attached to the main chain) It can be derived by deriving the number distribution curve of the short chain branches of the polymer with ) as the y-axis. In addition, the average value and deviation of the crystal forming unit may be derived based on the molecular weight distribution of the polymer and the number distribution of short chain branches.

According to one embodiment of the present invention, the average value of the crystal forming units of the polymer may be calculated by Equation 2 below.

[Equation 2]

In Equation 2, m _ccu,i is the average value of the ith molecular weight crystal generating unit of the polymer, L _bb is the number of carbon atoms in the main chain, and n _br is the number of short chain branches.

Equation 2 is applicable to all polymers in which short chain branches exist, and the average value of the molecular weight crystal generating units of the polymer, which can be calculated by Equation 2, depends on the number of short chain branches per 1,000 carbon atoms of the polymer as follows. can be expressed differently.

According to an embodiment of the present invention, when the number of short chain branches per 1,000 carbon atoms of the polymer is 20 or more, the average value of crystal forming units of the polymer may be calculated by Equation 3 below.

[Equation 3]

In Equation 3, m _ccu,i is the average value of the ith molecular weight crystal generating unit of the polymer, M is the molecular weight of the polymer, M _br is the molecular weight of the short chain branch, and SCB / C1000 is the short chain branch per 1,000 carbons is the number of

The above Equation 3 and the following Equation 4 are for deriving the average value of the crystal forming unit reflecting the crystallization behavior of the short chain branch from the polymer molecular weight and the number distribution of the short chain branch. When the number of short chain branches per 1,000 carbons of the polymer is 20 or more, the short chain branches act as an element that hinders the crystal, and it is difficult to ignore the effect, so in Equation 3, the short chain branches interfere with the crystal. indicated the extent to which In addition, when the number of short chain branches per 1,000 carbons to which Equation 3 can be applied is 20 or more, the polymer may include linear low density polyethylene (LLDPE).

In addition, according to an embodiment of the present invention, when the number of short chain branches per 1,000 carbon atoms of the polymer is less than 20, the average value of crystal forming units of the polymer may be calculated by Equation 4 below.

[Equation 4]

In Equation 4, m _ccu,i is the average value of the ith molecular weight crystal generating unit of the polymer, M is the molecular weight of the polymer, M _br is the molecular weight of the short chain branch, and SCB / C1000 is the short chain branch per 1,000 carbons is the number of

When the number of short chain branches per 1,000 carbon atoms to which Equation 4 can be applied is less than 20, high density polyethylene (HDPE) may be included.

According to one embodiment of the present invention, the deviation of the crystal production unit may be calculated by Equation 5 below.

[Equation 5]

In Equation 5, σ _ccu,i is the deviation of the ith molecular weight crystal generating unit of the polymer, m _ccu,i is the average value of the ith molecular weight crystal generating unit of the polymer, L _bb is the number of carbon atoms in the main chain, and n _br is is the number of short chain branches.

The deviation of the crystal generating unit may be derived using Equation 5 above.

According to one embodiment of the present invention, the step (S2) is the step (S21) of deriving the distribution of crystal forming units of the polymer using a Gaussian distribution from the average value and deviation of the crystal forming units of the polymer (S21) and the above (S21) ) using the Gibbs-Thomson equation from the distribution of crystal generating units of the polymer derived in step (S22) of predicting the crystal distribution of the polymer according to the temperature.

According to one embodiment of the present invention, the probability distribution of the crystal producing units of the polymer in the step (S21) can be calculated using the Gaussian distribution represented by Equation 6 from the average value and the deviation value of the crystal producing units.

[Equation 6]

In Equation 6, m _ccu,i and σ _ccu,i are as defined in Equations 2 to 5, CCU is the value of the entire crystal unit of the polymer, and f(CCU,i) is the i-th polymer It is the probability that the crystal generating unit of the molecular weight will produce a crystal, and P(CCU) represents the probability distribution of the crystal generating unit throughout the polymer.

When the probability distribution of the crystal production unit is high, it means that the sizes of crystals that can be formed are diverse, and when the probability distribution of the crystal production units is low, it means that the size of crystals that can be generated is limited.

In addition, according to one embodiment of the present invention, the crystal distribution of the polymer according to the temperature in the step (S22) can be calculated using Equation 7 below from the distribution of crystal generating units calculated by Equation 6 above.

[Equation 7]

In Equation 7, CCU is the crystal production unit value of the entire polymer, T ₀ is the equilibrium temperature, T _C is the crystallization temperature of the polymer, and α and β are the crystal distribution of the polymer according to the temperature. As representing the constant to be determined, the constant α means the interfacial energy value of the polymer crystallization process and is 10 to 22, and β is a semi-empirical parameter and is -30 to -15 depending on the type of sample to be measured.

Equation 7 is derived based on the Gibbs-Thomson equation based on the thermodynamic crystallization theory of Gibbs-Thomson.

According to an embodiment of the present invention, in the step (S2), the crystal distribution of the polymer according to the temperature derived from Equation 7 and the determination of the polymer according to the temperature measured using the true temperature rising elution fractionation (TREF) When the distributions are compared, the accuracy may be 80% or more, and the accuracy means that the reliability of the linear regression function represented by Equation 3 is high. As a specific example, when the accuracy is 80% or more, the error value between the internal pressure prediction value derived from the average value and deviation of the crystal generating units calculated in step (S2) and the actually measured internal pressure value is low, and the linearity of Equation 3 is This means that the accuracy of the regression function is high. In particular, a high-density polyethylene polymer having a density of 0.92 to 0.97 g/cm ³ and a crystallinity of 30 to 70% may have an accuracy of about 92.5% or more, and a linear low-density polyethylene polymer under the same conditions may have an accuracy of about 81.9% or more. there is.

According to an embodiment of the present invention, the molecular weight distribution and short chain branch distribution of the polymer having the predicted withstand pressure value exceeding 10.2 MPa can be derived by Equation 8 below.

[Equation 8]

In Equation 8, M is the molecular weight of the polymer,

SCB/C1000 is the number of short chain branches per 1,000 carbons, a _o is -21.444, a ₁ is 13.409, a ₂ is -2.2117, and a ₃ is 0.1251.

In addition, according to one embodiment of the present invention, in the polymer quality prediction method of the present invention, the polymer may be polyolefin, and the polyolefin is ethylene, propylene, 1-butene, 1-pentene, 1-hexene, 4-methyl- Polymerizing at least one monomer selected from the group consisting of 1-pentene, 1-octene, 1-decene, 1-dodecene, 1-tetradecene, 1-hexadecene, 1-octadecene, and 1-eicosene It can be a polymer or a copolymer.

The method for predicting the quality of a polymer of the present invention can predict in advance the long-term withstand pressure characteristics of a product manufactured using a polymer resin, particularly a polyolefin resin, and is preferably used for small diameter and/or large diameter high pressure pipes. It may be suitable for predicting long-term withstand pressure characteristics.

Hereinafter, embodiments of the present invention will be described in detail so that those skilled in the art can easily implement the present invention. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein.

Example 1: Derivation of the linear regression function of Equation 1

(1) Actual measurement of molecular weight distribution of polymer and number distribution of short chain branches

1 shows the molecular weight distribution and the number distribution of short chain branches of high-density polyethylene resin 184K.

High-density polyethylene resin 184K (weight average molecular weight 184,000 g/mol, density 0.947 g/cm ³ , crystallinity 65.2%) was measured for continuous molecular weight distribution using gel permeation chromatography (GPC). A molecular weight distribution curve was obtained with the logarithmic value (LogM) of the molecular weight as the x-axis and the molecular weight distribution (dw/dLogM) relative to the logarithmic value as the y-axis, and shown as a continuous solid line in FIG. 1 below.

Then, using an infrared spectroscopy (FT-IR) device, the log value (LogM) of the molecular weight obtained by gel permeation chromatography is set as the x-axis, and the short per 1,000 carbons for the molecular weight value obtained by the FT-IR device The number of chain branches (the number of short chain branches attached to the main chain per 1,000 carbon atoms constituting the main chain, units/1,000C, short chain branches are branched chains with 2 to 7 carbon atoms attached to the main chain) is y A distribution curve of the number of short chain branches serving as an axis was obtained, and is indicated by a discontinuous blue dotted line in FIG. 1 below.

At this time, the measuring equipment and measuring conditions of the gel permeation chromatography are as follows.

<Measuring device>

Water Pl-GPC220

<Measurement conditions>

A Polymer Laboratories PLgel MIX-B 300 mm long column was used for evaluation using a Waters PL-GPC220 instrument. The evaluation temperature is 160 ℃, 1,2,4-trichlorobenzene (1,2,4-Trichlorobenzene) was used as a solvent, the flow rate was 1 mL / min, and the sample was prepared at a concentration of 10 mg / 10 mL Supplied in an amount of 200 μL, polystyrene standards (molecular weight: 2,000 g/mol, 10,000 g/mol, 30,000 g/mol, 70,000 g/mol, 200,000 g/mol, 700,000 g/mol, 2.000.000 g/mol, 4,000,000 g/mol, 10,000,000 g/mol) was used to determine the molecular weight using a calibration curve.

In addition, the FT-IR apparatus and measurement conditions are as follows.

<Measuring device>

PerkinElmer Spectrum 100 FT-IR

<Measurement conditions>

It was measured using a PerkinElmer Spectrum 100 FT-IR connected to GPC (PL-GPC220).

Specifically, when the molecular weight distribution curve is drawn with the log value (log M) of the weight average molecular weight (M) as the X-axis and the molecular weight distribution (dwt/dlog M) for the log value as the Y-axis, the total area SCB (Short Chain Branch) content (number of short chain branches per 1,000 carbons constituting the main chain, units/1,000C, short chain branches) at the left and right borders of 60% of the middle except for the 20% of the left and right ends It is a branched chain having 2 to 7 carbon atoms bonded to the main chain.) was measured and the BOCD Index was calculated using Equation 9 below. The BOCB (Broad Orthogonal Comonomer Distribution) is a value indicating how concentrated the content of a comonomer such as an alpha olefin is in a high molecular weight main chain. As the number of short chain branches increases toward the high molecular weight, the BOCD value increases. At this time, the SCB content on the high molecular weight side and the SCB content on the low molecular weight side mean the SCB content values at the right and left boundaries of the middle 60% range, respectively. After pretreatment by melting in 1,2,4-Trichlorobenzene at 160 °C for 10 hours, measurement was performed at 160 °C using a PerkinElmer Spectrum 100 FT-IR connected to high-temperature GPC (PL-GPC220).

[Equation 9]

BOCD　Index = (high molecular weight side SCB content - low molecular weight side SCB content) / (low molecular weight side SCB content)

(2) Comparison of the result of measuring the crystal distribution of polymer according to temperature and the result of crystal distribution of polymer according to temperature according to the prediction method of the present invention

Figure 2 shows the result of measuring the crystal distribution according to the temperature of the high-density polyethylene resin 184K and the result of the crystal distribution according to the temperature according to the prediction method of the present invention.

The crystal distribution of the high-density polyethylene resin 184K according to temperature was measured using a TREF (Temperature Rising Elution Fractionation) method. A crystal distribution curve according to temperature was obtained with temperature (T) as the x-axis and molecular weight distribution (dw/dT) with respect to the temperature as the y-axis, and indicated by a continuous black line in FIG. 2 below.

In comparison, the measured results of the molecular weight distribution and the number distribution of short chain branches of the high-density polyethylene resin 184K shown in FIG. 2 were applied to Equations 4 to 7 to predict the crystal distribution according to the temperature of the high-density polyethylene resin , The prediction results are indicated by discontinuous red dotted lines in FIG. 2 below.

At this time, the measurement method of TREF is as follows.

TREF was measured using PolymerChar's TREF machine and using o-dichlorobenzene as a solvent in the range of -20 °C to 120 °C. Specifically, 80 mg of the polymer sample was dissolved in 20 ml of an o-dichlorobenzene solvent at 135° C. for 30 minutes and then stabilized at 95° C. for 30 minutes. After introducing this into a TREF column, it was cooled to -20°C at a temperature decreasing rate of 0.5°C/min, and held for 2 minutes. Thereafter, while heating from -20 °C to 130 °C at a temperature decrease rate of 1 °C/min, o-dichlorobenzene as a solvent was flowed through the column at a flow rate of 0.5 mL/min to measure the concentration of the eluted polymer.

Equations 4 to 7 used to predict the crystal distribution according to temperature based on the measured results of the molecular weight distribution of the high-density polyethylene resin 184K and the number distribution of short chain branches are as follows.

[Equation 4]

In Equation 4, m _ccu,i is the average value of the ith molecular weight crystal generating unit of the polymer, M is the molecular weight of the polymer, M _br is the molecular weight of the short chain branch, and SCB / C1000 is the short chain branch per 1,000 carbons is the number of

When the number of short chain branches per 1,000 carbon atoms to which Equation 4 can be applied is less than 20, high density polyethylene (HDPE) may be included.

[Equation 5]

In Equation 5, σ _ccu,i is the deviation of the ith molecular weight crystal generating unit of the polymer, m _ccu,i is the average value of the ith molecular weight crystal generating unit of the polymer, L _bb is the number of carbon atoms in the main chain, and n _br is is the number of short chain branches.

[Equation 6]

In Equation 6, m _ccu,i and σ _ccu,i are as defined in Equations 4 to 7, CCU is the value of the entire crystal generating unit of the polymer, and f(CCU,i) is the i-th polymer It is the probability that the crystal generating unit of the molecular weight will produce a crystal, and P(CCU) represents the probability distribution of the crystal generating unit throughout the polymer.

[Equation 7]

In Equation 7, CCU is the crystal production unit value of the entire polymer, T ₀ is the equilibrium temperature, T _C is the crystallization temperature of the polymer, and α and β are the crystal distribution of the polymer according to the temperature. Indicates the constant to be determined.

Referring to FIG. 2 , it can be seen that the result of predicting the crystal distribution of the high-density polyethylene resin according to the prediction method of the present invention is very similar to the TREF analysis result of the actual high-density polyethylene resin.

(3) Derivation of the linear regression function of Equation 1

After confirming that the result of the crystal distribution according to the temperature predicted by the prediction method based on the high-density polyethylene 184K and the TREF analysis result actually measured are similar, the high-density polyethylene having the sample name, withstand pressure, and withstand pressure prediction value shown in Table 1 below ( HDPE) 15 types were prepared.

division sample name type Pressure resistance (MPa) Pressure prediction (MPa) One PE 20 HDPE 10.32 10.06822 2 PE 3 HDPE 11.45 11.18693 3 PE 21 HDPE 10.14 10.36944 4 PE 22 HDPE 9.63 9.68673 5 PE 23 HDPE 10.26 10.05853 6 PE 13 HDPE 10.45 10.20889 7 PE 24 HDPE 10.3 10.30607 8 PE 25 HDPE 10.35 10.51606 9 PE 26 HDPE 10.4 10.43059 10 PE 27 HDPE 9.58 10.10111 11 PE 16 HDPE 10.42 10.19536 12 PE 28 HDPE 10.08 10.1228 13 PE 29 HDPE 10.41 10.14444 14 PE 12 HDPE 9.87 10.12226 15 PE 17 HDPE 9.99 10.15124

The measurement results of each molecular weight distribution and the number distribution of short chain branches for the 15 types of high-density polyethylene were applied to Equations 4 to 7 to derive a crystal distribution curve according to the temperature of each of the 15 types of high-density polyethylene. Based on the crystal distribution curve according to the temperature of each of the 15 types of high-density polyethylene, the average value (m _ccu ) and the standard deviation value (σ _ccu ) of all crystal production units of each polymer are calculated, and the crystal production unit A linear regression function represented by Equation 1 was derived from the average value and standard deviation of , and the internal pressure value measured by the actual ISO 9080 method. As a graph displaying predicted values, the predicted values of the withstand pressure of each sample calculated by Equation 1 above are indicated on the X-axis in FIG. [Equation 1]

P = 11.0019 × m _ccu ^-0.08 × σ _ccu ^-0.15 + 3.8414

In Equation 1, m _ccu is the average value of the crystal forming units of the polymer, and σ _ccu is the deviation of the crystal forming units of the polymer.

Experimental Example 1: Accuracy Verification of the Linear Regression Function of Equation 1

35 types of high-density polyethylene resins (HDPE) and 24 types of linear low-density polyethylene (LLDPE) having a density of 0.92 g/cm ³ to 0.97 g/cm ³ and a crystallinity of 30% to 70% as shown in Table 2 were prepared.

division sample name type Density (g/cm ³ ) Mw (g/mol) BOCD Crystallinity (%) One PE 30 HDPE 0.9434 160,043 4.4 62.43 2 PE 31 HDPE 0.9460 170,997 1.9 65.43 3 PE 32 HDPE 0.9444 180,379 0.93 64.62 4 PE 33 HDPE 0.9442 210,294 0.84 65.16 5 PE 8 HDPE 0.9440 212,654 0.87 65.22 6 PE 9 HDPE 0.9465 166,876 1.41 65.69 7 PE 10 HDPE 0.9456 198,182 0.93 65.17 8 PE 12 HDPE 0.9432 184,763 1.19 67.18 9 PE 14 HDPE 0.9464 185,664 1.38 61.26 10 PE 13 HDPE 0.9477 184,498 1.45 62.72 11 PE 18 HDPE 0.9468 176,324 1.33 60.81 12 PE 16 HDPE 0.9444 174,841 0.91 62.31 13 PE 34 HDPE 0.9436 191,971 0.61 66.01 14 PE 17 HDPE 0.9466 169,556 0.51 61.56 15 PE 18 HDPE 0.9473 178,087 0.69 63.89 16 PE 28 HDPE 0.9452 177,873 0.65 67.75 17 PE 29 HDPE 0.9468 187,457 0.67 63.52 18 PE 23 HDPE 0.9475 188,421 1.01 61.43 19 PE 35 HDPE 0.9471 175,568 0.84 63.37 20 PE 36 HDPE 0.9476 175,460 0.95 57.36 21 PE 37 HDPE 0.9470 150,693 0.78 62.19 22 PE 38 HDPE 0.9480 171,164 0.75 64.34 23 PE 39 HDPE 0.9479 145,539 0.99 71.32 24 PE 40 HDPE 0.9473 154,727 0.96 63.45 25 PE 41 HDPE 0.9482 151,207 0.47 64.76 26 PE 42 HDPE 0.9480 146,192 0.49 65.53 27 PE 43 HDPE 0.9478 146,379 1.08 65.70 28 PE 44 HDPE 0.9464 160,452 1.34 61.32 29 PE 45 HDPE 0.9489 152,992 1.22 61.94 30 PE 46 HDPE 0.9467 159,158 1.03 63.39 31 PE 47 HDPE 0.9463 167,276 0.4 63.57 32 PE 48 HDPE 0.9475 160,977 0.65 63.32 33 PE 49 HDPE 0.9476 160,655 0.87 61.85 34 PE 50 HDPE 0.9482 181,724 2.1 62.11 35 PE 20 HDPE 0.9467 203,081 3.26 61.09 36 PE 51 LLDPE 0.9162 143,000 13.25986 - 37 PE 52 LLDPE 0.9171 113,000 9.027358 - 38 PE 53 LLDPE 0.9177 126,000 9.104695 - 39 PE 54 LLDPE 0.9198 120,000 7.354053 - 40 PE 55 LLDPE 0.9195 99,000 14.01592 - 41 PE 56 LLDPE 0.9191 94,000 15.26164 - 42 PE 57 LLDPE 0.9189 98,000 8.455305 - 43 PE 58 LLDPE 0.9189 99,000 9.460663 - 44 PE 59 LLDPE 0.9215 120,000 11.82211 - 45 PE 60 LLDPE 0.9195 122,000 6.270674 - 46 PE 61 LLDPE 0.9196 115,000 3.842905 - 47 PE 62 LLDPE 0.9198 112,000 4.127137 - 48 PE 63 LLDPE 0.9196 116,000 7.285705 - 49 PE 64 LLDPE 0.92 111,000 6.896074 - 50 PE 65 LLDPE 0.9202 117,000 7.813168 - 51 PE 66 LLDPE 0.9215 120,000 1.396253 - 52 PE 67 LLDPE 0.9217 113,000 3.98112 - 53 PE 68 LLDPE 0.9216 114,000 4.23385 - 54 PE 69 LLDPE 0.9218 117,000 3.742811 - 55 PE 70 LLDPE 0.9199 118,000 7.354053 - 56 PE 71 LLDPE 0.9198 120,000 8.127984 - 57 PE 72 LLDPE 0.9198 121,000 8.901916 - 58 PE 73 LLDPE 0.92 109,000 5.155863 - 59 PE 74 LLDPE 0.9196 106,000 6,92959 -

(BOCB stands for Broad Orthogonal Comonomer Distribution and is a value indicating how concentrated the content of comonomers such as alpha olefins is in the high molecular weight main chain. The BOCD value increases as the number of short chain branches goes toward the high molecular weight. BOCD was measured in the same way as in (1) of Example 1, and the density was measured at 23 ° C. using a METTLER TOLEDO XS104 device according to ASTM D1505 standard.) First, gel permeation chromatograph The molecular weight distribution curve and the number distribution curve of short chain branches were derived using (GPC) and infrared spectroscopy (FT-IR). The measurement method using the gel permeation chromatograph (GPC) and infrared spectroscopy (FT-IR) apparatus is the same as described in (1) of Example 1.

Then, the crystal distribution according to the temperature was measured using the TREF (Temperature Rising Elution Fractionation) method for the 35 types of high-density polyethylene resins and 24 types of linear low-density polyethylene, and the molecular weight of the 35 types of high-density polyethylene resins and 24 types of linear low-density polyethylene. A crystal distribution according to the calculated temperature was derived by applying the measurement results of the distribution and the number distribution of short chain branches to the above-described Equations 4 to 7. The measurement method using the TREF is the same as described in (2) of Example 1 above.

As a result of comparing the crystal length value for each temperature with respect to the actual TREF measured value and the calculated crystal distribution according to the temperature for the 35 types of high-density polyethylene resins and 24 types of linear low-density polyethylene described in Table 4, the high-density polyethylene It was confirmed that an accuracy of 92.5% was exhibited for 35 types of resin, and an accuracy of 81.9% was shown for 24 types of linear low-density polyethylene resin.

Two of the 35 types of high-density polyethylene (HDPE) were arbitrarily selected, and two types of the linear low-density polyethylene (LLDPE) were randomly selected, and samples 1 to 4 were shown in Table 3, respectively. The polymer molecular weight distribution of sample 2, the distribution graph of the number of short chain branches, and the crystal distribution graph according to temperature are shown in (a) and (b) of FIG. 4, and the molecular weight distribution of samples 3 and 4 and the number distribution of short chain branches The graph and the crystal distribution graph according to temperature are shown in (c) and (d) of FIG. 4 .

division sample name type Density (g/cm ³ ) Mw (g/mol) BOCD Crystallinity (%) sample 1 PE 36 HDPE 0.9444 180,379 0.93 64.62 sample 2 PE 37 HDPE 0.9460 170,997 1.9 65.43 sample 3 PE 74 LLDPE 0.9196 106,000 6,92959 - sample 4 PE 59 LLDPE 0.9215 120,000 11.82211 -

Referring to FIG. 4, sample 1 (sample name PE 36) and sample 2 (sample name PE 37) are HDPE, and the crystal distribution according to the temperature measured by the actual TREF and the temperature calculated using Equations 4 to 7 above. It can be seen that the crystal distribution according to is quite similar at low and high temperatures. Sample 3 (sample name PE 74) and sample 4 (sample name PE 59) are LLDPE, and as described above, the degree of similarity is lower than that of HDPE, but it can be seen that the crystal distribution tendency is similar at a temperature of 90 ° C. In this way, it was confirmed that the crystal distribution according to the temperature actually measured by TREF and the crystal distribution according to the calculated temperature were similar, and the accuracy of the average value and deviation of the crystal producing unit was high. In the case of medium PE 50, it can be confirmed that the actual internal pressure measured by the ISO9080 method is 10.62 MPa, and the predicted internal pressure calculated using Equation 1 from the average value and deviation of the crystal producing unit is 10.2 MPa, from which the internal pressure It was confirmed that the predicted value had an accuracy within 5% of the error value compared to the actual internal pressure. Accordingly, it was confirmed that the accuracy of the regression equation of Equation 1 below was also high. [Equation 1]

P = 11.0019 × m _ccu ^-0.08 × σ _ccu ^-0.15 + 3.8414

In Equation 1, m _ccu is the average value of the crystal forming units of the polymer, and σ _ccu is the deviation of the crystal forming units of the polymer.

Experimental Example 2: Derivation of number distribution of optimal short chain branches of polymer

In this experimental example, the internal pressure value required for the actual product was set to be greater than 10.2 MPa, and the optimum short chain branch of sample 5 (sample name opt_pert) satisfying the internal pressure value of 11.3695 MPa, which is greater than 10.2 MPa based on the above-mentioned Equation 1 The number distribution of was derived from Equation 8 below.

[Equation 8]

In Equation 8, M is the molecular weight of the polymer,

SCB/C1000 is the number of short chain branches per 1,000 carbons;

a _o is -21.444, a ₁ is 13.409, a ₂ is -2.2117, and a ₃ is 0.1251.

In addition, FIG. 5 shows the number distribution of optimal short chain branches of each sample calculated by Equation 8 for sample 5 (sample name opt_pert). Referring to FIG. 5, it can be seen that the pilot product actually manufactured from Sample 5 and the molecular weight distribution and the number distribution of short chain branches derived by the polymer quality prediction method of the present invention are similar. Through this, the polymer quality prediction method of the present invention can derive the optimal quality of polymers having optimal long-term pressure resistance characteristics in advance without manufacturing pipe products.

Claims (15)

measuring the molecular weight distribution of the polymer and the number distribution of short chain branches (S1);
Calculating average values and deviations of the crystal forming units of the polymer from the measured molecular weight distribution and the distribution of the number of short chain branches of the polymer (S2); and
A method for predicting polymer quality comprising the step (S3) of calculating an internal pressure prediction value from the average value and deviation of crystal forming units of the polymer.
The method of claim 1,
The method of predicting polymer quality further comprising the step (S4) of deriving molecular weight distribution and short chain branch distribution of the polymer from the pressure resistance prediction value.
The method of claim 1,
In step (S1), the molecular weight distribution of the polymer is measured using gel permeation chromatography.
The method of claim 1,
In the step (S1), the number distribution of the short chain branches is measured by using infrared spectroscopy to measure the number of short chain branches per 1,000 carbon atoms of the main chain of the polymer.
The method of claim 1,
The polymer quality prediction method in which the pressure prediction value (P) is calculated by Equation 1 below:
[Equation 1]
P = 11.0019 × m _ccu ^-0.08 × σ _ccu ^-0.15 + 3.8414
In Equation 1, m _ccu is the average value of the crystal forming units of the polymer, and σ _ccu is the deviation of the crystal forming units of the polymer.
The method of claim 1,
The method for predicting polymer quality, wherein the predicted pressure value is greater than 10.2 MPa.
The method of claim 1,
Polymer quality prediction method in which the average value of the crystal forming units of the polymer is calculated by Equation 2 below:
[Equation 2]

In Equation 2, m _ccu,i is the average value of the ith molecular weight crystal generating unit of the polymer, L _bb is the number of carbon atoms in the main chain, and n _br is the number of short chain branches.
The method of claim 1,
When the number of short chain branches per 1,000 carbons of the polymer is 20 or more, the average value of the crystal forming unit of the polymer is calculated by Equation 3 below: Polymer quality prediction method:
[Equation 3]

In Equation 3 above,
m _ccu,i is the average value of the ith molecular weight crystal generating unit of the polymer,
M is the molecular weight of the polymer, M _br is the molecular weight of short chain branches, and SCB/C1000 is the number of short chain branches per 1,000 carbons.
The method of claim 1,
When the number of short chain branches per 1,000 carbons of the polymer is less than 20, the average value of the crystal forming units of the polymer is calculated by Equation 4 below: Polymer quality prediction method:
[Equation 4]

In Equation 4, m _ccu,i is the average value of the ith molecular weight crystal generating unit of the polymer,
M is the molecular weight of the polymer, M _br is the molecular weight of short chain branches, and SCB/C1000 is the number of short chain branches per 1,000 carbons.
The method of claim 1,
Polymer quality prediction method in which the deviation of the crystal production unit is calculated by Equation 5 below:
[Equation 5]

In Equation 5, σ _ccu,i is the ith molecular weight crystal generation unit deviation of the polymer,
m _ccu,i is the average value of the ith molecular weight crystal generating unit of the polymer, L _bb is the number of carbon atoms in the main chain, and n _br is the number of short chain branches.
The method of claim 1,
The step (S2) includes the step (S21) of deriving the distribution of the crystal forming units of the polymer using the following Equation 6 from the average value and deviation of the crystal forming units of the polymer:
[Equation 6]

In Equation 6, m _ccu,i is the average value of the ith molecular weight crystal generating unit of the polymer, σ _ccu,i is the deviation of the ith molecular weight crystal generating unit of the polymer,
The CCU is the value of the crystal generating unit of the entire polymer, f(CCU,i) is the probability that the crystal generating unit of the ith molecular weight of the polymer will generate a crystal, and P(CCU) is the probability distribution of the crystal generating unit of the entire polymer indicate
The method of claim 11,
The (S2) step predicts the polymer quality including the step (S22) of predicting the crystal distribution of the polymer according to the temperature using Equation 7 below from the distribution of crystal generating units of the polymer derived in the step (S21). method:
[Equation 7]

In Equation 7, CCU is the value of the crystal generating units of the entire polymer,
T ₀ is the equilibrium temperature, T _C is the crystallization temperature of the polymer, α is 10 to 22, and β is -30 to -15.
The method of claim 2,
The polymer quality prediction method in which the molecular weight distribution and short chain branch distribution of the polymer is derived from Equation 8 below:
[Equation 8]

In Equation 8, M is the molecular weight of the polymer,
SCB/C1000 is the number of short chain branches per 1,000 carbons, a _o is -21.444, a ₁ is 13.409, a ₂ is -2.2117, and a ₃ is 0.1251.
The method of claim 1,
Polymer quality prediction method, wherein the polymer is polyolefin.
According to claim 14,
The polyolefin is ethylene, propylene, 1-butene, 1-pentene, 1-hexene, 4-methyl-1-pentene, 1-octene, 1-decene, 1-dodecene, 1-tetradecene, 1-hexadecene, A method for predicting the quality of a polymer obtained by polymerizing at least one monomer selected from the group consisting of 1-octadecene and 1-eicosene.

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