Method and apparatus for polymerization process monitoring and model predictive control

Abstract

The present invention relates to a computer-implemented method for polymerization process monitoring and model predictive control comprising the steps of: providing (S1) at least one parameter set comprising catalyst information, kinetic modeling information, and calculated polymer property information; and modeling (S2) the reaction performance of a catalyst used in the polymerization process using a reaction network, the reaction network comprising a plurality of reactions of a catalyst, the reactions of which are modeled by a discrete number of a plurality of site types of the catalyst, and the catalyst is modeled as the sum of the contributions of the plurality of site types.

Description

[Technical field]

[0001] The present invention relates to polymerization process monitoring and model predictive control, and in particular to a method of polymerization process monitoring and model predictive control. [Background technology]

[0002] Methods for controlling polymerization reactors are known and include mathematical models for the polymerization process and optimizers to define the best operating options.

[0003] Detailed kinetics of polymer reaction engineering models are described, for example, in the following references:

[0004] The paper by Touloupidis V., published in Macromol. React. Eng., 8, pp. 508-527, 2014 and titled “Catalytic Olefin Polymerization Process Modeling: Multi-Scale Approach and Modeling Guidelines for Micro-Scale / Kinetic Modeling”, describes a multi-scale polymerization process modeling framework including polymerization kinetics (micro-scale), system thermodynamics (meso-scale), and reactor performance (macro-scale) as well as presents a methodology to develop mathematical models for catalytic olefin polymerization.

[0005] A paper by Soares JBP published in 2014 in Macromol.React.Eng., 8, 235-259 and titled "The Use of Instantaneous Distributions in Polymerization Reaction Engineering" describes how the method of instantaneous distributions can be used to model the microstructure of polymers made under different polymerization conditions.

[0006] The paper by Soares J., Touloupidis V., published in 2019 in "Multimodal Polymers with Supported Catalysts: Design and Production," edited by Jeremic D., Prades F. and Albunia A., Springer International Publishing, and titled "Polymerization Kinetics and the Effect of Reactor Residence Time on Polymer Microstructure," describes the effect of reactor residence time on polymerization kinetics and polymer microstructure.

[0007] A 2020 article in Macromol. React. Eng. by Touloupidis V., Rittenschober G., and Paulik C., titled "An Integrated PRE Methodology for Capturing the Reaction Performance of Single- and Multi-site Type Catalysts Using Bench-Scale Polymerization Experiments," describes a system and method for capturing the reaction performance of single-site and multi-site type catalysts.

[0008] The model can predict polymerization kinetics and polymer product molecular properties (molecular weight distribution, comonomer content distribution and average comonomer content) based on first principles (e.g., mass balance equations), as well as several second-order polymer properties including polymer density and melt index based on a series of correlations. [Prior art documents] [Non-patent literature]

[0009] [Non-Patent Document 1] Macromol.React.Eng., 8, pp. 508-527. [Non-Patent Document 2] Macromol.React.Eng., 8, 235-259 [Non-Patent Document 3] Jeremic D., Prades F. and Albunia A., eds., “Multimodal Polymers with Supported Catalysts: Design and Production”, Springer International Publishing [Non-Patent Document 4] Touloupidis V., Rittenschober G., Paulik C., "An Integrated PRE Methodology for Capturing the Reaction Performance of Single- and Multi-site Type Catalysts Using Bench-Scale Polymerization Experiments", Macromol. React. Eng. 2020 Summary of the Invention [Means for solving the problem]

[0010] These and other objects are solved by the subject matter of the present invention as defined by the independent claims. Further embodiments are defined by the dependent claims. The invention therefore makes it possible to enable new quality control concepts aimed at a better definition of polymer properties, both in the scope of process monitoring and control.

[0011] According to a first aspect of the present invention, a computer-implemented method for polymerization process monitoring and model predictive control is provided.

[0012] The method includes providing at least one parameter set including catalyst information, kinetic modeling information, and calculated polymer property information; and modeling the reaction performance of a catalyst used in a polymerization process using a reaction network, the reaction network including a plurality of reactions of the catalyst, the reactions of the catalyst being modeled by a discrete number of site types of the catalyst, and the catalyst being modeled as a sum of contributions of the plurality of site types.

[0013] In other words, the present invention advantageously offers, as a difference compared to the prior art, the enabling of new quality control concepts aimed at better defining polymer properties, both in the scope of process monitoring and polymer property oriented model predictive control.

[0014] According to the concept of the invention, kinetic parameters capable of describing the reaction performance of the catalyst are fed into a model together with the operating conditions, which is capable of predicting the expected reactive concentration values ​​in the reactor(s), the polymerization rate (and consequently the split) per reactor, and the polymer microstructure per reactor, e.g. as the molecular weight distribution MWD, the comonomer content CC.

[0015] Additionally, the rheological model provides a second level prediction of the expected complex viscosity at the outlet of each reactor (for a range of shear rates). This model prediction is compared at regular intervals (typically every 4-6 hours) with experimental fast frequency sweep measurements of corresponding reactor samples and with the corresponding desired fast frequency sweep set points, allowing control of selected manipulated variables.

[0016] According to one exemplary embodiment of the invention, the method further comprises predicting expected reactive concentration values, polymerization rates per reactor, or polymer microstructures per reactor based on the modeled reaction performance.

[0017] According to one exemplary embodiment of the present invention, modeling the reaction performance of the catalyst further comprises using a rheological model to predict an expected complex viscosity.

[0018] According to an exemplary embodiment of the present invention, the set of parameters further includes a feed rate used in the polymerization process, a temperature used in the polymerization process, or a pressure used in the polymerization process.

[0019] According to one exemplary embodiment of the present invention, the method further comprises modeling at least one value of a molecular property based on the modeled reaction performance.

[0020] According to one exemplary embodiment of the present invention, the method further comprises modeling at least one value of the rheological property based on the modeled reaction performance.

[0021] According to one exemplary embodiment of the present invention, the modeled reaction performance is used for process monitoring by an optimizer, which is preferably connected to a controller for controlling the polymerization process.

[0022] According to one exemplary embodiment of the present invention, the modeled reaction performance is used for process monitoring by predicting process and polymer parameters, and the optimizer is connected to a controller for controlling the polymerization process.

[0023] According to one exemplary embodiment of the present invention, the modeled reaction performance is used in an offline mode.

[0024] According to a second aspect of the present invention there is provided a computer program product comprising computer readable instructions which, when loaded and executed on a processor, performs a method according to any one of the embodiments of the first aspect or the first aspect itself.

[0025] According to a third aspect of the present invention there is provided an apparatus configured for polymerization process monitoring and model predictive control, the apparatus including a data memory and a processor.

[0026] The data memory is configured to provide at least one parameter set including catalyst information, kinetic modeling information, and calculated polymer property information.

[0027] The processor is configured to model reaction performance of a catalyst used in a polymerization process using a reaction network, the reaction network including a plurality of reactions of the catalyst, the reactions of the catalyst being modeled by a discrete number of a plurality of site types of the catalyst, and the catalyst being modeled as a sum of contributions of the plurality of site types.

[0028] According to one embodiment of the invention, the processor is further configured to predict expected reactive concentration values, polymerization rates per reactor, or polymer microstructures per reactor based on the modeled reaction performance.

[0029] According to one embodiment of the invention, the processor is further configured to model the reaction performance of the catalyst with respect to using the rheological model to predict the expected complex viscosity.

[0030] According to one embodiment of the present invention, the set of parameters further comprises a feed rate used in the polymerization process, a temperature used in the polymerization process, or a pressure used in the polymerization process.

[0031] According to one embodiment of the invention, the processor is further configured to model at least one value of the molecular property based on the modeled reaction performance.

[0032] According to one embodiment of the invention, the processor is further configured to model at least one value of the rheological property based on the modeled reaction performance.

[0033] The computer program for carrying out the method of the present invention may be stored on a computer readable medium, which may be a floppy disk, a hard disk, a CD, a DVD, a Universal Serial Bus (USB) storage device, a Random Access Memory (RAM), a Read Only Memory (ROM) and an Erasable Programmable Read Only Memory (EPROM).

[0034] The computer readable medium may also be a data communications network, for example the Internet, which allows the program code to be downloaded using a connection via WLAN or 3G / 4G or any other wireless data technology.

[0035] The methods, systems, and devices described herein may be implemented as software in a Digital Signal Processor (DSP), microcontroller, or any other side processor, or as hardware circuitry in an application specific integrated circuit, ASIC, CPLD, or FPGA.

[0036] The invention may be implemented in digital electronic circuitry, or in computer hardware, firmware, software, or in combinations of them, for example in available hardware of conventional mobile devices or in new hardware specifically adapted for processing the methods described herein. [Brief description of the drawings]

[0037] A more complete appreciation of the present invention and its attendant advantages may be better understood by reference to the following schematic drawings, which are not to scale.

[0038] [Figure 1] FIG. 1 shows a schematic diagram of a method for polymerization process monitoring and model predictive control. [Diagram 2] FIG. 2 shows a schematic diagram of an apparatus for polymerization process monitoring and model predictive control according to an exemplary embodiment of the present invention. [Diagram 3] FIG. 3 shows a schematic diagram of a model predictive control concept according to an exemplary embodiment of the present invention. [Figure 4] FIG. 4 shows a schematic diagram of a detailed dynamic model predictive control concept according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0039] The illustrations in the drawings are schematic and not to scale. In different drawings, similar or identical elements are provided with the same reference numbers.

[0040] Generally, the same parts, units, entities or steps are provided with the same reference numbers in several figures.

[0041] FIG. 1 shows a schematic diagram of a method for polymerization process monitoring and model predictive control.

[0042] In particular, FIG. 1 illustrates a method for polymerization process monitoring and model predictive control, the method comprising the steps of:

[0043] The first step of the method involves providing S1 at least one parameter set that includes catalyst information, kinetic modeling information, and calculated polymer property information.

[0044] As a second step of the method, a step S2 of modelling the reaction performance of a catalyst used in the polymerisation process using a reaction network is carried out, the reaction network comprising a plurality of reactions of the catalyst, the reactions of the catalyst being modelled by a discrete number of a plurality of site types of the catalyst, and the catalyst being modelled as the sum of the contributions of the plurality of site types.

[0045] FIG. 2 shows a schematic diagram of an apparatus for polymerization process monitoring and model predictive control.

[0046] An apparatus 100 for polymerization process monitoring and model predictive control is provided, the apparatus 100 including a data memory 10 configured to provide at least one parameter set including catalyst information, kinetic modeling information, and calculated polymer property information.

[0047] The apparatus 100 further includes a processor 20 configured to model reaction performance of a catalyst used in a polymerization process using a reaction network, the reaction network including a plurality of reactions of the catalyst, the reactions of the catalyst being modeled by a discrete number of a plurality of site types of the catalyst, and the catalyst being modeled as a sum of contributions of the plurality of site types.

[0048] FIG. 3 shows a schematic diagram of a model predictive control concept according to an exemplary embodiment of the present invention.

[0049] The kinetic model includes the entire reaction network calculated by the modeled process 301, and the characterization data 302 includes data on MWD, CCD, and rheology. The controlled variables 303 are continuously measured. Online measurements 304 on temperature, pressure, or concentration are provided to a model 305 configured to provide model predictive control and to a controller 306 configured to provide process control.

[0050] A particular model 305 is set up with the dynamic parameters and fed to a controller 306 .

[0051] Feedback to the process is provided with manipulated variables 307 to the process reactor.

[0052] According to an exemplary embodiment of the present invention, in contrast to currently used models, this new kinetic model contains a kinetic description of the reaction network of elementary reactions taking place at catalytically active centers.

[0053] Furthermore, it is postulated that the behavior of Ziegler-Natta catalysts can be explained by a discrete number of different site types, each of which acts as a "single-site catalyst".

[0054] The dynamics of the entire reaction network are calculated for each site type individually: the macroscopic behavior of the catalyst is the sum of the contributions of the different site types.

[0055] For the purposes of describing the models, the following nomenclature is used:

[0056]

number

[0057] The reaction network consists of the following elementary reactions: Site Activation: Co-catalyst:

number

number

[0058] Chain Initiation:

number

[0059] Chain Propagation:

number

number

number

[0060] Deactivation: With hydrogen:

number

number

[0061] All of the above reactions are assumed to be first order. Having calculated the rate of each reaction at each site type, the following values ​​are accessible, which describe the overall conversion of the reactants:

[0062] Total amount of polymer consisting of monomer 1 formed per time interval:

number

number

number

[0063] Amount of cocatalyst reacted per time interval:

number

[0064] Amount of hydrogen reacted per time interval:

number

[0065] The following formula can be derived for each type of site. Changes in potential sites of type k per time interval:

number

[0066] Change in activated sites of type k per time interval:

number

[0067] Change in inactivated sites of type k per time interval:

number

[0068] Change in reactive sites of type k per time interval:

number

[0069] Change in reactive sites of type k (last inserted monomer = monomer 2) per time interval:

number

[0070] Change in reactive sites of type k (last inserted monomer = monomer 3) per time interval:

number

[0071] Amount of polymer consisting of monomer 1 formed at site type k per time interval:

number

[0072] Amount of polymer consisting of monomer 2 formed at site type k per time interval:

number

[0073] Amount of polymer consisting of monomer 3 formed at site type k per time interval:

number

[0074] Total growth rate for site type k:

number

[0075] The sum of the chain transfer (and quenching) rates for site type k:

number

[0076] Instantaneous comonomer content of monomer 2 at site type k:

number

[0077] Instantaneous comonomer content of monomer 3 at site type k:

number

[0078] Having calculated the instantaneous comonomer content at site type k, the instantaneous average molecular weight of the polymer formed at site type k can be calculated as follows:

number

[0079] From the instantaneous average molecular weight, R Prop,k and R Trans,k Using the instantaneous number average molecular weight (M n,k ) can be derived.

number

number

number

[0080] Considering that the polydispersity index of the single-site type is 2, the instantaneous weight-average molecular weight (M w,k )teeth,

number

number

[0081] The total instantaneous MWD of the polymer formed across all site types of the catalyst is the sum of the instantaneous MWD of the different site types.

number

number

number

[0082] FIG. 4 shows a schematic diagram of a detailed dynamic model predictive control concept according to an exemplary embodiment of the present invention.

[0083] Monitoring and control of polymerization process: The complexity of the chemical and physical phenomena occurring within a series of polymerization reactors, the overlapping effects of the system's input variables (including flow rates, temperatures and pressures), and the continuous and dynamic nature of the process necessitate effective monitoring and control methods - methods for manipulating the variables of interest so that the controlled variable values ​​remain within specified specification ranges (set points). According to the basic feedback control scheme described above, operational action is based on feeding back process output information to a controller.

[0084] This control methodology has been used for the past 20 years, and the feedback information may refer to reactive concentration values, polymer density or melt index values. This control method includes a mathematical model for the polymerization process and an optimizer to define the best operating options. The model is based on a material balance equation and a set of empirical correlations for the prediction of polymerization rate and several polymer properties including polymer density and melt index.

[0085] For example, monomer conversion is a function of temperature, pressure, hydrogen content and comonomer content. The correlation is in the form of a product of a correction factor, a pre-exponential factor, terms describing the effects of the concentrations and / or partial pressures of the monomer and other reaction partners, terms describing the mass and catalytic activity of the catalyst in the reactor, and an Arrhenius-type term capturing the effect of temperature. The nonlinear behavior of these parameters is fitted to match observed plant data.

[0086] This method works well from a control standpoint because the models tend to be accurate. For example, the current setup is successful in controlling process conditions / stabilization and controlling polymerization conditions. Production speed H2 / monomer ratio Provide

[0087] However, since no detailed kinetic considerations are included, model predictions regarding the polymer microstructure and hence its physical and chemical properties such as molecular weight distribution (MWD), comonomer content (CC) and comonomer content distribution (CCD) and subsequent rheological behavior cannot be achieved.

[0088] This limitation prevents us from reaching trait-oriented control capabilities. What is missing is Product characteristic control Monitoring and control of polymer properties (MWD, CC) Rheological properties (complex viscosity, melt index) It is.

[0089] The invention therefore offers a series of advantages. i. Improved control during transitions ii. Improved Product Integrity iii. Transition optimization iv. Reduce the need for test runs v. Process Understanding and Troubleshooting Tools vi. Deeper knowledge of polymer structures vii. Improving product quality performance

[0090] The proposed method of polymerization process monitoring and model predictive control provides a new powerful tool for polymerization reactor operation. The proposed method of polymerization process monitoring and model predictive control can predict polymer microstructure properties including MWD, CC over MWD and CCD based on the kinetic parameters of the catalyst in use and the process conditions (feed rate, temperature, pressure).

[0091] Furthermore, the proposed method of polymerization process monitoring and model predictive control is able to predict the complex viscosity of the produced polymer (within a certain range of shear rates).

[0092] Furthermore, the proposed method of polymerization process monitoring and model predictive control can provide predictions of molecular and rheological properties that can be further used as the basis for second level structure-property correlations for additional polymer properties (e.g., correlations for crack resistance, yield stress, etc.).

[0093] Furthermore, the proposed method of polymerization process monitoring and model predictive control can be used for process monitoring that dynamically provides important information to plant operators in the control room.

[0094] Furthermore, the proposed method of polymerization process monitoring and model predictive control can be used in offline mode (not connected to the plant) for engineering studies (product and process development) on alternative process conditions or reactor settings. Furthermore, the proposed method of polymerization process monitoring and model predictive control can use a fast frequency sweep method to receive control feedback and close the loop to enable model predictive control operation.

[0095] Furthermore, the proposed method of polymerization process monitoring and model predictive control enables property-oriented control targeting intrinsic polymer microstructural properties, which is not currently feasible.

[0096] Furthermore, the proposed method of polymerization process monitoring and model predictive control can increase the model validity of the stabilization control, leading to improved control during the transition.

[0097] Use of the proposed method provides additional freedom regarding the variables that can be manipulated, since now there is a way to predict the effect those variables will have on the process (e.g., manipulating the split or reactor temperature).

[0098] It should be noted that embodiments of the present invention are described with reference to different subject matter: in particular, some embodiments are described with reference to method type claims, whereas other embodiments are described with reference to device type claims.

[0099] However, those skilled in the art will understand from the above and following description that, unless otherwise noted, any combination of features belonging to one type of subject matter, as well as any combination between features relating to different subjects, is considered to be disclosed in this application, however, all features may be combined to provide synergistic effects that exceed the simple sum of the features.

[0100] While the invention has been illustrated and described in detail in the drawings and the foregoing description, such illustration and description is to be considered illustrative or exemplary and not restrictive, and the invention is not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art and practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

[0101] In the claims, the term "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality. A single processor or controller or other unit may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage. Any reference signs in the claims should not be interpreted as limiting the scope.

Claims

1. 1. A computer-implemented method for polymerization process monitoring and model predictive control, comprising: providing at least one parameter set (S1) including catalyst information, kinetic modeling information, and calculated polymer property information; (S2) modeling the reaction performance of a catalyst used in the polymerization process using a reaction network, the reaction network including a plurality of reactions of the catalyst, the reactions of the catalyst being modeled by a discrete number of a plurality of site types of the catalyst, and the catalyst being modeled as a sum of the contributions of the plurality of site types; A method comprising:

2. 10. The method of claim 1, further comprising predicting expected reactive concentration values, polymerization rates per reactor, or polymer microstructures per reactor based on the modeled reaction performance.

3. 3. The method of claim 1 or claim 2, wherein the step of modeling (S2) the reaction performance of the catalyst further comprises using a rheological model to predict an expected complex viscosity.

4. 4. The method of claim 1, wherein the set of parameters further comprises a feed rate used in the polymerization process, a temperature used in the polymerization process, or a pressure used in the polymerization process.

5. 5. The method of claim 1, further comprising the step (S3) of modelling at least one value of a molecular property based on the modelled reaction performance.

6. 6. The method of claim 1, further comprising the step (S3) of modelling at least one value of a rheological property based on the modelled reaction performance.

7. 7. The method of any one of claims 1 to 6, wherein the modelled reaction performance is used for process monitoring by an optimizer, preferably connected to a controller for controlling the polymerization process.

8. The method of any one of claims 1 to 7, wherein the modelled reaction performance is used in an off-line mode.

9. An apparatus (100) for polymerization process monitoring and model predictive control, comprising: a data memory (10) configured to provide at least one parameter set including catalyst information, kinetic modeling information, and calculated polymer property information; A processor (20) configured to model reaction performance of a catalyst used in a polymerization process using a reaction network, the reaction network including a plurality of reactions of the catalyst, the reactions of the catalyst being modeled by a discrete number of a plurality of site types of the catalyst, and the catalyst being modeled as a sum of contributions of the plurality of site types; 13. An apparatus comprising:

10. 10. The apparatus of claim 9, wherein the processor (20) is further configured to predict expected reactive concentration values, polymerization rates per reactor, or polymer microstructures per reactor based on the modeled reaction performance.

11. 11. The apparatus of claim 9 or claim 10, wherein the processor (20) is further configured to model reaction performance of the catalyst in terms of using a rheological model to predict an expected complex viscosity.

12. 12. The apparatus of claim 9, wherein the set of parameters further comprises a feed rate used for the polymerization process, a temperature used for the polymerization process, or a pressure used for the polymerization process.

13. 13. The apparatus of any one of claims 9 to 12, wherein the processor (20) is further configured to model at least one value of a molecular property based on the modeled reaction performance.

14. 14. The apparatus of any one of claims 9 to 13, wherein the processor (20) is further configured to model at least one value of a rheological property based on the modeled reaction performance.

15. A computer readable medium comprising instructions which, when executed by a computer, cause the computer to perform the steps of the method of any one of claims 1 to 8.

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