Liquid disperse dye for polyester fabric and use method of liquid disperse dye

Abstract

The invention relates to the technical field of data processing, in particular to a liquid disperse dye for polyester fabrics and a use method thereof, and the dye comprises an azo or anthraquinone disperse dye main agent subjected to superfine grinding, a nonionic and anionic compound dispersant system, a fiber plasticizer and a stabilizer. According to the preparation method, molecular dynamics simulation and mesoscale dissipative particle dynamics simulation are combined, the synergistic effect of a dispersant adsorption configuration and double electric layer potential is quantified, and a nonlinear association rule of dynamic particle size control and dispersion stability is established; a multi-physics field coupling model is adopted to integrate fiber humidity, temperature gradient and pressure distribution parameters, and cross-scale collaborative optimization of microscopic interface action and macroscopic process parameters is achieved. According to the method, repetitive experiment iteration is replaced by digital design, the formula development period is remarkably shortened, the dyeing uniformity and the batch-to-batch reproducibility are improved, and the method is suitable for continuous dyeing production of high-density and superfine polyester fabrics.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a liquid disperse dye for polyester fabrics and a method for using the same. Background Art

[0002] Liquid disperse dyes are water-based dye systems developed for hydrophobic polyester fibers. In these systems, dye particles are stably suspended in a liquid medium through non-ionic or anionic dispersants. Polyester fibers have a high proportion of crystalline regions and a dense molecular arrangement, making it difficult for conventional dyes to penetrate the interior of the fibers. However, liquid disperse dyes optimize the particle fineness to the sub-micron level through dynamic particle size control technology. Under high-temperature and high-pressure dyeing conditions, the thermal motion of dye molecules intensifies, prompting the particles to break through the glass transition region on the fiber surface and gradually migrate to the amorphous region to complete adsorption and diffusion. During the preparation process, dispersant molecules form a double electric layer or steric hindrance effect on the surface of dye particles, inhibiting particle agglomeration and maintaining the stability of the system. At the same time, they act synergistically with fiber plasticizers to reduce the activation energy of dyeing. This dosage form reduces dust pollution compared to powdered dyes and has improved batch-to-batch reproducibility. It is suitable for continuous dyeing processes such as padding and jig dyeing. After dyeing, floating color on the fiber surface can be effectively removed through reduction cleaning to meet the requirements of color fastness standards.

[0003] During the formulation development of liquid disperse dyes, the multivariable non-linear relationship between dynamic particle size control and the synergistic effect of dispersants makes it difficult to model the dispersion stability of dye particles. The existing experience-driven mode is difficult to accurately quantify the coupling mechanism of the double electric layer potential distribution, steric hindrance effect on the surface of dye particles, and the viscoelasticity of the system. As a result, the optimization of formulation parameters relies on a large number of repetitive experimental iterations, lacking a digital design method based on molecular dynamics simulation and multi-physics field coupling algorithms, resulting in an extended formulation development cycle and low efficiency in adapting process parameters. This problem belongs to the technical field of computer-aided molecular design. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a liquid disperse dye for polyester fabrics and a method for using the same, which are used to solve the technical problems of difficult dispersion stability modeling caused by the multivariable non-linear relationship between dynamic particle size control and the synergistic effect of dispersants during the formulation development of existing liquid disperse dyes, as well as the long development cycle and low efficiency in adapting process parameters caused by the lack of a digital design method based on molecular dynamics simulation and multi-physics field coupling algorithms.

[0005] To solve the above technical problems, the specific technical solutions of the present invention are as follows: A liquid disperse dye for polyester fabrics and a method for using the same provided by the present invention include the following components in mass percentage: 40% to 60% of the main disperse dye, where the main disperse dye is an azo or anthraquinone disperse dye that has been ultra-finely pulverized to a sub-micron particle size; 15% to 25% of a dispersant system, where the dispersant system includes a non-ionic block copolymer dispersant and an anionic sulfonate dispersant; 5% to 10% of a fiber plasticizer, where the fiber plasticizer is a benzoate or phosphate compound; 3% to 8% of a stabilizer, where the stabilizer includes a rheology modifier and an antioxidant; 0.5% to 2% of a pH regulator for maintaining the system pH value at 5 to 6; The balance is deionized water; Among them, the ratio of the main disperse dye, the dispersant system, and the fiber plasticizer is generated through the following steps: Calculate the binding energy data of the surface energy of the dye particles and the adsorption configuration of the dispersant through a molecular dynamics simulation model to generate the dispersant compounding ratio parameters; Simulate the intermolecular force data between the fiber plasticizer and the amorphous region of polyester through the molecular dynamics simulation model to generate the plasticizer concentration optimization parameters; Based on the synergistic action threshold of the dispersant compounding ratio parameters and the plasticizer concentration optimization parameters, determine the mass percentage ratio of each component.

[0006] Furthermore, the usage method of the liquid disperse dye for polyester fabrics includes the following steps: Immerse the polyester fabric in a pretreatment solution containing a penetrant for wetting treatment, and collect the fiber wetness data through the sensor in the pretreatment solution to generate the pretreated fabric data; Input the pretreated fabric data into a multi-physics field coupling model, and the multi-physics field coupling model calculates the appropriate dyeing process parameters based on the fiber wetness data and the structural parameters of the polyester fabric. The dyeing process parameters include temperature gradient, pressure distribution, and liquor ratio; Perform the dyeing operation according to the dyeing process parameters. Among them, the temperature gradient data is generated in the gradient heating stage, the adsorption kinetic data is generated according to the dye adsorption rate in the constant temperature adsorption stage, and the pressure distribution data is generated in the dynamic pressure adjustment stage, and integrated into the dyeing process data; Input the dyeing process data into a molecular dynamics simulation system, and based on the binding energy data of the surface energy of the dye particles and the adsorption configuration of the dispersant, real-time correct the disperse dye ratio and process parameters; Perform reduction cleaning and post-treatment on the dyed fabric, and generate color fastness feedback data through a color fastness tester; Compare the color fastness feedback data with the preset standards in the formulation database, and update the formulation parameters in the formulation database through a machine learning algorithm to form a closed-loop optimization link.

[0007] Further, for the method of using the liquid disperse dye for polyester fabrics, input the pre-treated fabric data into a multi-physical field coupling model, and the multi-physical field coupling model calculates the appropriate dyeing process parameters based on the fiber moisture data and the structural parameters of the polyester fabric. The dyeing process parameters include temperature gradient, pressure distribution, and liquor ratio, and it includes: Based on the fiber surface characteristic parameters in the pre-treated fabric data, construct an adsorption configuration model of non-ionic dispersant molecules on the surface of dye particles to generate a steric hindrance effect parameter; Through the mesoscopic dissipative particle dynamics simulation, calculate the double-layer potential distribution data of anionic dispersants on the surface of dye particles to generate an electrostatic repulsion effect parameter; Input the steric hindrance effect parameter and the electrostatic repulsion effect parameter into a synergistic effect analysis model, and determine the compounding ratio of the dispersants according to the preset synergistic effect threshold.

[0008] Further, for the method of using the liquid disperse dye for polyester fabrics, input the steric hindrance effect parameter and the electrostatic repulsion effect parameter into a synergistic effect analysis model, and determine the compounding ratio of the dispersants according to the preset synergistic effect threshold, including: Based on the temperature gradient data in the dyeing process parameters, predict the diffusion coefficient of the plasticizer in the amorphous region of polyester through molecular dynamics simulation to generate plasticizer penetration efficiency data; Simulate the fiber swelling rate affected by the plasticizer concentration on the liquor ratio in the dyeing process parameters through a macroscopic hydrodynamics model to generate swelling rate optimization parameters; Input the plasticizer penetration efficiency data and the swelling rate optimization parameters into a dynamic optimization algorithm, and adjust the amount of fiber plasticizer added according to the preset penetration-swelling correlation rule.

[0009] Further, for the method of using the liquid disperse dye for polyester fabrics, perform a dyeing operation according to the dyeing process parameters, and the temperature gradient data generated in the gradient heating stage includes: In the initial stage, raise the temperature of the dye liquor from 50 °C to 130 °C - 135 °C at a rate of 1.5 °C / min - 2 °C / min, and generate temperature gradient data through a temperature sensor; In the constant temperature adsorption stage, adjust the holding time according to the dye adsorption rate monitored in real time by a spectral analyzer to generate adsorption kinetics data; Input the temperature gradient data and adsorption kinetics data into the multi-physics coupling model to predict the uniformity of dye distribution inside the fiber, and feedback the prediction result to the dynamic pressure regulation stage.

[0010] Furthermore, for the method of using the liquid disperse dye for polyester fabrics, the step of inputting the temperature gradient data and adsorption kinetics data into the multi-physics coupling model to predict the uniformity of dye distribution inside the fiber, and feedbacking the prediction result to the dynamic pressure regulation stage includes: Based on the temperature gradient data generated in the gradient heating stage, use a viscosity sensor to collect the dye liquor viscosity data in real time, and input it into the rheology model to calculate the viscoelastic modulus of the system; According to the mapping relationship between the viscoelastic modulus and the pressure distribution output by the multi-physics coupling model, adjust the roller pressure parameters of the pad dyeing process; Feedback the adjusted roller pressure parameters to the formulation database, and optimize the addition ratio of the rheological modifier for subsequent batches through machine learning algorithms.

[0011] Furthermore, for the method of using the liquid disperse dye for polyester fabrics, the step of performing reduction cleaning and post-treatment on the dyed fabric and generating color fastness feedback data by a color fastness tester includes: According to the unfixed dye concentration in the dyeing process data, use a high performance liquid chromatograph to detect the dye residue, and automatically configure a cleaning solution containing sodium dithionite and NaOH; Perform correlation analysis on the color fastness feedback data and the historical process data in the formulation database to generate a cleaning solution concentration adjustment instruction; Transmit the adjustment instruction to the penetrant addition module in the pretreatment stage to adjust the composition of the pretreatment liquid, forming a closed-loop process parameter optimization link.

[0012] Advantages of the present invention; The beneficial effects of the present invention are reflected in the following aspects: through a multi-scale modeling method combining molecular dynamics simulation and mesoscopic dissipative particle dynamics simulation, the synergistic effect between the adsorption configuration of the dispersant and the double-layer potential is quantified, and a non-linear correlation rule for dispersion stability and dynamic particle size control is established, breaking through the limitations of traditional empirical modeling; based on a multi-physics field coupling algorithm, fiber humidity, temperature gradient, and pressure distribution parameters are integrated to achieve cross-scale collaborative design of microscopic interface interactions and macroscopic process parameters, improving the matching efficiency of dyeing process parameters; a closed-loop feedback link from formulation optimization to process execution is constructed, and through real-time iterative correction of dyeing process data and color fastness feedback, the compounding ratio of the dispersant, the concentration of the plasticizer, and the addition amount of the rheological modifier are dynamically adjusted, significantly shortening the development cycle and improving dyeing uniformity; combined with digital design methods to replace repetitive experiments, reducing the dependence on manual experience in formulation development, reducing the modeling error rate of dispersion stability and the matching time of process parameters by more than 50%, and ultimately achieving the high efficiency and batch-to-batch consistency of the polyester fabric dyeing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the present invention, the accompanying drawings required for use in the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the drawings.

[0014] Figure 1 It is a flowchart of a method for using a liquid disperse dye for polyester fabrics provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention will be described in detail below with reference to the accompanying drawings. To better understand the objectives of the present invention, the present invention will be further described in detail below.

[0016] A liquid disperse dye for polyester fabrics and a method for using the same provided by the present invention include the following components in mass percentages: 40% - 60% of the main disperse dye, and the main disperse dye is an azo or anthraquinone disperse dye that has been ultrafinely pulverized to a submicron particle size; 15% - 25% of the dispersant system, and the dispersant system includes a nonionic block copolymer dispersant and an anionic sulfonate dispersant; 5% to 10% of a fiber plasticizer, where the fiber plasticizer is a benzoate or phosphate compound; 3% to 8% of a stabilizer, where the stabilizer includes a rheology modifier and an antioxidant; 0.5% to 2% of a pH regulator for maintaining the system pH value at 5 to 6; The balance is deionized water; Among them, the ratio of the main disperse dye, the dispersant system and the fiber plasticizer is generated through the following steps: Calculate the binding energy data of the surface energy of the dye particles and the adsorption configuration of the dispersant through a molecular dynamics simulation model to generate the compounding ratio parameters of the dispersant; Simulate the intermolecular force data between the fiber plasticizer and the amorphous region of polyester through the molecular dynamics simulation model to generate the optimized parameters of the plasticizer concentration; Based on the synergistic action threshold of the compounding ratio parameters of the dispersant and the optimized parameters of the plasticizer concentration, determine the mass percentage ratio of each component.

[0017] The liquid disperse dye for polyester fabrics provided by the present invention comprises the following components and its preparation method. The main disperse dye is composed of azo or anthraquinone disperse dyes and is processed to a submicron particle size through an ultrafine pulverization process to improve the dispersion uniformity of the dye particles in the liquid phase medium. The dispersant system consists of a nonionic block copolymer dispersant and an anionic sulfonate dispersant. The nonionic dispersant forms a steric hindrance by adsorbing on the surface of the dye particles through a hydrophobic chain, and the anionic dispersant inhibits particle aggregation through electrostatic repulsion. The two work together to maintain the suspension stability of the dye. The fiber plasticizer is selected from benzoate or phosphate compounds, and its molecular structure can penetrate into the amorphous region of polyester fibers, reduce the glass transition temperature of the fibers, expand the internal free volume, and promote the diffusion of dye molecules. The stabilizer includes a rheology modifier and an antioxidant. The rheology modifier forms a three-dimensional network structure through hydrogen bonding to adjust the thixotropy of the system to prevent sedimentation during storage, and the antioxidant inhibits the oxidative degradation of the dye during high-temperature dyeing. The pH regulator uses citric acid or sodium dihydrogen phosphate to control the system pH within the range of 5 - 6 to avoid hydrolysis of the dispersant and aggregation of dye particles. Deionized water is used as the liquid phase medium with a conductivity not exceeding 10 μS / cm to reduce the influence of ionic interference on the dispersion stability.

[0018] The ratio of the main disperse dye, dispersant system and fiber plasticizer is optimized by molecular dynamics simulation. The molecular dynamics simulation model first calculates the binding energy data of the surface energy of the dye particles and the adsorption configuration of the dispersant to generate the compounding ratio parameters of the dispersant; subsequently, it simulates the intermolecular force data between the fiber plasticizer and the amorphous region of polyester to generate the optimized concentration parameters of the plasticizer. The binding energy data reflects the adsorption strength and coverage efficiency of the dispersant on the surface of the dye particles, and the intermolecular force data characterizes the affinity and diffusion path between the plasticizer and the fiber. Based on the synergy threshold of the compounding ratio parameters of the dispersant and the optimized concentration parameters of the plasticizer, the mass percentage ratio of each component is determined. The synergy threshold is calculated by a multi-physical field coupling model, which integrates the interaction relationships of the steric hindrance effect of the dispersant, electrostatic repulsion and the penetration efficiency of the plasticizer to realize the systematic optimization of the formulation parameters.

[0019] Please refer to Figure 1 , specifically, the method for using the liquid disperse dye for polyester fabrics includes the following steps: Step S101, impregnate the polyester fabric in a pretreatment solution containing a penetrant for wetting treatment, collect fiber wetness data through a sensor in the pretreatment solution to generate pretreatment fabric data; Step S102, input the pretreatment fabric data into a multi-physical field coupling model, and the multi-physical field coupling model calculates the suitable dyeing process parameters based on the fiber wetness data and the structural parameters of the polyester fabric. The dyeing process parameters include temperature gradient, pressure distribution and liquor ratio; Step S103, perform the dyeing operation according to the dyeing process parameters. Among them, temperature gradient data is generated in the gradient heating stage, adsorption kinetic data is generated according to the dye adsorption rate in the constant temperature adsorption stage, and pressure distribution data is generated in the dynamic pressure adjustment stage, which is integrated into the dyeing process data; Step S104, input the dyeing process data into the molecular dynamics simulation system, and based on the binding energy data of the surface energy of the dye particles and the adsorption configuration of the dispersant, the ratio of the disperse dye and the process parameters are corrected in real time; Step S105, perform reduction cleaning and post-treatment on the dyed fabric, and generate color fastness feedback data through a color fastness tester; Step S106, compare the color fastness feedback data with the preset standards in the formula database, and update the ratio parameters in the formula database through a machine learning algorithm to form a closed-loop optimization link.

[0020] The method for using liquid disperse dyes for polyester fabrics provided by the present invention comprises the following steps. The polyester fabric is impregnated in a pretreatment liquid containing a penetrant. The sensor in the pretreatment liquid monitors the wetting state of the fiber surface in real time through capacitive or optical sensing technology to generate fiber wetness data. This data reflects the fiber porosity and surface tension characteristics and provides input for the calculation of subsequent process parameters. The fiber wetness data and the structural parameters of the polyester fabric are jointly input into a multi-physics field coupling model. The structural parameters include fiber linear density, yarn twist, and fabric density. The model calculates the dyeing process parameters by coupling thermodynamic equations and hydrodynamic equations and outputs a combination of temperature gradient, pressure distribution, and liquor pickup rate parameters.

[0021] The dyeing process parameters are used to guide the execution of the dyeing operation. In the gradient heating stage, a segmented temperature control strategy is adopted. In the initial stage, the temperature of the dye liquor is increased at a set rate, and the temperature sensor collects the temperature change curve in real time to generate temperature gradient data. In the constant temperature adsorption stage, the attenuation rate of the dye concentration in the dye liquor is monitored by an on-line spectral analyzer to generate dye adsorption kinetic data. In the dynamic pressure adjustment stage, the roller gap is adjusted according to the feedback of the pressure sensor of the padding equipment to generate dynamic pressure distribution data. After the above data is integrated, a dyeing process data set is formed, recording the spatio-temporal variation laws of temperature, adsorption rate, and pressure parameters.

[0022] The dyeing process data is input into a molecular dynamics simulation system. The system reconstructs the dye-dispersant-fiber interface interaction model based on the binding energy data of the surface energy of the dye particles and the adsorption configuration of the dispersant. The binding energy data is obtained by calculating the intermolecular potential energy function and reflects the coverage and adsorption stability of the dispersant on the surface of the dye particles. The system adjusts the compounding ratio of the dispersant and the concentration of the fiber plasticizer in real time according to the output of the model and generates a formulation parameter adjustment instruction adapted to the current process conditions.

[0023] After dyeing, the fabric is treated with a reduction cleaning liquid. The configuration of the cleaning liquid is automatically adjusted according to the concentration of unfixed dyes in the dyeing process data. The residual amount of dyes on the fiber surface is detected by a high performance liquid chromatograph. Combining the dry / wet rubbing fastness and soaping fastness data measured by a color fastness tester, a color fastness feedback data set is generated. The color fastness feedback data is associated with the historical process data in the formulation database. The machine learning algorithm identifies the mapping law between process parameters and dyeing performance through feature extraction and pattern matching and updates the threshold of the formulation parameters in the database.

[0024] The updated formulation parameters are fed back to the pretreatment stage to adjust the penetrant concentration and pretreatment time, forming a closed-loop optimization link across process stages. The adjustment of the pretreatment liquid composition is executed by a penetrant addition module. The module adjusts the dosage of the non-ionic surfactant according to the instruction to optimize the fiber wetting efficiency. The closed-loop link realizes the adaptive optimization of the dyeing process through the iterative cycle of real-time data collection, model calculation, and parameter correction.

[0025] Specifically, for the method of using a liquid disperse dye for polyester fabrics, when inputting the pre-treated fabric data into a multi-physical field coupling model, the multi-physical field coupling model calculates the adapted dyeing process parameters based on the fiber moisture data and the structural parameters of the polyester fabric. The dyeing process parameters include temperature gradient, pressure distribution, and liquor ratio, and they are as follows: Based on the fiber surface characteristic parameters in the pre-treated fabric data, an adsorption configuration model of non-ionic dispersant molecules on the surface of dye particles is constructed to generate a steric hindrance effect parameter; Through the mesoscopic dissipative particle dynamics simulation, the double-layer potential distribution data of anionic dispersants on the surface of dye particles is calculated to generate an electrostatic repulsion effect parameter; The steric hindrance effect parameter and the electrostatic repulsion effect parameter are input into a synergistic effect analysis model, and the compounding ratio of the dispersants is determined according to a preset synergistic effect threshold.

[0026] In the method of using a liquid disperse dye for polyester fabrics provided by the present invention, the specific steps of inputting the pre-treated fabric data into the multi-physical field coupling model are as follows. The fiber surface characteristic parameters in the pre-treated fabric data include fiber surface roughness, porosity, and chemical functional group distribution, which are obtained by characterization with an atomic force microscope or a scanning electron microscope. Based on the fiber surface characteristic parameters, an adsorption configuration model of non-ionic dispersant molecules on the surface of dye particles is constructed, and the van der Waals force between the hydrophobic segments of the dispersant and the surface of the dye particles is simulated using a molecular docking algorithm to generate a steric hindrance effect parameter. The steric hindrance effect parameter characterizes the thickness and coverage density of the physical barrier formed by the dispersant molecules on the particle surface, reflecting the ability to inhibit particle aggregation.

[0027] The mesoscopic dissipative particle dynamics simulation is based on the fiber moisture and dye particle size distribution data in the pre-treated fabric data to establish a dye-dispersant-water three-phase system model. During the simulation, the sulfonate ions of the anionic dispersant form a double-layer structure on the surface of the dye particles, and the double-layer potential distribution data is calculated by solving the Poisson-Boltzmann equation. The potential distribution data reflects the variation law of the zeta potential on the particle surface with the pH value and ionic strength, generating an electrostatic repulsion effect parameter to quantify the intensity of the electrostatic repulsion between particles.

[0028] Spatial steric effect parameter and electrostatic repulsion parameter input synergistic effect analysis model. The model integrates the contribution degrees of the two parameters through a weighted fusion algorithm. The preset synergistic effect threshold is calibrated by the stability test data in the historical process database. When the weighted sum of the spatial steric effect parameter and the electrostatic repulsion parameter reaches the threshold, it is determined that the blending ratio of the dispersant meets the suspension stability requirements. The model outputs the mass ratio range of the non-ionic and anionic dispersants to guide the optimization of the blending ratio of the dispersant system. The optimized blending ratio parameter is fed back to the multi-physical field coupling model for the calculation iteration of the dyeing process parameters.

[0029] Specifically, for the method of using the liquid disperse dye for polyester fabrics, inputting the spatial steric effect parameter and the electrostatic repulsion parameter into the synergistic effect analysis model and determining the blending ratio of the dispersant according to the preset synergistic effect threshold includes: Based on the temperature gradient data in the dyeing process parameters, predicting the diffusion coefficient of the plasticizer in the amorphous region of polyester by molecular dynamics simulation to generate plasticizer penetration efficiency data; Simulating the fiber swelling rate affected by the plasticizer concentration on the liquor pickup rate in the dyeing process parameters through a macroscopic hydrodynamics model to generate swelling rate optimization parameters; Inputting the plasticizer penetration efficiency data and the swelling rate optimization parameters into a dynamic optimization algorithm to adjust the amount of fiber plasticizer added according to the preset penetration-swelling correlation rule.

[0030] In the method of using the liquid disperse dye for polyester fabrics provided by the present invention, the specific steps of inputting the spatial steric effect parameter and the electrostatic repulsion parameter into the synergistic effect analysis model are as follows. The temperature gradient data in the dyeing process parameters are collected in real time by a temperature sensor, reflecting the temperature change rate and distribution uniformity of the dye liquor in the heating stage. Based on the temperature gradient data, a molecular chain conformation model of the amorphous region of polyester is constructed by molecular dynamics simulation to simulate the diffusion path of the plasticizer molecules inside the fiber and generate plasticizer penetration efficiency data. The penetration efficiency data characterize the ability of the plasticizer to lower the glass transition temperature of the fiber and affect the migration rate of the dye molecules into the fiber interior.

[0031] The macroscopic hydrodynamics model establishes a flow model of the dye liquor in the fiber pores according to the liquor pickup rate data in the dyeing process parameters. The liquor pickup rate data are obtained by calculating the mass change rate after the fabric is padded, reflecting the amount of dye liquor attached to the fiber surface and the penetration depth. The model simulates the swelling and deformation behavior of the fiber at different plasticizer concentrations and outputs swelling rate optimization parameters. The swelling rate optimization parameters quantify the volume expansion degree of the amorphous region of the fiber and are directly related to the dye adsorption capacity and dyeing uniformity.

[0032] The plasticizer penetration efficiency data and swelling rate optimization parameter input dynamic optimization algorithm fuses data according to the preset penetration-swelling correlation rule. The penetration-swelling correlation rule is established through historical experimental data and defines the non-linear mapping relationship between the penetration efficiency and the swelling rate. When the ratio of the plasticizer penetration efficiency data to the swelling rate optimization parameter exceeds the preset threshold, the algorithm generates a plasticizer concentration adjustment instruction to dynamically increase or decrease the addition amount of the fiber plasticizer. The adjusted plasticizer concentration parameter is fed back to the dyeing process parameter calculation module to optimize the temperature gradient and pressure distribution parameters in the subsequent dyeing stage. Through the multi-parameter linkage adjustment, the coordinated adaptation of the plasticizer concentration and the compounding ratio of the dispersant is realized, and the stability and reproducibility of the dyeing process are improved.

[0033] Specifically, for the method of using the liquid disperse dye for polyester fabrics, the dyeing operation is performed according to the dyeing process parameters, and the temperature gradient data generated in the gradient heating stage includes: In the initial stage, the temperature of the dye liquor is raised from 50 °C to 130 °C - 135 °C at a rate of 1.5 °C / min - 2 °C / min, and the temperature gradient data is generated by a temperature sensor. In the constant temperature adsorption stage, the holding time is adjusted according to the dye adsorption rate monitored in real time by a spectral analyzer to generate adsorption kinetic data. The temperature gradient data and the adsorption kinetic data are input into the multi-physical field coupling model to predict the uniformity of the dye distribution inside the fiber, and the prediction result is fed back to the dynamic pressure regulation stage.

[0034] In the method of using the liquid disperse dye for polyester fabrics provided by the present invention, the gradient heating process in the execution stage of the dyeing process parameters is as follows. In the initial stage, the temperature of the dye liquor is raised at a set rate through a temperature control system, and the temperature sensor uses a distributed thermocouple array or an infrared temperature measurement module to collect the temperature changes in different areas of the dye vat in real time to generate temperature gradient data. The temperature gradient data records the heating rate, the temperature fluctuation range and the heat distribution uniformity index, providing a basis for the subsequent process parameter optimization. The thermocouple array is arranged along the axial and radial directions of the dye vat to capture the spatial differences of the temperature field, and the infrared temperature measurement module avoids the influence of the dye liquor disturbance on the data accuracy through non-contact measurement.

[0035] In the constant temperature adsorption stage, the on-line spectral analysis technology is adopted, and the ultraviolet-visible spectrophotometer monitors the attenuation trend of the dye concentration in the dye liquor in real time. The spectrophotometer continuously samples through a flow cell and calculates the dye adsorption rate based on the Lambert-Beer law to generate adsorption kinetic data. The adsorption kinetic data reflects the migration efficiency of the dye molecules from the liquid phase to the fiber surface and constitutes the core control variables of the dyeing process together with the temperature gradient data. The system dynamically adjusts the holding time according to the change of the adsorption rate, and terminates the constant temperature stage when the adsorption rate is lower than the preset threshold to avoid color difference caused by excessive consumption of the dye.

[0036] The temperature gradient data and adsorption kinetics data are input into the multi - physical field coupling model. The model simulates the temperature distribution inside the fiber through the heat conduction equation and calculates the penetration depth of dye molecules in the amorphous region of the fiber in combination with Fick's diffusion law. The model integrates the coupling effect of the temperature field and the concentration field to predict the uniformity index of the dye distribution across the fiber cross - section. The prediction results are visually displayed in the form of a numerical cloud map showing the concentration gradient of the dye inside the fiber. When the uniformity index is lower than the target value, a warning signal is generated. The warning signal triggers parameter correction in the dynamic pressure regulation stage, compensating for local penetration deficiencies by adjusting the pressure distribution of the padding rollers to optimize the uniformity of the dye distribution. The corrected pressure parameters are synchronously fed back to the formulation database, providing an iterative benchmark for setting process parameters in subsequent batches.

[0037] Specifically, for the method of using the liquid disperse dye for polyester fabrics, the process of inputting the temperature gradient data and adsorption kinetics data into the multi - physical field coupling model to predict the uniformity of the dye distribution inside the fiber and feeding back the prediction results to the dynamic pressure regulation stage includes: Based on the temperature gradient data generated during the gradient heating stage, the viscosity data of the dye liquor is collected in real - time through a viscosity sensor and input into a rheology model to calculate the visco - elastic modulus of the system. According to the mapping relationship between the visco - elastic modulus and the pressure distribution output by the multi - physical field coupling model, the roller pressure parameters of the padding process are adjusted. The adjusted roller pressure parameters are fed back to the formulation database, and the addition ratio of the rheological modifier for subsequent batches is optimized through machine learning algorithms.

[0038] In the method of using the liquid disperse dye for polyester fabrics provided by the present invention, the processing and feedback adjustment process of the temperature gradient data and adsorption kinetics data are as follows. Based on the temperature gradient data generated during the gradient heating stage, the viscosity changes of the dye liquor in different temperature ranges are collected in real - time through a rotary viscosity sensor. The viscosity sensor generates an original viscosity data set based on the linear relationship between shear stress and shear rate. The dye liquor viscosity data is input into a rheology model, and the model analyzes the visco - elastic modulus of the system through the generalized Maxwell equation. The visco - elastic modulus includes the storage modulus and the loss modulus, characterizing the rheological behavior stability of the dye liquor under dynamic shear conditions.

[0039] The mapping relationship between the pressure distribution output by the multi - physical field coupling model is generated through finite element analysis. This relationship reflects the non - linear correlation between the pressure in the roller gap and the fiber penetration depth in the padding process. According to the mapping relationship between the visco - elastic modulus and the pressure distribution, the roller pressure parameters of the padding process are adjusted. Specifically, the roller gap is dynamically adjusted by a servo motor to make the local pressure distribution adapt to the rheological characteristics of the dye liquor. The pressure parameter adjustment is based on the threshold range of the visco - elastic modulus. When the storage modulus exceeds the preset range, the roller gap is increased to reduce the shear rate and avoid dye accumulation on the fiber surface.

[0040] The adjusted roller pressure parameters are fed back to the formulation database, which integrates the addition ratio of rheological modifiers and viscoelastic modulus data in historical batches through time series analysis. The machine learning algorithm uses a random forest regression model to extract the characteristic correlation rules between the concentration of rheological modifiers and the viscoelastic modulus, and generates optimized addition ratio parameters. The optimized parameters are used to update the pretreatment process of subsequent batches. By adjusting the dosage of non-ionic rheological modifiers, the viscoelastic modulus of the dye liquor is stabilized within the target range. The parameter optimization results are synchronously fed back to the multi-physical field coupling model, forming a closed-loop control link from process execution to formulation adjustment, and realizing the iterative improvement of dyeing uniformity and process stability.

[0041] Specifically, for the method of using a liquid disperse dye for polyester fabrics, the dyed fabric is subjected to reduction cleaning and post-treatment, and the color fastness feedback data generated by a color fastness tester includes: According to the concentration of unfixed dyes in the dyeing process data, the dye residue amount is detected by a high-performance liquid chromatograph, and a cleaning solution containing sodium dithionite and NaOH is automatically prepared; The color fastness feedback data is correlated with the historical process data in the formulation database to generate an adjustment instruction for the cleaning solution concentration; The adjustment instruction is transmitted to the penetrant addition module in the pretreatment stage to adjust the composition of the pretreatment solution, forming a closed-loop process parameter optimization link.

[0042] In the method of using a liquid disperse dye for polyester fabrics provided by the present invention, the reduction cleaning and post-treatment steps after dyeing are as follows. The concentration of unfixed dyes in the dyeing process data is detected by a high-performance liquid chromatograph. When detecting, a C18 reversed-phase chromatographic column is used to separate the dye liquor residues. The mobile phase is a methanol-water mixed solvent, and a UV detector quantitatively analyzes the characteristic peak area of the dye at a specific wavelength to generate dye residue amount data. The cleaning solution preparation system automatically adjusts the dosing ratio of sodium dithionite and NaOH according to the residue amount data. The concentration of sodium dithionite is positively correlated with the concentration of unfixed dyes, and the concentration of NaOH is dynamically adjusted based on the pH value of the dye liquor to maintain the reduction potential stability of the cleaning system.

[0043] The color fastness feedback data is obtained by a standard color fastness tester, which simulates dry rubbing, wet rubbing and soaping conditions according to preset quality standards to quantify the degree of dye shedding on the fabric surface. The color fastness data is correlated with the historical process data in the formulation database. The database stores the process parameters, formulation ratios and color fastness results of previous dyeings. The correlation analysis uses a clustering algorithm to identify the potential correlations between process parameters and color fastness indicators, extracts key influencing factors through a decision tree model, and generates an adjustment instruction for the cleaning solution concentration. The adjustment instruction includes a correction value for the concentration of sodium dithionite and a pH adjustment range for the pretreatment solution.

[0044] The instruction for adjusting the cleaning solution concentration is transmitted to the penetrant addition module in the pretreatment stage, and the module adjusts the injection amount of non-ionic penetrant through a metering pump. The adjustment of the penetrant concentration is based on the pH adjustment range in the instruction. When the pH value is lower than the target range, the proportion of anionic penetrant is increased to improve the wettability of the fiber surface. The composition of the adjusted pretreatment solution is monitored by a real-time conductivity sensor, and the data is fed back to the dyeing process parameter calculation module to form a closed-loop optimization link from post-treatment to pretreatment. The closed-loop link iteratively corrects the penetrant concentration and cleaning solution parameters, gradually converges to the optimal process combination, and realizes the consistency control between dyeing batches.

[0045] The present invention solves the problems of the prior art through the following technical solutions. First, a multi-scale modeling method combining molecular dynamics simulation and mesoscopic dissipative particle dynamics simulation is adopted to quantify the dynamic action mechanism of the dispersant-dye-fiber system. The molecular dynamics simulation constructs a binding energy model of the surface energy of dye particles and the adsorption configuration of the dispersant, and analyzes the steric hindrance effect of non-ionic dispersants; the mesoscopic simulation calculates the double-layer potential distribution of anionic dispersants to quantify the electrostatic repulsion. The two simulation data are input into a synergistic action analysis model, and the compounding ratio of the dispersant is optimized through a preset threshold to establish the correlation rules between dispersion stability and dynamic particle size control, breaking through the bottleneck of multi-variable non-linear modeling.

[0046] Secondly, the cross-scale process parameter collaborative design is realized based on the multi-physics field coupling algorithm. The multi-physics field coupling model integrates parameters such as fiber moisture, temperature gradient, and pressure distribution, and calculates the dyeing process parameters through the coupling of thermodynamic equations and fluid mechanics equations. The dynamic optimization algorithm correlates the plasticizer penetration efficiency data output by the molecular dynamics simulation with the fiber swelling rate parameter of the macroscopic fluid mechanics model to generate the plasticizer concentration threshold adapted to different fabric structures. The cross-scale data fusion eliminates the disconnection between the microscopic interface action and the macroscopic process parameters in the experience-driven mode, and improves the parameter adaptation efficiency.

[0047] Finally, a closed-loop feedback link from formula optimization to process execution is constructed. The data during the dyeing process is input into the molecular dynamics simulation system in real time. Combining the color fastness feedback data and the historical process database, the compounding ratio of the dispersant, the concentration of the plasticizer, and the addition amount of the rheological modifier are iteratively corrected through machine learning algorithms. The closed-loop link realizes the adaptive matching of process parameters and formula components through the linkage control of dynamic pressure regulation, optimization of the pretreatment solution composition, and adjustment of the cleaning solution parameters. The digital design method replaces the traditional trial-and-error experiment, shortening the dispersion stability modeling and process development cycle.

[0048] The specific implementation manner of the present invention is as follows. In the dyeing process of polyester fabrics, first, a liquid disperse dye is prepared: An azo or anthraquinone disperse dye is processed by a high-pressure homogenizer to a submicron particle size (100 - 500 nm), and is compounded with a nonionic block copolymer dispersant (such as Pluronic F127, with a proportion of 8 - 12%) and an anionic lignosulfonate dispersant (with a proportion of 7 - 13%) to form a dispersant system. The binding energy of a benzoate plasticizer (such as diethyl phthalate, with a proportion of 5 - 10%) with the amorphous region of polyester is predicted by molecular dynamics simulation, and the concentration is optimized to lower the glass transition temperature of the fiber. A rheology modifier (such as a polyurethane associative thickener, with a proportion of 3 - 5%) and antioxidant BHT (0.1 - 0.5%) are added to the system, the pH is adjusted to 5 - 6, and then it is dispersed in deionized water to form a stable suspension.

[0049] During the dyeing operation, the polyester fabric is immersed in a pretreatment solution containing fatty alcohol polyoxyethylene ether (treated at 60 - 70 °C for 10 - 15 minutes), and the fiber wetness data is collected by a capacitive sensor. Based on the wetness data and the fabric density parameters, a multi-physical field coupling model calculates the dyeing process parameters: the initial heating rate is 1.5 - 2 °C / min (from 50 °C to 130 - 135 °C), the padding liquor ratio is 70 - 80%, and the dynamic pressure regulation range is 0.2 - 0.6 MPa. The steric hindrance effect (adsorption layer thickness 2 - 5 nm) of the nonionic dispersant and the zeta potential (-30 to -50 mV) of the anion dispersant are simulated by dissipative particle dynamics at the mesoscopic scale, and the mass ratio (1:0.8 - 1.2) of the two is determined through a synergistic effect analysis model. The diffusion coefficient of the plasticizer (1×10⁻ 10 to 5×10⁻ 10 m² / s) and the correlation with the fiber swelling rate (15 - 25%) are simulated by molecular dynamics, and a dynamic optimization algorithm outputs an instruction to adjust the plasticizer concentration.

[0050] After dyeing, the concentration of unfixed dye is detected by high-performance liquid chromatography (≤0.5 mg / L), and a reduction cleaning solution containing sodium dithionite (2 - 3 g / L) and NaOH (1 - 2 g / L) is automatically prepared. The color fastness test data (dry rubbing ≥ grade 4, soaping ≥ grade 4 - 5) is correlated with the data in the historical process library, and a machine learning algorithm extracts the mapping rule between the concentration of the rheology modifier (0.1 - 0.3%) and the viscoelastic modulus (storage modulus 100 - 300 Pa), and feeds it back to the pretreatment liquid penetrant module to adjust the dosing amount (1 - 3%). The closed-loop optimization link iterates through multi-batch data, reducing the compounding error rate of the dispersant and shortening the process development cycle.

Claims

1. A liquid disperse dye for polyester fabrics, characterized in that, Comprising components with the following mass percentages: 40% - 60% of the main disperse dye, where the main disperse dye is an azo or anthraquinone disperse dye ultra - micro - pulverized to sub - micron particle size; 15% - 25% of the dispersant system, where the dispersant system contains a non - ionic block copolymer dispersant and an anionic sulfonate dispersant; 5% - 10% of the fiber plasticizer, where the fiber plasticizer is a benzoate or phosphate compound; 3% - 8% of the stabilizer, where the stabilizer includes a rheology modifier and an antioxidant; 0.5% - 2% of the pH regulator, used to maintain the system pH value at 5 - 6; The balance is deionized water; Among them, the ratio of the main disperse dye, the dispersant system and the fiber plasticizer is generated through the following steps: Calculating the binding energy data of the surface energy of the dye particles and the adsorption configuration of the dispersant through a molecular dynamics simulation model to generate the dispersant compounding ratio parameters; Simulating the intermolecular force data between the fiber plasticizer and the amorphous region of polyester through the molecular dynamics simulation model to generate the plasticizer concentration optimization parameters; Determining the mass percentage ratio of each component based on the synergistic action threshold of the dispersant compounding ratio parameters and the plasticizer concentration optimization parameters.

2. The method for using a liquid disperse dye for polyester fabrics according to claim 1, characterized in that, Including the following steps: Immersing the polyester fabric in a pretreatment solution containing a penetrant for wetting treatment, collecting fiber wetness data through a sensor in the pretreatment solution to generate pretreatment fabric data; Inputting the pretreatment fabric data into a multi - physical - field coupling model, where the multi - physical - field coupling model calculates the appropriate dyeing process parameters based on the fiber wetness data and the structural parameters of the polyester fabric, and the dyeing process parameters include temperature gradient, pressure distribution and liquor ratio; Performing a dyeing operation according to the dyeing process parameters, where temperature gradient data is generated in the gradient heating stage, adsorption kinetic data is generated according to the dye adsorption rate in the constant - temperature adsorption stage, pressure distribution data is generated in the dynamic pressure regulation stage, and integrated into the dyeing process data; Inputting the dyeing process data into a molecular dynamics simulation system, and based on the binding energy data of the surface energy of the dye particles and the adsorption configuration of the dispersant, the disperse dye ratio and process parameters are corrected in real time; Performing reduction cleaning and post - treatment on the dyed fabric, and generating color fastness feedback data through a color fastness tester; Comparing the color fastness feedback data with the preset standards in the formula database, and updating the ratio parameters in the formula database through a machine learning algorithm to form a closed - loop optimization link.

3. The method for using a liquid disperse dye for polyester fabrics according to claim 2, characterized in that, The step of inputting the pretreatment fabric data into a multi - physical - field coupling model, where the multi - physical - field coupling model calculates the appropriate dyeing process parameters based on the fiber wetness data and the structural parameters of the polyester fabric, and the dyeing process parameters include temperature gradient, pressure distribution and liquor ratio includes: Based on the fiber surface characteristic parameters in the pretreatment fabric data, constructing an adsorption configuration model of non - ionic dispersant molecules on the surface of dye particles to generate the steric hindrance effect parameters; Calculating the double - layer potential distribution data of anionic dispersants on the surface of dye particles through the mesoscopic dissipative particle dynamics simulation to generate the electrostatic repulsion effect parameters; Input the steric hindrance effect parameter and the electrostatic repulsion parameter into the synergy analysis model, and determine the compounding ratio of the dispersant according to the preset synergy threshold.

4. The method for using a liquid disperse dye for polyester fabrics according to claim 3, characterized in that, The step of inputting the steric hindrance effect parameter and the electrostatic repulsion parameter into the synergy analysis model and determining the compounding ratio of the dispersant according to the preset synergy threshold includes: Based on the temperature gradient data in the dyeing process parameters, predict the diffusion coefficient of the plasticizer in the amorphous region of polyester by molecular dynamics simulation, and generate plasticizer penetration efficiency data; Simulate the fiber swelling rate affected by the plasticizer concentration on the liquor uptake rate in the dyeing process parameters through a macroscopic hydrodynamics model, and generate swelling rate optimization parameters; Input the plasticizer penetration efficiency data and the swelling rate optimization parameters into a dynamic optimization algorithm, and adjust the plasticizer addition amount of the fiber according to the preset penetration-swelling correlation rule.

5. The method for using a liquid disperse dye for polyester fabrics according to claim 2, wherein Perform the dyeing operation according to the dyeing process parameters, wherein generating the temperature gradient data in the gradient heating stage includes: In the initial stage, raise the temperature of the dye liquor from 50 °C to 130 °C - 135 °C at a rate of 1.5 °C / min - 2 °C / min, and generate temperature gradient data through a temperature sensor; In the constant temperature adsorption stage, adjust the holding time according to the dye adsorption rate monitored in real time by a spectral analyzer, and generate adsorption kinetics data; Input the temperature gradient data and the adsorption kinetics data into the multi-physical field coupling model, predict the uniformity of the dye distribution inside the fiber, and feedback the prediction result to the dynamic pressure regulation stage.

6. The method for using a liquid disperse dye for polyester fabrics according to claim 5, characterized in that, The step of inputting the temperature gradient data and the adsorption kinetics data into the multi-physical field coupling model, predicting the uniformity of the dye distribution inside the fiber, and feedbacking the prediction result to the dynamic pressure regulation stage includes: Based on the temperature gradient data generated in the gradient heating stage, collect the dye liquor viscosity data in real time through a viscosity sensor, and input it into a rheology model to calculate the viscoelastic modulus of the system; Adjust the roller pressure parameter of the pad dyeing process according to the mapping relationship between the viscoelastic modulus and the pressure distribution output by the multi-physical field coupling model; Feedback the adjusted roller pressure parameter to the formula database, and optimize the addition ratio of the rheological modifier for subsequent batches through a machine learning algorithm.

7. The method for using a liquid disperse dye for polyester fabrics according to claim 2, characterized in that, Perform reduction cleaning and post-treatment on the dyed fabric, and generate color fastness feedback data through a color fastness tester, including: According to the concentration of unfixed dye in the dyeing process data, detect the dye residue amount by a high performance liquid chromatograph, and automatically prepare a cleaning solution containing sodium dithionite and NaOH; Perform correlation analysis on the color fastness feedback data and the historical process data in the formula database to generate a cleaning solution concentration adjustment instruction; Transmit the adjustment instruction to the penetrant addition module in the pretreatment stage to adjust the composition of the pretreatment solution, and form a closed-loop process parameter optimization link.

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