CN111462828B - Method and device for predicting polymerization Mooney viscosity in real time - Google Patents
Method and device for predicting polymerization Mooney viscosity in real time Download PDFInfo
- Publication number
- CN111462828B CN111462828B CN201910108335.8A CN201910108335A CN111462828B CN 111462828 B CN111462828 B CN 111462828B CN 201910108335 A CN201910108335 A CN 201910108335A CN 111462828 B CN111462828 B CN 111462828B
- Authority
- CN
- China
- Prior art keywords
- polymerization
- nitrile rubber
- mooney viscosity
- molecular weight
- molecular
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000006116 polymerization reaction Methods 0.000 title claims abstract description 133
- 238000000034 method Methods 0.000 title claims abstract description 63
- 229920000459 Nitrile rubber Polymers 0.000 claims abstract description 162
- NLHHRLWOUZZQLW-UHFFFAOYSA-N Acrylonitrile Chemical compound C=CC#N NLHHRLWOUZZQLW-UHFFFAOYSA-N 0.000 claims abstract description 70
- 238000009826 distribution Methods 0.000 claims abstract description 68
- 241001441571 Hiodontidae Species 0.000 claims abstract description 22
- 238000006243 chemical reaction Methods 0.000 claims description 26
- 230000008569 process Effects 0.000 claims description 20
- 229920000642 polymer Polymers 0.000 claims description 13
- 230000035484 reaction time Effects 0.000 claims description 6
- 238000004088 simulation Methods 0.000 claims description 6
- 238000003756 stirring Methods 0.000 claims description 5
- 238000004519 manufacturing process Methods 0.000 abstract description 14
- 238000012360 testing method Methods 0.000 abstract description 11
- 238000012545 processing Methods 0.000 description 12
- 238000002360 preparation method Methods 0.000 description 10
- 229920001971 elastomer Polymers 0.000 description 7
- 239000005060 rubber Substances 0.000 description 7
- 230000001419 dependent effect Effects 0.000 description 5
- 230000000704 physical effect Effects 0.000 description 5
- KAKZBPTYRLMSJV-UHFFFAOYSA-N Butadiene Chemical compound C=CC=C KAKZBPTYRLMSJV-UHFFFAOYSA-N 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 3
- 150000001875 compounds Chemical class 0.000 description 3
- 239000007788 liquid Substances 0.000 description 3
- 239000003921 oil Substances 0.000 description 3
- 235000019198 oils Nutrition 0.000 description 3
- 238000000611 regression analysis Methods 0.000 description 3
- QGZKDVFQNNGYKY-UHFFFAOYSA-N Ammonia Chemical compound N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 description 2
- 229920000715 Mucilage Polymers 0.000 description 2
- 241000183024 Populus tremula Species 0.000 description 2
- 239000000853 adhesive Substances 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 239000000839 emulsion Substances 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000002156 mixing Methods 0.000 description 2
- 125000002560 nitrile group Chemical group 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 238000012797 qualification Methods 0.000 description 2
- 239000002994 raw material Substances 0.000 description 2
- 239000000376 reactant Substances 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 241001465754 Metazoa Species 0.000 description 1
- 229910021529 ammonia Inorganic materials 0.000 description 1
- 239000010775 animal oil Substances 0.000 description 1
- 238000003556 assay Methods 0.000 description 1
- 239000004568 cement Substances 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 230000015271 coagulation Effects 0.000 description 1
- 238000005345 coagulation Methods 0.000 description 1
- 239000000084 colloidal system Substances 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 238000007334 copolymerization reaction Methods 0.000 description 1
- 238000004132 cross linking Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 238000011143 downstream manufacturing Methods 0.000 description 1
- 238000001035 drying Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000010556 emulsion polymerization method Methods 0.000 description 1
- 238000001704 evaporation Methods 0.000 description 1
- 230000008020 evaporation Effects 0.000 description 1
- 238000001125 extrusion Methods 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 238000001879 gelation Methods 0.000 description 1
- 238000010249 in-situ analysis Methods 0.000 description 1
- 238000006317 isomerization reaction Methods 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 229920002521 macromolecule Polymers 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 239000002480 mineral oil Substances 0.000 description 1
- 235000010446 mineral oil Nutrition 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000000178 monomer Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 150000003254 radicals Chemical class 0.000 description 1
- 229920005604 random copolymer Polymers 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000000344 soap Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 239000002904 solvent Substances 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000008961 swelling Effects 0.000 description 1
- 230000002194 synthesizing effect Effects 0.000 description 1
- 235000015112 vegetable and seed oil Nutrition 0.000 description 1
- 239000008158 vegetable oil Substances 0.000 description 1
Landscapes
- Addition Polymer Or Copolymer, Post-Treatments, Or Chemical Modifications (AREA)
Abstract
The invention provides a method and a device for predicting the viscosity of polymerized Mooney in real time, wherein the method comprises the following steps: predicting the molecular weight, molecular distribution, bound acrylonitrile content, branching degree and gel content of the target nitrile rubber; and inputting the molecular weight, the molecular distribution, the combined acrylonitrile content, the branching degree and the gel content into a polymerization Mooney viscosity model to obtain the polymerization Mooney viscosity of the target nitrile rubber. The method can realize the real-time prediction of the polymerization Mooney viscosity of the nitrile rubber, avoid the hysteresis of the manual test data result and save the production cost to the maximum extent.
Description
Technical Field
The invention relates to a measurement method, in particular to a method and a device for predicting the viscosity of polymerized Mooney in real time, and belongs to the technical field of nitrile rubber machinery and industrial automation control.
Background
In the nitrile rubber industry, the polymeric mooney viscosity is related to factors such as polymer molecular weight and narrow distribution range, polarity of macromolecular backbone, side group polarity, steric hindrance of side groups, branching, crosslinking (gelation), and temperature.
On the one hand, polymeric mooney viscosity is being widely used as an indicator of the plasticity of raw rubber and of the rubber compound, which is one of the most important characteristics of the processability of the rubber compound. The processing performance of the nitrile rubber can be judged by the viscosity of the polymerized Mooney. The polymerization Mooney viscosity is high, which shows that the average molecular weight is large, the plasticity is small, the raw rubber is not easy to plastify, the rubber is not easy to mix uniformly, and the extrusion performance is also poor; on the contrary, the average molecular weight is small, the plasticity is large, the rubber is easy to stick to rollers during mixing, and the strength of the vulcanized product can be influenced.
On the other hand, the polymerization Mooney viscosity is also an important index for monitoring the quality of the synthetic nitrile rubber, is a key index for representing the processing performance of the nitrile rubber, is one of important indexes for controlling the quality of raw rubber, is one of the most concerned technical indexes of nitrile rubber product factories and processing enterprises, and has close relation with the physical and mechanical properties and the processing performance of products.
Nitrile rubber (NBR) is a random copolymer prepared from butadiene and acrylonitrile by free radical emulsion copolymerization. At present, the low-temperature continuous emulsion polymerization method is mainly adopted for production, and the molecular structure of the nitrile rubber contains polar groups of nitrile groups and unsaturated double bonds. Therefore, NBR has better oil resistance and has been widely used in various oil-resistant nitrile rubber products.
In recent years, the development of the nitrile rubber industry in China is rapid, and according to the existing building device level, the nitrile rubber productivity in China is expected to reach about 23 ten thousand tons in 2020. High yields have led to a substantial saturation of nitrile rubber in the market and even an excess of productivity will occur. Along with the aggravation of market competition, the market puts forward higher requirements on the mechanical properties and the processing technology of the nitrile rubber, particularly the requirements on the polymerization Mooney viscosity of the nitrile rubber products, the control range is narrower and narrower, the most interesting index of downstream processing enterprises is the polymerization Mooney viscosity for the same series of NBR products, the polymerization Mooney viscosity of the products among batches fluctuates, and the additional cost brought by the processing enterprises for adjusting the processing formula and the process and the stability of the processing technology are increased. Therefore, it is necessary to reduce the gap by researching the polymerization influencing factors and the abatement countermeasures and to improve the quality grade and the central control qualification rate. Further, it is necessary to study the main influence factors of the polymerization Mooney viscosity and the interrelation between them, and predict how the change of NBR microstructure affects the trend of the change of the polymerization Mooney viscosity, so that the polymerization Mooney viscosity of nitrile rubber can be predicted in real time.
Disclosure of Invention
The invention provides a method and a device for predicting the polymerization Mooney viscosity in real time, which can realize the real-time prediction of the polymerization Mooney viscosity of the nitrile rubber, avoid the hysteresis of the result of manual test data and save the production cost to the maximum extent.
The invention provides a method for predicting the viscosity of polymerized Mooney in real time, which comprises the following steps:
predicting the molecular weight, molecular distribution, bound acrylonitrile content, branching degree and gel content of the target nitrile rubber;
and inputting the molecular weight, the molecular distribution, the combined acrylonitrile content, the branching degree and the gel content into a polymerization Mooney viscosity model to obtain the polymerization Mooney viscosity of the target nitrile rubber.
A method of predicting polymeric mooney viscosity in real time as described above, wherein the method further comprises:
and obtaining the polymerization Mooney viscosity model.
The method for predicting the polymeric mooney viscosity in real time, wherein the obtaining the polymeric mooney viscosity model comprises the following steps:
collecting a plurality of nitrile rubber samples;
establishing a correlation database of influence factors and polymerization Mooney viscosity according to the plurality of nitrile rubber samples;
generating the polymeric mooney viscosity model according to the association database;
wherein the influencing factors include the molecular weight, molecular distribution, bound acrylonitrile content, degree of branching, and gel content.
The method for predicting the polymerization mooney viscosity in real time, wherein the method for predicting the molecular weight, the molecular distribution, the combined acrylonitrile content, the branching degree and the gel content of the target nitrile rubber comprises the following steps:
and predicting the molecular weight, molecular distribution, bound acrylonitrile content, branching degree and gel content of the target nitrile rubber according to the technological parameters for preparing the target nitrile rubber.
The method for predicting the polymerization mooney viscosity in real time, wherein the predicting the molecular weight, the molecular distribution, the combined acrylonitrile content, the branching degree and the gel content of the target nitrile rubber according to the technological parameters for preparing the target nitrile rubber comprises the following steps:
inputting the technological parameters into a Polymer Plus of a high molecular polymerization simulation model to obtain the molecular weight, molecular distribution, combined acrylonitrile content, branching degree and gel content of the target nitrile rubber.
The method for predicting the polymerization Mooney viscosity in real time, wherein the polymerization Mooney viscosity model is shown in a formula 1,
ML＝[A 1 ×(M w ×PDI) x ]+[(B 2 ×AN％)+(C 3 ×Mw×AN％)]+[2.327×λ×100]+[3.153×G×100]
1 (1)
Wherein ML is Mooney viscosity, mw is molecular weight, PDI is molecular weight distribution, AN% is bound acrylonitrile content, lambda is branching degree, G is gel content, A 1 、B 2 And C 3 And respectively, constants related to the target nitrile rubber grades.
The invention also provides a device for predicting the viscosity of the polymerized Mooney in real time, which comprises the following steps:
the prediction module is used for predicting the molecular weight, molecular distribution, combined acrylonitrile content, branching degree and gel content of the target nitrile rubber;
and the determining module is used for inputting the molecular weight, the molecular distribution, the combined acrylonitrile content, the branching degree and the gel content into a polymerization Mooney viscosity model to obtain the polymerization Mooney viscosity.
A real-time predicting device for polymeric mooney viscosity as defined above, wherein said device further comprises:
and the acquisition module is used for acquiring the polymerization Mooney viscosity model.
The device for predicting the polymerization mooney viscosity in real time as described above, wherein the obtaining module is specifically configured to:
collecting a plurality of nitrile rubber samples;
establishing a correlation database of influence factors and polymerization Mooney viscosity according to the plurality of nitrile rubber samples;
generating the polymeric mooney viscosity model according to the association database;
wherein the influencing factors include the molecular weight, molecular distribution, bound acrylonitrile content, degree of branching, and gel content.
The device for predicting the polymerization mooney viscosity in real time as described above, wherein the prediction module is specifically configured to:
and predicting the molecular weight, molecular distribution, bound acrylonitrile content, branching degree and gel content of the target nitrile rubber according to the technological parameters for preparing the target nitrile rubber.
The method and the device for predicting the polymerization Mooney viscosity in real time are used for obtaining the polymerization Mooney viscosity of the target nitrile rubber by inputting the obtained molecular weight, molecular distribution, combined acrylonitrile content, branching degree and gel content of the target nitrile rubber into a polymerization Mooney viscosity model. In the embodiment of the invention, the molecular weight, the molecular distribution, the combined acrylonitrile content, the branching degree and the gel content are taken as main microstructure influencing factors influencing the Mooney viscosity of the nitrile rubber, the molecular weight, the molecular distribution, the combined acrylonitrile content, the branching degree and the gel content of the target nitrile rubber are brought into a Mooney viscosity model, and the Mooney viscosity of the target nitrile rubber produced under the current working condition is calculated, so that the real-time prediction of the Mooney viscosity of the nitrile rubber is realized, and the method has the advantages of accurate prediction and low cost.
Drawings
FIG. 1 is a flow chart of a method for predicting the viscosity of polymeric mooney in real time according to an embodiment of the invention;
FIG. 2 is a flow chart of a method for predicting the viscosity of polymeric mooney in real time according to another embodiment of the invention;
FIG. 3 is a schematic flow chart of S100A of the method for predicting the viscosity of polymeric mooney in real time according to the invention;
FIG. 4 is a flow chart of a method for predicting the viscosity of polymeric mooney in real time according to another embodiment of the invention;
FIG. 5 is a graph comparing the results obtained by the polymerization Mooney viscosity prediction method of the present invention with the results of an actual analytical test.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described in the following in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a flow chart of a method for predicting the viscosity of polymeric mooney in real time according to an embodiment of the invention. As shown in fig. 1, the method for predicting the mooney viscosity of the polymer provided in this embodiment may include:
s101: the molecular weight, molecular distribution, bound acrylonitrile content, degree of branching, and gel content of the target nitrile rubber are predicted.
In this step, the target nitrile rubber is the nitrile rubber to be tested for the Mooney viscosity of the polymer, and is in or about to be in the production preparation stage.
The molecular weight, molecular distribution, bound acrylonitrile content, branching degree and gel content of the target nitrile rubber are obtained by predicting the target nitrile rubber in the production preparation stage or the stage to be produced. The present invention is not limited to the method of predicting it, and the molecular weight, molecular distribution, bound acrylonitrile content, branching degree and gel content thereof may be predicted by, for example, the raw material of the target nitrile rubber or the working conditions of the production line of the target nitrile rubber.
The molecular weight of nitrile rubber is also referred to as the relative molecular mass of nitrile rubber, which is related to the type of nitrile rubber, and the different types of nitrile rubber have different molecular weights, and the sum of the relative atomic masses of the individual atoms in the chemical formula or molecular number of each nitrile rubber is the molecular weight of the nitrile rubber. The nitrile rubber is a medium and high molecular compound, so the molecular weight of the nitrile rubber is generally more than 10 ten thousand.
The molecular distribution of nitrile rubber means that nitrile rubber is a polymer, and the molecular weight or the degree of polymerization and the molecular chain length of the synthetic nitrile rubber are not uniform due to the probability factor in the polymerization reaction, so that there is a molecular distribution (also referred to as a molecular weight distribution).
The bound acrylonitrile content of the nitrile rubber refers to the acrylonitrile content in the nitrile rubber. Acrylonitrile is a raw material for synthesizing nitrile rubber, and the nitrile rubber has higher swelling stability to nonpolar or low-polar animal and vegetable oil, mineral oil, liquid fuel and solvent due to the existence of the polar nitrile group of the acrylonitrile, and the higher the content of the combined acrylonitrile, the better the oil resistance of the nitrile rubber.
The degree of branching of nitrile rubber is used to indicate the degree of isomerization of the paraffins in nitrile rubber, in general, the higher the degree of branching the more complex the branching.
The gel content of nitrile rubber refers to the mass content of gel in nitrile rubber. The gel is insoluble and infusible gel with linear molecules forming a network structure after being crosslinked by nitrile rubber. Gel is a special dispersion system, colloid particles or polymer macromolecules in nitrile rubber are mutually connected to form a frame to form a space network structure, and liquid or gas is filled in the gaps of the structure, and the property of the gel is between solid and liquid.
The molecular weight, molecular distribution, combined acrylonitrile content, branching degree and gel content of the target nitrile rubber are used as main microcosmic influencing factors of the polymerization mooney viscosity of the nitrile rubber, and the polymerization mooney viscosity of the target nitrile rubber is obtained by acquiring the molecular weight, molecular distribution, combined acrylonitrile content, branching degree and gel content of the target nitrile rubber in real time in the production stage of the target nitrile rubber.
S102: and inputting the molecular weight, the molecular distribution, the combined acrylonitrile content, the branching degree and the gel content into a polymerization Mooney viscosity model to obtain the polymerization Mooney viscosity of the target nitrile rubber.
The polymerization Mooney viscosity model is used for calculating the polymerization Mooney viscosity of the nitrile rubber according to the predicted molecular weight, molecular distribution, combined acrylonitrile content, branching degree and gel content of the target nitrile rubber.
It will be appreciated that the polymeric mooney viscosity model is a computational model comprising an independent variable and a dependent variable, wherein the independent variable comprises at least molecular weight, molecular distribution, bound acrylonitrile content, branching degree and gel content, and the dependent variable is polymeric mooney viscosity, and the polymeric mooney viscosity is correspondingly changed due to change of at least one parameter of molecular weight, molecular distribution, bound acrylonitrile content, branching degree and gel content. Thus, when different polymeric mooney viscosities are predicted, due to molecular weight, molecular distribution, bound acrylonitrile content, degree of branching, and gel content, different polymeric mooney viscosities are obtained.
In the step, the molecular weight, molecular distribution, combined acrylonitrile content, branching degree and gel content are input into a polymerization Mooney viscosity model, and the polymerization Mooney viscosity of the nitrile rubber with the above-mentioned each prediction parameters is obtained through the polymerization Mooney viscosity model.
In the embodiment, the molecular weight, the molecular distribution, the bound acrylonitrile content, the branching degree and the gel content of the target nitrile rubber are taken as microscopic mechanism influence factors of the polymerization mooney viscosity of the nitrile rubber, the molecular weight, the molecular distribution, the bound acrylonitrile content, the branching degree and the gel content of the target nitrile rubber are predicted, and the predicted molecular weight, the molecular distribution, the bound acrylonitrile content, the branching degree and the gel content are input into a polymerization mooney viscosity model, so that the polymerization mooney viscosity model can finally obtain the polymerization mooney viscosity of the target nitrile rubber in the production preparation stage or the to-be-produced polymerization mooney viscosity through the predicted molecular weight, the molecular distribution, the bound acrylonitrile content, the branching degree and the gel content because the polymerization mooney viscosity model is a correlation calculation formula between the molecular weight, the molecular distribution, the gel content and the polymerization mooney viscosity.
If the polymerization Mooney viscosity obtained by the polymerization Mooney viscosity model meets the requirement, the production can be continued according to the currently formulated technological parameters; if the polymeric mooney viscosity obtained by the polymeric mooney viscosity model does not meet the requirements, the gap between the polymeric mooney viscosity obtained by the polymeric mooney viscosity model and the required polymeric mooney viscosity may need to be reduced by researching influencing factors, adjusting processes and other measures, so that the high-grade product rate and the qualification rate of the nitrile rubber are improved.
The method for predicting the polymerization Mooney viscosity in real time can realize the prediction of the polymerization Mooney viscosity of the nitrile rubber in real time, avoid the hysteresis of the manual test data result, and timely reflect whether the polymerization Mooney viscosity of the current nitrile rubber reaches the standard, thereby reducing the additional cost brought by processing enterprises for adjusting the processing formula and the process and improving the process stability of production and preparation.
Fig. 2 is a flow chart of a method for predicting the viscosity of polymeric mooney in real time according to another embodiment of the invention. On the basis of the above embodiment, as shown in fig. 2, before the step S102, the embodiment of the present invention further includes:
step S100A: a polymeric mooney viscosity model was obtained.
In this step, it is necessary to obtain a polymerization mooney viscosity model so that the polymerization mooney viscosity model can obtain the polymerization mooney viscosity of the target nitrile rubber according to the molecular weight, the molecular distribution, the bound acrylonitrile content, the branching degree and the gel content.
It should be noted that, in fig. 2, step S100A is schematically disposed between S101 and S102, but it is not limited that step S100A must be performed after step S101; fig. 2 is merely a schematic representation of the step S100A before the step S102, and does not limit the order of execution of the steps S101 and S100A.
The following sections of the embodiments of the present invention describe a specific implementation manner of the step S100A.
Exemplary, fig. 3 is a flow chart of S100A of the method for real-time prediction of polymeric mooney viscosity of the present invention. As shown in fig. 3, obtaining the polymeric mooney viscosity model includes:
s201: a plurality of nitrile rubber samples are collected.
The nitrile rubber sample refers to a nitrile rubber sample which is already prepared, and the number of the nitrile rubber samples is not less than 100.
In order to ensure the accuracy of the polymerization Mooney viscosity model, each nitrile rubber sample needs to be prepared with different process parameters, so that the molecular weight, molecular distribution, bound acrylonitrile content, branching degree and gel content of each nitrile rubber sample are different.
S202: and establishing a relational database of influence factors and the polymerization Mooney viscosity according to a plurality of nitrile rubber samples.
Among the influencing factors are molecular weight, molecular distribution, bound acrylonitrile content, degree of branching, and gel content.
That is, the molecular weight, molecular distribution, bound acrylonitrile content, branching degree, gel content, and polymerized mooney viscosity of each nitrile rubber sample are collected such that the molecular weight, molecular distribution, bound acrylonitrile content, branching degree, gel content of each nitrile rubber sample is a set, assuming that each independent variable X comprises the molecular weight, molecular distribution, bound acrylonitrile content, branching degree, gel content of one nitrile rubber sample, and the polymerized mooney viscosity of that nitrile rubber sample is a dependent variable Y.
If n nitrile rubber samples are collected, a database of associations of influencing factors and polymeric mooney viscosities is established. The association database includes independent variables x= { X1, X2,..and dependent variables y= { Y1, Y2,..and Yn }, where X1 of the 1 st nitrile rubber sample corresponds to Y1, X2 of the 2 nd nitrile rubber sample corresponds to Y2, and Xn of the n th nitrile rubber sample corresponds to Yn.
Wherein, the molecular weight, molecular distribution, bound acrylonitrile content, branching degree, gel content and polymerization Mooney viscosity of a plurality of samples can be collected by adopting a detection method conventional in the art, and the invention does not limit the detection method in particular.
S203: and generating an aggregate Mooney viscosity model according to the association database.
And taking X in the association database as an independent variable and Y as a dependent variable, extracting non-Gaussian information of the independent variable X by a multiple regression analysis method, and establishing a linear regression coefficient and a regression value to obtain the aggregate Mooney viscosity model.
In the building process, the significance test and the correlation coefficient test are required to be carried out, and the residual error test is carried out, if the correlation degree manuscript is highly significant and the residual error is normally distributed, the obtained polymerization Mooney viscosity model is applicable. If the correlation coefficient is lower, regression is not ideal, and the model should be modified again to ensure that the results of the significance test and the correlation coefficient test are ideal, so that the regression equation is established, and the final suitable polymer Mooney viscosity model is obtained.
Of course, in the embodiment of the present invention, the polymerized mooney viscosity model may be obtained by other realizations, which is not limited in the embodiment of the present invention.
In the embodiment of the invention, a relational database between the microstructure influence factors of the nitrile rubber and the polymerization Mooney viscosity is established based on the sampling analysis test results of a plurality of different nitrile rubber samples. And (3) performing multiple regression analysis on the data in the established database by adopting a multiple statistical analysis method to obtain a correlation model between the microstructure influence factors of the nitrile rubber and the polymerization Mooney viscosity, namely a polymerization Mooney viscosity model. The polymerization Mooney viscosity model is obtained so as to input the predicted molecular weight, molecular distribution, combined acrylonitrile content, branching degree and gel content into the polymerization Mooney viscosity model, and the polymerization Mooney viscosity model can finally obtain the polymerization Mooney viscosity of the target nitrile rubber through the predicted molecular weight, molecular distribution, combined acrylonitrile content, branching degree and gel content because the polymerization Mooney viscosity model is a correlation calculation formula between the molecular weight, molecular distribution, combined acrylonitrile content, branching degree and gel content and the polymerization Mooney viscosity, so that the polymerization Mooney viscosity of the target nitrile rubber in a production preparation stage or to be produced and prepared in a production manner can be obtained in real time.
The method for predicting the polymerization Mooney viscosity in real time can realize the prediction of the polymerization Mooney viscosity of the nitrile rubber in real time, avoid the hysteresis of the manual test data result, and timely reflect whether the polymerization Mooney viscosity of the current nitrile rubber reaches the standard, thereby reducing the additional cost brought by processing enterprises for adjusting the processing formula and the process and improving the process stability of production and preparation.
Fig. 4 is a flow chart of a method for predicting the viscosity of polymeric mooney in real time according to another embodiment of the invention. On the basis of the above embodiment, as shown in fig. 4, embodiment S101 of the present invention includes:
s301: according to the technological parameters for preparing the target nitrile rubber, the molecular weight, molecular distribution, combined acrylonitrile content, branching degree and gel content of the target nitrile rubber are predicted.
In this step, the molecular weight, molecular distribution, bound acrylonitrile content, branching degree and gel content of the target nitrile rubber are predicted according to the preparation process parameters of the target nitrile rubber.
The inventor finds that the special process parameters in the preparation process of the nitrile rubber have great influence on the physical properties of the nitrile rubber, so the inventor creatively correlates the process parameters with the physical properties of the nitrile rubber.
In the present invention, the process parameters include: the preparation method comprises the steps of preparing a reaction formula, feeding flow, polymerization temperature, reaction time, the number of polymerization kettles, the volume and pressure of the reaction kettles, polymerization conversion rate, stirring rotation speed and device yield of target nitrile rubber; the physical properties of nitrile rubber are molecular weight, molecular distribution, bound acrylonitrile content, branching degree and gel content.
That is, since the process parameters for preparing the target nitrile rubber are known during or before the preparation of the target nitrile rubber, the molecular weight, molecular distribution, bound acrylonitrile content, branching degree, and gel content of the target nitrile rubber can be predicted by the process parameters of the target nitrile rubber.
The following sections of the embodiments of the present invention describe a specific implementation manner of the step S301.
By way of example, specific predictions can be made using the Polymer Plus model in the large general flow simulation System Aspen Plus.
In the nitrile rubber polymerization model in Polymer Plus, materials and auxiliaries required for polymerization, such as butadiene (stream BD), acrylonitrile (stream ACN), soaps and OTHER reagents (stream OTHER), molecular weight regulators and the like, are fed into a continuous line collecting and feeding pipe by respective conveying pumps for mixing, enter a reaction system from the bottom of a first polymerization kettle, are discharged from the top of the first polymerization kettle, enter the bottom of a second polymerization kettle, and the like, for example, 8 polymerization kettles are sequentially passed, and the molecular weight regulators can be added to the 4 th to 6 th polymerization kettles for controlling the polymerization Mooney viscosity of the polymerization reactants. The final reactant is added with a terminator at the outlet of the last kettle to terminate the polymerization reaction. The process flow simulation belongs to the white box operation process, when a polymerization model is established, a reaction equation is required to be defined according to a reaction mechanism, reaction dynamics and thermodynamic parameters are input, and a reaction formula and operation process conditions for producing the nitrile rubber with the corresponding brand are determined. After the polymerization model is established, the actual technological parameters (reaction formula, feeding flow, polymerization temperature, reaction time, number of polymerization kettles, volume and pressure of the reaction kettles, polymerization conversion rate, stirring rotation speed and device yield) of the device site are simulated, and the polymerization model is converged and then the data of the combined acrylonitrile content, molecular weight distribution, branching degree and gel content of the corresponding brand products are calculated. The simulation results substantially match the in-situ analysis assay data.
The volume of each reaction kettle is 42m 3 A stirrer is provided. The reactor was equipped with a cooling tube to remove the heat of reaction by evaporation of ammonia. The reaction time is about 10-20 h, the reaction temperature is 5-8 ℃, and the monomer conversion rate is about 72%. Taking nitrile rubber NBR3305E as a research object, under the condition of adopting a standard polymerization formula, the data input in the polymerization model comprises: reaction formula, feed flow, polymerization temperature, reaction time, number of polymerization kettles, reaction kettle volume and pressure, polymerization conversion rate, stirring rotation speed and device yield for preparing target nitrile rubber. The polymerization model respectively calculates the total number of emulsion particles in each component and each reaction kettle in the system according to the reaction rate equation, and solves the molecular weight and the distribution thereof, the combined acrylonitrile content, the branching degree and the gel content of the nitrile rubber under the formula and the process conditions by adopting a differential equation and a series of algebraic equations.
Of course, in embodiments of the present invention, other realizations may be used to predict the molecular weight, molecular distribution, bound acrylonitrile content, branching degree, and gel content of the target nitrile rubber, which is not limited in the embodiments of the present invention.
Further, the invention takes a 5 ten thousand tons/year nitrile rubber device of Lanzhou petrochemical company as a research object, 100 batches of polymerized mucilage are collected, after coagulation and drying, the polymerized mooney viscosity, the molecular weight and the distribution thereof, the acrylonitrile content, the branching degree and the gel content of the obtained mucilage are combined, and a polymerized mooney viscosity model is obtained by a multiple regression analysis method, wherein the polymerized mooney viscosity model is shown in the following formula 1:
ML＝[A 1 ×(M w ×PDI) x ]+[(B 2 ×AN％)+(C 3 ×Mw×AN％)]+[2.327×λ×100]+[3.153×G×100]1 (1)
Wherein ML is Mooney viscosity, M w The molecular weight, PDI, the molecular weight distribution, AN% the bound acrylonitrile content, lambda the branching degree, G the gel content, A 1 、B 2 And C 3 And respectively, constants related to the target nitrile rubber grades.
Illustratively, when the target nitrile rubber grade is NBR3305E, A 1 、B 2 And C 3 29.52,1.86, -6.858 ×10 respectively -5 ；
When the target nitrile rubber is NBR3308E, A 1 、B 2 And C 3 32.16,2.01, -7.682 ×10 respectively -5 。
The polymerization Mooney viscosity prediction method in the embodiment of the invention utilizes Aspen Plus and Ploymer Plus tools to establish a polymerization reaction model of the nitrile rubber device, simulates the actual working condition of the device site and predicts the microstructure physical property data of the nitrile rubber product. Substituting the microstructure physical property data of the product into a polymerization Mooney viscosity model, and predicting to obtain the polymerization Mooney viscosity value of the cement produced under the actual working condition of the device. The method can realize the real-time prediction of the polymerization mooney viscosity of the nitrile rubber, avoid the hysteresis of the manual test data result and save the production cost to the maximum extent.
FIG. 5 is a graph comparing the results obtained by the polymerization Mooney viscosity prediction method of the present invention with the results of an actual analytical test.
As shown in fig. 5, the dashed line is a curve formed by connecting the polymerization mooney viscosities of 15 groups of nitrile rubber obtained by the prediction method of the invention, wherein in the implementation process, polymer Plus is adopted to establish a nitrile rubber polymerization reaction model and related process parameters as the basis for predicting molecular weight, molecular distribution, combined acrylonitrile content, branching degree and gel content, and formula 1 is adopted as the polymerization mooney viscosity model of the nitrile rubber; the solid line is a curve obtained by connecting the actual polymerization Mooney viscosities obtained by analyzing and detecting the 15 groups of nitrile rubbers which are prepared.
As can be seen from FIG. 5, the predicted result and the measured result of the method are basically consistent, and the correlation is 0.79, which indicates that the polymeric Mooney viscosity model based on the device steady state polymerization model and the regression of a large number of data of microstructure physical analysis of the nitrile rubber can be used for predicting the polymeric Mooney viscosity of the nitrile rubber better.
The invention also provides a structural schematic diagram of a device for predicting the viscosity of the polymerized mooney in real time, which comprises:
the prediction module is used for predicting the molecular weight, molecular distribution, combined acrylonitrile content, branching degree and gel content of the target nitrile rubber;
and the determining module is used for inputting the molecular weight, the molecular distribution, the combined acrylonitrile content, the branching degree and the gel content into the polymerization Mooney viscosity model to obtain the polymerization Mooney viscosity.
In one possible implementation, the apparatus for predicting the mooney viscosity of the polymer in real time further comprises:
and the acquisition module is used for acquiring the polymerization Mooney viscosity model.
In one possible implementation manner, the acquiring module is specifically configured to:
collecting a plurality of nitrile rubber samples;
establishing a correlation database of influence factors and polymerization Mooney viscosity according to the plurality of nitrile rubber samples;
generating the polymeric mooney viscosity model according to the association database;
wherein the influencing factors include the molecular weight, molecular distribution, bound acrylonitrile content, degree of branching, and gel content.
In one possible implementation, the prediction module is specifically configured to:
and predicting the molecular weight, molecular distribution, bound acrylonitrile content, branching degree and gel content of the target nitrile rubber according to the technological parameters for preparing the target nitrile rubber.
The technical scheme in the embodiment of the method for predicting the viscosity of the polymeric mooney in real time provided by the embodiment of the invention is similar in technical principle and technical effect, and is not repeated here.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (3)
1. A method for predicting the viscosity of polymeric mooney in real time, comprising the steps of:
inputting process parameters of the target nitrile rubber into a Polymer Plus of a high molecular polymerization simulation model to obtain the molecular weight, molecular distribution, combined acrylonitrile content, branching degree and gel content of the target nitrile rubber;
the technological parameters include: preparing a reaction formula, a feed flow, a polymerization temperature, a reaction time, the number of polymerization kettles, the volume and pressure of the reaction kettles, a polymerization conversion rate, a stirring rotating speed and a device yield of the target nitrile rubber;
obtaining a polymeric mooney viscosity model comprising: collecting a plurality of nitrile rubber samples; establishing a correlation database of influence factors and polymerization Mooney viscosity according to the plurality of nitrile rubber samples; generating the polymeric mooney viscosity model according to the association database; wherein the influencing factors include the molecular weight, molecular distribution, bound acrylonitrile content, degree of branching, and gel content;
and inputting the molecular weight, the molecular distribution, the combined acrylonitrile content, the branching degree and the gel content into a polymerization Mooney viscosity model to obtain the polymerization Mooney viscosity of the target nitrile rubber.
2. The method for predicting the viscosity of polymeric mooney according to claim 1, wherein the model of the viscosity of polymeric mooney is represented by formula 1,
ML＝[A 1 ×(M w ×PDI) x ]+[(B 2 ×AN％)+(C 3 ×Mw×AN％)]+[2.327×λ×100]+[3.15
3 XG 100 1
Wherein ML is Mooney viscosity, M w The molecular weight, PDI, the molecular weight distribution, AN% the bound acrylonitrile content, lambda the branching degree, G the gel content, A 1 、B 2 And C 3 And respectively, constants related to the target nitrile rubber grades.
3. A device for real-time prediction of polymeric mooney viscosity, comprising:
the prediction module is used for inputting the technological parameters of the target nitrile rubber into a Polymer Plus of a high molecular polymerization simulation model to obtain the molecular weight, molecular distribution, combined acrylonitrile content, branching degree and gel content of the target nitrile rubber; the technological parameters include: preparing a reaction formula, a feed flow, a polymerization temperature, a reaction time, the number of polymerization kettles, the volume and pressure of the reaction kettles, a polymerization conversion rate, a stirring rotating speed and a device yield of the target nitrile rubber;
the acquisition module is used for collecting a plurality of nitrile rubber samples, establishing a correlation database of influence factors and the polymerization Mooney viscosity according to the nitrile rubber samples, and generating the polymerization Mooney viscosity model according to the correlation database; wherein the influencing factors include the molecular weight, molecular distribution, bound acrylonitrile content, degree of branching, and gel content;
and the determining module is used for inputting the molecular weight, the molecular distribution, the combined acrylonitrile content, the branching degree and the gel content into a polymerization Mooney viscosity model to obtain the polymerization Mooney viscosity.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910108335.8A CN111462828B (en) | 2019-01-18 | 2019-01-18 | Method and device for predicting polymerization Mooney viscosity in real time |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910108335.8A CN111462828B (en) | 2019-01-18 | 2019-01-18 | Method and device for predicting polymerization Mooney viscosity in real time |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111462828A CN111462828A (en) | 2020-07-28 |
| CN111462828B true CN111462828B (en) | 2023-09-26 |
Family
ID=71679906
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910108335.8A Active CN111462828B (en) | 2019-01-18 | 2019-01-18 | Method and device for predicting polymerization Mooney viscosity in real time |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111462828B (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2409434A1 (en) * | 2002-10-17 | 2004-04-17 | Bayer Inc. | Polymer blends comprising low molecular weight nitrile rubber |
-
2019
- 2019-01-18 CN CN201910108335.8A patent/CN111462828B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2409434A1 (en) * | 2002-10-17 | 2004-04-17 | Bayer Inc. | Polymer blends comprising low molecular weight nitrile rubber |
Non-Patent Citations (2)
| Title |
|---|
| 丁腈橡胶性能影响因素研究;王志忠;《《中国优秀硕士学位论文全文数据库》》;摘要 * |
| 影响乳聚丁苯橡胶门尼粘度的因素;戚银城等;《弹性体》(第03期);正文左栏第三段-第一页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111462828A (en) | 2020-07-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6663919B2 (en) | Systems and methods for predicting and controlling the properties of a species during a time-dependent process | |
| CN101458506A (en) | Industrial polypropylene producing melt index flexible measurement method based on combination neural net | |
| CN111863151B (en) | Polymer molecular weight distribution prediction method based on Gaussian process regression | |
| CN101482750A (en) | Cobalt oxalate granularity prediction method in hydrometallurgical synthesis process | |
| CN111595727A (en) | Method for establishing evaluation for rapidly predicting asphalt-aggregate adhesiveness and asphalt toughness | |
| CN111899798A (en) | Crude oil data management method, system, device and storage medium | |
| Saeb et al. | Controlled grafting of vinylic monomers on polyolefins: a robust mathematical modeling approach | |
| CN107703099B (en) | On-line continuous detection method and polymerization reaction method | |
| CN111462828B (en) | Method and device for predicting polymerization Mooney viscosity in real time | |
| CN112434739A (en) | Chemical process fault diagnosis method of support vector machine based on multi-core learning | |
| CN1916792A (en) | Soft measuring method of industrial process under condition of small sample | |
| Cavalcante de Sá et al. | Modeling of Isoprene Solution Coordinative Chain Transfer Polymerization | |
| Chang et al. | Fundamental modeling in anionic polymerization processes | |
| CN113627037A (en) | Crude oil cutting-based straight-run asphalt property prediction method | |
| JP4017516B2 (en) | Method for evaluating physical properties of polymer products | |
| US6862562B1 (en) | Computer method and apparatus for determining state of physical properties in a chemical process | |
| de Sá et al. | Modelling and parameter estimation of trans‐β‐farnesene coordination polymerization | |
| He et al. | Combined mechanistic and genetic programming approach to modeling pilot NBR production: influence of feed compositions on rubber Mooney viscosity | |
| Curteanu | Machine Learning Techniques Applied to a Complex Polymerization Process | |
| KR101837464B1 (en) | Method for simulating polypropylene polymerization | |
| CN110197031A (en) | A kind of flexible measurement method for the reaction of high density polyethylene (HDPE) polymerization cascade | |
| Mikhailova et al. | Application of computer simulation by the Monte Carlo method to the study of compositional heterogeneity of the product of copolymerization | |
| Zheng et al. | Machine learning for revealing the relationship between the process–structure–properties of polypropylene in-reactor alloys | |
| WO2001025863A1 (en) | Computer method and apparatus for determining state of physical properties in a chemical process | |
| CN110867216B (en) | Step modeling method of industrial p-tert-octyl phenol synthesis reaction multicomponent prediction model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |