Self-adaptive polymerization process kinetic parameter adjusting method

Abstract

The invention discloses a self-adaptive polymerization process kinetic parameter adjusting method, and belongs to the field of chemical engineering. The method comprises the following steps: establishing a polymerization reaction microcosmic quality model; screening actual estimable kinetic parameters and carrying out preliminary fitting solution; classifying the screening parameters according to the influence on molecular weight distribution and sorting according to sensitivity; and an optimization objective function is constructed, parameter optimization is carried out by adopting a particle swarm algorithm on the basis of the initial optimal value, and different types of parameters adopt a differentiated optimization algorithm. The method aims at adaptively adjusting the parameters based on the industrial data of the small samples to replace complicated model parameter adjustment, and the method can automatically adjust the parameters based on the difference between the calculated molecular weight distribution curve and the field industrial data, so that the microcosmic quality results under a plurality of working conditions are consistent with the field data.

Description

Technical Field

[0001] The invention relates to the field of chemical engineering, and in particular to an adaptive polymerization process kinetic parameter adjustment method. Background Art

[0002] Polymers are composed of a series of repeating structural units, and their microscopic quality characteristics (such as molecular weight distribution) have an important influence on the intrinsic properties of polymers. However, in actual production processes, polymerization reactions usually rely on macroscopic quality indicators (for example, average molecular weight) for control and guidance. However, this method fails to fully reflect the detailed information of the chain structure and is difficult to meet the needs of high-performance polymer products for refined control. In order to accurately obtain the macroscopic and microscopic quality characteristics of polymers, simulation of the polymerization process has become a key technology. Especially in large-scale industrial production, simulation technology plays a vital role in product design, process optimization and operation adjustment. However, the polymerization reaction process model is large in scale and has many parameters, and the parameter estimation model is difficult to solve. In addition, it is generally difficult to obtain large batches of industrial data, which further increases the difficulty of parameter estimation. Therefore, developing a small sample and easy-to-solve number adjustment method has become the key to solving this problem. Summary of the invention

[0003] In order to solve the problems in the prior art, the present invention proposes an adaptive polymerization process kinetic parameter adjustment method. The method classifies and grades the kinetic parameters and performs adaptive parameter adjustment by constructing a weighted function. The method includes kinetic modeling, constructing a kinetic and molecular weight distribution correlation function, and a two-layer iterative optimization algorithm.

[0004] The present invention is achieved through the following technical solutions:

[0005] In one aspect, the present invention provides an adaptive method for adjusting kinetic parameters of a polymerization process, comprising:

[0006] 1) Establish a microscopic quality model of the polymerization process;

[0007] 2) Screen the kinetic parameters that can be actually estimated during the polymerization reaction, and fit the micro-mass model to solve the preliminary optimal values ​​of the kinetic parameters after screening;

[0008] 3) Classify the kinetic parameters obtained by the screening according to their effects on the molecular weight distribution of the polymer, and sort the kinetic parameters in each category from high to low in terms of sensitivity;

[0009] 4) Construct an optimization objective function. On the basis of the preliminary optimal value, perform particle swarm optimization on each kinetic parameter based on the optimization objective function. Different optimization objective functions are used for different categories of parameters.

[0010] On the other hand, the present invention also proposes an adaptive polymerization process kinetic parameter adjustment system for implementing the above-mentioned polymerization process kinetic parameter adjustment method.

[0011] The beneficial effects of the present invention are:

[0012] (1) The adaptive kinetic parameter adjustment method proposed in the present invention can perform parameter adjustment and estimation on a smaller industrial sample data scale. Compared with the conventional kinetic parameter estimation method, the model scale is smaller and more conducive to solving.

[0013] (2) The adaptive dynamic parameter adjustment method proposed in the present invention is intended to replace the complicated manual parameter adjustment and make it more efficient and reliable.

[0014] (3) The adaptive kinetic parameter adjustment method proposed in the present invention is simple to implement, has good accuracy, and is suitable for simulating various olefin polymerization reaction processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The purpose, features and advantages of the present invention can be further understood by describing the embodiments of the present invention in combination with the accompanying drawings and attached tables and demonstrating the effects thereof, wherein:

[0016] Figure 1 It is a schematic diagram of the adaptive parameter adjustment method;

[0017] Figure 2 This is a comparison between the adaptive parameter adjustment results of a certain industrial grade (grade 1) and the molecular weight distribution data of a single kettle on site;

[0018] Figure 3 Comparison of the adaptive parameter adjustment results of a certain industrial grade (grade 1) with the molecular weight distribution data of the two kettles on site;

[0019] Figure 4 This is a comparison of the adaptive parameter adjustment results of a certain industrial brand (brand 1) and the molecular weight distribution data of three kettles on site.

[0020] Figure 5 The comparison between the adaptive parameter adjustment results of a certain industrial grade (grade 2) and the molecular weight distribution data of a single kettle on site;

[0021] Figure 6 Comparison of the adaptive parameter adjustment results of a certain industrial grade (grade 2) with the molecular weight distribution data of the second kettle on site;

[0022] Figure 7 This is a comparison of the adaptive parameter adjustment results of a certain industrial brand (brand 2) and the molecular weight distribution data of three kettles on site. DETAILED DESCRIPTION

[0023] The present invention is described in more detail below with reference to the attached tables and drawings of the present invention, so as to describe the specific implementation process of the present invention to those skilled in the art.

[0024] Example 1

[0025] Next, an adaptive polymerization process kinetic parameter adjustment method provided by this embodiment is described in detail.

[0026] This embodiment provides an adaptive polymerization process kinetic parameter adjustment method such as Figure 1 As shown, the following steps are included:

[0027] 1) Establish a microscopic quality model of the polymerization process;

[0028] 2) Conduct feasibility estimation of kinetic parameters;

[0029] 3) Classify and grade the kinetic parameters and calibrate their sensitivity in influencing the model;

[0030] 4) Construct a parameter adjustment function that relates the kinetic parameters to the molecular weight distribution and perform adaptive parameter adjustment.

[0031] Step 1). Construction of micro-mass dynamics model

[0032] In this step, an improved microscopic mass kinetic model for the copolymerization of ethylene and butene is established. The model is based on the following assumptions:

[0033] (1) Consider the activation reaction between the co-catalyst and the monomer and comonomer, simplifying it into a linear relationship. Assume that the activation rate of the catalyst is positively correlated with the catalyst concentration and temperature;

[0034] (2) It is assumed that there is a nonlinear relationship between the rate of chain growth reaction and the monomer concentration, comonomer concentration and catalyst activity;

[0035] (3) Chain transfer reactions include chain transfer of hydrogen, monomer, and comonomer, and catalyst deactivation is modeled through the interaction between catalyst and comonomer.

[0036] On this basis, we established a general reactor model, which involves reaction kinetics, thermodynamics, material balance, energy balance and phase balance, and can simulate the three-phase reaction (gas-liquid-solid) inside the reactor. The reaction equation is shown in Table 1.

[0037] Table 1 Copolymerization mechanism

[0038] Reaction Type Reaction Description Catalyst activation #timg##timg# Chain Initiation #timg##timg# Chain Growth #timg##timg##timg##timg# Transfer to monomer #timg##timg##timg##timg# Shift to hydrogen #timg##timg# Transfer to co-catalyst #timg##timg# Beta transfer #timg##timg# Chain inactivation #timg##timg# #timg#

[0039] in It is a potential active site of a catalyst. It is a co-catalyst. It is a monomer. It is a comonomer, It is a transfer agent. is the active site, is the inactivated active site, It is a single The chain length of the active free radical is Living polymers, It is a single The chain length of the active free radical is Living polymers, The chain length is of dead polymers. represent The index of the active site, ranging from 1 to 5. are the kinetic rate constants for the different reactions, where To promote the reaction rate of the activation catalyst; is the reaction rate of comonomer activation; is the chain initiation rate between the active site and monomer A; is the chain initiation rate between the active site and monomer B; is the chain growth rate, which indicates the growth reaction rate between the living polymer with monomer A or B as the activity and monomer A or B; is the chain transfer rate, which indicates the reaction rate of the living polymer with monomer A or B as the activity transferring to monomer A or B; and It indicates the reaction rate of the transfer of the living polymer with monomer A / B as the activity to the transfer agent; and It represents the reaction rate of the transfer of the active living polymer from monomer A / B to the co-catalyst; and represents the self-transfer of living polymers with monomer A / B as the activity ( transfer) rate; is the reaction rate of the active site and the deactivation of the living polymer with monomer A / B as the activity.

[0040] Step 2). Adaptive estimation and screening of kinetic parameters

[0041] The polymerization reaction kinetics mechanism is the core component of the polymerization process model. It reveals the basic characteristics of the process and directly affects the various performance indicators of the final polymer product (such as melt index, polydispersity index, average molecular weight and molecular weight distribution, etc.).

[0042] This step determines which parameters can be effectively adaptively adjusted through sensitivity analysis of reaction kinetic parameters and research on confidence intervals. The specific steps of the screening process for parameter estimability analysis are as follows: first, pre-select parameters. At this stage, those parameters with relatively low sensitivity are eliminated from the subset of parameters to be estimated; then, parameter estimation is performed to obtain the optimal estimated values ​​of the parameters in the current subset; finally, post-optimization analysis is performed. At this stage, parameters with large confidence intervals for the optimal values ​​are also removed from the subset of parameters to be estimated. This process gradually eliminates parameters with low credibility by continuously looping the above steps until the maximum confidence interval ratio of the remaining parameters in the subset of parameters to be estimated is lower than the set threshold. At this point, the obtained parameter set is the actual estimable parameter set.

[0043] This step is specifically implemented through the following sub-steps:

[0044] Parameter screening based on sensitivity analysis: All possible kinetic parameters (such as chain growth rate constants) are screened using the micro-mass model. , chain termination rate constant The sensitivity of each kinetic parameter is calculated by numerical simulation through the model in step 1. The sensitivity is used to describe the influence of the parameter on the molecular weight distribution of the polymer. The kinetic parameters are screened according to the sensitivity, and the parameters with greater influence on the molecular weight distribution of the polymer, such as catalyst concentration, reaction temperature, hydrogen concentration, etc., are retained, and the parameters with less sensitivity are eliminated to ensure that the computing resources are concentrated on the key parameters. Calculated according to the following formula:

[0045]

[0046] Among them, the For the The active site corresponds to Kinetic parameters The sensitivity of For the The probability density parameter of the polymer molecular weight distribution on each active site is represents partial derivative, ln represents logarithm, and the sensitivity of the logarithmic scale is calculated in the formula, which can make the comparison of kinetic parameters at different scales more objective.

[0047] Assume that the sensitivity of a parameter is the largest among all parameters, and its value is , in this embodiment, the sensitivity is kept not less than Parameters.

[0048] Parameter screening based on estimability analysis: In order to improve the reliability of parameter estimation, the present invention further determines which kinetic parameters can be adaptively adjusted by step-by-step analysis of the retained reaction kinetic parameters, applying error analysis and confidence interval analysis. In this process, the Bayesian estimation method is used to evaluate the uncertainty of the parameters and exclude unreliable parameters (confidence interval ratio exceeds 20%). The confidence interval ratio is defined as:

[0049]

[0050] in represents the optimal estimate of the parameter, They represent the upper and lower limits of the confidence interval of the parameter respectively.

[0051] Preliminary parameter estimation: For the actual estimable parameter set obtained after two screenings, the least squares method (LSM) is used to fit the micro-mass model to obtain the preliminary optimal kinetic parameter values ​​as the initial parameter values ​​for subsequent optimization.

[0052] Step 3). Classification and sensitivity ranking of kinetic parameters

[0053] In order to improve the accuracy of adaptive estimation, the present invention proposes a multi-step adaptive parameter estimation method, which successfully transforms the complex nonlinear ill-conditioned problem into multiple simple, low nonlinear sub-problems by classifying and grading the kinetic parameters, thereby effectively avoiding the problem of initial value sensitivity and reducing the difficulty of solving. This method not only improves the accuracy of parameter estimation, but also can better adapt to the complex situation in the polymerization process under industrial scale.

[0054] Specifically, the kinetic parameters obtained by screening in step 2) are classified according to their effects on molecular weight distribution (MWD) and graded according to sensitivity.

[0055] In this embodiment, the parameters are divided into four categories:

[0056] : Positively correlated with molecular weight, affecting the position of molecular weight distribution;

[0057] : Negatively correlated with molecular weight, affecting the position of molecular weight distribution;

[0058] : Positively correlated with molecular weight, affecting the shape of molecular weight distribution;

[0059] : Negatively correlated with molecular weight and affects the shape of molecular weight distribution.

[0060] in and The parameters of this type play a decisive role in the average molecular weight of the polymer, usually including catalyst concentration, reaction temperature, etc. and The parameters of this type mainly affect the width of the molecular weight distribution or the peak degree of the distribution, and usually include hydrogen concentration, comonomer ratio, etc.

[0061] After the classification is completed, the parameters in each category are sorted from high to low according to sensitivity, that is, is the most sensitive parameter, and It is the least sensitive parameter.

[0062] Step 4). Adaptive parameter adjustment

[0063] One of the core innovations of the present invention is the adaptive parameter adjustment method, which dynamically adjusts the key parameters in the polymerization process based on the above kinetic model and correlation function to achieve the optimization of the target molecular weight distribution. In this step, the optimization objective function is first constructed, and then the particle swarm optimization algorithm is used to optimize each kinetic parameter. Different types of parameters are designed with differentiated optimization algorithms. The specific process is as follows:

[0064] Construction of optimization objective function: For and Class parameters, by calculating the difference between the molecular weight distribution of the initial value of the parameter and the target molecular weight distribution, the optimization objective function is established:

[0065]

[0066] in, is the set of kinetic parameters to be optimized, is the current molecular weight distribution, is the target molecular weight distribution, represents the square of the 2-norm.

[0067] for and Class parameters, by calculating the molecular weight distribution and the standard deviation of the target molecular weight distribution, the optimization objective function is established:

[0068]

[0069] in and Calculate the molecular weight distribution With target molecular weight The standard deviation of .

[0070] In the objective function, molecular weight distribution Based on the parameters to be estimated and simulated by the micro-quality model; based on the classified parameter set, the functional correlation between the molecular weight distribution and the parameters to be estimated is as follows:

[0071]

[0072]

[0073]

[0074] in For chain length, is the molecular weight distribution curve calculated based on the adaptive estimation algorithm, Active site The molecular weight distribution sub-curve on Active site The molecular weight distribution curve on , represents the kinetic parameter that affects the position of the molecular weight distribution, , Represents the kinetic parameters that affect the molecular weight distribution. , , , is the index of each parameter set (consecutive integers starting from zero), is the probability density parameter of molecular weight distribution, , , , They are , , and The adaptive estimation weighting function is defined as follows:

[0075]

[0076]

[0077]

[0078]

[0079] in and is the coefficient that controls the weight decay speed ( Take 0.5 to 2, Take 0.3~1.0).

[0080] Molecular weight distribution curve Multiple active sites Subcurve on Each sub-curve is a nominal curve. Obtained through parameter transformation.

[0081] Design of optimization algorithm: Based on the above optimization objective function, the particle swarm optimization (PSO) algorithm is used to solve the optimization problem of the actual estimable parameter set. In the PSO algorithm, each parameter in the actual estimable parameter set is a particle, the dynamic parameter to be optimized is the position of the particle, and the amplitude and direction of the parameter update are the particle speed. The update formula of the particle swarm is as follows:

[0082]

[0083] in, is the velocity of the particle at time t and time t+1, is the position of the particle at time t, is the historical optimal position of the particle, is the global optimal position, is the inertia weight, , is the acceleration constant, , Is a random number.

[0084] for and Class parameters, the objective function of each particle is to minimize the position error, that is, to minimize the difference between the molecular weight distribution and the target distribution; and Class parameters, the objective function is to minimize the molecular weight distribution shape error, that is, ,in and are the standard deviations of the calculated curve and the target curve respectively.

[0085] Parameter update and feedback adjustment: In each optimization iteration of the PSO algorithm, the parameter values ​​of the micro-mass kinetic model in the initial stage of the reaction are adjusted according to the particle swarm optimization results, and the polymerization process is simulated by the micro-mass kinetic model. Through real-time feedback, the optimization target is continuously reduced until the error between the molecular weight distribution and the target value is less than the predetermined threshold.

[0086] This method can adapt to the changes in reaction conditions in the reactor in real time. After each optimization iteration, the weight function is dynamically adjusted according to the reaction results. , , , The attenuation coefficient in and , thereby ensuring the stability of the polymerization process and the quality of the final polymer.

[0087] Example 2

[0088] In this embodiment, a three-reactor series system for producing polyethylene slurry is involved, which includes three identical continuous stirred tank reactors (CSTR). In this system, three reactors are connected in sequence, namely the first reactor, the second reactor and the third reactor, and the liquid phase material of the previous reactor is transferred to the next reactor or output as a product after flash evaporation and condensation. In this embodiment, n-hexane (C6H14) is used as a solvent, ethylene, butene and hydrogen are used as gaseous raw materials, and a Ziegler-Natta catalyst is used for olefin copolymerization.

[0089] This technology covers the basic reaction types involved in olefin polymerization using Ziegler-Natta catalysts, including catalyst activation, chain initiation, polymer chain growth, chain transfer, and catalyst failure. Considering the complexity of the Ziegler-Natta copolymerization mechanism and the strong coupling and nonlinear characteristics of its reaction process, these factors often make it difficult for the calculation process to converge. In the simplified model proposed in the present invention, the catalyst activation reaction only involves the activation of the co-catalyst, monomer, and co-monomer; the chain growth reaction only considers the chain extension of the monomer and co-monomer; the chain transfer reaction includes the chain transfer of the monomer, hydrogen, co-catalyst, and its own chain transfer.

[0090] This simplified model not only ensures the high accuracy of the model, but also significantly reduces the complexity of modeling. In the Ziegler-Natta catalyst system, ethylene and comonomers such as butene react in a polymerization reactor to produce copolymers with different chain lengths and compositions. Establishing a reactor model based on an accurate reaction mechanism is crucial to determining the composition and molecular weight distribution of the copolymer. The reactor model includes comprehensive considerations of polymerization reaction kinetics, thermodynamics, material balance, energy balance and phase equilibrium. In view of the particularity of the copolymer system, different chain lengths need to be balanced separately. In the modeling process, the moment model is often used to represent the relationship between different chain lengths to simplify the complexity of the model.

[0091] This algorithm is completed based on the model of Python environment. First, the ethylene butene copolymerization process model is established, and its equation is shown below.

[0092] Polymer chain growth equation:

[0093]

[0094]

[0095] in, and The chain length is The formation rate of vinyl polymer chains and butene polymer chains, and The chain length is -1 and The concentration of vinyl polymer chains, and The chain length is -1 and The concentration of butene-based polymer chains;

[0096] Dead polymer formation equation:

[0097]

[0098] in, Indicates the length is The generation rate of dead polymer chains, and denote the chain transfer rate constants for ethylene and butene chains, respectively;

[0099] Catalyst consumption equation:

[0100]

[0101] in, is the consumption rate of the catalyst, is the activation rate constant of the catalyst and co-catalyst, is the activation rate constant of the catalyst and butene monomer, is the concentration of promoter, is the catalyst concentration;

[0102] Co-catalyst consumption equation:

[0103]

[0104] in, is the consumption rate of the co-catalyst, is the chain transfer rate constant between the co-catalyst and the active center, , is the concentration of vinyl active center and butene active center;

[0105] Active chain initiation equation:

[0106]

[0107] in, is the rate of initial active chain generation, , is the initiation rate constant of the monomer, is the initial active chain concentration, , , , is the chain transfer rate constant, is the hydrogen concentration, is the active chain inactivation rate constant;

[0108] Dead catalyst generation equation:

[0109]

[0110] in, is the generation rate of dead catalyst, , is the deactivation rate constant of the vinyl active center and the butenyl active center;

[0111] Monomer consumption equation:

[0112]

[0113]

[0114] in, is the consumption rate of ethylene monomer, is the consumption rate of butene monomer, , , , is the chain transfer rate constant between the monomer and the active center;

[0115] Hydrogen consumption equation:

[0116]

[0117] in, is the consumption rate of hydrogen, is the zero-order moment of the active polymer with monomer A as the active site, is the zero-order moment of the active polymer with monomer B as the active site.

[0118] Then, adaptive estimation and screening of kinetic parameters are performed. After screening, the kinetic parameters are classified and ranked from high to low sensitivity as follows:

[0119] (Positively correlated with molecular weight, affecting position): None.

[0120] (negatively correlated with molecular weight, affecting position): , , , , , .

[0121] (Positively correlated with molecular weight, affecting shape): , , , .

[0122] (negatively correlated with molecular weight, affects shape): , , .

[0123] Then the adaptive parameter adjustment is performed. In the parameter optimization process of parameter adjustment, the particle swarm algorithm optimization of different types of parameters is as follows: and Class parameter, that is, a negatively correlated parameter related to the molecular weight distribution position:

[0124] Particle swarm initialization and objective function: Initialize the position and velocity of the particles. The objective function of each particle is to minimize the position error, that is, minimize the difference between the molecular weight distribution and the target distribution.

[0125] Particle swarm iterative search: By updating the particle position and velocity through multiple iterations, searching for the global optimal solution, the optimized Class parameters.

[0126] for and Class parameters (positively and negatively correlated with the shape of the molecular weight distribution, respectively):

[0127] Particle swarm initialization and objective function: Initialize the position and velocity of the particles. The objective function is to minimize the shape error of the molecular weight distribution.

[0128] Particle swarm iterative search: Also through multiple iterations, the position and velocity of the particles are updated to search for the global optimal solution, and finally the optimized and Class parameters.

[0129] After the optimization of the X and Y parameters is completed, the optimized parameters are updated to the reaction kinetics model. Based on the new optimization parameters, the model is solved again to verify whether the optimized molecular weight distribution meets the expectations. The target molecular weight distribution is compared with the molecular weight distribution obtained by actual calculation. If the error meets the set threshold, the optimization process ends and the final optimization result is output. If the error is greater than or equal to the set threshold, return to the parameter adjustment module, readjust the parameters, and continue the optimization until the error is less than the predetermined threshold.

[0130] This method uses a multi-step optimization method to further improve the accuracy of the model by gradually fine-tuning the parameters before meeting the target error threshold. During the optimization process, the weights of each objective function are dynamically adjusted according to the sensitivity analysis results to ensure that the final optimization result can minimize the error. According to this method, the adaptive parameter adjustment of the data of an industrial process is carried out, and the results are as follows Figure 2-Figure 7 shown. Figure 2 , Figure 5 The comparison between the adaptive parameter adjustment results of two different ethylene-butene copolymers (different industrial grades) and the molecular weight distribution data of one kettle on site; Figure 3 , Figure 6 The results of adaptive parameter adjustment for the production of two different ethylene-butene copolymers are compared with the molecular weight distribution data of two reactors on site; Figure 4 , Figure 7 The results of adaptive parameter adjustment for the production of two different ethylene-butene copolymers are compared with the molecular weight distribution data of three reactors on site. Based on this solution, parameter adjustment based on multiple sets of industrial data is achieved, and the model molecular weight distribution is consistent with the industrial test data.

[0131] Example 3

[0132] In the present embodiment, a kind of adaptive polymerization process kinetic parameter regulating system is also provided, and this system is used to realize the above-mentioned embodiment.The terms "module", "unit" etc. used below can realize the combination of software and / or hardware of predetermined function.Although the system described in the following embodiments is preferably realized with software, the realization of hardware, or the combination of software and hardware is also possible.

[0133] The self-adaptive polymerization process kinetic parameter adjustment system comprises:

[0134] Model building module, used to build micro-quality models of polymerization processes;

[0135] Parameter screening module, used to screen the kinetic parameters that can actually be estimated during the polymerization reaction;

[0136] Initial value solving module, used to solve the initial optimal value of the kinetic parameters after screening;

[0137] A classification and sorting module is used to classify and sort the kinetic parameters obtained by screening;

[0138] The parameter adjustment module is used to construct an optimization objective function and optimize various kinetic parameters based on the optimization objective function.

[0139] For the system embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment, and the implementation methods of the remaining modules will not be repeated here. The system embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of the present invention. Ordinary technicians in this field can understand and implement it without paying creative work.

[0140] The embodiments of the system of the present invention can be applied to any device with data processing capabilities, and the device with data processing capabilities can be a device or apparatus such as a computer. The system embodiments can be implemented by software, or by hardware or a combination of software and hardware. Taking software implementation as an example, as a device in a logical sense, the corresponding computer program instructions in the non-volatile memory are read into the memory by the processor of any device with data processing capabilities and run.

[0141] The above-mentioned embodiments only express several implementation methods of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. For ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for adaptively adjusting kinetic parameters of a polymerization process, characterized in that: include: 1) Establish a microscopic quality model of the polymerization process; 2) Screen the kinetic parameters that can be actually estimated during the polymerization reaction, and fit the micro-mass model to solve the preliminary optimal values ​​of the kinetic parameters after screening; 3) Classify the kinetic parameters obtained by the screening according to their effects on the molecular weight distribution of the polymer, and sort the kinetic parameters in each category from high to low in terms of sensitivity; 4) Construct an optimization objective function. On the basis of the preliminary optimal value, perform particle swarm optimization on the sorted kinetic parameters based on the optimization objective function. Different optimization objective functions are used for different categories of parameters.

2. The method for adjusting the adaptive polymerization process kinetic parameters according to claim 1, characterized in that: In step 2), the actually estimable kinetic parameter is selected from the group consisting of a chain growth rate constant, a chain termination rate constant, a chain transfer rate constant, an activation rate constant and a deactivation rate constant.

3. The method for adjusting the adaptive polymerization process kinetic parameters according to claim 1, characterized in that: In step 1), the microscopic mass model of the polymerization reaction process includes a plurality of equations for solving the generation or consumption rate of living polymers, dead polymers, catalysts, and reactant monomers according to the concentration of substances.

4. The method for adjusting the adaptive polymerization process kinetic parameters according to claim 1, characterized in that: In step 2), the kinetic parameters that can be actually estimated during the screening polymerization reaction are specifically: 2.1) Numerical simulation based on the micro-mass model is performed to calculate the sensitivity of all kinetic parameters that affect the molecular weight distribution of the polymer and eliminate the kinetic parameters with low sensitivity; The sensitivity calculation formula is: ； Among them, the For the The active site corresponds to Kinetic parameters The sensitivity of For the The probability density parameter of the polymer molecular weight distribution on each active site is represents partial derivative, ln represents logarithm; 2.2) Using the Bayesian estimation method to perform uncertainty assessment on the retained kinetic parameters, obtain the confidence intervals of the kinetic parameters, and eliminate the kinetic parameters whose confidence interval ratio exceeds a threshold. The confidence interval ratio is the ratio of the width of the confidence interval of the kinetic parameter to the optimal estimate of the kinetic parameter, and the optimal estimate of the kinetic parameter is obtained by maximum a posteriori estimation.

5. The method for adjusting the adaptive polymerization process kinetic parameters according to claim 4, characterized in that: In step 2.1), the kinetic parameters with a sensitivity lower than 1% of the maximum sensitivity value were eliminated.

6. The method for adjusting the adaptive polymerization process kinetic parameters according to claim 1, characterized in that: In step 2), the least square method is used to fit the micro-quality model.

7. The method for adjusting the adaptive polymerization process kinetic parameters according to claim 1, characterized in that: In step 3), the kinetic parameters obtained by screening are classified according to their effects on the molecular weight distribution of the polymer, specifically into four categories: The kinetic parameter that is positively correlated with the molecular weight and affects the position of the molecular weight distribution is kind; The kinetic parameter that is negatively correlated with the molecular weight and affects the position of the molecular weight distribution is kind; The kinetic parameter that is positively correlated with the molecular weight and affects the shape of the molecular weight distribution is kind; The kinetic parameter that is negatively correlated with the molecular weight and affects the shape of the molecular weight distribution is kind.

8. The method for adjusting the adaptive polymerization process kinetic parameters according to claim 7, characterized in that: In step 4), for and The dynamic parameters of the class, the optimization objective function is: ； in, is the set of kinetic parameters after screening in step 2), To estimate the molecular weight distribution curve, is the target molecular weight distribution curve, represents the square of the two-norm; for and The dynamic parameters of the class, the optimization objective function is: ； in, and Estimated molecular weight distribution curves With target molecular weight The standard deviation of Estimation of molecular weight distribution curve The calculation formula is: ； ； ； in, For chain length, To estimate the molecular weight distribution curve, Active site The molecular weight distribution sub-curve on Active site The molecular weight distribution curve on , , and express kind, kind, Class and Kinetic parameters of the class, , , , is the index of the kinetic parameter in each category, For the The probability density parameter of the molecular weight distribution on each active site is , , , They are , , and The adaptive estimation weighting function of .

9. The method for adjusting the adaptive polymerization process kinetic parameters according to claim 1, characterized in that: In step 4), the particle swarm optimization is performed on each kinetic parameter based on the optimization objective function, specifically: each kinetic parameter in the actual estimable parameter set is taken as a particle, the kinetic parameter value to be optimized is taken as the particle position, the amplitude and direction of the kinetic parameter update is taken as the particle speed, and the particle swarm is iteratively updated based on the optimization objective function.

10. An adaptive polymerization process kinetic parameter adjustment system, used to implement the polymerization process kinetic parameter adjustment method according to claim 1, characterized in that: The system includes: Model building module, used to build micro-quality models of polymerization processes; Parameter screening module, used to screen the kinetic parameters that can actually be estimated during the polymerization reaction; Initial value solving module, used to solve the initial optimal value of the kinetic parameters after screening; A classification and sorting module is used to classify and sort the kinetic parameters obtained by screening; The parameter adjustment module is used to construct an optimization objective function and optimize various kinetic parameters based on the optimization objective function.

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