Emulsion preparation process optimization method using multiple light scattering technology and application thereof

Abstract

The application provides an emulsion preparation process optimization method using a multiple light scattering technology and application thereof, and the emulsion preparation process optimization method comprises the following steps: (1) selecting emulsions to be optimized with different homogenization durations to perform multiple light scattering scanning, recording light intensity change rates, establishing a t1-η-C correlation model, and obtaining a t1 optimal interval; (2) fixing t1 in the optimal interval, changing homogenization intensity, obtaining emulsions to be optimized with different particle diameters, performing multiple light scattering scanning, recording light intensity change rates, and establishing a P-D-C correlation model; (3) taking C minimization as an objective, determining the optimal combination of t1 and P according to the correlation models of steps (1) and (2), and completing process optimization. The method provided by the application uses a correlation model to obtain an optimal combination, and realizes optimization of an emulsion process.

Description

Technical Field

[0001] This invention belongs to the field of cosmetic technology, specifically relating to an optimization method for emulsion preparation using multiple light scattering technology and its application. Background Technology

[0002] Emulsions are an important type of dispersion system with wide applications in cosmetics, battery slurries, and other fields. However, emulsion systems face numerous challenges during production and storage. For example, cosmetic emulsions exhibit thermodynamic instability and gradually revert to a stable phase-separated state over time. Furthermore, emulsion stability is a crucial parameter for evaluating product quality, requiring rigorous assessment during product development, production, and storage. Its macroscopic stability is determined by a ternary coupling of viscosity, particle size, and interfacial network. Traditional optimization approaches include the following steps: ① First, single-factor experiments (temperature, homogenization pressure, emulsifier concentration, etc.) are conducted to measure the endpoint viscosity or average particle size; ② Then, using viscosity / particle size as the response value, the optimal process is found using response surface methodology (RSM) or artificial neural network (ANN). However, the entire process relies on "macroscopic steady-state indices" (η, D50) as the sole feedback, making it impossible to capture early flocculation, Ostwald ripening, and other microstructural evolutions online. This results in a large experimental workload, lengthy cycles, poor reproducibility, and unsatisfactory optimization of the process. Its shortcomings can be summarized into the following four points: (1) Offline and destructive detection: It is impossible to use samples from the early stage of production, and macroscopic indicators can only be obtained "after the fact", making it impossible to detect early microscopic instability in advance.

[0003] (2) Huge experimental workload: with η or D 50 To obtain a unique response, 30-50 full factorial or RSM experiments are required, which is costly and time-consuming.

[0004] (3) Viscosity-particle size are not decoupled: The optimal homogeneous strength P often falls in the "high energy consumption zone" or "excessive homogeneity zone", which accelerates the ripening process.

[0005] (4) Unexplainable model: When the laboratory optimal parameters are scaled up to the production line, they fail directly due to the difference in heat transfer / shear of the equipment, resulting in poor reproducibility.

[0006] This means that the most commonly used optimization processes still rely on the experience of skilled technicians, lacking scientific guidance and exhibiting a degree of uncertainty. Therefore, providing a scientific, simple, and efficient method for optimizing emulsion preparation processes has become an urgent problem to be solved. Summary of the Invention

[0007] To address the shortcomings of existing technologies, the present invention aims to provide a method for optimizing emulsion preparation processes using multiple light scattering (MLS) technology and its application. The method provided by this invention employs MLS technology, overcoming the limitations of traditional light scattering techniques in terms of detection range and accuracy. It can meet the needs of stability determination for emulsions of different types and concentrations, and utilizes correlation models to obtain the optimal combination, thereby optimizing the emulsion process. This overcomes the drawbacks of traditional methods, such as high subjectivity and long processing times, and improves the accuracy and efficiency of emulsion stability assessment.

[0008] To achieve this objective, the present invention adopts the following technical solution: On one hand, the present invention provides a method for optimizing emulsion preparation process using multiple light scattering technology, the method comprising the following steps: (1) Select emulsions to be optimized with different homogenization times and perform multiple light scattering scans. Record the rate of change of light intensity, establish a t1-η-C correlation model, and obtain the optimal t1 interval; (2) Fix t1 within the optimal range, change the homogenization intensity to obtain emulsions with different particle sizes to be optimized, and perform multiple light scattering scans to record the light intensity change rate and establish a PDC correlation model. (3) With the goal of minimizing C, determine the optimal combination of t1 and P based on the correlation model of steps (1) and (2) to complete the process optimization.

[0009] Where t1 is the homogenization time, η is the emulsion viscosity, C is the rate of change of light intensity, P is the homogenization intensity, and D is the particle size (D1). 50 ).

[0010] The aforementioned specific method employs multiple light scattering technology, which overcomes the limitations of traditional light scattering technology in terms of detection range and accuracy. It can meet the needs of stability determination of emulsions of different types and concentrations, and uses correlation models to obtain the optimal combination, thereby optimizing the emulsion process. It overcomes the shortcomings of traditional methods, such as strong subjectivity and long cycle time, and improves the accuracy and efficiency of emulsion stability assessment.

[0011] Preferably, the t1-η-C correlation model in step (1) includes the η-C correlation model and the t1-η correlation model.

[0012] Preferably, the η-C correlation model is a multiple function with lnη as the independent variable and C as the dependent variable.

[0013] Preferably, the t1-η correlation model is an exponential function with t1 as the independent variable and η as the dependent variable.

[0014] Preferably, the PDC association model in step (2) includes a DC association model and a PD association model.

[0015] Preferably, the DC association model is a multiple function with D as the independent variable and C as the dependent variable.

[0016] Preferably, the PC association model is an exponential function with P as the independent variable and D as the dependent variable.

[0017] The aforementioned specific model function can more accurately determine the optimal homogenization duration and homogenization intensity, thereby improving the accuracy of the method.

[0018] On the other hand, the present invention also provides the application of the emulsion preparation process optimization method described above in the production and preparation of cosmetics.

[0019] Compared with the prior art, the present invention has the following beneficial effects: This invention provides an optimized method for emulsion preparation using multiple light scattering technology, which has the following advantages: (1) This invention optimizes the emulsion process by changing the homogenization time t1 and homogenization intensity with a fixed formula, performing multiple scans and measurements, establishing a correlation model, and obtaining the optimal combination. This overcomes the shortcomings of traditional methods, which are highly subjective and have long cycles, and improves the accuracy and efficiency of emulsion stability assessment. (2) The present invention uses multiple light scattering technology, which breaks through the limitations of traditional light scattering technology in terms of detection range and detection accuracy, and can meet the needs of stability determination of emulsions of different types and concentrations, and is particularly suitable for opaque or high concentration dispersion systems. (3) By establishing the t1-η-C correlation model and the PDC correlation model, this invention achieves accurate measurement of emulsion viscosity and particle size, providing multiple indicators for comprehensive evaluation of emulsion stability and overcoming the shortcomings of single evaluation indicators in the prior art. (4) By optimizing the homogenization time t1 and homogenization intensity of the sample, the present invention establishes a systematic experimental condition control mechanism, which effectively solves the problem of the influence of key parameters such as sample cell placement temperature and time on emulsion stability. (5) This invention establishes a correlation model through multiple scans and measurements, enabling in-depth mining and utilization of experimental data, which helps to extract representative indicators and parameters to comprehensively evaluate the stability of the emulsion. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention, the following describes the technical solution of the present invention in conjunction with preferred embodiments of the present invention. However, the present invention is not limited to the scope of the embodiments.

[0021] The sample composition to be optimized is as follows in the following example: Example 1 This embodiment provides a method for optimizing emulsion preparation process, the specific steps of which are as follows: (1) Establish the η-C correlation model: Different homogenization times t1 were set, with a gradient of 0.5-7 minutes (covering the stages of "insufficient emulsification → sufficient emulsification → over-emulsification"). Five different sets of samples were prepared, and MLS scans were performed on the five sets of samples to record the rate of change of light intensity, which was then fitted. Viscosity is a physical parameter that directly controls the rate of change of light intensity; therefore, a correlation model between viscosity and the rate of change of light intensity needs to be established to derive the homogenization time. For different formulations, the homogenization time required to reach the same viscosity varies; therefore, the homogenization time must be determined based on viscosity.

[0022] The specific data is as follows (all results were tested at 25℃): The η-C correlation model is obtained as C = 2.675⋅(lnη). 2 -48.59⋅(lnη)+221.65.

[0023] (2) Determine the optimal range of homogenization time t1 based on the η-C correlation model. When C ≤ 3%, early stability is considered acceptable, corresponding to 5679 ≥ η ≥ 3531 mPa·s.

[0024] The t1-η correlation model is established as η=5.35⋅t. 4.9525 With +3031.7, the optimal interval for t1 is found to be 2.5-3.5 min.

[0025] (3) Establish PDC association model With t1 fixed as the optimal range, five groups of samples were prepared by varying the homogenization pressure P, and the particle size and light intensity change rate were measured. A fitting analysis was then performed. Particle size is a physical parameter that directly controls the light intensity change rate; therefore, a correlation model between particle size and light intensity change rate needs to be established to derive the homogenization pressure. For different formulations, the required homogenization pressure to achieve the same particle size varies; therefore, the homogenization pressure must be determined based on the particle size.

[0026] The data is as follows: First, establish the DC correlation model: C = -0.0138D 50 2 +0.8009D 50 -1.7445, R 2 =0.9969, when C≤3%, we can find D. 50 ≤6.7 or D 50 ≥51, considering the actual situation, choose D. 50≤6.7 is considered the optimal range for particle size.

[0027] Establish a PD association model: D 50 =54.484P -0.465 R 2 =0.9619, so the range of values ​​for P is P≥100.

[0028] Taking into account both energy consumption and stability, the minimum value of P is selected as 100 bar.

[0029] (4) Joint optimization With the objective of minimizing the rate of change of light intensity C, and considering constraints on energy consumption and production efficiency, the optimal combination of process parameters (t1, P) is determined as follows: Process conditions: Stability constraint: C≤3% (good early stability) Energy consumption constraint: 100≤P≤220 bar (the upper limit is 220 bar to avoid energy waste caused by excessive homogenization) Efficiency constraint: 2.5 ≤ t1 ≤ 3.5 min (to ensure production efficiency) In contrast, the conditions before optimization were P=30 bar and t1=10 min. Under these conditions, C=6.0%, which showed poor stability and did not meet production requirements.

[0030] Example 2 This embodiment provides a method for optimizing emulsion preparation process. Specifically, except for step (1), the η-C correlation model obtained is: C = -3.2325 × 10⁻⁶. -12 η 3 +3.4697×10 -7 η 2 -6.2101×10 -3 Except for η+ 20.5015, the rest is the same as in Example 1.

[0031] When C≤3%, η≥8560, t1≥4.

[0032] The final process conditions are as follows: Stability constraint: C≤3% Energy consumption constraint: 100 ≤ P ≤ 220 bar Efficiency constraint: t1≥4 Under these process conditions, the actual product yield (C) is 2.3%, which meets production requirements. However, the average time is longer than that of the optimal embodiment, resulting in increased production energy consumption and a certain difference from the optimal embodiment.

[0033] Example 3 This embodiment provides an optimization method for emulsion preparation process. Except for step (3), which is adjusted as follows, the other steps are the same as in embodiment 1.

[0034] The final process conditions are as follows: Step (3) First, establish the DC association model: C = 0.1838D 50 1.3981 When C ≤ 3%, find D. 50 ≤0.274. At this point, according to the PD association model: D 50 =54.484P -0.465 P far exceeds the equipment's operating range and does not meet normal production requirements.

[0035] As can be seen from the above, the method provided by the present invention can efficiently, scientifically and accurately optimize the emulsion preparation process, and improve the accuracy and efficiency of emulsion stability assessment.

[0036] The applicant declares that this invention illustrates the optimized emulsion preparation process using multiple light scattering technology and its application through the above embodiments. However, this invention is not limited to the above embodiments, meaning that this invention does not necessarily rely on the above embodiments for implementation. Those skilled in the art should understand that any improvements to this invention, equivalent substitutions of raw materials for the product, additions of auxiliary components, and selection of specific methods all fall within the protection and disclosure scope of this invention.

[0037] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various simple modifications can be made to the technical solution of the present invention, and these simple modifications all fall within the protection scope of the present invention.

[0038] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not describe the various possible combinations separately.

Claims

1. A method for optimizing emulsion preparation process using multiple light scattering technology, characterized in that, The method for optimizing the emulsion preparation process includes the following steps: (1) Select emulsions to be optimized with different homogenization times and perform multiple light scattering scans. Record the rate of change of light intensity, establish a t1-η-C correlation model, and obtain the optimal t1 interval; (2) Fix t1 within the optimal range, change the homogenization intensity to obtain emulsions with different particle sizes to be optimized, and perform multiple light scattering scans to record the light intensity change rate and establish a PDC correlation model. (3) With the goal of minimizing C, determine the optimal combination of t1 and P based on the correlation model of steps (1) and (2) to complete the process optimization; Where t1 is the homogenization time, η is the emulsion viscosity, C is the light intensity change rate, P is the homogenization intensity, and D is the particle size.

2. The method for optimizing the emulsion preparation process according to claim 1, characterized in that, The t1-η-C correlation model in step (1) includes the η-C correlation model and the t1-η correlation model.

3. The method for optimizing the emulsion preparation process according to claim 2, characterized in that, The η-C correlation model is a multiple function with lnη as the independent variable and C as the dependent variable.

4. The method for optimizing the emulsion preparation process according to claim 2 or 3, characterized in that, The t1-η correlation model is an exponential function with t1 as the independent variable and η as the dependent variable.

5. The method for optimizing the emulsion preparation process according to any one of claims 1-4, characterized in that, The PDC association model in step (2) includes the DC association model and the PD association model.

6. The method for optimizing the emulsion preparation process according to claim 5, characterized in that, The DC association model is a multiple function with D as the independent variable and C as the dependent variable.

7. The method for optimizing the emulsion preparation process according to claim 5 or 6, characterized in that, The PC association model is an exponential function with P as the independent variable and D as the dependent variable.

8. The application of an emulsion preparation process optimization method according to any one of claims 1-7 in the production and preparation of cosmetics.

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