Anti-solvent spray crystallization flow parameter optimization method based on experiment and numerical simulation and storage medium

Abstract

The invention provides an anti-solvent jet crystallization flow parameter optimization method based on experiment and numerical simulation. The method comprises the following steps: step 1, carrying out an anti-solvent jet crystallization experiment on a crystal solution sample; 2, obtaining grain size distribution of crystals filtered in the anti-solvent spray crystallization experiment, and obtaining the average grain size of the experiment; 3, establishing a numerical simulation model for calculating a fluid mechanics coupling grain number balance equation; 4, performing numerical simulation based on the numerical simulation model to obtain a numerical simulation average grain size; 5, adjusting the crystallization kinetic parameters for numerical simulation until the deviation between the numerical simulation average grain size and the experimental average grain size is smaller than a set threshold value; and 6, carrying out numerical simulation based on the adjusted crystallization kinetic parameters, and determining flow parameters for realizing the target grain size distribution. According to the method, numerical simulation is further carried out on the basis of the obtained crystallization kinetic parameters, the flow parameters for achieving the target grain size distribution are determined, and theoretical guidance is provided for experiments.

Description

Technical Field

[0001] The present invention relates to the technical field of enhanced crystallization, and particularly to an optimization method for the flow parameters of anti-solvent jet crystallization based on experiments and numerical simulations and a storage medium. Background Art

[0002] The core objective of crystallization operations is to produce crystal particles with controllable size, shape, crystal form, and chemical purity. The average particle size and particle size distribution are important parameters for evaluating crystallization products, directly affecting the performance of crystal particles in subsequent applications and the efficiency of post-treatment processes. During the crystallization process, the particle size and distribution are comprehensively affected by dynamic factors such as nucleation, growth, aggregation, and breakage, and these factors are closely related and mutually restricted. Therefore, achieving precise control of crystal particle size is a challenging and far-reaching task.

[0003] Among various crystallization methods, anti-solvent crystallization promotes crystal precipitation by adding an anti-solvent to a solution to reduce the solubility of the solute, and is particularly suitable for heat-sensitive substances and systems where the solubility is less affected by temperature changes. Jet crystallization uses a rapid injection mixer to enhance the mixing effect, and through the intense interaction between fluids, a highly supersaturated crystallization environment is formed in a very short time, which is very suitable for the anti-solvent crystallization process. Inside the injection mixer, the solution can be quickly mixed to produce a uniform supersaturation region, thereby achieving continuous preparation of crystal particles with a narrow particle size distribution. In anti-solvent jet crystallization, the nucleation and growth processes play a key role in the final crystal particle size. Therefore, accurately obtaining the nucleation rate and growth rate is crucial for optimizing the microstructure and macroscopic properties of crystals.

[0004] The nucleation rate and growth rate are key parameters describing the dynamic process of crystal formation, usually expressed as functions of supersaturation. However, due to the limitations of existing experimental techniques, most anti-solvent jet crystallization studies can only explore influencing factors from a macroscopic perspective, and it is difficult to directly measure parameters such as the nucleation rate and growth rate, thus unable to deeply reveal the microscopic process of the crystallization mechanism. To overcome this limitation, a method combining theoretical models and experiments is needed to obtain key data such as the nucleation rate and growth rate through numerical simulations. Finally, through numerical simulations under different operating condition parameters, a theoretical basis is provided for optimizing the crystallization process and precisely controlling the crystal particle size distribution in experiments. Summary of the Invention

[0005] The present invention designs and provides an optimization method for the flow parameters of anti-solvent spray crystallization based on experiments and numerical simulations in view of the deficiencies existing in the above-mentioned prior art. The purpose is to solve the problem that the nucleation rate and growth rate in the anti-solvent spray crystallization process cannot be directly measured by experiments. The method of the present invention obtains the crystal size distribution (CSD) after the anti-solvent spray crystallization experiment through a scanning electron microscope (SEM), and combines the CFD-PBE numerical simulation method to inversely calculate parameters such as the nucleation rate and growth rate in the anti-solvent spray crystallization process. Based on the obtained crystallization kinetic parameters, further numerical simulations are carried out to determine the flow parameters for achieving the target grain size distribution, providing theoretical guidance for experiments.

[0006] The purpose of the present invention is achieved through the following technical solutions:

[0007] In the first aspect, the present invention provides an optimization method for the flow parameters of anti-solvent spray crystallization based on experiments and numerical simulations, including the following steps:

[0008] Step 1: Conduct an anti-solvent spray crystallization experiment on the crystal solution sample;

[0009] Step 2: Obtain the crystal size distribution after filtration of the anti-solvent spray crystallization experiment through microstructure characterization, and obtain the experimental average grain size;

[0010] Step 3: Establish a numerical simulation model that couples computational fluid dynamics and population balance equations based on the basic equations of fluid dynamics and the population balance equation;

[0011] Step 4: Divide the grid and set the boundary conditions, set the initial crystallization kinetic parameters, and perform numerical simulation and solution based on the numerical simulation model in Step 3 to obtain the numerical simulation average grain size;

[0012] Step 5: Compare the numerical simulation average grain size with the experimental average grain size. When the deviation between the two is greater than the set threshold, adjust the crystallization kinetic parameters and perform numerical simulations until the deviation between the numerical simulation average grain size and the experimental average grain size is less than the set threshold;

[0013] Step 6: Perform numerical simulations based on the adjusted crystallization kinetic parameters to determine the flow parameters for achieving the target grain size distribution.

[0014] Furthermore, the process parameters set for the anti-solvent spray crystallization experiment in Step 1 include:

[0015] Set process parameters and conduct anti-solvent jet crystallization experiments through a jet mixer. The anti-solvent is injected from the top of the mixing tube, and the jet solution is evenly sprayed through the jet holes symmetrically distributed on the mixer tube wall, mixing with the anti-solvent in the mixing tube. By controlling the velocity ratio of the jet to the anti-solvent and the solution concentration, a filtered crystal suspension is finally obtained at the outlet of the mixing tube.

[0016] Furthermore, in step 2, a scanning electron microscope is used to analyze the morphology and statistically analyze the particle size distribution of the filtered crystal suspension. The scanning electron microscope images are imported into image processing software to calculate the equivalent diameter of the particles, count all particle sizes, generate a particle size distribution histogram or curve, and obtain the experimental average grain size.

[0017] Furthermore, the hydrodynamic coupled population balance equation in step 3 is as follows:

[0018]

[0019] J = k b ·S b G = k g ·S g

[0020] where n(L; x, t) is the crystal number density, L is the crystal characteristic size, t is the residence time, u i is the time-averaged velocity in the i direction, x i is the coordinate component in the i-th direction, Γ t is the turbulent diffusion coefficient, J is the nucleation rate, G is the growth rate, and S is the supersaturation level;

[0021] k b is the nucleation rate constant, b is the nucleation rate exponent, k g is the growth rate constant, and g is the growth rate exponent.

[0022] Furthermore, in step 4, the geometric shape of the jet mixer used for numerical simulation based on the numerical simulation model in step 3 is the same as that used in step 1, and the set process parameters are the same.

[0023] Furthermore, the initial crystallization kinetic parameters in step 4 include the nucleation rate and the growth rate.

[0024] Furthermore, step 4 includes: dividing the unstructured hexahedron grid through ICEM, solving the basic hydrodynamic equations through the commercial software Ansys Fluent, using the turbulence model and the standard wall function, solving the hydrodynamic coupled population balance equation for the crystallization process, and coupling the population balance equation with the flow field through a user-defined function.

[0025] Further, step 6 includes: based on the adjusted crystallization kinetic parameters obtained in step 5, adjusting other flow parameters, successively performing numerical simulations based on the numerical simulation model in step 3, analyzing the specific influence rules of different combinations of flow parameters on the crystal size distribution during the crystallization process, comparing the particle size distribution results under different flow parameter settings, and screening out the optimal parameter combination that can achieve the target particle size distribution.

[0026] Further, the key flow parameters include: solution flow rate, solution temperature, solute mass fraction, antisolvent flow rate, antisolvent temperature.

[0027] On the other hand, the present invention provides a computer storage medium, and the computer storage medium is a server workstation;

[0028] The server workstation stores a computer program executed by an electronic device. When the computer program runs on the electronic device, it enables the electronic device to execute the steps in the method for optimizing the flow parameters of antisolvent injection crystallization based on experiments and numerical simulations.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. The present invention can avoid a large number of experiments and obtain crystallization kinetic parameters only through comparative analysis from a small number of experiments and corresponding numerical simulations.

[0031] 2. Based on the obtained crystallization kinetic parameters, the present invention can perform numerical simulations on the antisolvent injection crystallization process under different working conditions and obtain the crystal size distribution under the corresponding working conditions.

[0032] 3. Realize the design of crystallization process parameters in experiments and regulate the crystal size distribution in experiments. Based on the particle size distribution obtained from numerical simulations, the corresponding process parameters under the target particle size distribution are deduced inversely. Description of the Drawings

[0033] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0034] Figure 1 It is the flowchart of the present invention;

[0035] Figure 2 It is the surface morphology image obtained by scanning electron microscope in Example 1 of the present invention;

[0036] Figure 3This is a schematic diagram of the three-dimensional model and mesh generation for the anti-solvent spray crystallization flow numerical simulation in Example 1 of the present invention.

[0037] Figure 4 This is a histogram of the crystal size distribution curves obtained from the experiment and numerical simulation in Example 1 of the present invention. Detailed implementation manners

[0038] To make the above objects, features, and advantages of the present application more apparent and understandable, the following describes the detailed implementation manners of the present application with reference to the accompanying drawings. Many specific details are set forth in the following description to facilitate a thorough understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the connotation of the present application. Therefore, the present application is not limited by the specific embodiments disclosed below.

[0039] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order different from that shown or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or modules does not necessarily have to be limited to those steps or modules clearly listed, but may include other steps or modules not clearly listed or inherent to these processes, methods, products, or devices. The naming or numbering of steps that appear in the present application does not mean that the steps in the method flow must be executed in the time / logical order indicated by the naming or numbering. The named or numbered process steps can be changed in the execution order according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved. The division of units that appears in the present application is a logical division, and there may be other division methods in actual implementation. For example, multiple units can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection shown or discussed with each other can be through some interfaces. The indirect coupling or communication connection between units can be electrical or other similar forms, which are not limited in the present application. And the units or subunits described as separate components may or may not be physically separated, may or may not be physical units, or may be distributed to multiple circuit units. Some or all of the units can be selected according to actual needs to achieve the purpose of the present application solution.

[0040] Example 1

[0041] AsFigure 1 As shown in Figure 1 , this embodiment provides an optimization method for the flow parameters of anti-solvent jet crystallization based on experiments and numerical simulations, including the following steps:

[0042] Step 1: Conduct an anti-solvent jet crystallization experiment on a crystal solution sample;

[0043] In this embodiment, in the anti-solvent jet crystallization experiment, the anti-solvent is water, the temperature is 2°C, the jet solution is a DMSO solution of DAAF, the temperature is 85°C, and it is carried out through a jet mixer. The anti-solvent flows in from the top of the mixing tube, and the jet solution is evenly sprayed into the mixing tube through jet holes symmetrically distributed vertically on the tube wall to be quickly sheared and mixed with the anti-solvent water. The flow rate of the jet solution is 0.06 m / s, the flow rate of the anti-solvent is 0.4 m / s, the mass concentration of the jet solution is controlled to be 0.07, and at the outlet of the mixing tube, the crystal suspension is vacuum filtered through a Buchner funnel;

[0044] Step 2: Obtain the crystal particle size distribution after filtering the anti-solvent jet crystallization experiment through microstructure characterization, and obtain the experimental average grain size;

[0045] Use a scanning electron microscope (SEM) to conduct morphology tests and crystal size distribution (CSD) statistics on the filtered crystal suspension. When conducting morphology tests, adjust the electron beam focus and working distance, and use the SEM detector to ensure clear surface morphology images are obtained. As Figure 2 shown, import the collected SEM images into NanoMeasurer image processing software, count the size data of all particles, and generate a histogram and particle size distribution curve.

[0046] Step 3: Establish a numerical simulation model that couples computational fluid dynamics and population balance equations based on the basic equations of fluid dynamics and population balance equations;

[0047] The mathematical model couples the population balance equation with the flow field, and can intuitively examine the influence of mixing on the crystallization process. The form of the population balance equation coupled with the flow field is as follows:

[0048]

[0049] J = k b ·S b G = k g ·S g

[0050] Among them, n(L; x, t) is the crystal number density, L is the crystal characteristic size, t is the residence time, u i is the time-averaged velocity in the i direction, x i is the coordinate component in the i-th direction, Γ tk is the turbulent diffusion coefficient, J is the nucleation rate, G is the growth rate, and S is the supersaturation;

[0051] k b is the nucleation rate constant, b is the nucleation rate exponent, k g is the growth rate constant, and g is the growth rate exponent.

[0052] Step 4: Divide the grid and set the boundary conditions, set the initial crystallization kinetics parameters, and perform numerical simulation and solution based on the numerical simulation model in Step 3 to obtain the average grain size of the numerical simulation;

[0053] In this step, the geometry of the jet mixer used in the numerical simulation is the same as that used in Step 1. The geometric dimensions are: the length of the jet mixer is 330 mm, the inner diameter is 10 mm, the inner diameter of the nozzle is 5 mm, and the diameter of the jet hole is 0.2 mm; the unstructured hexahedral grid is divided by ICEM; the model control equations are solved by the commercial software Ansys Fluent. The standard k-ε turbulence model and the standard wall function are used to solve the CFD-PBE coupling equation of the crystallization process. The crystallization nucleation function and the crystallization growth function are defined through the user-defined function (UDF), and the material transport equation, the population balance equation, and the micromixing model are coupled with the flow field;

[0054] Step 5: Compare the average grain size of the numerical simulation with the average grain size of the experiment. When the deviation between the two is greater than the set threshold, adjust the crystallization kinetics parameters and perform numerical simulation until the deviation between the average grain size of the numerical simulation and the average grain size of the experiment is less than the set threshold;

[0055] Set the process parameters of the anti-solvent jet crystallization numerical simulation to be the same as those in Step 1. The concentration of the jet solution is controlled at 0.07, the incident velocity is 0.06 m / s, the temperature is 85 °C, the flow rate of the anti-solvent is 0.4 m / s, and the temperature is 2 °C. Adjust the values of the nucleation rate and the growth rate, count the average grain size obtained from the numerical simulation, compare it with the average grain size obtained from the experiment in Step 2, calculate the ratio of the absolute difference between the two to the experimental data in Step 2, and then perform the following operations:

[0056] If the ratio error is greater than the set threshold of 0.15 and the average crystal particle size obtained from the numerical simulation is smaller than the average crystal particle size counted in Step 2, reduce the nucleation rate and increase the growth rate, and repeat the above numerical simulation operations until the ratio is less than the threshold and then stop the calculation. At this time, the values of the nucleation rate and the growth rate set in the numerical simulation are accepted as the final actual values;

[0057] If the data error is less than the set threshold of 0.15, stop the calculation. At this time, the values of the parameters of the nucleation rate and the growth rate used in the calculation are accepted as the final actual values.

[0058] The comparison of the particle size distributions obtained from the final numerical simulation and experiments is as Figure 4 shown. From the comparison results, it can be seen that the two are in good agreement.

[0059] Step 6: Conduct numerical simulation based on the adjusted crystallization kinetic parameters to determine the flow parameters for achieving the target grain size distribution.

[0060] Based on the nucleation rate and growth rate data obtained in Step 5, adjust the jet velocity, anti-solvent flow rate, temperature, etc., and conduct systematic numerical simulation experiments in sequence. Through numerical simulation, obtain the specific influence of different flow parameters on the crystal particle size distribution during the crystallization process, and the best parameter combination that can achieve the target particle size distribution can be screened out. Repeating this simulation experiment, the obtained particle size distribution curves are consistent, and the stability is verified. Therefore, it can be seen that through the combination of engineering experiments and numerical simulation, the present invention can obtain relatively accurate crystallization kinetic parameters during the anti-solvent jet crystallization flow process, and then determine the flow parameters for achieving the target grain size distribution, providing theoretical guidance for experiments and scientific basis and reliable reference for optimizing the actual crystallization process and precisely controlling the crystal particle size distribution.

[0061] Example 2

[0062] A specific embodiment of the present invention also provides a computer storage medium.

[0063] The computer storage medium is a server workstation;

[0064] The server workstation stores a computer program executed by an electronic device. When the computer program runs on the electronic device, the electronic device is caused to execute the steps of the anti-solvent jet crystallization flow parameter optimization method according to an embodiment of the present invention.

[0065] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application.

[0066] It should be understood that the parts not elaborated in this specification belong to the prior art.

[0067] It should be understood that the above description of the preferred embodiment is relatively detailed, and it should not be considered as a limitation to the protection scope of the present invention patent. Under the inspiration of the present invention, those of ordinary skill in the art can also make substitutions or deformations without departing from the protection scope defined by the claims of the present invention, and all fall within the protection scope of the present invention. The scope of protection claimed by the present invention shall be subject to the appended claims.

Claims

1. A method for optimizing flow parameters of antisolvent jet crystallization based on experiments and numerical simulations, characterized in that: The steps include: Step 1: Perform an antisolvent spray crystallization experiment on a crystal solution sample; Step 2: Obtain the crystal size distribution after filtration in the antisolvent jet crystallization experiment through microstructural characterization, and obtain the experimental average grain size; Step 3: Establish a numerical simulation model of computational fluid dynamics coupled with the particle balance equation based on the basic equations of fluid dynamics and the particle balance equation; Step 4: Divide the grid and set boundary conditions, set initial crystallization kinetic parameters, perform numerical simulation based on the numerical simulation model in step 3, and obtain the average grain size of the numerical simulation; Step 5: Compare the numerical simulation average grain size with the experimental average grain size. When the deviation between the two is greater than the set threshold, adjust the crystallization kinetic parameters to perform numerical simulation until the deviation between the numerical simulation average grain size and the experimental average grain size is less than the set threshold. Step 6: Perform numerical simulation based on the adjusted crystallization kinetics parameters to determine the flow parameters to achieve the target grain size distribution.

2. The method for optimizing antisolvent injection crystallization flow parameters based on experiments and numerical simulations according to claim 1, characterized in that: The process parameters for setting the anti-solvent spray crystallization experiment in step 1 include: The process parameters were set and an antisolvent jet crystallization experiment was carried out through a jet mixer. The antisolvent was injected from the top of the mixing tube, and the jet solution was evenly sprayed from the jet holes symmetrically distributed on the wall of the mixer tube and mixed with the antisolvent in the mixing tube. By controlling the speed ratio of the jet and the antisolvent and the solution concentration, a filtered crystal suspension was finally obtained at the outlet of the mixing tube.

3. The method for optimizing antisolvent jet crystallization flow parameters based on experiments and numerical simulations according to claim 1, characterized in that: In step 2, a scanning electron microscope is used to perform morphological analysis and particle size distribution statistics on the filtered crystal suspension, and the scanning electron microscope image is imported into an image processing software to calculate the equivalent diameter of the particles, count all particle sizes, generate a particle size distribution histogram or curve, and obtain the experimental average grain size.

4. The method for optimizing antisolvent injection crystallization flow parameters based on experiments and numerical simulations according to claim 1, characterized in that: The fluid mechanics coupled population balance equation in step 3 is: J=k b ·S b G=k g ·S g Where n(L; x, t) is the number density of crystals, L is the characteristic size of the crystal, t is the residence time, and u i is the average speed in direction i, x i is the coordinate component in the i-th direction, Γ t is the turbulent diffusion coefficient, J is the nucleation rate, G is the growth rate, and S is the supersaturation degree; k b is the nucleation rate constant, b is the nucleation rate exponent, k g is the growth rate constant and g is the growth rate exponent.

5. The method for optimizing antisolvent jet crystallization flow parameters based on experiments and numerical simulations according to claim 4, characterized in that: The geometric shape of the jet mixer used for the numerical simulation in step 4 based on the numerical simulation model in step 3 is the same as that used in step 1, and the set process parameters are the same.

6. The method for optimizing antisolvent jet crystallization flow parameters based on experiments and numerical simulations according to claim 5, characterized in that: The initial crystallization kinetic parameters in step 4 include nucleation rate and growth rate.

7. The method for optimizing antisolvent jet crystallization flow parameters based on experiments and numerical simulations according to claim 6, characterized in that: The step 4 includes: dividing the unstructured hexahedral grid by ICEM, solving the basic fluid dynamics equations by the commercial software Ansys Fluent, solving the fluid mechanics coupled population balance equation of the crystallization process by using the turbulence model and the standard wall function, and coupling the population balance equation with the flow field by a custom function.

8. The method for optimizing antisolvent jet crystallization flow parameters based on experiments and numerical simulations according to claim 6, characterized in that: The step 6 includes: adjusting other flow parameters based on the adjusted crystallization kinetic parameters obtained in step 5, performing numerical simulations based on the numerical simulation model in step 3, analyzing the specific influence of different flow parameter combinations on the crystal particle size distribution during the crystallization process, comparing the particle size distribution results under different flow parameter settings, and screening out the best parameter combination that can achieve the target particle size distribution.

9. The method for optimizing antisolvent jet crystallization flow parameters based on experiments and numerical simulations according to claim 8, characterized in that: Key flow parameters include: solution flow rate, solution temperature, solute mass fraction, antisolvent flow rate, and antisolvent temperature.

10. A computer storage medium, characterized in that: The computer storage medium is a server workstation; The server workstation stores a computer program executed by an electronic device, and when the computer program is run on the electronic device, the electronic device executes the steps in the anti-solvent jet crystallization flow parameter optimization method based on experiments and numerical simulations according to any one of claims 1 to 9.

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