KR20220046341A - Method for predicting properties of polyethylene resin - Google Patents

Abstract

The present invention relates to a method for predicting properties of a polyethylene resin, which can reliably predict a change in properties of a polyethylene resin in advance from melting characteristics of the polyethylene resin and a content ratio by structure. The method for predicting properties of a polyethylene resin comprises the steps of: a first step of measuring a melt index (MI), for a polyethylene resin to be measured, under load conditions of 2.16 kg and 21.6 kg according to ASTM D1238, and deriving a melt flow rate ratio (MFRR) thereof; a second step of measuring a change in molecular weight distribution through gel permeation chromatography (GPC) and a change in complex viscosity through a dynamic frequency sweep test, for the polyethylene resin to be measured, and deriving the content of long chain branches (LCB) having 8 or more carbon atoms per 1,000 atoms of a branched resin included in the polyethylene resin to be measured; a third step of performing multiple liner regression on the relationship of y (rheological properties of the polyethylene resin to be measured), x_1 (melt index under the load condition of 2.16 kg), x_2 (melt flow index ratio), and x_3 (content of LCB) with a linear function; and a fourth step of predicting rheological properties of a polyethylene resin from the multiple linear regression function.

Description

Method for predicting physical properties of polyethylene resin

The present invention relates to a method for predicting physical properties of a polyethylene resin, and relates to a method for predicting thermal processing properties affecting processability and quality when a polyethylene resin is applied as a film from the melt characteristics of the polyethylene resin and the content ratio for each structure.

The physical properties of the polymer are greatly affected by structural parameters such as the shape of the polymer, the weight average molecular weight, and the polydispersity index. In general, a polymer is classified into a linear polymer or a branched polymer according to a shape, and the branched polymer refers to a structure formed by branching various types of chains in a main chain of a polymer. In particular, in the case of a branched polymer, the molecular weight, distribution, and number of side chains greatly affect the physical properties of the polymer. Therefore, in order to effectively control the physical properties of the polymer, it is necessary to accurately grasp the structure of the polymer.

On the other hand, heat shrinkable film refers to a film used for product packaging by inducing contraction of the film in one or both directions by heat at a constant temperature. It is widely used for the convenience of manual and automatic transfer. Heat-shrinkable film is generally manufactured by a blown film forming method, and is stretched in the machine direction (MD: Machine Direction) and transverse direction (TD: Transverse Direction) when the film is manufactured and cooled with stress.

This heat-shrinkable film is used in the shrink packaging process to induce stress relief through heat treatment after packaging the product to be packaged, and as a result, shrinkage occurs. During blown film molding, the draw down ratio (DDR: draw down ratio) and expansion ratio (BUR: blow up ratio) are adjusted through repeated experiments to achieve the desired shrinkage ratio.

On the other hand, the most widely used heat-shrinkable film is low-density polyethylene (LDPE), which usually satisfies the MD shrinkage of about 60% or more, but the TD shrinkage varies from about 10% to 20% depending on the intrinsic properties of the polymer. Therefore, it is important to control it. In particular, it is important to raise the TD shrinkage rate, which is significantly lower than the MD shrinkage rate, to an appropriate range. Since this shrinkage characteristic has a great influence on the processability of the manufacturing process and the quality of the manufactured product, predicting the TD shrinkage rate is very important for improving productivity.

However, there are no clear standards or numerical ranges for analyzing the tendency of TD shrinkage change according to polymer properties, and specific control factors for controlling this are not known. Until now, there has been a problem in that in order to derive the desired TD shrinkage rate, numerous trials and errors are required through actual experiments.

Accordingly, the present invention is to provide a method for predicting the physical properties of a polyethylene resin that can reliably predict the change in the physical properties of the polyethylene resin.

The present invention in order to solve the above problems,

For the polyethylene resin to be measured, in accordance with ASTM D1238, the melt index (MI) is measured under load conditions of 2.16 kg and 21.6 kg, and their melt flow rate ratio (MFRR) is derived. first step;

For the polyethylene resin to be measured, the molecular weight distribution change through gel permeation chromatography (GPC) and the complex viscosity change through the dynamic frequency sweep test are respectively measured, and from this, the carbon of the branched resin included in the measurement target polyethylene resin a second step of deriving the content of a long chain branch (LCB) having 8 or more carbon atoms per 1,000 atoms;

The rheological property of the polyethylene resin to be measured is set to y, the melt index of the load condition of 2.16 kg derived in the first step is set to x ₁ , the melt flow index ratio is set to x ₂ , and the second a third step of multiple linear regression of the relationship of y, x ₁ , x ₂ and x ₃ to a linear function by setting the long chain branching content derived in the step to x ₃ ;

From the multiple linear regression function, it provides a method for predicting the physical properties of a polyethylene resin, including a fourth step of predicting the rheological properties of the polyethylene resin.

As described above, according to the present invention, the change in physical properties of the polyethylene resin can be reliably predicted in advance from the melting characteristics of the polyethylene resin and the content ratio for each structure.

Therefore, by applying the method for predicting the physical properties of the polyethylene resin of the present invention, the target heat shrinkage properties are predicted, and accordingly, the manufacturing process into a film can be more effectively performed, and the quality of the film can be effectively predicted.

1 is a graph showing the GPC molecular weight distribution for Examples 1 to 5.
2 is a graph showing the complex viscosity of ARES (Advanced Rheometric Expansion System) for Examples 1 to 5.

Hereinafter, a method for predicting physical properties of a polyethylene resin according to an embodiment of the present invention will be described in detail for each step.

The terminology used herein is used to describe exemplary embodiments only, and is not intended to limit the invention. The singular expression includes the plural expression unless the context clearly dictates otherwise. In this specification, terms such as "comprises", "comprising" or "have" are intended to designate the presence of an embodied feature, step, element, or a combination thereof, but one or more other features or steps; It should be understood that the possibility of the presence or addition of components, or combinations thereof, is not precluded in advance.

In addition, terms such as first, second, third, etc. are used to describe various components, and the terms are used only for the purpose of distinguishing one component from other components.

Since the invention can have various changes and can have various forms, specific embodiments are illustrated and described in detail below. However, this is not intended to limit the invention to the specific disclosed form, it should be understood to include all modifications, equivalents and substitutes included in the spirit and scope of the invention.

When a polyethylene resin is manufactured into a product, the physical properties of the final product vary depending on the detailed conditions of the thermoforming process. Since it is necessary to consider the change pattern according to the snow-type process conditions, there were problems such as complexity and time consuming.

To solve this problem, the present inventors set the melting characteristics and content ratio of each structure of the polyethylene resin as an independent variable, and set the rheological property change aspect of the polyethylene resin as a dependent variable, to derive a multiple linear regression function, and to satisfy the reliability range. Constants and coefficients of the regression function were derived, respectively. Through this, by reliably predicting the TD shrinkage rate in advance, it was confirmed that the quality of the product could be determined in advance, and the present invention was completed.

First, in one embodiment of the invention, the polyethylene resin to be measured is in the form of a mixture, and includes a resin classified into a linear resin and a branched resin depending on the structure.

Here, the branched resin may be a resin in which a weight average molecular weight ratio of the side chain to the main chain is 40% or less, or a weight average molecular weight of only the side chain is 3,000 or more. When the weight average molecular weight ratio of the side chain to the main chain exceeds 40%, since the side chain may act as a part of the main chain rather than the side chain depending on the position occupied by the main chain, the analysis reliability for the branched resin may be reduced. In addition, when the weight average molecular weight of the side chain of the branched resin is less than 3,000, the difference in rheological properties of the polymer depending on the side chain is insignificant, so the use of the method for predicting physical properties of the present invention may be limited.

In addition, the branched resin may be classified into a comb-like resin, a star-like resin, and an H-type resin depending on the shape of the side chain bonded to the main chain. Polyethylene resin following a general polymerization method is a branched resin and a comb resin is predominant.

In one embodiment of the invention, the polyethylene resin to be measured further includes a linear resin in addition to the branched resin.

The linear resin is divided into a first linear resin having a low molecular weight and a second linear resin having a high molecular weight according to a weight average molecular weight. The low molecular weight first linear resin may have a weight average molecular weight of less than 80,000, preferably 50,000 or less, or 30,000 to 50,000, and the high molecular weight second linear resin may have a weight average molecular weight of 80,000 or more, preferably 90,000 or more, or 90,000 to It can be 150,000.

In summary, in the method for predicting physical properties of the present invention, the polyethylene resin to be measured is a branched resin, a low molecular weight first linear resin having a weight average molecular weight of less than 80,000, and a high molecular weight second linear resin having a weight average molecular weight of 80,000 or more It can be a mixture containing there is.

On the other hand, the content of a long chain branch (LCB) in the side chain of the branched resin corresponds to one of the independent variables of the multiple linear regression function to be described later.

For reference, the branch of the polyethylene resin is that alpha-olefin comonomers such as 1-butene, 1-hexene, and 1-octene are incorporated into the carbon chain of the main chain during the polymerization process to produce a branched form, When the number of carbon atoms per 1,000 main chain carbon atoms is 2 to 7, it is a short chain branch (SCB), and when it has 8 or more carbon atoms per 1,000 main chain carbon atoms, it means a long chain branch (LCB).

Hereinafter, a method for measuring a polyethylene resin according to an embodiment of the present invention will be described for each step.

According to one embodiment of the invention, first, for the polyethylene resin to be measured, the melt index (MI) is measured under load conditions of 2.16 kg and 21.6 kg according to ASTM D1238, respectively, and their melt flow index ratio ( Melt Flow Rate Ratio (MFRR) is derived (Step 1).

The melt flow index ratio means a value obtained by dividing the melt index measured under the load condition of 21.6 kg by the melt index measured under the load condition of 2.16 kg.

Specifically, the melt index refers to the weight (g) of the polymer melted for about 10 minutes under the two load conditions (2.16 kg, 21.6 kg) at a temperature of about 190° C., and the melt index ratio is 21.6 kg. It means a value obtained by dividing the melt index measured under the load condition by the melt index measured under the load condition of 2.16 kg.

The melt index and melt flow index ratio are due to changes in the weight average molecular weight and molecular weight distribution of the polymer and are indicators indicating the flowability of the polymer in the molten state. These are major factors affecting the shrinkage behavior of polymers, and the melt index and melt flow index ratio correspond to independent variables (x ₁ , x ₂ ) of a multiple linear regression function to be described later.

According to an embodiment of the present invention, for the polyethylene resin to be measured, a change in molecular weight distribution through gel permeation chromatography (GPC) and a change in complex viscosity through a dynamic frequency sweep test are respectively measured, and from this, the polyethylene resin to be measured The content of a long chain branch (LCB) having 8 or more carbon atoms per 1,000 carbon atoms of the branched resin included in the resin is derived (second step).

In the molecular weight distribution change through gel permeation chromatography (GPC) in the second step, for the measurement target polyethylene resin, a total molecular weight distribution curve is derived by gel permeation chromatography (GPC) analysis, and in the molecular weight distribution curve region The weight average molecular weight and mass fraction are derived by deconvolution of a plurality of sub-molecular weight distribution curves that are included and have the shape of a normal distribution curve.

Through the combination of the weight average molecular weight and mass fraction derived here and the data derived from the complex viscosity change through the dynamic frequency sweep test to be described later, the content of Long Chain Branch (LCB) with 8 or more carbon atoms per 1,000 carbon atoms This is derived

The polymer resin to be measured includes a branched resin, a low molecular weight first linear resin having a weight average molecular weight of less than 80,000, and a high molecular weight second linear resin having a weight average molecular weight of 80,000 or more. Accordingly, the sub-molecular weight distribution curve for each resin structure ( C) specifically includes a first linear resin sub-molecular weight distribution curve (C-1), a second linear resin sub-molecular weight distribution curve (C-2) and a branched resin sub-molecular weight distribution curve (C-3), respectively .

The weight average molecular weight data of the entire resin is derived through the total molecular weight distribution curve (C), and the weight average molecular weight of each resin structure, their total weight through the sub molecular weight distribution curves (C-1 to C-3) for each resin structure. Derive each mass fraction for the sum in wt%.

The molecular weight distribution curve is a gel permeation chromatography (GPC) method applied, and the weight average molecular weight (Mw) and number average molecular weight (Mn) are measured, and the log value (log Mw) of the weight average molecular weight (Mw) is x It is derived using the axis and the molecular weight distribution (dwt/dlog) for log values as the y-axis.

Specifically, the molecular weight distribution curve may be analyzed using a GPC instrument such as Waters PL-GPC220 using a Polymer Laboratories PLgel MIX-B 300 mm long column.

In this case, the temperature during analysis is about 160° C., and 1,2,4-trichlorobenzene may be used as a solvent. In addition, the flow rate is measured at a rate of 1 mL/min, and a resin sample can be prepared at a concentration of 10 mg/10 mL, and then supplied in an amount of 200 µL. In addition, as a standard material for GPC analysis, a polystyrene resin may be used, and the molecular weight of the standard material may be 2,000, 10,000, 30,000, 70,000, 200,000, 700,000, 2,000,000, 4,000,000, or 10,000,000. A molecular weight distribution curve may be derived by performing GPC analysis using a calibration curve formed using such a standard material.

In the complex viscosity change through the dynamic frequency sweep test of the second step, the complex viscosity measured while giving a change in shear stress from 0.5 rad/s to 500 rad/s with respect to the polyethylene resin to be measured The weight average molecular weight and number of long chain branches (LCBs) having 8 or more carbon atoms per 1,000 main chain carbon atoms of the branched resin derived from Finally, through the combination of the weight average molecular weight and number of Long Chain Branches (LCB) derived here and the data derived from the molecular weight distribution change through the above-mentioned gel permeation chromatography (GPC), 8 or more carbon atoms per 1,000 carbon atoms The content of Long Chain Branch (LCB) is derived.

Specifically, the complex viscosity change through the dynamic frequency sweep test proceeds through the Advanced Rheometric Expansion System (ARES), and the viscosity graph of the resin while giving a change in shear stress from 0.5 rad/s to 500 rad/s for the polymer to be measured to derive, From this, the structural characteristics of the resin are derived, and the weight average molecular weight and number of long chain branches having 8 or more carbon atoms per 1,000 main chain carbon atoms are derived.

[R . G. Larson, Macromolecules 2001, 34, 4556-4571], the numerical analysis model of Doi-Edwards can be used.

The content of a long chain branch (LCB) having 8 or more carbon atoms per 1,000 carbon atoms derived from the second step is an important factor in determining the flow and mechanical properties affecting the physical properties and processability of the resin, and the long chain branching The content of is one of a plurality of independent variables (x ₃ ) of the multiple linear regression function to be described later.

According to an embodiment of the invention, the rheological properties of the polyethylene resin to be measured are set to y, the melt index (MI _2.16 ) of the load condition of 2.16 kg derived in the first step is set to x ₁ , and the melt flow The exponential ratio (MFRR = MI _21.6 /MI _2.16 ) is set to x ₂ , and the long chain branching content derived in the second step is set to x ₃ , so that the relationship between y, x ₁ , x ₂ and x ₃ is first-order Multiple linear regression can be performed as a function (3rd step), and from the multiple linear regression functions for y, x ₁ , x ₂ and x ₃ , the physical properties of the polyethylene resin are reliably predicted (4th step).

According to one embodiment of the invention, the physical property of the polyethylene resin to be predicted is the TD shrinkage rate, which affects the processing of the polyethylene resin into a film, and in particular, is an index that has a great influence on setting thermoforming conditions. By reliably predicting the TD shrinkage rate according to the prediction method derived by the present inventors, thermoforming conditions suitable for this can be set, thereby reducing processing costs and improving productivity through high-speed production.

In particular, it was confirmed that the predicted physical property values for the TD shrinkage of the polyethylene resin predicted in this way were almost identical to the actual measured properties of the same polyethylene resin, that is, the TD shrinkage ratio and the error range were within about 3%±0.1%.

The measured physical property, TD shrinkage, is measured by immersing a polyethylene resin specimen (size of about 10 * 10 cm) to be measured in silicone oil at about 150 ° C. for about 20 seconds according to ASTM D2732, then taking it out and cooling it at room temperature. can be calculated as

Shrinkage (%) = 100 - (length before shrinkage * length after shrinkage)/ length before shrinkage

The multiple linear regression is a regression analysis when there are two or more independent variables. A method of finding a linear equation that obtains a regression coefficient representing the amount of change according to each independent variable and minimizes the sum of the squares of the residuals in a multidimensional space made of them. It can be performed, and the linear expression according to this is called a multiple linear regression function.

According to one embodiment of the invention, the multiple linear regression function may be expressed by the following Equation 2:

[Equation 2]

y' = a * x ₁ ' + b * x ₂ '+ c * x ₃ ' + d

In the above formula,

y' is the predicted physical property of the polyethylene resin to be measured, specifically, the TD shrinkage,

x ₁ ' is the melt index under 2.16 kg load condition,

x ₂ ' is the melt flow index ratio, specifically, a value obtained by dividing the melt index measured under a load condition of 21.6 kg by the melt index measured under a load condition of 2.16 kg,

x ₃ ' is the content of long chain branches having 8 or more carbon atoms per 1,000 main chain carbon atoms,

a, b and c are the coefficients determined in the multiple linear regression step,

d is a constant determined in the multiple linear regression step.

From the multiple linear regression function, the TD shrinkage rate change pattern, which is a target physical property, can be reliably predicted from the melting characteristics of the polyethylene resin and the content ratio for each structure. Therefore, using the prediction method of one embodiment, it becomes very easy to determine in advance the conditions for processing the polyethylene resin into a film.

According to an embodiment of the present invention, a sixth step of actually measuring the physical properties of the polyethylene resin to be measured and comparing the predicted physical properties of the polyethylene resin in the fifth step with the measured physical properties is further performed to increase the reliability of the prediction method of the present application can be checked

Specifically, the step of comparing the predicted physical properties of the polyethylene resin with the actual measured properties is to calculate their error values and check whether the error values are less than a predetermined error reference value, and the multiple linear regression function of the present application The predicted physical properties measured through this method have very high reliability with an error value of about 3%±0.1% compared to the actual measured properties.

That is, in this method, in consideration of the target physical properties of the polyethylene resin to be manufactured into a product, appropriate thermoforming process conditions can be determined in advance through the method for predicting physical properties of one embodiment, and through this, the productivity can be further improved. .

In the manufacturing method of the other embodiment described above, a general method for manufacturing a polyethylene resin may be followed except for the method for predicting the physical properties of the embodiment, and thus, additional description thereof will be omitted.

Hereinafter, preferred embodiments are presented to help the understanding of the present invention. However, the following examples are only for illustrating the present invention, and the present invention is not limited thereto.

Determination of regression function for predicting physical properties of polyethylene resin

Polyethylene resin to be measured

First polyethylene resin: SE0327B manufactured by LG Chem (Density: 0.9311 g/cm ² ; MI: 0.292 g/10min);

Second polyethylene resin: SE0327N manufactured by LG Chem (Density: 0.9304 g/cm ² ; MI: 0.292 g/10min);

Third polyethylene resin: SE0327N manufactured by LG Chem (density: 0.9306 g/cm ² ; MI: 0.304 g/10min);

4th polyethylene resin: Enable 2703MC manufactured by ExxonMobil (Density: 0.9277 g/cm ² ; MI: 0.285 g/10min);

5th polyethylene resin Enable 2703MC manufactured by ExxonMobil (Density: 0.9279 g/cm ² ; MI: 0.250 g/10min)

Determination of the regression function

(1) Density analysis

For the first to fifth polyethylene resins, the densities were measured at 23° C. using an XS104 device from METTLER TOLEDO in accordance with ASTM D1505, and are shown in Table 1 below.

(2) structural analysis

For the first to fifth polyethylene resins, a change in molecular weight distribution through gel permeation chromatography (GPC) and a change in complex viscosity through a dynamic frequency sweep test were respectively measured.

First, molecular weight distribution curves were derived for the first to fifth polyethylene resins. A plurality of sub-molecular weight distribution curves included in the molecular weight distribution curve region and having the shape of a normal distribution curve were deconvolved.

Molecular weight distribution curves for the first to fifth polyethylene resins are derived with the log value (log Mw) of the weight average molecular weight (Mw) as the x-axis and the molecular weight distribution (dwt/dlog) for the log value as the y-axis, , which is shown in FIG. 1 . Through this GPC analysis, the weight average molecular weight and mass fraction of each resin structure were measured. For the GPC analysis, the following analysis conditions and methods were applied.

Polymer Laboratories PLgel MIX-B 300 mm length column was used for analysis using GPC instrument of Waters PL-GPC220. The temperature at the time of analysis was about 160° C., and 1,2,4-trichlorobenzene was used as a solvent. In addition, the flow rate was measured at a rate of 1 mL/min, and the resin sample was prepared at a concentration of 10 mg/10 mL, and then supplied in an amount of 200 μL. In addition, polystyrene resin was used as a standard material for GPC analysis, and the molecular weight of these standard materials was 2,000, 10,000, 30,000, 70,000, 200,000, 700,000, 2,000,000, 4,000,000, or 10,000,000.

Next, for the first to fifth polyethylene resins, a composite viscosity change graph was derived while giving a change in shear stress from 0.5 rad/s to 500 rad/s, which is shown in FIG. 2 . The weight average molecular weight and number of long chain branches (LCBs) having 8 or more carbon atoms per 1,000 main chain carbon atoms of the branched resin were derived from the complex viscosity graph.

The content (%) of the long chain branch (LCB) is derived from the weight average molecular weight and mass fraction of each of the derived resin structures and the weight average molecular weight and number of the long chain branch (LCB) , and the results are shown in Table 1.

(3) Melt index analysis

With respect to the first to fifth polyethylene resins, using a machine of Gottfertt MI-4 according to ASTM D1238, at a temperature of about 190° C. under the two load conditions (2.16 kg, 21.6 kg) for about 10 minutes. The weight (g) of the resulting polymer was measured, and the melt index (MI _2.16 ) measured under the load condition of 2.16 kg is shown in Table 1.

Next, the melt index ratio (MFRR) value obtained by dividing the melt index measured under the load condition of 21.6 kg by the melt index measured under the load condition of 2.16 kg is shown in Table 1.

division polyethylene resin Example 1 Example 2 Example 3 Example 4 Example 5 density g/cm ² 0.9311 0.9304 0.9306 0.9277 0.9279 MI (2.16 kg) g/10min 0.292 0.292 0.304 0.285 0.250 MFRR - 63.43 60.18 60.48 50.29 54.00 GPC Mn (g/mol) 38840 38897 40614 40160 35388 Mw (g/mol) 106316 10334 108404 118624 121825 MWD 2.74 2.66 67 2.95 3.44 high molecular weight
linear wt% 60.2 59.5 59.1 65.6 65.6 Mw 100.7K 93.7K 97.4K 119.0K 113.6K low molecular weight
linear wt% 9.9 8.9 9.8 12.3 11.2 Mw 45.0K 51.1K 45.5K 38.4K 41.6K branched wt% 29.8 31.5 31.1 22 23.2 Mw 166.1K 163.8K 163.8K 222.8K 218.0K LCB Mw 29.9K 32.6K 33.2K 41.8K 45.9K Number of LCB chains pcs/1,000C 0.053 0.051 0.052 0.031 0.031 LCB
content % 3.18 3.34% 3.40% 2.18% 2.16%

(3) Determination of multiple linear regression functions

For the first to fifth polyethylene resins, the target predicted TD shrinkage ratio is y', and for the three independent variables, the melt flow index (MI _2.16 ) is x ₁ ', the melt flow index ratio (MFRR) ) is x ₂ ', and the content of long chain branches having 8 or more carbon atoms per 1,000 main chain carbon atoms is x ₃ ', and these relationships are multiplied linearly regressed as a linear function.

Based on the statistical values of the first to fifth polyethylene resins, the regressed function is, y' = (-30.73)*x ₁ ' + (0.206)*x ₂ '+ (1.705)*x ₃ ' + (10.725) ), and through this, the TD shrinkage rate, which is the predicted physical property of the polyethylene resin to be measured, was derived.

regression statistics multiple correlation coefficients 0.997882 coefficient of determination 0.995769 adjusted coefficient of determination 0.983076 standard error 0.228938 number of observations 5

To check the usefulness of the regression model through the coefficient of determination of the regression analysis, it was checked whether the coefficient of determination and the adjusted coefficient of determination were close to 1. The corresponding values are 0.9978, 0.9957, and 0.9830, which means that the correlation between the dependent variable and the independent variable is high.

Test Example: Evaluation of Reliability of Prediction Results of Physical Properties of Polyethylene Resins

(1) Preparation of test specimens

First, film specimens for shrinkage were prepared using the first to fifth polyethylene resins. Using a 1-layer blown film extuder manufactured by Eugene Engineering Co., a film specimen having a thickness of 50±5 μm was prepared in a size of 10 * 10 mm under the following conditions.

Die diameter: 100mm

Die gap: 2mm

Frost Line: 22cm

Blow up ratio: 2.5

(2) Reliability evaluation

Next, each film specimen was immersed in silicone oil at about 150° C. for 20 seconds according to ASTM D2732, cooled at room temperature, and TD (Transverse Direction) shrinkage was measured according to the following method.

Shrinkage (%) = 100 - (length before shrinkage * length after shrinkage) / length before shrinkage

Next, the error range of the predicted TD shrinkage rate and the measured TD shrinkage rate according to the derived multiple linear regression function were evaluated, and the results are shown in Table 3.

division polyethylene resin One 2 3 4 5 Actual TD shrinkage ℃ 20.2 19.7 19.8 16.0 17.9 Predicted TD shrinkage ℃ 20.2 19.8 19.6 16.0 17.8 reliability % 100 99.5 99 100 99.4 error evaluation % 0 0.5 One 0 0.6

As can be seen in Table 3, the TD shrinkage rate of the polyethylene resin was predicted by applying the multiple regression function derived according to an embodiment of the invention, and it was confirmed that the reliability was very good.

Claims (13)

For the polyethylene resin to be measured, in accordance with ASTM D1238, the melt index (MI) is measured under load conditions of 2.16 kg and 21.6 kg, and their melt flow rate ratio (MFRR) is derived. first step;
For the polyethylene resin to be measured, the molecular weight distribution change through gel permeation chromatography (GPC) and the complex viscosity change through the dynamic frequency sweep test are respectively measured, and from this, the carbon of the branched resin included in the measurement target polyethylene resin a second step of deriving the content of a long chain branch (LCB) having 8 or more carbon atoms per 1,000 atoms;
The rheological property of the polyethylene resin to be measured is set to y, the melt index of the load condition of 2.16 kg derived in the first step is set to x ₁ , the melt flow index ratio is set to x ₂ , and the second a third step of multiple linear regression of the relationship of y, x ₁ , x ₂ and x ₃ to a linear function by setting the long chain branching content derived in the step to x ₃ ;
From the multiple linear regression function, comprising a fourth step of predicting the rheological properties of the polyethylene resin, a method for predicting physical properties of a polyethylene resin.
The method of claim 1,
The measurement target polyethylene resin includes a branched resin, a low molecular weight first linear resin having a weight average molecular weight of less than 80,000, and a high molecular weight second linear resin having a weight average molecular weight of 80,000 or more,
A method for predicting physical properties of polyethylene resins.
3. The method of claim 2,
The branched resin, the weight average molecular weight ratio of the side chain to the main chain is 40% or less, or the weight average molecular weight of only the side chain is 3,000 or more,
A method for predicting physical properties of polyethylene resins.
The method of claim 1,
In the first step, the melt flow index ratio is a value obtained by dividing the melt index measured under the load condition of 21.6 kg by the melt index measured under the load condition of 2.16 kg,
A method for predicting physical properties of polyethylene resins.
The method of claim 1,
In the molecular weight distribution change through gel permeation chromatography (GPC) of the second step,
For the polyethylene resin to be measured, a total molecular weight distribution curve is derived by gel permeation chromatography (GPC) analysis, and a plurality of sub-molecular weight distribution curves included in the molecular weight distribution curve region and having the shape of a normal distribution curve are separated and set ( deconvolution) to derive the weight average molecular weight and mass fraction,
A method for predicting physical properties of polyethylene resins.
The method of claim 1,
In the complex viscosity change through the dynamic frequency sweep test of the second step,
Long Chain Branch (LCB) having 8 or more carbon atoms per 1,000 main chain carbon atoms of the branched resin from the measured complex viscosity while varying the shear stress from 0.5 rad/s to 500 rad/s with respect to the polyethylene resin to be measured To derive the weight average molecular weight and number of
A method for predicting physical properties of polyethylene resins.
6. The method of claim 5,
The measurement target polyethylene resin includes a branched resin, a low molecular weight first linear resin having a weight average molecular weight of less than 80,000, and a high molecular weight second linear resin having a weight average molecular weight of 80,000 or more,
The plurality of sub molecular weight distribution curves include a branched resin sub molecular weight distribution curve, a first linear resin sub molecular weight distribution curve, and a second linear resin sub molecular weight distribution curve,
A method for predicting physical properties of polyethylene resins.
6. The method of claim 5,
The measurement target polyethylene resin includes a branched resin, a low molecular weight first linear resin having a weight average molecular weight of less than 80,000, and a high molecular weight second linear resin having a weight average molecular weight of 80,000 or more,
The weight average molecular weight derived by deconvolution of the plurality of sub molecular weight distribution curves is the weight average molecular weight of each of the branched resin, the first linear resin, and the second linear resin included in the measurement target polyethylene resin,
The mass fractions are the respective mass fractions of their total sum,
A method for predicting physical properties of polyethylene resins.
The method of claim 1,
In the third step, the regressed multiple linear regression function is expressed by Equation 2 below,
Method of predicting the physical properties of polyethylene resin:
[Equation 2]
y' = a * x ₁ ' + b * x ₂ '+ c * x ₃ ' + d
In the above formula,
y' is the predicted physical property of the polyethylene resin to be measured,
x ₁ ' is the melt index under 2.16 kg load condition,
x ₂ ' is the melt flow index ratio,
x ₃ ' is the content of long chain branches having 8 or more carbon atoms per 1,000 main chain carbon atoms of the branched resin included in the measurement target polyethylene resin,
a, b and c are the coefficients determined in the multiple linear regression step,
d is a constant determined in the multiple linear regression step.
The method of claim 1,
In the fourth step, the rheological property of the polyethylene resin is the shrinkage rate in the TD direction of the polyethylene resin,
A method for predicting physical properties of polyethylene resins.
The method of claim 1,
Further comprising a fifth step of measuring the physical properties of the measurement target polyethylene resin, and comparing the measured physical properties with the predicted physical properties of the polyethylene resin in the fourth step,
A method for predicting physical properties of polyethylene resins.
12. The method of claim 11,
A fifth step of comparing the predicted physical properties of the polyethylene resin with the measured physical properties is to calculate their error values and to check whether the error values are less than a predetermined error reference value,
A method for predicting physical properties of polyethylene resins.
12. The method of claim 11,
Actual physical properties of the polyethylene resin are TD shrinkage measured according to ASTM D2732,
A method for predicting physical properties of polyethylene resins.

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