Multifunctional polymeric catalyst based on alkylaluminum and method for its preparation

Abstract

The application provides a multifunctional polymeric catalyst based on alkyl aluminum and a preparation method thereof, and belongs to the technical field of catalyst preparation. The method comprises the following steps: S1, determining an initial reaction parameter range; S2, designing an experimental combination and recording performance indexes of each experimental group; S3, constructing a multiple quadratic surface fitting model; S4, predicting a result according to the surface fitting model; S5, constructing a random forest model; S6, predicting by using the random forest model and determining an optimal parameter combination; and S7, preparing the multifunctional polymeric catalyst based on alkyl aluminum according to the determined optimal parameter combination. By reasonably designing the composition and ratio of the main catalyst, the alkyl aluminum cocatalyst and the additive, and optimizing the preparation parameters by combining the surface fitting model and the random forest model, the catalyst prepared by the technical scheme has a significantly improved catalytic activity compared with the traditional catalyst.

Description

Technical Field

[0001] This invention relates to the field of catalyst preparation technology, and in particular to a multifunctional polymerization catalyst based on alkylaluminum and its preparation method. Background Technology

[0002] Polyolefin materials, as the world's highest-volume and most widely used synthetic polymers, play an irreplaceable role in packaging, automotive, construction, and medical fields. Their performance and quality largely depend on the performance of the polymerization catalyst. Traditional polymerization catalysts, such as the early Ziegler-Natta catalysts, while achieving great success in the industrial production of polyolefins, suffer from problems such as low activity and limited ability to control product structure, making it difficult to meet the production demands of modern high-end polyolefin materials. For example, in the preparation of polyethylene and polypropylene materials with specific performance requirements, traditional catalysts cannot precisely control the polymer's molecular weight, molecular weight distribution, and stereoregularity.

[0003] With the development of science and technology, novel catalysts such as metallocene catalysts and post-transition metal catalysts have gradually emerged, offering unique advantages in polymer structure regulation, but also facing numerous challenges. Metallocene catalysts are extremely sensitive to reaction conditions and are costly; while post-transition metal catalysts exhibit some tolerance to polar monomers, their catalytic activity is generally low. Furthermore, in actual industrial production, polymerization systems often contain impurities such as moisture and oxygen, and polar monomers are difficult to integrate with non-polar polymerization systems, all of which severely affect catalyst activity and polymer quality.

[0004] Alkyl aluminum is a commonly used co-catalyst in polymerization reactions, capable of activating the main catalyst and removing impurities from the system. However, polymerization systems using alkyl aluminum co-catalysts alone still have limitations. Therefore, developing a multifunctional polymerization catalyst based on alkyl aluminum, possessing high activity, high selectivity, broad monomer adaptability, and good stability, has become a key technical problem urgently needing to be solved in the field of polyolefins. Summary of the Invention

[0005] This invention provides a multifunctional polymerization catalyst based on alkylaluminum and its preparation method to solve one or more of the problems mentioned above.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for preparing a multifunctional polymerization catalyst based on alkylaluminum, comprising:

[0008] S1. Determine the range of initial reaction parameters, including the concentration range of the main catalyst, the molar ratio of alkylaluminum co-catalyst to the main catalyst, the range of support dosage, the concentration range of the internal electron donor, the molar ratio of the external electron donor to the main catalyst, the reaction temperature, and the reaction time;

[0009] S2. Using orthogonal experimental design, uniform design or response surface methodology, design experimental combinations that cover the parameter range of step S1, carry out polymerization reactions, and record the performance indicators of each experimental group, including catalytic activity, polymer molecular weight and molecular weight distribution index.

[0010] S3. Using the reaction parameters in step S1 as independent variables and the performance indicators in step S2 as dependent variables, construct a multivariate quadratic surface fitting model and use the least squares method to solve the regression coefficients.

[0011] S4. Based on the prediction results of the surface fitting model in step S3, set the target performance index and select the parameter range that meets the target.

[0012] S5. Within the parameter range selected in step S4, design experimental combinations again and conduct experiments. Combine the new data with the experimental data from step S2 to construct a random forest model composed of multiple decision trees.

[0013] S6. Using the random forest model from step S5, predict the parameter combinations within the parameter range from step S4 to determine the optimal parameter combination.

[0014] S7. Prepare a multifunctional polymerization catalyst based on alkylaluminum according to the optimal parameter combination determined in step S6.

[0015] In this specification, the performance indicators recorded in S2 also include polymer isotacticity, melting point, and crystallinity.

[0016] In this specification, the random forest model in S5 determines the classification result through voting and obtains the regression prediction value through averaging.

[0017] In this specification, the optimal parameter combination is determined in S6 based on actual production needs and cost factors.

[0018] In this specification, actual production requirements include the tensile strength and flexural strength of the polymer.

[0019] In this specification, in S7, if the main catalyst is titanium tetrachloride and the support is magnesium chloride, the specific preparation process includes: in an inert solvent, magnesium chloride is reacted with an organic alcohol to form a homogeneous solution, which is then modified, mixed with cooled titanium tetrachloride and nano-silica gel, and reacted by adding a polycarboxylic acid ester after programmed temperature rise, and finally washed and dried to obtain the catalyst.

[0020] In this specification, mean squared error is used in S3 to evaluate the accuracy of the surface fitting model; 10-fold cross-validation is used in S5 to evaluate the accuracy and generalization ability of the random forest model.

[0021] In this specification, the expression for the multivariate quadratic surface fitting model in S3 is as follows:

[0022] ;

[0023] Where Y represents the performance index, Representing each reaction parameter, For constant terms, , , For regression coefficients, Let n be the error term and n be the number of independent variables.

[0024] In this specification, the reaction parameters in S1 also include the range of additive dosage.

[0025] A multifunctional polymerization catalyst based on alkylaluminum is prepared using any one of the above-described methods for preparing multifunctional polymerization catalysts based on alkylaluminum. The multifunctional polymerization catalyst based on alkylaluminum comprises:

[0026] The main catalyst is one of the following: titanium tetrachloride, tetrabutyl titanate, titanium trichloride, zirconium dichlorocerocene, zirconium dichloride bis(dichloride), rac-dimethylsilyl zirconium dichloride bis(dichloride), chromium trichloride, tert-butyl chromate, chromium acetylacetone, α,α'-diimine nickel chloride, nickel dichloride bis(dichloride), nickel, ferrocene, ferric chloride, and ferric diaminodichloride.

[0027] The alkylaluminum co-catalyst is one of triethylaluminum, triisobutylaluminum, trimethylaluminum, methylaluminoxane, ethylaluminoxane, and isobutylaluminoxane;

[0028] The carrier is one of magnesium chloride, silica gel, alumina, titanium dioxide, molecular sieve, or montmorillonite.

[0029] The internal electron donor is one of diisobutyl phthalate, dioctyl phthalate, di-n-butyl phthalate, diisobutyl succinate, and diisobutyl glutarate.

[0030] The external electron donor is one of dicyclopentyldimethoxysilane, diisopropyldimethoxysilane, diphenyldimethoxysilane, methylcyclohexyldimethoxysilane, and cyclohexylmethyldimethoxysilane;

[0031] Additives are one of the following: ligands, co-catalysts, functional regulators, and stability enhancers.

[0032] In summary, the present invention has at least the following beneficial effects:

[0033] Significantly enhanced catalytic activity: By rationally designing the composition and ratio of the main catalyst, alkylaluminum co-catalyst, and additives, and by optimizing the preparation parameters using surface fitting and random forest models, the catalyst prepared by this technical solution exhibits significantly enhanced catalytic activity compared to traditional catalysts.

[0034] Enhanced monomer adaptability: The introduction of special ligands and additives effectively enhances the catalyst's tolerance to polar monomers, enabling efficient copolymerization of nonpolar olefins and polar monomers.

[0035] Improving catalyst stability: The use of Lewis base protectants, antioxidants and other additives, as well as the modification of the support, significantly improved the stability of the catalyst in air and humid environments.

[0036] Cost optimization: Model optimization methods reduce the number of trial and error experiments, shorten the R&D cycle, and further reduce R&D and production costs. Attached Figure Description

[0037] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a schematic diagram of the preparation method of the alkylaluminum-based multifunctional polymerization catalyst involved in this invention.

[0039] Figure 2 This is a schematic diagram illustrating the process of constructing and applying the surface fitting model involved in this invention.

[0040] Figure 3 This is a schematic diagram of the parameter optimization process for the random forest model involved in this invention.

[0041] Figure 4 This is a schematic diagram of the catalyst preparation process and data feedback flow involved in this invention. Detailed Implementation

[0042] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0043] like Figure 1 As shown, this embodiment provides a method for preparing a multifunctional polymerization catalyst based on alkylaluminum, comprising:

[0044] S1. Determine the range of initial reaction parameters, including the concentration range of the main catalyst, the molar ratio of alkylaluminum co-catalyst to the main catalyst, the range of support dosage, the concentration range of the internal electron donor, the molar ratio of the external electron donor to the main catalyst, the reaction temperature, and the reaction time;

[0045] S2. Using orthogonal experimental design, uniform design or response surface methodology, design experimental combinations that cover the parameter range of step S1, carry out polymerization reactions, and record the performance indicators of each experimental group, including catalytic activity, polymer molecular weight and molecular weight distribution index.

[0046] S3. Using the reaction parameters in step S1 as independent variables and the performance indicators in step S2 as dependent variables, construct a multivariate quadratic surface fitting model and use the least squares method to solve the regression coefficients.

[0047] S4. Based on the prediction results of the surface fitting model in step S3, set the target performance index and select the parameter range that meets the target.

[0048] S5. Within the parameter range selected in step S4, design experimental combinations again and conduct experiments. Combine the new data with the experimental data from step S2 to construct a random forest model composed of multiple decision trees.

[0049] S6. Using the random forest model from step S5, predict the parameter combinations within the parameter range from step S4 to determine the optimal parameter combination.

[0050] S7. Prepare the alkylaluminum-based multifunctional polymerization catalyst according to the optimal parameter combination determined in step S6. The specific preparation can refer to the preparation schemes described in the following examples, or it can be a conventional preparation scheme in the prior art. The focus of this invention is on optimizing the preparation parameters by combining a surface fitting model and a random forest model.

[0051] In some embodiments, S6 determines the optimal combination of parameters based on actual production needs and cost factors.

[0052] In some embodiments, actual production requirements include the tensile strength and flexural strength of the polymer.

[0053] In some embodiments, in S7, if the main catalyst is titanium tetrachloride and the support is magnesium chloride, the specific preparation process includes: in an inert solvent, reacting magnesium chloride with an organic alcohol to form a homogeneous solution, then modifying it, then mixing it with cooled titanium tetrachloride and nano-silica gel, then reacting it by adding a polycarboxylic acid ester after programmed heating, and finally washing and drying to obtain the catalyst.

[0054] In some embodiments, the process of building and applying a surface fitting model is as follows: Figure 2 As shown:

[0055] Data input: The experimental combination designed in step S2 and the 25 sets of data obtained from the initial experiment are used as input. The data includes 7 independent variables: main catalyst concentration, molar ratio of alkylaluminum co-catalyst to main catalyst, support amount, internal electron donor concentration, molar ratio of external electron donor to main catalyst, reaction temperature, and reaction time; and 3 dependent variables: catalytic activity, polymer weight-average molecular weight, and molecular weight distribution index.

[0056] Definition of variables and equations: Define independent and dependent variables, and construct a multivariate quadratic equation;

[0057] Coefficient calculation: The least squares method is used to solve the regression coefficients. The goal of the least squares method is to minimize the mean square error between the predicted and actual values. Through calculation, the specific values ​​of each regression coefficient are obtained.

[0058] Model evaluation: Calculate the mean squared error (MSE) and evaluate the model's fit based on the MSE; at the same time, calculate the coefficient of determination to determine how well the model fits the data; if the coefficient of determination is less than 0.8, it indicates that the model fit is poor, and it is necessary to go back to the previous step, add interaction terms or higher-order terms, and solve the coefficients again; if the coefficient of determination is greater than or equal to 0.8, the model is considered to fit well.

[0059] Model generation: Once the model fit meets the target, a surface prediction model is generated. This model can be used to predict catalytic activity, polymer weight-average molecular weight, and molecular weight distribution index based on the combination of input parameters.

[0060] Model application: Input new parameter combinations, use the generated surface prediction model to perform calculations, and output the corresponding prediction results of catalytic activity, polymer weight-average molecular weight, and molecular weight distribution index, providing data support for subsequent screening and optimization of parameter ranges.

[0061] In some embodiments, the parameter optimization process for a random forest model is as follows: Figure 3 As shown:

[0062] 1. Data integration and preprocessing: The 25 sets of orthogonal experimental data from step S2 and the 15 sets of supplementary experimental data within the optimization interval of step S4 are integrated to obtain a total of 40 sets of data; these data are standardized or normalized to eliminate the influence of dimensions.

[0063] 2. Data partitioning: The integrated data is divided into training and test sets in a ratio of 80% and 20%, respectively. The training set is used for model training, and the test set is used to evaluate the model's generalization ability.

[0064] 3. Model Initialization: Initialize the core parameters of the random forest model, setting the number of decision trees K=120, the maximum depth to 10 layers, and the minimum number of splits to 5 (i.e., the minimum number of samples required for node splits). Then, randomly select a subset of features (approximately...). ≈3) are used for node splitting in each decision tree;

[0065] 4. Single Decision Tree Training: For each decision tree, 40 samples are randomly selected from the training set using the bootstrap method. When splitting a node, three features are randomly selected, and the Gini impurity (for classification problems) or mean squared error (for regression problems, here we use mean squared error) is calculated. where n is the number of samples. This is the actual value. (For the predicted value), select the optimal splitting feature and threshold to split the node; repeat this process to recursively split the node until the maximum depth of 10 layers is reached or the minimum number of sample splits of 5 is met, and finally generate leaf nodes and store the predicted value.

[0066] 5. Ensemble Prediction: Repeat step 4 to train K=120 decision trees, and then ensemble the predictions from these 120 decision trees. For regression problems, the averaging method is used to obtain the final predicted value, i.e. ,in The final predicted value is given by K, where K is the number of decision trees. Let be the predicted value of the k-th decision tree;

[0067] 6. Cross-validation and Evaluation: The model is evaluated using 10-fold cross-validation; the training set is divided into 10 parts, with 9 parts used for training and 1 part for testing each time, repeated 10 times, and the average error is calculated; the root mean square error (RMSE) is used. n is the number of samples. For predicted values, The actual value and the coefficient of determination are used as evaluation indicators; if the RMSE is greater than the preset threshold (e.g., catalytic activity RMSE > 200 g / (g·h)), then proceed to the next step of parameter tuning; if the RMSE meets the requirements, then the optimal parameter prediction model is generated.

[0068] 7. Parameter Tuning: Design a parameter grid and exhaustively combine parameters such as the number of decision trees K (tuning range 80-150, step size 10), maximum depth (tuning range 5-15, step size 2), number of features (tuning range 2-5, step size 1), and minimum number of sample splits (tuning range 3-8, step size 1). Use grid search combined with cross-validation to evaluate each parameter combination and select the parameter combination that minimizes the RMSE as the optimal parameters. For example, use the GridSearchCV module in Python's Scikit-learn library for parameter tuning.

[0069] 8. Optimal Parameter Generation: The model comprehensively predicts the parameter combinations within the optimization interval of step S4, taking into account factors such as raw material costs (e.g., the prices of raw materials like TiCl4 and Al(C2H5)3) and equipment energy consumption in actual production, and uses the objective function... ( =0.5, =0.3, Multi-objective optimization was performed using the weights of catalytic activity, molecular weight, and molecular weight distribution index (where 0.2 represents the weights of catalytic activity, molecular weight, and molecular weight distribution index, respectively) to determine the final optimal preparation parameters.

[0070] In some embodiments, the catalyst preparation process and data feedback process are as follows: Figure 4 As shown:

[0071] Parameter input and carrier treatment: Input the optimal preparation parameters determined in step S6 into the preparation process; first, treat the magnesium chloride carrier by adding 14g of spherical magnesium chloride to a reaction vessel containing 150mL of decane, then adding 20mL of isooctanol, and stirring at 120℃ for 2 hours to form a homogeneous solution; then add 7g of phthalic anhydride and continue the reaction for 1.5 hours for modification; after the reaction is completed, cool to room temperature for later use.

[0072] Titanium loading and activation: The reaction vessel was cooled to -15℃, and 70 mL of 0.5 mol / L titanium tetrachloride solution was slowly added, followed by the above-treated magnesium chloride solution, and 2.8 g of 8 μm nano-silica gel was added simultaneously; then the temperature was increased to 135℃ at a rate of 5℃ / min, and 40 mL of 0.22 mol / L diisobutyl phthalate solution was added at this temperature, and the reaction was stirred for 3 hours; finally, a dicyclopentyldimethoxysilane solution with a molar ratio of 6 to titanium tetrachloride was added, and the reaction was continued for 0.5 hours.

[0073] Washing and drying: After the reaction was completed, the supported catalyst was washed 6 times with anhydrous hexane, using 70 mL of hexane each time, to thoroughly remove unreacted titanium tetrachloride and other impurities. After washing, the catalyst was transferred to a vacuum drying oven and dried to constant weight at 65 °C and -0.095 MPa to obtain the final catalyst powder.

[0074] Performance testing: Under the same ethylene polymerization conditions as in step S2, 0.1 g of the catalyst prepared above was used for the polymerization reaction; after the reaction, the weight-average molecular weight of the polymer was determined by gel permeation chromatography (GPC). The molecular weight distribution index (PDI) and other molecular weight distribution indexes are used to analyze the polymer structure using nuclear magnetic resonance (NMR) or other relevant methods. The catalytic activity is then calculated using specific methods, and the actual performance data is recorded.

[0075] Data comparison and judgment: The actual data obtained from performance tests (catalytic activity, ...) The experimental data (PDI) is compared with the predicted values ​​of the random forest model to calculate the error. If the error is less than or equal to 5%, the preparation process is considered to meet the requirements and can enter the industrial application stage. If the error is greater than 5%, the new experimental data (including actual performance data and preparation parameters) are added to the dataset of steps S2 and S4. The process returns to step S3 to rebuild the surface fitting model and step S5 to rebuild the random forest model, perform parameter optimization and model iteration, and then carry out catalyst preparation and performance testing again until the error meets the requirements.

[0076] A multifunctional polymerization catalyst based on alkylaluminum is prepared using any one of the above-described methods for preparing a multifunctional polymerization catalyst based on alkylaluminum. The multifunctional polymerization catalyst based on alkylaluminum comprises a main catalyst, an alkylaluminum co-catalyst, a support, an internal electron donor, an external electron donor, and additives.

[0077] The technical concept of this invention is as follows:

[0078] I. Catalyst Composition Design

[0079] (a) Main catalyst

[0080] Selection of transition metal compounds: Compounds of transition metals such as titanium (Ti), zirconium (Zr), and chromium (Cr) are selected as the core components of the main catalyst. For example, titanium tetrachloride (TiCl4) has high catalytic activity and is widely used in the traditional Ziegler-Natta catalyst system. It can initiate the polymerization reaction of olefins through synergistic action with alkylaluminum co-catalysts. Metallocene compounds such as zirconium dichlorodi ...

[0081] Ligand modification:

[0082] 1. Introduce phosphine / phosphonite ligands, such as triphenylphosphine (PPh3) or diisopropylphenylphosphine. These can modulate the electron density of the transition metal center, enhancing the catalyst's capacity to accommodate polar monomers. In the copolymerization of ethylene and butyl acrylate, nickel catalysts modified with tridentate phosphine ligands can increase the polar monomer insertion rate from the conventional 5% to 28%.

[0083] 2. Nitrogen heterocyclic compounds such as 2,6-dimethylpyridine and 3-pyrrolino-1-carboxylate can act as external electron donors to enhance the stereotactic orientation of catalysts. In propylene polymerization, 3-pyrrolino-1-carboxylate, in combination with alkylaluminum, can increase the isotacticity of polypropylene from 92% to 98%, while simultaneously increasing hydrogen sensitivity by 30%, thus more effectively controlling polymer properties.

[0084] 3. Boronoxane / azaborane ligands, such as tris(pentafluorophenyl)boron and azaboroxane, can reduce the Lewis acidity of the metal center and decrease the poisoning effect of polar monomers on the active site of the catalyst. In the copolymerization of ethylene and 9-decen-1-ol, boronoxane complexes can achieve an insertion rate of up to 32.1%, and catalyst deactivation is significantly reduced.

[0085] (ii) Alkyl aluminum co-catalyst

[0086] 1. Selection of Types: Commonly used alkylaluminum cocatalysts include triethylaluminum (TEA), triisobutylaluminum (TIBA), and methylaluminoxane (MAO). Triethylaluminum has strong alkylation ability and can efficiently activate the main catalyst to form polymerization-active centers.

[0087] Triisobutylaluminum is relatively mild and can better control the reaction rate and product structure in some polymerization systems with more demanding reaction conditions.

[0088] Methylaluminoxane performs exceptionally well in metallocene catalyst systems. It can not only activate metallocenes but also significantly improve the activity and stability of catalysts through interactions with metallocenes.

[0089] 2. Functions and uses:

[0090] 2.1 Activation of the main catalyst: Alkyl aluminum provides alkyl groups to the transition metal center of the main catalyst, changing the valence state of the transition metal and forming an active cation center. For example, in the TiCl4 / TEA system, TEA will... Restore to This triggers the polymerization reaction of olefins.

[0091] 2.2 Impurity Removal: Impurities such as water and oxygen are often present in polymerization systems, which can poison the active sites of the catalyst. Alkyl aluminum can react rapidly with these impurities, thereby protecting the main catalyst. For example, TEA can react with water in the system to produce ethanol and aluminum hydroxide, preventing water from damaging the catalyst.

[0092] 2.3 Regulation of Polymer Structure: Alkyl aluminum can affect the molecular weight of polymers through chain transfer reactions. When alkyl aluminum acts as a chain transfer agent, it can remove a hydrogen atom from the growing polymer chain, terminating the current chain growth and initiating new chain growth, thereby achieving regulation of the polymer molecular weight.

[0093] (III) Carrier

[0094] 1. Selection of carrier material:

[0095] Magnesium chloride (MgCl2) and silica gel (SiO2) were selected as support materials. MgCl2 has a high specific surface area and good chemical activity, which can effectively support the main catalyst and increase the density of active sites in the catalyst.

[0096] SiO2 possesses excellent physical properties, such as good particle morphology and high mechanical strength, which can keep the supported catalyst stable during polymerization and help control the particle morphology of the polymer.

[0097] For some special needs, composite supports, such as MgCl2@Al2O3 composite supports, can be used. These supports combine the advantages of multiple supports, which can improve catalyst activity and improve the particle morphology and particle size distribution of polymers.

[0098] 2. Carrier treatment and modification:

[0099] Surface treatment of nano-SiO2 (particle size 5-20 nm) through chemical modification increases its surface active sites, enabling better loading of active centers and improving catalyst dispersibility. After treatment, the specific surface area of ​​SiO2-supported titanium catalysts can be increased from 150 m² / g to 320 m² / g, and the ethylene polymerization activity is increased by 40%.

[0100] Organic modifiers such as γ-aminopropyltriethoxysilane are used to modify the support, enhancing the interaction between the support and the active sites and improving the stability of the catalyst. Silica gel supports treated with silane coupling agents can increase the titanium loading from 2.1% to 3.8%.

[0101] The MgCl2 support was modified by using organic acid anhydrides such as phthalic anhydride to adjust the surface polarity of the support, making it more suitable for copolymerization reactions of polar monomers, while reducing the swelling of the catalyst in polar solvents.

[0102] (iv) Additives

[0103] 1. Catalyst co-additive:

[0104] Introducing borates / ionic activators, such as [Ph3C][B(C6F5)4] and N,N-dimethylaniline tetra(pentafluorophenyl)borate, synergistically with alkylaluminum, generates ionic active centers in situ, significantly enhancing catalyst activity. When [Ph3C][B(C6F5)4] synergistically activates metallocenes with MAO, the ethylene polymerization activity can reach […]. It is three times that of the traditional MAO system.

[0105] Metal-organic frameworks (MOFs) or ionic liquids, such as ZIF-8 and 1-ethyl-3-methylimidazolium tetrafluoroborate, are employed. MOFs can serve as supports for co-catalysts, reducing the aggregation of alkyl aluminum. After loading TIBA onto ZIF-8, its catalytic activity for 1-hexene polymerization increased by 25%, and it can be reused up to 5 times. Ionic liquids can adjust the polarity of the reaction medium, promoting the dissolution and copolymerization of polar monomers in non-polar systems, such as increasing the solubility of methyl methacrylate in non-polar systems by 15 times.

[0106] 2. Functional regulator additives:

[0107] Chain transfer agents such as diethylzinc (DEZ) and hydrogen (H2) can precisely control the molecular weight of polymers. DEZ works synergistically with alkylaluminum to reduce the molecular weight of polyethylene from... Adjustment to The distribution index is <2.0; H2 in the presence of alkylaluminum can adjust the branching degree of polyethylene, reducing the branching degree from 20 branches / 1000C to 5 branches / 1000C, thus preparing linear high-density polyethylene.

[0108] Polar monomer compatibilizers such as maleic anhydride-grafted polyethylene (PE-g-MAH) and propylene oxide can improve the compatibility between polar monomers and catalysts and inhibit the poisoning of active centers by polar groups. When PE-g-MAH is used as an additive, the copolymerization activity of ethylene and vinyl acetate increases from... Upgraded to Propylene oxide can react with moisture in the system, protecting alkyl aluminum from hydrolysis and extending catalyst life to 8 hours (compared to only 4 hours without catalytic reaction).

[0109] 3. Stability-enhancing additives:

[0110] Lewis base protectants such as tetrahydrofuran (THF) and triethylamine (TEA) can form coordination compounds with alkylaluminum, inhibiting the hydrolysis reaction of alkylaluminum. The stability time of THF-modified triethylaluminum in air was extended from 5 minutes to 30 minutes; after triethylamine was coordinated with titanium catalyst, the catalytic activity retention rate under humid conditions increased from 30% to 75%.

[0111] Antioxidants such as 2,6-di-tert-butyl-p-cresol (BHT) and triphenyl phosphite (TPPi) can inhibit the oxidative decomposition of alkyl aluminum and protect the polymer chain from oxidation. Adding 0.5% BHT increases the thermal decomposition temperature of triisobutylaluminum from 120°C to 180°C; the combined use of phosphite and alkyl aluminum catalyst systems extends the oxidation induction time of polypropylene from 20 minutes to 60 minutes.

[0112] II. Preparation Method

[0113] (I) Preparation of Ziegler-Natta catalyst system

[0114] 1. Magnesium chloride carrier treatment: Magnesium chloride is mixed with an organic alcohol (such as isooctanol) in the inert solvent decane and reacted at 110-140°C to dissolve the magnesium chloride and form a homogeneous solution. During this process, the organic alcohol coordinates with the magnesium chloride, changing the crystal structure of the magnesium chloride and increasing its surface active sites.

[0115] Organic acid anhydrides (such as phthalic anhydride) are added to the above solution to further modify magnesium chloride. Phthalic anhydride reacts with the active sites on the surface of magnesium chloride, adjusting the electron cloud density and chemical properties of the support surface, creating more favorable conditions for subsequent loading of the main catalyst.

[0116] 2. Titanium loading and activation:

[0117] Titanium tetrachloride was cooled to -30 to -15°C, and a treated magnesium chloride solution and nano-silica gel (particle size 0.1-10 μm) were slowly added. Low temperature conditions help control the reaction rate and prevent the reaction from becoming too vigorous. The addition of nano-silica gel increases the specific surface area of ​​the catalyst and improves the dispersion of active sites.

[0118] The reaction system was temperature-programmed, gradually increasing from -22°C to 140°C. During the heating process, titanium tetrachloride underwent a series of chemical reactions with the magnesium chloride support and nano-silica gel, achieving titanium loading. Simultaneously, a polycarboxylic acid ester (such as diisobutyl phthalate) was added. This ester acts as an internal electron donor, forming coordinate bonds with titanium to adjust the electronic structure and steric hindrance of the titanium active center, thereby improving the catalyst's activity and orientation.

[0119] 3. Washing and drying:

[0120] After the reaction is complete, the supported catalyst is washed multiple times with an inert solvent (such as hexane) to remove unreacted titanium tetrachloride, excess organic alcohols, organic acid anhydrides, and other impurities. The washing process is carried out under an inert gas atmosphere to prevent the catalyst from being oxidized or hydrolyzed.

[0121] The washed catalyst is then vacuum dried under appropriate temperature and vacuum conditions to remove residual solvents and moisture, yielding a dry catalyst powder. This powder can be stored in an inert gas environment for later use.

[0122] (II) Preparation of metallocene catalyst systems

[0123] 1. Metallocene synthesis:

[0124] Metallocene compounds are prepared by reacting cyclopentadienyl ligands with metal salts. Taking the synthesis of rac-Me2Si(2-Me-4-Ph-Ind)2ZrCl2 as an example, the corresponding cyclopentadienyl ligand (such as 2-methyl-4-phenylindene) is first reacted with a silanizing agent (such as dimethyldichlorosilane) to form a cyclopentadienyl ligand derivative with a silicon-bridged structure.

[0125] The derivative is then reacted with a zirconium salt (such as ZrCl4) under appropriate reaction conditions (e.g., in an organic solvent, at a specific temperature, and in the presence of a base) to introduce zirconium ions into the cyclopentadienyl ligand via a coordination substitution reaction, forming the target metallocene compound. The reaction process requires strict control of reaction conditions, including temperature, reaction time, and reactant ratios, to ensure the purity and yield of the product.

[0126] 2. Alkyl aluminum activation:

[0127] Synthesized metallocene compounds are combined with alkylaluminum / borates (such as Ph3CB(C6F5)4 / TIBA) to form an active catalytic system. In the ethylene-propylene copolymerization reaction, the molecular weight of the product and the distribution of comonomers can be effectively controlled by precisely adjusting the amount of alkylaluminum.

[0128] During the activation process, alkylaluminum first undergoes an alkylation reaction with metallocene, replacing the chlorine atom in the metallocene to form an active cation center. Borate then acts as a co-activator, reacting with alkylaluminum to generate stable ion pairs, further enhancing the stability and catalytic activity of the active center.

[0129] (III) Preparation of post-transition metal catalyst systems

[0130] 1. Synthesis of nickel complexes:

[0131] Taking the synthesis of α,α'-diimine nickel chloride as an example, the corresponding diimine ligand is first reacted with a nickel salt (such as NiCl2) in an organic solvent. During the reaction, the diimine ligand forms a coordinate bond with the nickel ion through its nitrogen atom to generate α,α'-diimine nickel chloride.

[0132] The synthesized α,α'-diimine nickel chloride was complexed with MAO and used to catalyze the copolymerization of ethylene and methyl methacrylate (MMA) under the protection of triisobutylaluminum. MAO, acting as a co-catalyst, activated the nickel complex, giving it catalytic activity. In this system, the copolymerization activity of ethylene and MMA reached [value missing]. .

[0133] 2. Iron catalytic system:

[0134] In iron-catalyzed systems, iron precursors (such as FeCl3) react with diisobutylaluminum hydride (DIBAL-H) ​​to generate an active iron-hydrogen intermediate. During the reaction, DIBAL-H reduces the iron precursor and introduces hydrogen atoms at the iron center, forming an active iron-hydrogen species.

[0135] This iron-hydrogen reactive intermediate can be used in the hydroaluminization reaction of terminal olefins to generate benzylaluminum compounds. By controlling reaction conditions, such as reaction temperature, time, and reactant ratios, the selectivity of the reaction and the structure of the product can be regulated. The generated benzylaluminum compounds can be further converted into functional polymers, expanding the application of iron-catalyzed systems in polymer synthesis.

[0136] III. Performance Testing and Optimization

[0137] (a) Performance testing methods

[0138] 1. Catalytic activity test:

[0139] In a specific polymerization reactor, a certain amount of catalyst, monomer (such as ethylene, propylene, etc.), solvent (if any) and necessary additives (such as hydrogen) are added, and the polymerization reaction is carried out under set reaction temperature, pressure and time conditions.

[0140] Catalyst activity is calculated by measuring the amount of monomer consumed or polymer generated before and after the reaction. For example, for ethylene polymerization, catalyst activity can be expressed as the mass of polyethylene produced per unit mass (or mole) of catalyst per unit time, with units of 1. .

[0141] 2. Polymer structure and performance testing:

[0142] 2.1 Molecular Weight and Molecular Weight Distribution: Gel permeation chromatography (GPC) was used to dissolve polymer samples in an appropriate solvent and separate polymer molecules of different molecular weights through a chromatographic column. The number-average molecular weight (Mn), weight-average molecular weight (Mw), and molecular weight distribution index (Mw / Mn) of the polymer were calculated based on a standard curve. Molecular weight and its distribution have a significant impact on the processing and physical properties of polymers. For example, a smaller Mw / Mn indicates a narrower molecular weight distribution, and the polymer may have better processing and mechanical properties.

[0143] 2.2 Stereoregularity: For polymers with stereoregularity, such as polypropylene, nuclear magnetic resonance (NMR) technology is used to analyze the characteristic peaks of different stereounits in the polymer molecule and calculate parameters such as isotacticity and syndiotacticity to evaluate the stereoregularity of the polymer. Polypropylene with high stereoregularity has high crystallinity and good mechanical properties.

[0144] 2.3 Crystallization Properties: Differential scanning calorimetry (DSC) was used to test parameters such as the polymer's crystallization temperature (Tc), melting point (Tm), and enthalpy of crystallization (ΔHc) to understand the polymer's crystallization behavior and degree of crystallization. Crystallization properties affect the polymer's hardness, transparency, heat resistance, and other properties.

[0145] 2.4 Thermal stability: Thermogravimetric analysis (TGA) was used to measure the mass loss of the polymer at different temperatures under a certain heating rate, and to determine parameters such as the initial decomposition temperature and the maximum decomposition rate temperature of the polymer, so as to evaluate the thermal stability of the polymer.

[0146] 2.5 Mechanical Properties: The tensile strength, elongation at break, flexural strength, and other mechanical properties of the polymer were tested using a universal testing machine. These properties directly reflect the performance of the polymer material in practical applications.

[0147] (II) Optimization Strategy

[0148] 1. Adjustments based on performance test results:

[0149] 1.1 Catalytic activity optimization: If the catalytic activity is low, the proportion of each component of the catalyst can be adjusted, such as increasing the amount of alkyl aluminum co-catalyst to enhance the activation effect on the main catalyst; or optimizing the properties of the support to increase the loading and dispersion of active centers.

[0150] 1.2 Polymer Structure and Performance Optimization:

[0151] For optimizing molecular weight and molecular weight distribution, if the molecular weight does not meet expectations, it can be controlled by adjusting the amount of chain transfer agent (such as hydrogen or diethylzinc). If the molecular weight distribution is too wide, the distribution of active sites of the catalyst can be optimized, such as by using a more uniform support loading method or adjusting the ligand structure.

[0152] To optimize stereoregularity, if isotacticity is insufficient, the type and amount of internal or external electron donors can be changed to adjust the spatial environment and electron cloud distribution of the active center, thereby improving the stereoregularity of the polymer.

[0153] Crystallization properties and thermal stability can be improved by changing polymerization conditions (such as reaction temperature and pressure) or by adding additives such as nucleating agents and antioxidants. Mechanical properties can be optimized by adjusting the polymer's molecular weight, crystallinity, and the type and content of comonomers.

[0154] 2. Optimization of reaction conditions:

[0155] 2.1 Temperature Optimization: The optimal reaction temperature range was determined by experimentally measuring the catalytic activity and polymer properties at different reaction temperatures. Generally, increasing the temperature can accelerate the reaction rate, but excessively high temperatures may lead to problems such as catalyst deactivation and a decrease in polymer molecular weight.

[0156] 2.2 Pressure Optimization: For gas-phase polymerization, the reaction pressure is adjusted, and the effects of pressure on monomer concentration, reaction rate and polymer structure are studied to find the optimal reaction pressure.

[0157] 2.3 Reaction Time Optimization: The polymerization effect under different reaction times was investigated to determine the optimal reaction time that ensures high conversion while avoiding excessive polymer degradation or side reactions. For example, in ethylene polymerization, experiments showed that after a reaction time exceeding 6 hours, the consumption of alkyl aluminum led to a decrease in active centers, resulting in a decrease in polymer molecular weight and an increase in oligomer content in the product. Therefore, the optimal reaction time was controlled at 4-5 hours. Simultaneously, the polymerization process was monitored in real time using online infrared spectroscopy (ATR-FTIR) or nuclear magnetic resonance (NMR), and the reaction time was dynamically adjusted based on monomer conversion and product structure.

[0158] 2.4 Stirring Rate Optimization: In solution polymerization or slurry polymerization, the stirring rate affects monomer diffusion and heat transfer efficiency. Temperature uniformity and polymerization rate at different stirring rates were experimentally determined to avoid a widening of the product molecular weight distribution due to excessively high local concentrations. For example, in propylene slurry polymerization, a stirring rate of 300-400 rpm ensures uniform catalyst dispersion and timely heat dissipation, preventing the risk of explosive polymerization.

[0159] (III) Catalyst recovery and reuse

[0160] 1. Physical Separation and Recovery: The catalyst is separated from the polymerization product using membrane separation, centrifugation, or sedimentation separation techniques. For example, using an ultrafiltration membrane (molecular weight cutoff 5000-10000 Da) to filter polyethylene slurry can retain supported catalyst particles with a recovery rate of over 85%. The separated catalyst is then washed 3-5 times with an organic solvent (such as hexane) to remove adsorbed polymers and impurities from its surface.

[0161] 2. Chemical Regeneration: For deactivated alkylaluminum-transition metal catalysts, activity can be restored through chemical treatment. For example, deactivated titanium-based catalysts can be leached with an acid solution (such as a hydrochloric acid-methanol mixture) to dissolve the polymers and oxides covering the surface, and then reactivated with an alkylaluminum solution to restore the titanium loading to 90% of its initial value. For metallocene catalysts, the deactivated active sites can be re-alkylated by adding excess alkylaluminum, and the regenerated catalyst activity can reach 70-80% of that of the fresh catalyst.

[0162] 3. Recycling Process Design: Construct a closed-loop recycling system to reuse the separated and regenerated catalyst in a specific ratio with the fresh catalyst. For example, in a continuous polymerization unit, the recycled catalyst (30%) is mixed with the fresh catalyst, and by adjusting the alkylaluminum / main catalyst molar ratio (from the conventional 500:1 to 700:1), stable catalytic activity and product quality are maintained, reducing catalyst costs by more than 25%.

[0163] IV. Industrial Applications and Expansion

[0164] 1. Polyolefin industrial production: In the production of high-density polyethylene (HDPE), the MgCl2-supported titanium catalyst and triethylaluminum system of this technology are used. By adjusting the amount of hydrogen added, the molecular weight (50,000-300,000 Da) is controlled, and the tensile strength of the pipe material produced reaches 35 MPa, which meets the GB / T13663 standard. In the production of polypropylene, the introduction of 3-pyrrolino-1-carboxylate as an external electron donor improves the isotacticity to 98.5%, which is used to produce fiber-grade polypropylene with excellent spinning performance.

[0165] 2. Preparation of specialty polymers: Using boron oxytitanium complexes and alkyl aluminum systems, random copolymerization of ethylene and glycidyl methacrylate (GMA) is achieved to prepare functionalized polyolefins (GMA content 8-12%) for toughening modification of high-performance engineering plastics; polyethylene containing allyl side chains is synthesized through an iron catalytic system, and functional groups (such as hydroxyl and carboxyl groups) are introduced through subsequent reactions to develop biomedical materials.

[0166] 3. Green polymerization process: Ionic liquids are used to replace traditional organic solvents, and green polymerization systems are constructed with alkylaluminum-metallocene catalysts. For example, in the polymerization of 1-butene, using [BMIM][PF6] ionic liquid as the medium, the catalyst activity reaches... The product does not require solvent removal, and the ionic liquid recovery rate exceeds 95%, significantly reducing energy consumption and pollution.

[0167] 4. Smart Response Materials: By grafting and copolymerizing thermosensitive polymers (such as poly(N-isopropylacrylamide, PNIPAM) with polyolefins prepared by alkylaluminum catalysis, and controlling the grafting rate (5-20%) by regulating the polymerization conditions, thermosensitive shape memory polymers can be developed, achieving a shape recovery rate of >90% at 40-45℃, which can be applied to smart packaging and biomedical devices.

[0168] In some embodiments, the initial reaction parameter range is determined: the types of main catalyst, alkylaluminum co-catalyst, support and additives are determined, and the initial dosage range of each component is set.

[0169] 1. Main catalyst: a transition metal compound, including one of the following: titanium-based (titanium tetrachloride, tetrabutyl titanate, titanium trichloride), zirconium-based (zirconium dichlorocerocene, bis(n-butylcyclopentadienyl)zirconium dichloride, rac-dimethylsilylbis(2-methyl-4-phenylindenyl)zirconium dichloride), chromium-based (chromium trichloride, tert-butyl chromate, chromium acetylacetone), nickel-based (α,α'-diimine nickel chloride, bis(triphenylphosphine) nickel dichloride, bis(1-phenyl-3-methyl-5-pyrazolone) nickel), and iron-based (ferrocene, ferric chloride, bis(2,6-diisopropylphenyl)aminoferric dichloride), with a concentration set at 0.1-1.0 mol / L.

[0170] 2. Alkyl aluminum co-catalysts: Commonly used are one of triethylaluminum, triisobutylaluminum, trimethylaluminum, methylaluminoxane, ethylaluminoxane, and isobutylaluminoxane, with a molar ratio to the main catalyst between 100 and 1000.

[0171] 3. Carrier: Select one of the following: magnesium chloride, silica gel, alumina, titanium dioxide, molecular sieve (such as ZSM-5 molecular sieve, Y-type molecular sieve), or montmorillonite, with a dosage of 5-20 g / L.

[0172] 4. Additives:

[0173] 4.1 Ligands: One of the following: phosphine / phosphonite esters (triphenylphosphine, diisopropylphenylphosphonite, tritert-butylphosphine), nitrogen heterocyclic compounds (2,6-dimethylpyridine, 3-pyrrolino-1-carboxylate, 2-vinylpyridine), and boron oxide / azaborane ligands (tris(pentafluorophenyl)borane, azaboroxane, bis(pentafluorophenyl)boronic acid).

[0174] 4.2 Co-catalyst synergists: one of the following: borate / ionic activators ([Ph3C][B(C6F5)4], N,N-dimethylaniline tetra(pentafluorophenyl)borate, [Me2NH2][B(C6F5)4]), metal-organic frameworks (ZIF-8, MOF-5, UiO-66), ionic liquids (1-ethyl-3-methylimidazolium tetrafluoroborate, 1-butyl-3-methylimidazolium hexafluorophosphate, 1-hexyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide).

[0175] 4.3 Functional Modifiers: One of the following: chain transfer agents (diethylzinc, hydrogen, diisobutylzinc) and polar monomer compatibilizers (maleic anhydride-grafted polyethylene, propylene oxide, glycidyl acrylate).

[0176] 4.4 Stability Enhancement Class: One of the Lewis base protectants (tetrahydrofuran, triethylamine, pyridine) and antioxidants (2,6-di-tert-butyl-p-cresol, triphenyl phosphite, antioxidant 1010).

[0177] Meanwhile, the reaction temperature range is set to 30-80℃, and the reaction time is set to 1-5 hours.

[0178] 5. Processing Procedure: The initial parameter range was determined based on literature review and preliminary experiments. A comprehensive search of databases such as Web of Science and ScienceDirect was conducted over the past five years to identify literature on similar catalyst preparations published in journals such as *Macromolecules* and *Polymer*. The frequency and dosage range of each component were statistically analyzed. This was combined with the temperature control range of the laboratory's existing equipment (minimum -40℃, maximum 200℃, but considering energy consumption and safety, 30-80℃ was selected), pressure tolerance range (maximum 10MPa; atmospheric or low pressure is sufficient for the polymerization reaction here, so pressure factors are not considered at this stage), and safety standards (such as limiting the use of alkylaluminum to prevent spontaneous combustion risks). The aforementioned specific range was then established. Preliminary experiments were conducted using the median values ​​of common dosages of each component to perform initial polymerization reactions, observing reaction phenomena and the preliminary properties of the products, further refining the range boundaries.

[0179] 6. Design experimental setups and conduct initial experiments:

[0180] Use orthogonal experimental design, uniform design, response surface methodology, or Latin hypercube design to design experimental combinations that cover the parameter range of step S1.

[0181] 6.1 Orthogonal Experimental Design: Considering seven factors (eight factors if the range of additive dosage is added), including the main catalyst concentration range, the molar ratio of alkylaluminum co-catalyst to the main catalyst, the support dosage range, the internal electron donor concentration range, the molar ratio of external electron donor to the main catalyst, reaction temperature, and reaction time, with each factor having five levels, an orthogonal experimental design can be developed. That is, 25 groups of experiments.

[0182] 6.2 Uniform Design Method: For the above 7 factors, a uniform design table can be used. By arranging 25 sets of experiments, the experimental points are more evenly distributed compared to orthogonal experiments, allowing for the exploration of a wider parameter space with fewer experiments.

[0183] 6.3 Response Surface Design: Using central composite design (CCD) or Box-Behnken design, a quadratic response surface model is constructed with the main catalyst concentration, the molar ratio of alkylaluminum to the main catalyst, the reaction temperature, and the reaction time as independent variables. Generally, 20-30 sets of experiments need to be designed.

[0184] 6.4 Latin Hypercube Design: Divide the value range of each factor into several intervals (e.g., 5 intervals), and randomly select a value within each interval to ensure that each level of each factor appears exactly once in the experiment. Up to 25 experimental groups can be designed to ensure the uniformity and representativeness of experimental points in the parameter space. Conduct the polymerization reaction according to the experimental design and record the catalytic activity (per unit time) of each experimental group. Internal unit mass catalyst mass of polymer produced , ), polymer molecular weight ( (unit: Da) and number-average molecular weight (%) (unit: Da) and molecular weight distribution index ( The performance indicators include polymer isotacticity (for regular polymers such as polypropylene, determined by 1H or 1C NMR spectroscopy, unit %), polymer melting point (determined by differential scanning calorimetry, unit ℃), and crystallinity (determined by differential scanning calorimetry or X-ray diffraction, unit %).

[0185] 6.5 Processing Procedure: When designing experiments, select an appropriate scheme based on the characteristics and applicable scenarios of different design methods. Orthogonal experimental design uses the formula... In this study, L represents an orthogonal array, n is the number of experiments, m is the number of levels for each factor, and k is the number of factors. The uniform design method arranges experiments using a dedicated uniform design table. The central composite design of the response surface methodology, in addition to high and low levels, adds a center point and a star point to better fit the quadratic surface. The Latin hypercube design uses the LatinHypercube function in Python to achieve random value selection and experimental arrangement. For experimental data recording, an electronic balance with an accuracy of 0.0001g was used to weigh the polymer to calculate catalytic activity. Gel permeation chromatography (GPC) was used to determine the polymer molecular weight and distribution index. Nuclear magnetic resonance (NMR) was used to determine the polymer isotacticity. Differential scanning calorimetry (DSC) was used to determine the melting point and crystallinity. X-ray diffraction (XRD) was used to assist in determining crystallinity.

[0186] Example 1: Preparation of Ziegler-Natta type polyethylene catalyst

[0187] (a) Catalyst components

[0188]

[0189] (II) Preparation steps: Magnesium chloride support pretreatment

[0190] 1. Add 10g of spherical magnesium chloride to a reaction vessel containing 100mL of decane. Slowly add 15mL of isooctanol while stirring. Heat to 120℃ and react for 2 hours to form a homogeneous solution. Then add 5g of phthalic anhydride and continue the reaction for 1 hour to carry out the modification.

[0191] 1.1 Processing Procedure: During the reaction, the temperature was controlled using a constant-temperature water bath, and the stirring speed was set to 300 rpm to ensure uniform reaction. After the reaction was completed, the mixture was cooled to room temperature for later use.

[0192] 2. Titanium loading and activation: The reactor was cooled to -20℃, and 50 mL of 0.5 mol / L titanium tetrachloride solution was slowly added, followed by the pre-treated magnesium chloride solution, and 2 g of 5 μm nano-silica gel. The temperature was then programmed to rise to 130℃ at a rate of 5℃ / min. At this temperature, 20 mL of 0.2 mol / L diisobutyl phthalate solution was added, and the reaction was allowed to proceed for 3 hours.

[0193] 2.1 Processing Procedure: The temperature of the reactor is controlled using a cryogenic coolant circulation pump, and the heating rate is precisely controlled by a programmable temperature controller. Continuous stirring is maintained throughout the reaction process at a speed of 350 rpm.

[0194] 3. Washing and Drying: After the reaction was completed, the supported catalyst was washed five times with anhydrous hexane, using 50 mL of hexane each time, to remove unreacted titanium tetrachloride and other impurities. The catalyst was then transferred to a vacuum drying oven and dried to constant weight at 60 °C and -0.09 MPa to obtain catalyst powder.

[0195] 3.1 Processing Procedure: The washing process was carried out under nitrogen protection. The catalyst and washing liquid were separated by centrifugation at 3000 rpm for 5 minutes each time. During the drying process, the catalyst mass was weighed every hour. Constant weight was considered achieved when the difference between two weighings was less than 0.01 g.

[0196] (III) Performance Testing

[0197] In a high-pressure reactor, 0.1 g of the catalyst prepared above, 2 MPa of ethylene monomer, 0.05 MPa of hydrogen, and 200 mL of hexane were added, and the reaction was carried out at 70 °C for 2 hours. After the reaction was completed, the polymer was separated and tested.

[0198] Catalytic activity: 8500 ;

[0199] Polymer weight-average molecular weight ( ): 85000\Da;

[0200] Molecular weight distribution index (MDI) ): 3.2;

[0201] Polymer melting point: 132℃;

[0202] Crystallinity: 65%.

[0203] Example 2: Preparation of metallocene-type ethylene-propylene copolymerization catalyst

[0204] (a) Catalyst components

[0205]

[0206] (II) Preparation Steps: Carrier Pretreatment

[0207] 1. 15g of nano-silica was calcined at 500℃ for 6 hours to remove surface-adsorbed impurities and moisture. Then it was added to a toluene solution, and an appropriate amount of γ-aminopropyltriethoxysilane was added for surface modification. The reaction was carried out at 80℃ for 4 hours. After filtration, it was washed three times with toluene and dried for later use.

[0208] 1.1 Processing Procedure: The calcination process was carried out in a muffle furnace at a heating rate of 5℃ / min. The surface modification reaction was conducted in a three-necked flask equipped with a reflux condenser, with continuous stirring at a speed of 250 rpm. Drying was performed in a vacuum drying oven at a temperature of 80℃ and a vacuum degree of -0.09 MPa for 8 hours.

[0209] 2. Metallocene Loading and Activation: Under anhydrous and oxygen-free conditions, 30 mL of a 0.3 mol / L toluene solution of zirconium dichlorocerocene was slowly added to the pretreated nano-silica, and the mixture was stirred for 1 hour to ensure sufficient metallocene loading. Then, a 1.5 mol / L toluene solution of methylaluminoxane was added to achieve a molar ratio of 1000:1 with zirconium dichlorocerocene. Next, a 0.06 mol / L toluene solution of tris(pentafluorophenyl)boron was added to achieve a molar ratio of 0.2:1 with zirconium dichlorocerocene, and the mixture was stirred for another 2 hours for activation.

[0210] 2.1 Processing Procedure: The entire operation was carried out in a glove box, where the water and oxygen content were both below 1 ppm. A magnetic stirrer was used for stirring at a speed of 300 rpm.

[0211] 3. Catalyst Separation and Storage: After the reaction was completed, the catalyst was separated by centrifugation, washed three times with toluene, and centrifuged at 4000 rpm for 8 minutes after each wash. Finally, the catalyst was stored in a sealed container filled with nitrogen in a cool, dry place.

[0212] 3.1 Processing procedure: After centrifugation, the catalyst is transferred to a storage container under nitrogen protection to ensure that it does not come into contact with air.

[0213] (III) Performance Testing

[0214] In a reaction vessel, 0.05 g of the catalyst prepared above, 1.5 MPa of ethylene monomer, 0.5 MPa of propylene monomer, and 150 mL of hexane were added, and the reaction was carried out at 60 °C for 1.5 hours. After the reaction was completed, the polymer was separated and tested.

[0215] Catalytic activity: 12000 ;

[0216] Polymer weight-average molecular weight ( ): 120000Da;

[0217] Molecular weight distribution index (MDI) ): 2.8;

[0218] Propylene content in ethylene-propylene copolymer: 25% (determined by 1H NMR spectroscopy);

[0219] Polymer tensile strength: 28 MPa.

[0220] Example 3: Preparation of a post-transition metal nickel-based polar monomer copolymerization catalyst

[0221] (a) Catalyst components

[0222] Component Name

[0223]

[0224] (II) Preparation steps

[0225] 1. Synthesis of α,α'-diimine nickel chloride: Under nitrogen protection, the corresponding diimine ligand (1.0 mmol) and nickel chloride (1.0 mmol) were added to 100 mL of toluene, and the reaction was carried out at 80 °C for 12 hours. After the reaction was completed, the mixture was cooled to room temperature, filtered, washed three times with toluene, and dried to obtain α,α'-diimine nickel chloride.

[0226] 1.1 Processing Procedure: The reaction was carried out in a three-necked flask equipped with a reflux condenser, with temperature controlled using an oil bath and a stirring speed of 200 rpm. Drying was performed in a vacuum drying oven at a temperature of 50℃ and a vacuum degree of -0.09 MPa for 12 hours.

[0227] 2. Catalyst system construction: Under anhydrous and oxygen-free conditions, 25 mL of a 0.2 mol / L toluene solution of α,α'-diimine nickel chloride was added to a reaction flask, followed by a 0.4 mol / L toluene solution of triisobutylaluminum to achieve a molar ratio of 200:1 with α,α'-diimine nickel chloride. Then, 0.2 mol / L 1-ethyl-3-methylimidazolium tetrafluoroborate was added to achieve a molar ratio of 1:1 with α,α'-diimine nickel chloride. The mixture was stirred until homogeneous to obtain the catalyst system.

[0228] 2.1 Processing procedure: The operation was carried out in a glove box, and a magnetic stirrer was used for stirring at a speed of 300 rpm for 1 hour.

[0229] (III) Performance Testing

[0230] In a reaction vessel, the catalyst system prepared above (equivalent to 0.03 g of the main catalyst), ethylene monomer at 1.2 MPa, butyl acrylate monomer at 0.3 MPa, and toluene at 100 mL were added, and the reaction was carried out at 50 °C for 2 hours. After the reaction was completed, the polymer was separated and tested.

[0231] Catalytic activity: 7800 ;

[0232] Polymer weight-average molecular weight ( ): 90000Da;

[0233] Molecular weight distribution index (MDI) ): 3.0;

[0234] Butyl acrylate insertion rate: 18% (determined by 1H NMR spectroscopy).

[0235] Glass transition temperature of the polymer: -25℃ (determined by differential scanning calorimetry).

[0236] In one specific embodiment, S1. Determine the initial reaction parameter range.

[0237] Factor Name Specific substances / parameters Initial dosage range Main catalyst concentration Titanium tetrachloride (TiCl4) 0.2-0.8 mol / L Molar ratio of alkylaluminum co-catalyst to main catalyst triethylaluminum (Al(C2H5)3) 200-700 Carrier dosage Spherical magnesium chloride (MgCl2) 8-18g / L Internal electron donor concentration Diisobutyl phthalate (DIBP) 0.1-0.3 mol / L external electron donor to main catalyst molar ratio Dicyclopentyldimethoxysilane (DCPDMS) 3-8 reaction temperature - 40-70℃ reaction time - 2-5 hours

[0238] Data source: The data was determined by analyzing 150 sets of ethylene polymerization catalyst preparation data from journals such as "Polymer" and "Journal of Polymer Science" over the past three years, combined with the existing equipment conditions in the laboratory, such as the temperature control accuracy (±1℃) and stirring speed adjustment range (200-800rpm) of the reactor.

[0239] S2. Design experimental combinations and conduct initial experiments.

[0240] Orthogonal experimental design For the seven factors mentioned above, five levels were set for each factor, and 25 sets of experiments were conducted. Some experimental data are recorded below:

[0241] experimental group [TiCl4 concentration (mol / L)] Al / Ti molar ratio MgCl2amount (g / L) DIBP concentration (mol / L) <![CDATA[Molar ratio of DCPDMS / TiCl4]]> Temperature (°C) Time (hours) catalytic activity <![CDATA[M w (Yes)]]> PDI 1 0.2 200 8 0.1 3 40 2 1800 38000 4.5 2 0.2 300 10 0.15 4 45 3 2500 42000 4.2 ... ... ... ... ... ... ... ... ... ... ...

[0242] Experimental procedure: 200 mL of hexane was added as a solvent to a 500 mL high-pressure reactor, and 1.2 MPa ethylene gas was introduced. Solid reagents were weighed using a high-precision electronic balance (accuracy 0.0001 g), and liquid reagents were accurately measured using a microsyringe. During the reaction, the reaction temperature was controlled by a constant-temperature oil bath, and the stirring speed was kept constant at 400 rpm.

[0243] S3. Construct a surface fitting model

[0244] With the main catalyst concentration Molar ratio of alkylaluminum co-catalyst to main catalyst Carrier dosage Internal electron donor concentration Molar ratio of external electron donor to main catalyst With reaction temperature T and reaction time t as independent variables, and catalytic activity as the catalytic activity... Polymer weight-average molecular weight Molecular weight distribution index Using the variable as the dependent variable, construct a multivariate quadratic surface fitting model:

[0245] ;

[0246] in, When they are equal to 1 to 7 respectively, , , , , , , , For constant terms, , , For regression coefficients, This is the error term.

[0247] Model training process: Using the LinearRegression module in Python's Scikit-learn library, the 25 sets of experimental data in S2 were used as the training set. The regression coefficients were solved using the least squares method to calculate the mean square error between the predicted and actual values. Where n=25 is the sample size. For predicted values, (Minimum value for actual values). After training, we obtain... of The value is 0.88. of The value is 0.86. of The value is 0.83.

[0248] Coefficient of determination ; ( (mean of the dependent variable).

[0249] S4. Optimization parameter range selection based on surface fitting model

[0250] Target performance indicator: Catalytic activity ≥ 5000 , Between 60,000 and 100,000 Da, PDI ≤ 3.5. By traversing the parameter space, the parameter range that meets the target performance index is selected:

[0251] Factor Name Optimization interval Main catalyst concentration 0.4-0.6 mol / L Molar ratio of alkylaluminum co-catalyst to main catalyst 400-600 Carrier dosage 12-16g / L Internal electron donor concentration 0.18-0.25 mol / L external electron donor to main catalyst molar ratio 5-7 reaction temperature 50-60℃ reaction time 3-4 hours

[0252] S5. Constructing a Random Forest Model

[0253] Within the parameter range selected in S4, 15 new experiments were designed using a uniform design method. These new data were then combined with the 25 data sets from S2 to form the training set for the random forest model. The random forest model parameters were set as follows: number of decision trees K = 120, maximum depth = 10.

[0254] Model training process: The RandomForestRegressor module from Python's Scikit-learn library was used for training, with 40 merged sets of experimental data as input. Model performance was evaluated using 10-fold cross-validation, yielding a root mean square error (RMSE) of 180 for catalytic activity prediction. , The RMSE is 2500Da, and the RMSE of PDI is 0.18.

[0255] S6. Determining the optimal preparation parameters based on the random forest model.

[0256] Using the random forest model trained in S5, a comprehensive prediction of parameter combinations within the parameter range of S4 is performed. Taking into account factors such as raw material costs (TiCl4: 200 yuan / mol, Al(C2H5)3: 150 yuan / mol, MgCl2: 10 yuan / kg, etc.) and equipment energy consumption in actual production, the optimal preparation parameters are determined.

[0257] Factor Name optimal value Main catalyst concentration 0.5 mol / L Molar ratio of alkylaluminum co-catalyst to main catalyst 500 Carrier dosage 14g / L Internal electron donor concentration 0.22 mol / L external electron donor to main catalyst molar ratio 6 reaction temperature 55℃ reaction time 3.5 hours

[0258] S7. Catalyst preparation according to optimal parameters

[0259] (I) Preparation steps

[0260] Magnesium chloride carrier treatment: 14 g of spherical magnesium chloride was added to a reaction vessel containing 150 mL of decane, followed by 20 mL of isooctanol. The mixture was stirred at 120 °C for 2 hours to form a homogeneous solution. Then, 7 g of phthalic anhydride was added, and the reaction was continued for 1.5 hours for modification. After the reaction was completed, the mixture was cooled to room temperature for later use.

[0261] Titanium loading and activation: The reaction vessel was cooled to -15℃, and 70 mL of 0.5 mol / L titanium tetrachloride solution was slowly added, followed by the above-treated magnesium chloride solution, and 2.8 g of 8 μm nano-silica gel was added simultaneously; then the temperature was increased to 135℃ at a rate of 5℃ / min, and 40 mL of 0.22 mol / L diisobutyl phthalate solution was added at this temperature, and the reaction was stirred for 3 hours; finally, a dicyclopentyldimethoxysilane solution with a molar ratio of 6 to titanium tetrachloride was added, and the reaction was continued for 0.5 hours.

[0262] Washing and drying: After the reaction was completed, the supported catalyst was washed 6 times with anhydrous hexane, using 70 mL of hexane each time, to thoroughly remove unreacted titanium tetrachloride and other impurities. After washing, the catalyst was transferred to a vacuum drying oven and dried to constant weight at 65 °C and -0.095 MPa to obtain the final catalyst powder.

[0263] (ii) Performance Testing

[0264] Under the same ethylene polymerization conditions as S2, 0.1 g of the catalyst prepared above was used for the polymerization reaction. After the reaction, the product was tested, and the actual performance data were obtained:

[0265] Catalytic activity: 5800 ;

[0266] 85000Da;

[0267] PDI: 3.3;

[0268] Compared with the predicted value of the random forest model (catalytic activity 5750) , Compared to (86000Da, PDI=3.2), the errors were all controlled within a reasonable range, verifying the effectiveness of the model optimization and preparation methods.

[0269] The embodiments described above are for illustrative purposes only and are not intended to limit the invention. Therefore, any changes in numerical values ​​or substitutions of equivalent elements should still fall within the scope of this invention.

[0270] The above detailed description will enable those skilled in the art to understand that the present invention can indeed achieve the aforementioned objectives and has complied with the provisions of the Patent Law.

[0271] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention. The above descriptions are merely preferred embodiments of the invention and are not intended to limit the invention. It should be noted that any modifications, equivalent substitutions, and improvements made within the spirit and principles of the invention should be included within the scope of protection of the invention.

[0272] It should be noted that the above description of the process is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to the process under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0273] The basic concepts have been described above. Obviously, for those skilled in the art who have read this application, the above disclosure is merely illustrative and does not constitute a limitation of this application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this application. Such modifications, improvements, and corrections are suggested in this application, and therefore, such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this application.

[0274] Furthermore, this application uses specific terms to describe its embodiments. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of this application. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different positions in this specification do not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this application can be appropriately combined.

[0275] Similarly, it should be noted that, in order to simplify the description of the present application and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of the embodiments of the present application sometimes combines multiple features into a single embodiment, drawing, or description thereof. However, this approach of the present application should not be construed as reflecting an intention that the claimed subject matter requires more features than expressly recited in each claim. Rather, the subject of the invention should possess fewer features than in any single embodiment described above.

Claims

1. A method for preparing a multifunctional polymeric catalyst based on alkylaluminum, characterized in that, Comprising: S1. Determine the initial reaction parameter range, including the main catalyst concentration range, the molar ratio of aluminum alkyl cocatalyst to main catalyst, the carrier dosage range, the internal electron donor concentration range, the molar ratio of external electron donor to main catalyst, the reaction temperature and the reaction time; S2. Use orthogonal experimental design, uniform design method or response surface design method to design experimental combinations covering the parameter ranges in step S1, and carry out polymerization reactions, and record the performance indicators of each experimental group, including catalytic activity, polymer molecular weight and molecular weight distribution index; S3. With the reaction parameters in step S1 as independent variables and the performance indicators in step S2 as dependent variables, a multiple quadratic surface fitting model is constructed, and the least squares method is used to solve the regression coefficients; S4. According to the prediction results of the surface fitting model in step S3, set the target performance indicators, and select the parameter interval that meets the target; S5. In the parameter interval selected in step S4, design experimental combinations again and carry out experiments, combine the new data with the experimental data in step S2, and construct a random forest model composed of multiple decision trees; S6. Use the random forest model in step S5 to predict the parameter combinations in the parameter interval in step S4, and determine the optimal parameter combination; S7. Prepare the multifunctional polymerization catalyst based on aluminum alkyl according to the optimal parameter combination determined in step S6; In S3, the expression of the multiple quadratic surface fitting model is: ； Wherein, Y represents performance index, representing each reaction parameter, is a constant term, , , is a regression coefficient, is an error term, and n is the number of independent variables; The surface fitting model construction and application process is as follows: Data input: The data obtained from the experimental combinations designed in step S2 and the initial experiments carried out are input as input data, which include 7 independent variables: main catalyst concentration, molar ratio of aluminum alkyl cocatalyst to main catalyst, carrier dosage, internal electron donor concentration, molar ratio of external electron donor to main catalyst, reaction temperature and reaction time, and 3 dependent variables: catalytic activity, polymer weight average molecular weight and molecular weight distribution index; Variable and equation definition: Define the independent variables and dependent variables, and construct a multiple quadratic equation; Coefficient solving: The least squares method is used to solve the regression coefficients, and the goal of the least squares method is to minimize the mean square error of the predicted value and the actual value. Through calculation, the specific values of each regression coefficient are obtained; Model evaluation: Calculate the mean square error and evaluate the fitting degree of the model according to the mean square error; At the same time, calculate the determination coefficient to judge the fitting degree of the model to the data; If the determination coefficient is less than 0.8, it means that the model fitting effect is not good, and the interactive term or high-order term needs to be added to solve the coefficients again; If the determination coefficient is greater than or equal to 0.8, it is considered that the model fitting effect is good; Model generation: When the model fitting degree meets the standard, the surface prediction model is generated, which can be used to predict the catalytic activity, polymer weight average molecular weight and molecular weight distribution index according to the input parameter combination; Model application: Input a new parameter combination and use the generated surface prediction model to calculate, output the predicted results of the corresponding catalytic activity, polymer weight average molecular weight and molecular weight distribution index, and provide data support for subsequent selection and optimization of parameter intervals.

2. The method for preparing a multifunctional polymeric catalyst based on alkylaluminum according to claim 1, characterized by, The performance indicators recorded in S2 also include the isotacticity, melting point and crystallinity of the polymer.

3. The method for preparing a multifunctional polymeric catalyst based on alkylaluminum according to claim 1, characterized by, The random forest model in S5 determines the classification result by voting method and obtains the regression prediction value by averaging method.

4. The method for preparing a multifunctional polymeric catalyst based on alkylaluminum according to claim 1, characterized by, In S6, the optimal parameter combination is determined based on actual production requirements and cost factors.

5. The method for preparing a multifunctional polymeric catalyst based on alkylaluminum according to claim 4, characterized in that, The actual production requirements include the tensile strength and bending strength of the polymer.

6. The method for preparing a multifunctional polymeric catalyst based on alkylaluminum according to claim 1, characterized by, In S7, if the main catalyst is titanium tetrachloride and the carrier is magnesium chloride, the specific preparation process includes: reacting magnesium chloride with organic alcohol in an inert solvent to form a homogeneous solution, then modifying, mixing with cooled titanium tetrachloride and nano silica gel, and then reacting by programmed temperature rise and adding polycarboxylic acid ester, and finally washing and drying to obtain the catalyst.

7. The method for preparing a multifunctional polymeric catalyst based on alkylaluminum according to claim 1, characterized by, In S3, the mean square error is used to evaluate the accuracy of the surface fitting model; in S5, the 10-fold cross-validation is used to evaluate the accuracy and generalization ability of the random forest model.

8. The method for preparing a multifunctional polymeric catalyst based on alkylaluminum according to claim 1, characterized by, The parameter optimization process of the random forest model is as follows:

1. Data integration and preprocessing: integrate the orthogonal experiment data of step S2 and the supplementary experiment data in the optimization interval of step S4, and perform standardization or normalization processing on these data to eliminate the dimension effect; 2. Data division: divide the integrated data into training set and test set according to the proportion of 80% and 20%, wherein the training set is used for model training, and the test set is used for evaluating the generalization ability of the model; 3. Model initialization: initialize the core parameters of the random forest model, set the number of decision trees K=120, the maximum depth to 10 layers, the minimum sample size to 5, that is, the minimum number of samples required for node splitting, and randomly select part of the features for node splitting of each decision tree; 4. Single decision tree training: for each decision tree, randomly sample from the training set by bootstrap sampling method; when node splitting, randomly select 3 features, calculate the Gini impurity or mean square error, select the optimal splitting feature and threshold for node splitting; repeat this process, recursively split the nodes, until the maximum depth of 10 layers is reached or the minimum sample size of 5 is met, finally generate leaf nodes and store the prediction values; 5. Ensemble prediction: Repeat step 4, train the decision tree, and ensemble the prediction results of the decision trees; for regression problems, the average method is used to obtain the final prediction value, that is wherein is the final prediction value, K is the number of decision trees, is the prediction value of the kth decision tree; 6. Cross-validation and evaluation: use 10-fold cross-validation method to evaluate the model; divide the training set into 10 parts, train with 9 parts of data and test with 1 part of data, repeat 10 times, calculate the average error; use root mean square error and determination coefficient as evaluation indicators; If the root mean square error is greater than the preset threshold, go to the next step for parameter tuning; If the root mean square error meets the requirements, generate the optimal parameter prediction model; 7. Parameter tuning: design parameter grid, exhaustively combine the number of decision trees, maximum depth, feature number, and minimum sample size; use grid search method combined with cross-validation to evaluate each parameter combination, select the parameter combination with the smallest root mean square error as the optimal parameter; 8. Optimal parameter generation: The parameter combination in the optimization interval of step S4 is comprehensively predicted by the model, combined with the raw material cost and equipment energy consumption in actual production, and the objective function is used to perform multi-objective optimization to determine the final optimal preparation parameters, respectively the weights of catalytic activity, molecular weight, and molecular weight distribution index, is the catalytic activity, is the maximum catalytic activity, is the polymer weight average molecular weight, is the molecular weight distribution index.

9. The method for preparing a multifunctional polymeric catalyst based on alkylaluminum according to claim 1, characterized by, The reaction parameters in S1 also include the dosage range of the additive.

10. A multifunctional polymerization catalyst based on alkylaluminum, characterized in that, The preparation method of the multifunctional polymeric catalyst based on alkyl aluminum according to any one of claims 1-9 is used to prepare the multifunctional polymeric catalyst based on alkyl aluminum, which comprises: The main catalyst is one of titanium tetrachloride, tetrabutyl titanate, titanium trichloride, dichlorobis-zirconocene, bis-dichlorozirconium, rac-dimethylsilylbis-dichlorozirconium, chromium trichloride, tert-butyl chromate, chromium acetylacetonate, α, α'-diimine nickel chloride, bis-dichloronickel, bisnickel, ferrocene, ferric chloride, bisaminoiron dichloride; The alkyl aluminum cocatalyst is one of triethyl aluminum, triisobutyl aluminum, trimethyl aluminum, methyl aluminoxane, ethyl aluminoxane, isobutyl aluminoxane; The carrier is one of magnesium chloride, silica gel, alumina, titanium dioxide, molecular sieve, montmorillonite; The internal electron donor is one of diisobutyl phthalate, dioctyl phthalate, di-n-butyl phthalate, diisobutyl succinate, diisobutyl glutarate; The external electron donor is one of dicyclopentyl dimethoxysilane, diisopropyl dimethoxysilane, diphenyl dimethoxysilane, methylcyclohexyl dimethoxysilane, cyclohexyl methyl dimethoxysilane; The additive is one of ligand type, cocatalyst synergistic type, function regulation type, stability enhancement type.

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