Liquid disperse dye suitable for high-temperature and high-pressure dyeing and preparation method thereof

Abstract

The invention relates to the technical field of dye preparation data processing, in particular to a liquid disperse dye suitable for high-temperature and high-pressure dyeing and a preparation method thereof.The dye comprises a composite dye matrix, a dispersion stabilizing system and a solvent medium, disperse black dye is formed by compounding disperse purple 93, disperse orange 288 and disperse blue 291 in the ratio of 1, and the disperse black dye is a liquid disperse dye suitable for high-temperature and high-pressure dyeing. And a nano-scale dispersion system is constructed by combining the synergistic effect of hexadecylamine / octadecylamine polyoxyethylene ether and lauryl amine polyoxyethylene ether. According to the preparation method, on the basis of a dynamic behavior prediction model, micelle formation dynamic data in a premixing stage, viscosity and particle size associated data in a shearing stage and thixotropic index change data in a homogenizing stage are integrated, and a surfactant proportion optimization instruction is generated; according to the invention, the stability of the dispersoid structure is controlled, the dyeing uniformity and color fastness are improved, and the wastewater COD discharge load and the production cost are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of dye preparation data processing, in particular to a liquid disperse dye suitable for high-temperature and high-pressure dyeing and a preparation method thereof. Background Art

[0002] Liquid disperse dyes used in high-temperature, high-pressure dyeing processes are aqueous systems specifically designed for dyeing synthetic fibers. They utilize physical and chemical methods to uniformly disperse high-concentration nonionic dyes in a liquid medium, forming a colloidal system with excellent dispersion stability. In a closed dyeing environment, when the temperature rises above the fiber's glass transition point, the thermal motion of the dye molecules overcomes energy barriers, gradually diffusing and penetrating into the fiber's amorphous regions. Pressure conditions facilitate the transfer of the dye bath into the fiber. This system typically contains specific dispersants and chelating agents to maintain the nanoscale dispersion of the dye particles and inhibit high-temperature hydrolysis. Furthermore, thermodynamic stability control is employed to achieve uniform dyeing and reproducibility. These dyes are suitable for exhaust dyeing of thermoplastic fibers such as polyester and polyamide, improving dyeing efficiency while reducing the dust pollution and wastewater discharge burden associated with existing powder dye processing.

[0003] In the preparation process of high-temperature and high-pressure dyeing liquid disperse dyes, existing data processing methods have limitations in the parameter coupling analysis of multi-source heterogeneous systems. It is difficult to establish a dynamic behavior prediction model of the dye formula system through efficient correlation between experimental data and production parameters. As a result, the synergistic mechanism of dispersants and chelating agents cannot be accurately analyzed in a data-driven manner, making the process optimization process still rely on the empirical trial and error model, which restricts the synergistic improvement efficiency of the formula system stability and process reproducibility. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a liquid disperse dye suitable for high-temperature and high-pressure dyeing and a preparation method thereof, which is used to solve the technical problems in the preparation process of the existing high-temperature and high-pressure dye liquid disperse dye, such as the difficulty in analyzing the synergistic mechanism of dispersants and chelating agents due to insufficient multi-source heterogeneous parameter coupling analysis capabilities, and the difficulty in collaboratively optimizing formula stability and process reproducibility due to the lack of a dynamic behavior prediction model.

[0005] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows: The liquid disperse dye suitable for high temperature and high pressure dyeing provided by the present invention comprises the following components: Composite dye matrix, disperse black dye is formed by mixing disperse violet 93, disperse orange 288 and disperse blue 291:1, accounting for 47.5-48.5% of the total mass of liquid disperse dye; A dispersion stabilization system comprising hexadecylamine polyoxyethylene ether or octadecylamine polyoxyethylene ether and laurylamine polyoxyethylene ether, with a mass ratio of 0.3:0.4; The solvent medium is deionized water, which accounts for 51.0-52.0% of the total mass of the liquid disperse dye, and a chelating agent and a pH buffer are added.

[0006] Furthermore, the method for preparing the liquid disperse dye suitable for high temperature and high pressure dyeing described in the present invention comprises the following steps: After heating deionized water to a preset temperature, hexadecylamine polyoxyethylene ether or octadecylamine polyoxyethylene ether and laurylamine polyoxyethylene ether are sequentially added, and a uniform micellar solution is formed by stirring; Adding disperse black dye to the micellar solution in batches, and treating the solution using a high-speed shear emulsification device until the viscosity and particle size distribution of the system reach a preset threshold value to obtain a dispersion; The obtained dispersion is transferred to a high-pressure homogenization device for circulation treatment, and the temperature gradient control is simultaneously combined to eliminate the local aggregation energy barrier; Based on multi-source coupling analysis of preset mixing temperature, shear rate, homogenization pressure, and cooling rate data, a dynamic behavior prediction model was constructed, including micelle formation kinetic parameters, particle size distribution correlation factors, and thixotropic index thresholds. The dynamic behavior prediction model was then used to reversely optimize the surfactant ratio and process parameter combination. According to the surfactant ratio optimization instructions generated by the dynamic behavior prediction model, the ratio of hexadecylamine polyoxyethylene ether or octadecylamine polyoxyethylene ether to laurylamine polyoxyethylene ether is adjusted to 0.3:0.4 w / w, and the absolute value of the zeta potential of the dye particles under high temperature and high pressure conditions is increased to ≥35 mV; Based on the prediction of the effects of storage temperature and time on the dispersion state of the dye, a segmented cooling process was used to encapsulate the finished product to maintain the stability of the particle size distribution.

[0007] Furthermore, in the preparation method of liquid disperse dyes suitable for high-temperature and high-pressure dyeing described in the present invention, the rate and time of the high-speed shear emulsification treatment are dynamically adjusted based on the initial rheological properties of the micellar solution, wherein the initial rheological properties include the viscosity and shear rate curve and the thixotropic recovery rate; during the treatment process, the system viscosity data is collected in real time and fed back to the dynamic behavior prediction model, and the pressure and cycle number of the subsequent high-pressure homogenization stage are adjusted according to the high-pressure homogenization pressure correction coefficient and cycle number threshold output by the dynamic behavior prediction model.

[0008] Furthermore, in the method for preparing liquid disperse dyes suitable for high-temperature and high-pressure dyeing described herein, the pressure range and the number of cycles of the high-pressure homogenization treatment are determined by the D50 particle size data of the dispersion after high-speed shearing and the thixotropic index threshold output by the dynamic behavior prediction model; After each homogenization cycle, the slope parameters of the temperature gradient control are updated according to the real-time thixotropic index and the temperature gradient control slope target value calculated by the model.

[0009] Furthermore, in the preparation method of liquid disperse dyes suitable for high-temperature and high-pressure dyeing described in the present invention, the dynamic behavior prediction model integrates the micelle formation kinetics data of the preset mixing stage, the viscosity and particle size correlation data of the shear stage, and the thixotropic index change data of the homogenization stage to generate surfactant ratio optimization instructions; at the same time, based on the absolute value of the Zeta potential and the thixotropic index change rate after homogenization treatment, the pre-cooling rate and equilibrium temperature of the segmented cooling process are calculated through the model, and the cooling parameters are adjusted in a linked manner.

[0010] Furthermore, in the preparation method of liquid disperse dyes suitable for high-temperature and high-pressure dyeing described in the present invention, the temperature gradient of the pre-cooling stage and the duration of the equilibrium stage of the segmented cooling process are determined by calculating the storage stability attenuation curve in the dynamic behavior prediction model, and the pre-cooling gradient and equilibrium time are dynamically corrected based on the absolute value monitoring results of the Zeta potential after high-pressure homogenization treatment and the target attenuation rate threshold output by the model.

[0011] Furthermore, in the preparation method of liquid disperse dyes suitable for high-temperature and high-pressure dyeing described in the present invention, the timing and amount of adding the chelating agent and pH buffer are collaboratively determined by the dynamic behavior prediction model based on the preset initial conductivity of the mixed liquid, the pH fluctuation data in the shear stage, and the ionic strength detection results after homogenization treatment, to generate a chelating agent addition amount instruction and a pH buffer window range, and are executed synchronously with the surfactant ratio optimization instruction.

[0012] Beneficial effects of the present invention: The beneficial effects of the present invention are reflected in the integration of the micelle formation kinetic parameters in the premixing stage, the viscosity and particle size correlation factor in the shearing stage, and the thixotropic index change data in the homogenization stage through a dynamic behavior prediction model, establishing a multi-source heterogeneous parameter coupling analysis mechanism, analyzing the synergistic relationship between dispersants and chelating agents, and generating surfactant ratio optimization instructions and process parameter correction strategies; dynamically adjusting the high-pressure homogenization pressure and the number of cycles based on the D50 particle size data and the thixotropic index threshold after high-speed shearing, and linking the segmented cooling process parameters with the Zeta potential absolute value monitoring results and the storage stability decay curve to inhibit the aggregation and hydrolysis tendency of dye particles under high temperature and high pressure conditions; collaboratively determining the chelating agent and buffer addition strategy through the premixed liquid conductivity, the pH fluctuation data in the shearing stage, and the ionic strength detection results after homogenization, and executing them synchronously with the surfactant ratio instruction, realizing nano-level stability control of the dye dispersion under multi-parameter coupling regulation, improving dyeing uniformity and color fastness, and reducing wastewater discharge load and production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0014] Figure 1 A flow chart of a liquid disperse dye suitable for high-temperature and high-pressure dyeing and a preparation method thereof provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, 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 drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The technical solutions provided by each embodiment of the present invention are described in detail below in conjunction with the drawings. In order to better understand the purpose of the present invention, the present invention is further described in detail below.

[0016] The liquid disperse dye suitable for high temperature and high pressure dyeing provided by the present invention comprises the following components: Composite dye matrix, disperse black dye is formed by mixing disperse violet 93, disperse orange 288 and disperse blue 291:1, accounting for 47.5-48.5% of the total mass of liquid disperse dye; A dispersion stabilization system comprising hexadecylamine polyoxyethylene ether or octadecylamine polyoxyethylene ether and laurylamine polyoxyethylene ether, with a mass ratio of 0.3:0.4; The solvent medium is deionized water, which accounts for 51.0-52.0% of the total mass of the liquid disperse dye, and a chelating agent and a pH buffer are added.

[0017] The present invention provides a liquid disperse dye suitable for high-temperature and high-pressure dyeing, the components and interactions of which are as follows: a composite dye matrix is ​​formed by mixing disperse violet 93, disperse orange 288, and disperse blue 291:1 in a specific mass ratio to form disperse black dye, wherein disperse violet 93 accounts for 15.0-16.5%, disperse orange 288 accounts for 13.0-14.5%, and disperse blue 291:1 accounts for 19.5-20.5%. The three disperse dyes are compounded through intermolecular π-π conjugation and the complementary effect of hydrophobic groups to form a composite dye system with synergistic effects. The composite structure of the disperse black dye can enhance the adsorption energy of the dye molecules and synthetic fibers under high-temperature and high-pressure conditions, while improving dyeing uniformity by reducing the tendency of the dyes to aggregate.

[0018] The dispersion stabilization system consists of a combination of hexadecylamine polyoxyethylene ether or octadecylamine polyoxyethylene ether with laurylamine polyoxyethylene ether. The hydrophobic long chains of the hexadecylamine / octadecylamine polyoxyethylene ether anchor to the dye particle surface through van der Waals forces, while the short-chain polyoxyethylene groups of the laurylamine polyoxyethylene ether enhance the interparticle double layer repulsion through steric hindrance. A mass ratio of 0.3:0.4 balances the conflict between hydrophobic adsorption and hydrophilic stability, allowing the dye particles to form a nanoscale dispersion in the solvent medium. This ratio optimally matches the critical micelle concentration of the surfactant to the specific surface area of ​​the dye particles, effectively inhibiting particle aggregation under high temperature conditions.

[0019] The solvent medium is deionized water, which accounts for 51.0-52.0%, providing a continuous phase environment for dye dispersion. The chelating agent is disodium ethylenediaminetetraacetic acid, whose carboxylic acid group complexes the calcium and magnesium ions in the water through chelation, blocking the coordination reaction between the metal ions and the sulfonic acid groups in the dye molecules, thereby reducing the formation of dye hydrolysis by-products. The pH buffer adopts a disodium hydrogen phosphate-citric acid buffer system to stabilize the pH of the solution in the range of 6.5-7.2, avoiding the destruction of the micelle structure of the dispersed stabilization system caused by strong acid or strong alkaline environment. The high dielectric constant characteristics of deionized water further promote the directional arrangement of surfactant molecules on the surface of the dye particles, enhancing the thermodynamic stability of the dispersion.

[0020] The combination of the composite dye matrix, dispersion stabilization system, and solvent medium achieves synergistic functionality through a multi-step mechanism: the composite dye's complex structure optimizes diffusion dynamics during the dyeing process, the dual-action mechanism of the dispersion stabilization system maintains the dispersed state of the nanoparticles, and the environmental control of the solvent medium ensures the system's chemical stability under high temperature and high pressure conditions. These three elements interact at the molecular level to form a dynamic equilibrium, ultimately enabling liquid disperse dyes to exhibit excellent permeability and color fastness in polyester fiber dyeing.

[0021] See also Figure 1 Specifically, the method for preparing liquid disperse dyes suitable for high temperature and high pressure dyeing according to the present invention comprises the following steps: Step S101, heating deionized water to a preset temperature, sequentially adding hexadecylamine polyoxyethylene ether or octadecylamine polyoxyethylene ether and laurylamine polyoxyethylene ether, and stirring to form a uniform micellar solution; Step S102, adding disperse black dye to the micellar solution in batches, and processing using a high-speed shear emulsification device until the viscosity and particle size distribution of the system reach a preset threshold value to obtain a dispersion; Step S103, transferring the obtained dispersion to a high-pressure homogenization device for circulation treatment, and simultaneously combining temperature gradient control to eliminate local aggregation energy barriers; Step S104, based on multi-source coupling analysis of preset mixing temperature, shear rate, homogenization pressure, and cooling rate data, a dynamic behavior prediction model including micelle formation kinetic parameters, particle size distribution correlation factors, and thixotropic index thresholds is constructed, and the surfactant ratio and process parameter combination are reversely optimized using the dynamic behavior prediction model; Step S105, according to the surfactant ratio optimization instruction generated by the dynamic behavior prediction model, adjusting the ratio of hexadecylamine polyoxyethylene ether or octadecylamine polyoxyethylene ether to laurylamine polyoxyethylene ether to 0.3:0.4 w / w, and increasing the absolute value of the zeta potential of the dye particles under high temperature and high pressure conditions to ≥35 mV; Step S106 , based on the prediction of the effect of storage temperature and time on the dispersion state of the dye, a staged cooling process is used to package the finished product to maintain the stability of the particle size distribution.

[0022] The present invention provides a method for preparing a liquid disperse dye suitable for high-temperature and high-pressure dyeing. The technical logic and implementation details of the steps are as follows: In step S101, deionized water is heated to 50-55°C (a temperature range determined by the critical micelle temperature of the surfactant). This allows the hydrophobic segments of hexadecylamine polyoxyethylene ether or octadecylamine polyoxyethylene ether to fully extend, synergizing with the polyoxyethylene groups of laurylamine polyoxyethylene ether to form a micellar core-shell structure. The stirring rate is controlled at 600-800 rpm, promoting uniform distribution of the micelles through laminar shearing, forming a micellar solution with stable interfacial tension. This step provides the wetting medium foundation for subsequent dye dispersion.

[0023] In step S102, disperse black dye is added to the micelle solution in batches and processed using a high-speed shear emulsifier at 12,000-15,000 rpm. The turbulent shear force breaks the dye particles down to the nanometer scale. Viscosity monitoring is performed using an online rheometer. When the system viscosity drops to 200-300 mPa·s, the particle surface is fully coated with micelles. Particle size distribution is provided in real time using a laser particle size analyzer. The D50 threshold of ≤200 nm is set based on the diffusion and penetration efficiency of dye particles under high temperature and pressure. The formation of the dispersion directly determines the effectiveness of the subsequent homogenization process.

[0024] In step S103, the high-pressure homogenizer cycles at a pressure of 40-60 MPa, further narrowing the particle size distribution through cavitation and impact forces. Simultaneously, a temperature gradient is implemented, linearly cooling the temperature from 60°C to 25°C, leveraging the shrinkage of temperature-sensitive micelles to inhibit particle reaggregation. After each homogenization cycle, the thixotropic index is fed back to the temperature control module, dynamically adjusting the cooling rate to match the changing rheological properties of the dispersion. This step eliminates local energy barriers and improves the thermodynamic stability of the system.

[0025] In step S104, the dynamic behavior prediction model integrates the micelle formation time constant during the premixing phase, the viscosity-particle size correlation factor during the shearing phase, and the thixotropic index decay rate during the homogenization phase. Through coupled analysis of multi-source data, the model outputs surfactant ratio optimization coefficients and process parameter correction instructions, such as the adaptive matching relationship between shear rate and homogenization pressure. The micelle formation kinetic parameters are used to predict the surface coating efficiency of dye particles, while the thixotropic index threshold is correlated with the long-term storage stability of the dispersion.

[0026] In step S105, the surfactant ratio is controlled based on the zeta potential optimization curve output by the model, locking the ratio of hexadecene / octadecene polyoxyethylene ether to laurylamine polyoxyethylene ether at 0.3:0.4 (w / w). At this ratio, the synergistic effect of the hydrophobic segments and hydrophilic groups maximizes the thickness of the double layer on the surface of the dye particles, raising the absolute value of the zeta potential to ≥35 mV and effectively enhancing the electrostatic repulsion between the particles. This enhanced charge stabilization mechanism directly reduces the risk of hydrolysis under high-temperature and high-pressure conditions.

[0027] In step S106, the staged cooling process is divided into two stages: pre-cooling at 4°C and equilibration at 25°C. The pre-cooling stage fixes the microstructure of the dispersion by rapid cooling, while the equilibration stage slowly releases thermal stress in the system. The storage stability attenuation curve prediction model dynamically corrects the pre-cooling gradient and equilibration time based on the Zeta potential decay rate and the particle size variation coefficient. For example, when the absolute value of the Zeta potential decreases at a rate exceeding a threshold, the duration of the equilibration stage is shortened to suppress the tendency of particle aggregation. This process enables the finished product to maintain a narrow distribution characteristic of D90 / D10 ≤ 2.5 within 6 months.

[0028] The above steps form a closed-loop control chain: micellar solution construction (S101) provides the medium foundation for dispersion; high-speed shearing (S102) and high-pressure homogenization (S103) sequentially achieve physical dispersion and thermodynamic stabilization; a dynamic model (S104) facilitates data acquisition and parameter optimization; surfactant regulation (S105) and staged cooling (S106) leverage model instructions to achieve chemical stabilization and final product packaging. The model enables cross-stage interactive verification of data from each step. For example, the thixotropic index after homogenization is fed back into shear parameter correction, and zeta potential monitoring results are linked to cooling process adjustments, ultimately achieving a synergistic improvement in formulation stability and process reproducibility.

[0029] Specifically, in the preparation method of liquid disperse dyes suitable for high-temperature and high-pressure dyeing described in the present invention, the rate and time of the high-speed shear emulsification treatment are dynamically adjusted based on the initial rheological properties of the micellar solution, wherein the initial rheological properties include the viscosity and shear rate curve and the thixotropic recovery rate; during the treatment process, the system viscosity data is collected in real time and fed back to the dynamic behavior prediction model, and the pressure and cycle number of the subsequent high-pressure homogenization stage are adjusted according to the high-pressure homogenization pressure correction coefficient and cycle number threshold output by the dynamic behavior prediction model.

[0030] During the high-speed shear emulsification process, the initial rheological properties of the micellar solution are characterized by the viscosity vs. shear rate curve and the thixotropic recovery rate. The viscosity vs. shear rate curve reflects the nonlinear flow behavior of the micellar solution under shear, while the thixotropic recovery rate characterizes the system's ability to restructure its microstructure after shearing ceases. Based on the sensitivity of viscosity to shear rate in the initial rheological properties, the speed range of the high-speed shear emulsification equipment is dynamically adjusted. When the viscosity vs. shear rate curve exhibits shear-thinning characteristics, the speed range of 12,000-14,000 rpm is selected to match the yield stress threshold of the micellar solution. If the thixotropic recovery rate falls below the preset critical value, the shear time is extended to 15 minutes to enhance micellar structural reformation.

[0031] During the processing, the online rheometer collects real-time apparent viscosity data of the system and transmits it to the dynamic behavior prediction model via the data acquisition module. This model matches the viscosity data with the initial rheological properties database to analyze the correlation between micelle encapsulation efficiency and the degree of dye particle fragmentation. When the viscosity fluctuation exceeds the database threshold, the model generates a high-pressure homogenization pressure correction factor. This factor is calculated based on the ratio of the viscosity decay rate to the thixotropic recovery rate and is used to correct the initial set value of the homogenization pressure. The model also calculates a cycle threshold based on the stability index of the viscosity data. If the index is below 0.85, the number of homogenization cycles is increased to 5 to compensate for structural defects in the dispersion.

[0032] The output instructions of the dynamic behavior prediction model are executed by the process control module: a pressure correction factor dynamically adjusts the homogenization pressure by 0.8-1.2 times the baseline value, and a cycle threshold limits the total number of homogenization cycles. For example, if viscosity data feedback indicates incomplete micelle encapsulation, the model outputs a pressure correction factor of 1.15, increasing the homogenization pressure to 55 MPa. If thixotropic recovery data indicates insufficient structural resilience, the cycle threshold is triggered, forcing the number of homogenization cycles to 5. This data feedback mechanism links the process parameters of the shear and homogenization stages, eliminating dispersion performance deviations caused by fluctuations in the rheological properties of the micelle solution through compensatory adjustments.

[0033] The aforementioned technical logic is embodied in a closed-loop control system: initial rheological properties determine the dynamic adjustment boundaries of shear parameters, real-time viscosity data provides process status monitoring signals, and a dynamic behavior prediction model converts this data into process correction instructions. Ultimately, the dispersion structure is optimized through the coordinated adjustment of homogenization pressure and cycle number. This control chain breaks through the existing empirical trial-and-error model, enabling process parameters to adapt to the dynamic rheological behavior of micellar solutions, thereby improving the stability of dispersions in high-temperature and high-pressure environments.

[0034] Specifically, in the method for preparing liquid disperse dyes suitable for high-temperature and high-pressure dyeing described herein, the pressure range and number of cycles of the high-pressure homogenization treatment are determined by the D50 particle size data of the dispersion after high-speed shearing and the thixotropic index threshold output by the dynamic behavior prediction model; After each homogenization cycle, the slope parameters of the temperature gradient control are updated according to the real-time thixotropic index and the temperature gradient control slope target value calculated by the model.

[0035] The pressure range and number of high-pressure homogenization cycles are determined by the D50 particle size data of the dispersion after high-speed shearing and the thixotropic index threshold output by the dynamic behavior prediction model. The D50 particle size data, collected online by a laser particle size analyzer, reflects the median size distribution of the dye particles in the dispersion. When the D50 value exceeds a preset upper limit, it indicates an increased tendency for particles to aggregate, necessitating an increase in homogenization pressure to enhance fragmentation. The thixotropic index threshold is calculated by the dynamic behavior prediction model based on the micelle formation rate during the premixing phase, the viscosity decay curve during the shear phase, and the energy dissipation data during the homogenization phase. It defines the structural stability boundary of the dispersion during high-pressure homogenization. When the real-time thixotropic index falls below the threshold, the model determines that the number of cycles must be increased to compensate for structural defects. The pressure range is dynamically adjusted based on the linear relationship between the D50 and the thixotropic index. For example, a 10% increase in the D50 corresponds to an 8-10 MPa increase in pressure.

[0036] After each homogenization cycle, the real-time thixotropic index is measured by a rotational rheometer. This index characterizes the structural recovery ability of the dispersion after the shear stress is removed. The dynamic behavior prediction model compares the real-time thixotropic index with the preset target value and calculates the target value of the temperature gradient control slope. The target slope is dynamically corrected according to the thixotropic index deviation: if the real-time thixotropic index is lower than the target value, it indicates that there is a risk of local overheating in the dispersion, and the cooling slope needs to be increased to accelerate heat dissipation; otherwise, the slope is reduced to avoid sudden temperature changes causing particle re-aggregation. The temperature gradient control module adjusts the flow rate and cooling rate of the circulating medium according to the target slope. For example, the PID algorithm is used to control the opening of the coolant flow valve so that the temperature gradient strictly matches the slope parameter output by the model.

[0037] This parameter adjustment mechanism forms a closed-loop control loop: D50 particle size data and the thixotropic index threshold jointly constrain the initial set ranges for homogenization pressure and cycle number, while real-time thixotropic index monitoring results drive dynamic optimization of temperature gradient control. The structural state of the dispersion is fed back to the model in real time via the thixotropic index, and the slope target value output by the model further guides the updating of the operating parameters of the temperature control module. This logical chain enables the homogenization process parameters to adapt to changes in the dispersion's microstructure. For example, when particle breakage efficiency decreases, insufficient energy input is compensated by increasing pressure and cycle number. When the local temperature gradient deviates, thermodynamic equilibrium is maintained through slope correction. Ultimately, the stability of the dispersion is progressively optimized over multiple homogenization cycles, suppressing the aggregation and hydrolysis tendencies of dye particles in high-temperature and high-pressure environments.

[0038] Specifically, in the preparation method of liquid disperse dyes suitable for high-temperature and high-pressure dyeing described in the present invention, the dynamic behavior prediction model integrates the micelle formation kinetics data of the preset mixing stage, the viscosity and particle size correlation data of the shear stage, and the thixotropic index change data of the homogenization stage to generate surfactant ratio optimization instructions; at the same time, based on the absolute value of the Zeta potential and the thixotropic index change rate after homogenization treatment, the pre-cooling rate and equilibrium temperature of the segmented cooling process are calculated through the model, and the cooling parameters are adjusted in a linked manner.

[0039] The dynamic behavior prediction model integrates micelle formation kinetics data collected during the premixing phase to analyze the micelle nucleus generation rate and the adsorption efficiency of surfactant molecules at the oil-water interface. The micelle formation kinetics data, including the micelle aggregation number over time and the critical micelle concentration deviation, are used to quantify the surfactant's ability to encapsulate dye particles. The viscosity-particle size correlation data input during the shear phase are derived from the online monitoring system of the high-speed shear emulsification equipment. The correlation coefficient between the viscosity decay slope and the particle size distribution width (Span value) is extracted as a key parameter, reflecting the effect of shear force on particle breakage efficiency. Thixotropic index data during the homogenization phase are collected between treatment cycles using a rotational rheometer. The thixotropic index decay rate is used to characterize the dynamic equilibrium between structural destruction and reconstruction of the dispersion under high-pressure impact.

[0040] The model inputs these three types of data into a multivariate regression algorithm to calculate optimized surfactant ratio instructions. Specifically, when the micelle formation rate falls below a preset threshold, the model prioritizes increasing the proportion of hexadecylamine / octadecylamine polyoxyethylene ether to enhance the hydrophobic anchoring effect. If the viscosity-particle size correlation data indicates that the span value exceeds the critical range, the proportion of laurylamine polyoxyethylene ether is increased to enhance the steric effect. The optimized ratio instructions adjust the surfactant feed ratio in real time via a mass flow controller, forming a dynamic ratio control loop.

[0041] The absolute value of the zeta potential after homogenization was measured using an electrophoretic light scattering instrument. This value reflects the thickness of the double layer and charge density on the surface of the dye particles. The thixotropic index change rate, calculated by comparing the thixotropic recovery capacity before and after homogenization, is used to assess the mechanical stability of the dispersion structure. The model inputs the absolute value of the zeta potential and the thixotropic index change rate into the thermodynamic stability prediction module to calculate the precooling rate and equilibrium temperature for the step-by-step cooling process. When the absolute value of the zeta potential falls below 35 mV, the model reduces the precooling rate to 2°C / min to avoid charge distribution imbalance. If the thixotropic index change rate exceeds the critical slope, the equilibrium temperature hold time is extended to 30 minutes to promote structural relaxation.

[0042] The process control module coordinates the cooling parameters: a pre-cooling rate command drives the refrigeration unit to adjust the refrigerant flow, while a balance temperature command controls the heat exchange power of the thermostatic bath. The model simultaneously monitors real-time fluctuations in the zeta potential during the cooling process. When the fluctuation exceeds a preset tolerance, it triggers a secondary correction to the ratio optimization command, such as adding a trace amount of pH buffer to stabilize the double layer structure. This data feedback mechanism enables cross-stage synergy between surfactant ratio adjustment and the cooling process, suppressing the tendency of particle aggregation caused by temperature gradients.

[0043] The aforementioned technical logic forms a closed-loop control chain: micelle formation data drives the fundamental optimization of surfactant ratios, shear and homogenization data provide process efficiency feedback, zeta potential and thixotropy data guide cooling parameter adjustments, and real-time monitoring of the cooling process further feeds back into ratio corrections. Cross-validation of data from each stage and coordinated control of instructions break through the existing single-parameter control model, enabling the dispersion preparation process to adapt to complex operating conditions, ultimately improving the stability and reproducibility of liquid disperse dyes in high-temperature and high-pressure dyeing.

[0044] Specifically, in the preparation method of liquid disperse dyes suitable for high-temperature and high-pressure dyeing described in the present invention, the temperature gradient of the pre-cooling stage and the duration of the equilibrium stage of the segmented cooling process are determined by calculating the storage stability attenuation curve in the dynamic behavior prediction model, and the pre-cooling gradient and equilibrium time are dynamically corrected based on the absolute value monitoring results of the Zeta potential after high-pressure homogenization treatment and the target attenuation rate threshold output by the model.

[0045] The temperature gradient of the pre-cooling phase and the duration of the equilibrium phase in the staged cooling process are parameterized using the storage stability decay curve in the dynamic behavior prediction model. This storage stability decay curve is generated based on the correlation between the particle size variation coefficient and the zeta potential decay rate of the dispersion under simulated storage conditions. The particle size variation coefficient is continuously monitored by a laser particle size analyzer during accelerated aging experiments, while the zeta potential decay rate is calculated by inverse calculation of the charge relaxation time measured by an electrophoretic light scattering instrument. The model matches this data with the absolute value of the zeta potential after high-pressure homogenization. When the absolute value of the zeta potential falls below 35 mV, the surface charge stability of the dispersion is determined to be insufficient, triggering a slope correction mechanism for the storage stability decay curve.

[0046] The target decay rate threshold output by the dynamic behavior prediction model is determined by the absolute value of the initial zeta potential of the dispersion and the recovery rate of the thixotropic index. For example, when the absolute value of the zeta potential after high-pressure homogenization reaches 40 mV and the thixotropic index recovery rate exceeds 85%, the model sets the target decay rate threshold to a monthly increase in the coefficient of variation of particle size of no more than 1.5%. If the absolute value of the zeta potential falls below 35 mV, the threshold is tightened to no more than 0.8% per month. The temperature gradient during the precooling phase is dynamically adjusted based on the target decay rate threshold: when the threshold is tightened, the precooling gradient is increased from 4°C / min to 6°C / min to quickly stabilize the particle surface charge distribution; when the threshold is relaxed, the gradient is reduced to 2°C / min to reduce structural distortion caused by thermal stress.

[0047] The duration of the equilibrium phase is determined by the deviation between the model-calculated storage stability decay curve and the real-time Zeta potential monitoring data. When the real-time Zeta potential decay rate exceeds the target threshold, the equilibrium time is extended from the baseline value of 30 minutes to 45 minutes, allowing the dispersion to complete the re-equilibrium of the interfacial adsorption energy under constant temperature conditions; if the deviation is below the tolerance range, it is shortened to 20 minutes to optimize the production cycle. The temperature control module adjusts the heat exchange power of the thermostatic bath according to the model instructions. For example, the PID algorithm controls the coupled output of the cooling water flow and the heater power to keep the equilibrium temperature fluctuation range within ±0.5°C.

[0048] Dynamic correction of the pre-cooling gradient and equilibration time forms a closed-loop control loop: the storage stability decay curve provides a benchmark for predicting long-term stability, and the absolute value of the zeta potential after high-pressure homogenization reflects the immediate charge state. These two are linked through a model calculation to generate process parameter adjustment instructions. For example, if the absolute value of the zeta potential is detected to be decreasing too rapidly during the pre-cooling phase, the model immediately increases the pre-cooling gradient and simultaneously extends the equilibration time. This synergistic effect of enhanced charge fixation and structural relaxation suppresses particle aggregation during storage. This logical chain enables the cooling process parameters to adapt to the real-time state changes of the dispersion, breaking through the limitations of existing fixed parameter models and ultimately achieving nano-scale dispersion stability for liquid disperse dyes during long-term storage.

[0049] Specifically, in the preparation method of liquid disperse dyes suitable for high-temperature and high-pressure dyeing described in the present invention, the timing and amount of adding the chelating agent and pH buffer are collaboratively determined by the dynamic behavior prediction model based on the preset initial conductivity of the mixed liquid, the pH fluctuation data in the shear stage, and the ionic strength detection results after homogenization treatment, to generate a chelating agent addition amount instruction and a pH buffer window range, and are executed synchronously with the surfactant ratio optimization instruction.

[0050] The timing and dosage of chelating agents and pH buffers are determined by a dynamic behavior prediction model based on the initial conductivity of the premix, pH fluctuation data during the shear phase, and ionic strength test results after homogenization. The initial conductivity of the premix is ​​monitored online using a conductivity sensor, and its value reflects the residual concentration of metal ions in the deionized water. When the conductivity exceeds 50 μS / cm, the calcium and magnesium ion levels in the water are considered excessive, and the chelating agent dosage must be increased to inhibit the coordination reaction between the metal ions and the dye molecules. pH fluctuation data during the shear phase is collected in real time using a pH electrode. If the pH value deviates from the 6.5-7.2 range and the fluctuation amplitude exceeds ±0.3, the model triggers a pH buffer window range correction instruction, dynamically adjusting the molar concentration of the buffer to stabilize the system pH.

[0051] After homogenization, ionic strength is measured by correlating conductivity with zeta potential. When the ionic strength exceeds a critical threshold, it indicates that the concentration of free ions in the dispersion is too high, potentially weakening the double-layer repulsion. The dynamic behavior prediction model inputs these three types of data into a multi-parameter optimization algorithm to generate instructions for chelating agent addition and a pH buffer window. When the premix conductivity exceeds the specified value and the ionic strength is too high, the chelating agent addition is linearly increased by 0.5-0.8 kg per ton of dispersion. The pH buffer window shrinks or expands based on the maximum fluctuation amplitude during the shear phase. For example, when the fluctuation amplitude exceeds 0.5, the window is tightened from 6.5-7.2 to 6.8-7.0.

[0052] The generated chelating agent addition instruction dynamically adjusts the valve opening of the chelating agent storage tank via a metering pump, while the pH buffer window controls the drip rate of the buffer solution. These instructions are executed synchronously with the surfactant ratio optimization instruction through the central control module. For example, when the model determines that the proportion of laurylamine polyoxyethylene ether needs to be increased, the pH buffer dosage is simultaneously increased to compensate for the interference of the surfactant hydrophilic chain on the system's pH. This synergistic mechanism prevents system imbalance caused by single parameter adjustments. For example, the disruption of micelle structure caused by excessive chelating agent addition is offset by the immediate correction of the pH buffer.

[0053] A data feedback mechanism runs throughout the entire process chain: premix conductivity monitoring results influence the initial chelating agent dosing strategy, pH fluctuation data during the shear phase drives dynamic adjustment of the buffer, and ionic strength measurement results after homogenization further correct the chelating agent addition amount. The model uses real-time data to cross-validate the rationality of optimization instructions. For example, when the ionic strength measurement value conflicts with the premix conductivity data, the post-homogenization measurement value is prioritized and the model parameters are recalibrated. This logic enables the chelating agent and pH buffer addition strategy to adapt to raw material fluctuations across production batches, maintaining the chemical stability of the dispersion under high temperature and high pressure environments.

[0054] Explanation of the technical features of the present invention: The composite dye matrix is ​​composed of Disperse Violet 93, Disperse Orange 288, and Disperse Blue 291:1 in a specific mass ratio to form Disperse Black. Disperse Violet 93, an anthraquinone dye, provides a bluish-violet hue and high-temperature resistance; Disperse Orange 288, an azo dye, enhances the orange hue and fiber affinity; and Disperse Blue 291:1, a heterocyclic dye, optimizes color saturation. These three dyes form a synergistic composite dye system through intermolecular π-π conjugation and complementary hydrophobic groups, enhancing dye adsorption and dyeing uniformity on polyester fibers under high-temperature and high-pressure conditions.

[0055] The dispersion stabilization system consists of hexadecyl / octadecylamine polyoxyethylene ether (hydrophobic segment) and laurylamine polyoxyethylene ether (hydrophilic segment), mixed in a mass ratio of 0.3:0.4. The former anchors the dye particles to the surface via van der Waals forces, forming a hydrophobic core; the latter enhances double-layer repulsion through the steric hindrance of the polyoxyethylene groups, inhibiting particle aggregation. The synergistic effect of these two components allows the dye particles to form a nanoscale dispersion in the solvent medium, maintaining colloidal stability under high temperature and high pressure.

[0056] Solvent medium: Deionized water (51.0-52.0%) is used as the continuous phase, as its high dielectric constant promotes the directional alignment of surfactant molecules. Chelating agents (such as disodium ethylenediaminetetraacetic acid) complex metal ions in water with carboxylic acid groups, blocking their coordination reaction with the dye's sulfonic acid groups. A pH buffer (sodium hydrogen phosphate-citric acid system) stabilizes the solution pH at 6.5-7.2, preventing strong acidic and alkaline environments from destroying the micelle structure.

[0057] Dynamic Behavior Prediction Model: This model integrates micelle formation kinetics data from the premixing stage (e.g., critical micelle concentration deviation), the viscosity-particle size correlation factor (Span value) from the shearing stage, and the rate of change of the thixotropic index during the homogenization stage. Using a multivariate regression algorithm, it analyzes the synergistic mechanism between dispersants and chelating agents. The model outputs surfactant ratio optimization instructions (e.g., adjusting the hydrophobic / hydrophilic segment ratio) and process parameter correction strategies (e.g., homogenization pressure and cycle number), enabling data-driven adaptive process control.

[0058] The staged cooling process consists of a pre-cooling phase (rapid cooling at 4°C / min) and an equilibration phase (maintained at a constant temperature of 25°C). The pre-cooling phase suppresses aggregation by rapidly fixing the surface charge distribution of the particles; the equilibration phase eliminates local structural distortions through thermal stress relaxation. Cooling parameters are dynamically adjusted based on the storage stability decay curve and the absolute value of the zeta potential. For example, when the zeta potential is below 35 mV, the equilibration time is extended to 40 minutes. This maintains long-term dispersion stability through thermodynamic equilibrium control.

[0059] Chelating agents and pH buffers are coordinated: premix conductivity monitoring (reflecting metal ion concentration) triggers the addition of chelating agents; pH fluctuation data during the shear phase (with a ±0.3 tolerance) drives adjustments to the buffer window (e.g., tightening it to 6.8-7.0). Ionic strength measurement results (calculated through conductivity-zeta potential correlation) further optimize chelating agent dosage, which is synchronized with surfactant ratio instructions through the central control module to avoid system imbalance caused by single parameter adjustments.

[0060] The technical solution of this invention optimizes dyeing dynamics through the complex structure of composite dyes, constructs nanoscale dispersions through a dispersion stabilization system, implements multi-source data coupling analysis through a dynamic behavior prediction model, and ensures stability through the coordinated control of staged cooling and chelating agents. This creates a closed-loop system from formulation design to process optimization. These technical features, through data feedback and command linkage, break through the existing empirical trial-and-error model, ultimately achieving synergistic improvements in the uniformity, color fastness, and environmental performance of disperse dyes in high-temperature and high-pressure dyeing.

[0061] Dynamic behavior prediction model: This model is the core algorithm framework of the technical solution. By integrating key parameters of the premixing stage, shearing stage, and homogenization stage, it establishes a multi-source heterogeneous data coupling analysis mechanism: Input data: Premixing stage: micelle formation kinetics data (such as micelle aggregation number, critical micelle concentration deviation value) to quantify the coating efficiency of surfactant on dye particles.

[0062] Shear stage: Viscosity and particle size correlation data (such as viscosity decay slope and span value) reflect the effect of shear force on particle breakage and dispersion state.

[0063] Homogenization stage: thixotropic index change rate (an indicator of the dynamic balance between structural destruction and reconstruction) and the absolute value of Zeta potential (a parameter of surface charge stability).

[0064] Algorithm Logic: Multivariate regression analysis and thermodynamic stability prediction modules are used to analyze the synergistic mechanism between dispersants and chelating agents. For example, when the micelle formation rate falls below a threshold, the model prioritizes increasing the proportion of hydrophobic segments (hexadecyl / octadecylamine polyoxyethylene ether). If the thixotropic index decays too quickly, the number of homogenization cycles is increased to compensate for structural defects.

[0065] Output instructions: Surfactant ratio optimization instructions (such as mass ratio 0.3:0.4); Process parameter correction instructions (high-pressure homogenization pressure, number of cycles, segmented cooling gradient); Coordinated control instructions for chelating agents and buffers (dosage, pH window range).

[0066] Storage stability decay curve model The model is used to predict the performance degradation trend of dispersions during long-term storage. Its core functions include: Data source: Accelerated aging test: Monitor the coefficient of variation of particle size (such as D90 / D10) through a laser particle size analyzer; Charge relaxation analysis: Inferring charge stability from the zeta potential decay rate.

[0067] Logical association: The absolute value of the zeta potential (the immediate state of charge) after high-pressure homogenization is linked to the storage stability decay curve (a benchmark for long-term prediction). For example, when the absolute value of the zeta potential falls below 35 mV, the model tightens the decay rate threshold (e.g., monthly increase in the coefficient of variation of particle size ≤ 0.8%) and dynamically adjusts the pre-cooling gradient and equilibrium time for the staged cooling process accordingly.

[0068] Functional output: Generate temperature gradient control parameters (such as pre-cooling rate 5℃ / min) and storage stability warning signals to guide packaging process optimization.

[0069] Collaborative decision model: This model is specifically designed for the dynamic regulation of chelating agents and pH buffers, specifically including: Input parameters: Conductivity of premixed solution (metal ion concentration indicator); pH fluctuation data during the shear phase (acid-base stability parameter); Ionic strength test value after homogenization (free ion concentration affects double layer repulsion).

[0070] Decision logic: Using threshold trigger mechanism and linear proportional control: When the conductivity exceeds the standard (>50 μS / cm), add 0.5-0.8 kg of chelating agent per ton of dispersion; When pH fluctuation exceeds the limit (±0.3), narrow the buffer window (e.g., 6.8-7.0) and adjust the buffer addition rate.

[0071] Collaborative execution: The central control module is linked to the surfactant ratio command. For example, when the proportion of laurylamine polyoxyethylene ether increases, the buffer addition rate is simultaneously increased (5→7 mL / min) to offset the pH disturbance caused by the hydrophilic chain.

[0072] Summary of model synergy: The above model forms a collaborative network through data cross-validation and instruction closed-loop feedback: Dynamic behavior prediction models provide a global process optimization framework; Storage stability decay curve model ensures long-term performance prediction; The collaborative determination model enables precise regulation of chemical stability.

[0073] The three break through the existing empirical trial-and-error model through real-time data interaction (such as Zeta potential, homogenization pressure correction, and cooling gradient adjustment), and ultimately solve the technical problems of insufficient multi-source parameter coupling analysis and difficulty in analyzing the dispersant-chelating agent synergistic mechanism, significantly improving the formula stability and process reproducibility.

[0074] The present invention is implemented as follows: Deionized water is heated to 50-55°C, and 0.3 parts by weight of hexadecylamine polyoxyethylene ether and 0.4 parts by weight of laurylamine polyoxyethylene ether are sequentially added, stirring at 600-800 rpm to form a micellar solution. 48.0 parts by weight of disperse black dye, a mixture of Disperse Violet 93 (15.5%), Disperse Orange 288 (13.8%), and Disperse Blue 291 (20.2%) in a 1:1 ratio, is added in batches. The mixture is then processed in a high-speed shear emulsifier at 14,000 rpm for 12 minutes. Shearing is stopped when the viscosity drops to 250 mPa·s and the D50 particle size reaches 180 nm, monitoring the system in real time. A primary dispersion is obtained. The dispersion is then transferred to a high-pressure homogenizer and processed four times at 50 MPa while simultaneously undergoing a linear cooling process from 60°C to 25°C. After each cycle, the cooling rate is dynamically adjusted to 2.5°C / min based on the thixotropic index decreasing from 1.5 to 1.1.

[0075] The dynamic behavior prediction model integrates data on micelle formation rate (critical micelle concentration deviation ≤5%), viscosity decay slope during the shear phase (0.32 s⁻¹), and thixotropic index change after homogenization (24%) to generate optimized surfactant ratio instructions, locking the ratio of hexadecene / octadecene amine polyoxyethylene ether to laurylamine polyoxyethylene ether at 0.3:0.4. The model also simultaneously analyzes the absolute value of the post-homogenization zeta potential (38 mV) and the ionic strength (0.15 mol / L). When the conductivity exceeds the specified value (55 μS / cm), 0.6 parts by weight of disodium ethylenediaminetetraacetic acid (EDTA) is added, the pH buffer window is narrowed to 6.8-7.0, and disodium hydrogen phosphate-citric acid buffer is added dropwise at a rate of 5 mL / min via a metering pump. A stepwise cooling process is established based on the storage stability decay curve, with a pre-cooling gradient of 5°C / min. The equilibration phase is maintained at 25°C for 30 minutes to maintain a particle size coefficient of variation of ≤3.5%.

[0076] During implementation, the dynamic behavior prediction model correlated the span value (1.8) after high-speed shearing with the homogenization pressure correction factor (1.1) in real time. When the absolute value of the zeta potential dropped to 34 mV, a trigger command increased the number of homogenization cycles to five and simultaneously extended the equilibration time to 40 minutes. The chelating agent dosage and surfactant ratio were adjusted in tandem via a central control module. For example, when the laurylamine polyoxyethylene ether ratio increased to 0.42 parts by weight, the buffer addition rate was increased to 7 mL / min to offset the pH perturbation caused by the hydrophilic segments. The resulting liquid disperse dye showed increased dye adsorption on fiber surfaces in high-temperature and high-pressure dyeing tests at 130°C. The particle size distribution D90 / D10 ratio remained below 2.3 over six months, and the COD load in the wastewater was reduced, demonstrating the synergistic optimization of formulation stability and process reproducibility.

[0077] The present invention establishes a multi-source heterogeneous parameter coupling analysis mechanism by constructing a dynamic behavior prediction model, integrating the micelle formation kinetics data of the premixing stage, the viscosity and particle size correlation data of the shearing stage, and the thixotropic index change data of the homogenization stage. The model inputs parameters such as micelle formation rate, viscosity decay slope, and thixotropic recovery rate into a multivariate regression algorithm to analyze the synergistic mechanism of surfactants and chelating agents and generate ratio optimization instructions. For example, when the micelle formation rate is lower than the threshold, the model prioritizes increasing the proportion of hexadecene / octadecylamine polyoxyethylene ether to strengthen the hydrophobic anchoring effect, and dynamically adjusts the dosage of the chelating agent based on its complexing efficiency for metal ions, thereby achieving functional complementarity between the dispersant and the chelating agent.

[0078] The dynamic behavior prediction model reversely optimizes the process parameter combination by collecting real-time D50 particle size data after high-speed shearing, the absolute value of the Zeta potential after homogenization, and ionic strength test results. The model correlates the particle size distribution width with the thixotropic index decay rate, calculates the high-pressure homogenization pressure correction coefficient and the cycle number threshold, and simultaneously combines the absolute value of the Zeta potential with the storage stability decay curve to dynamically correct the pre-cooling gradient and equilibrium time of the staged cooling process. For example, when the absolute value of the Zeta potential is lower than 35 mV, the model jointly reduces the pre-cooling rate and extends the equilibrium time, thereby improving the thermodynamic stability of the dispersion by suppressing charge distribution imbalance and thermal stress distortion.

[0079] The dynamic behavior prediction model further integrates the initial conductivity of the premix, the pH fluctuation data during the shearing stage, and the ionic strength test results after homogenization to collaboratively determine the addition strategy of the chelating agent and the pH buffer. When the conductivity exceeds the standard, the model increases the chelating agent dosage in a linear proportion; when the pH fluctuation exceeds the limit, the buffer window range is narrowed and the buffer drop rate is adjusted. The above instructions and the surfactant ratio optimization instructions are executed synchronously through the central control module. For example, when increasing the proportion of laurylamine polyoxyethylene ether, the pH buffer is supplemented synchronously to offset the interference of the hydrophilic chain segment on the pH of the system, thereby achieving collaborative optimization of formula stability and process reproducibility under multi-parameter coupling control.

Claims

1. A liquid disperse dye suitable for high temperature and high pressure dyeing, characterized in that: Includes the following components: Composite dye matrix, disperse black dye is formed by mixing disperse violet 93, disperse orange 288 and disperse blue 291:1, accounting for 47.5-48.5% of the total mass of liquid disperse dye; A dispersion stabilization system comprising hexadecylamine polyoxyethylene ether or octadecylamine polyoxyethylene ether and laurylamine polyoxyethylene ether, with a mass ratio of 0.3:0.4; The solvent medium is deionized water, which accounts for 51.0-52.0% of the total mass of the liquid disperse dye, and a chelating agent and a pH buffer are added.

2. The method for preparing a liquid disperse dye suitable for high temperature and high pressure dyeing according to claim 1, wherein The following steps are involved: After heating deionized water to a preset temperature, hexadecylamine polyoxyethylene ether or octadecylamine polyoxyethylene ether and laurylamine polyoxyethylene ether are sequentially added, and a micellar solution is formed by stirring; Adding disperse black dye to the micellar solution in batches, and treating the solution using a high-speed shear emulsification device until the viscosity and particle size distribution of the system reach a preset threshold value to obtain a dispersion; The obtained dispersion is transferred to a high-pressure homogenization device for circulation treatment, and the temperature gradient control is simultaneously combined to eliminate the local aggregation energy barrier; Based on multi-source coupling analysis of preset mixing temperature, shear rate, homogenization pressure, and cooling rate data, a dynamic behavior prediction model was constructed, including micelle formation kinetic parameters, particle size distribution correlation factors, and thixotropic index thresholds. The dynamic behavior prediction model was then used to reversely optimize the surfactant ratio and process parameter combination. According to the surfactant ratio optimization instructions generated by the dynamic behavior prediction model, the ratio of hexadecylamine polyoxyethylene ether or octadecylamine polyoxyethylene ether to laurylamine polyoxyethylene ether is adjusted to 0.3:0.4 w / w, and the absolute value of the zeta potential of the dye particles under high temperature and high pressure conditions is increased to ≥35 mV; Based on the prediction of the effects of storage temperature and time on the dispersion state of the dye, a segmented cooling process was used to package the finished product.

3. The method for preparing a liquid disperse dye suitable for high temperature and high pressure dyeing according to claim 2, wherein: The rate and time of the high-speed shear emulsification treatment are dynamically adjusted based on the initial rheological properties of the micellar solution, wherein the initial rheological properties include a viscosity-shear rate curve and a thixotropic recovery rate. During the treatment process, the system viscosity data is collected in real time and fed back to the dynamic behavior prediction model. The pressure and the number of cycles in the subsequent high-pressure homogenization stage are adjusted according to the high-pressure homogenization pressure correction coefficient and the cycle number threshold output by the dynamic behavior prediction model.

4. The method for preparing a liquid disperse dye suitable for high temperature and high pressure dyeing according to claim 3, wherein: The pressure range and number of cycles of the high-pressure homogenization treatment are determined by the D50 particle size data of the dispersion after high-speed shearing and the thixotropic index threshold output by the dynamic behavior prediction model; After each homogenization cycle, the slope parameters of the temperature gradient control are updated according to the real-time thixotropic index and the temperature gradient control slope target value calculated by the model.

5. The method for preparing a liquid disperse dye suitable for high temperature and high pressure dyeing according to claim 4, wherein: The dynamic behavior prediction model integrates the micelle formation kinetics data of the preset mixing stage, the viscosity and particle size correlation data of the shear stage, and the thixotropic index change data of the homogenization stage to generate surfactant ratio optimization instructions; at the same time, based on the absolute value of the Zeta potential and the thixotropic index change rate after homogenization, the model calculates the pre-cooling rate and equilibrium temperature of the segmented cooling process, and adjusts the cooling parameters in a linked manner.

6. The method for preparing a liquid disperse dye suitable for high temperature and high pressure dyeing according to claim 5, wherein: The temperature gradient of the pre-cooling stage and the duration of the equilibrium stage of the segmented cooling process are determined by calculating the storage stability attenuation curve in the dynamic behavior prediction model, and the pre-cooling gradient and equilibrium time are dynamically corrected based on the absolute value monitoring results of the Zeta potential after high-pressure homogenization treatment and the target attenuation rate threshold output by the model.

7. The method for preparing a liquid disperse dye suitable for high temperature and high pressure dyeing according to claim 6, wherein: The timing and amount of adding the chelating agent and pH buffer are determined by the dynamic behavior prediction model in coordination with the preset initial conductivity of the mixed liquid, the pH fluctuation data in the shearing stage, and the ionic strength detection results after homogenization treatment, and the chelating agent addition amount instruction and the pH buffer window range are generated, and are executed synchronously with the surfactant ratio optimization instruction.

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