Method of application of liquid disperse dyes based on nanomaterial modification

Abstract

The present application relates to the technical field of dye preparation data processing, and particularly relates to an application method of liquid disperse dye based on nano material modification, which significantly improves the performance and application efficiency of the liquid disperse dye through surface functional modification of the nano material and multi-parameter collaborative optimization of the genetic algorithm. The modified nano surface amino functional group forms directional chemical bonding with the dye molecule, enhances the adsorption strength of the dye on the fiber surface and the thermal migration stability under high temperature environment, generates a globally optimal process combination through multi-objective iterative optimization, breaks through the limitations of low parameter matching efficiency and poor adaptability of the traditional empirical method, and reduces the COD value of dyeing wastewater. The present application realizes precise regulation and control of dyeing process parameters, efficient use of resources and closed-loop control of clean production, and takes into account dyeing quality, energy consumption optimization and environmental protection requirements.

Description

Technical Field

[0001] This invention relates to the field of dye preparation data processing technology, and in particular to the application method of liquid dispersed dyes modified with nanomaterials. Background Technology

[0002] Nanomaterial-modified liquid disperse dyes are a novel type of colorant that utilizes surface functionalization techniques to composite nanoparticles with dye molecules, improving dye performance through the interfacial and size effects of nanomaterials. Nanomaterials such as silica, zinc oxide, and carbon-based materials, after surface modification, can form stable physical or chemical bonds with dye molecules, effectively inhibiting dye aggregation, promoting uniform dispersion, and enhancing their interfacial compatibility with the fiber matrix. In high-temperature, high-pressure dyeing processes, nanocomposite structures can improve the thermal migration stability of dyes, reduce hydrolysis tendency, and significantly improve color fastness and color saturation. This technology also optimizes the adsorption kinetics of dyes on hydrophobic synthetic fibers by regulating the surface charge and functional group distribution of nanoparticles, making it suitable for efficient dyeing of synthetic fibers such as polyester and nylon, as well as blended fabrics. Furthermore, nanocomposite systems can impart photothermal stability to dyes, reducing degradation under ultraviolet or humid conditions. Its applications can be expanded to the coloring of high-value-added textiles and functional materials, while reducing the organic content in dyeing wastewater, meeting the requirements of clean production.

[0003] In the application of liquid disperse dyes modified by nanomaterials, there is a lack of efficient characterization models for the dynamic correlation between the surface modification parameters of nanoparticles and the composite state of dye molecules. This makes it difficult to accurately quantify the mapping relationship between key parameters such as the surface charge density and functional group distribution of nanomaterials and the dispersion stability and fiber adsorption efficiency of dyes, which restricts the intelligent optimization of process parameters and the real-time prediction of the performance of composite systems. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an application method for liquid dispersed dyes based on nanomaterial modification, which solves the problem of difficulty in quantifying the mapping relationship between key parameters and dispersion stability and fiber adsorption efficiency due to the lack of a dynamic correlation model between nanomaterial surface modification parameters and dye molecule complex state.

[0005] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows:

[0006] The present invention provides a method for applying liquid disperse dyes modified with nanomaterials, comprising: the following components by mass percentage:

[0007] Disperse Violet 93: 10.0%-12.0%;

[0008] Disperse Orange 288: 15.0%-17.0%;

[0009] Disperse Blue 291:1: 25.0%-28.0%;

[0010] Cetyl / octadecylamine polyoxyethylene ether: 0.5%-1.0%;

[0011] Laurylamine polyoxyethylene ether: 0.5%-1.0%;

[0012] Sodium lignosulfonate: 3.0%-5.0%;

[0013] Modified nano 1.5%-2.5%, the modified nano Surface functionalization modification with silane coupling agent;

[0014] Organosilicon defoamer: 0.3%-0.5%;

[0015] The remainder is deionized water.

[0016] Furthermore, the application method of the liquid dispersed dye based on nanomaterial modification according to the present invention includes the following steps:

[0017] Data on the porosity and surface roughness of polyester or nylon fibers are collected and input into a genetic algorithm model to generate a combination of parameters for sodium hydroxide concentration, treatment temperature, and time for alkali reduction treatment.

[0018] The fiber is subjected to alkali reduction treatment according to the combined parameters to control the surface roughness of the fiber.

[0019] The treated fibers are immersed in a dye bath, which is prepared by diluting liquid disperse dye at a bath ratio of 1:5 to 1:8 and adjusting the pH to 5.0 to 5.5.

[0020] The dyeing heating rate, holding time and dye liquor circulation flow rate are input into the genetic algorithm model. The optimal process parameters are obtained by iterative screening using dyeing depth, color fastness and wastewater COD as fitness indicators.

[0021] A gradient temperature dyeing process is executed based on the optimized process parameters.

[0022] After dyeing, the reduction cleaning and cationic color fixing parameters are dynamically adjusted based on the residual color data, and modified nano-materials in the wastewater are used to further adjust the parameters. Optimize sedimentation and filtration parameters using concentration and particle size distribution data.

[0023] Furthermore, in the application method of the liquid disperse dye based on nanomaterial modification described in this invention, the matching of the alkali reduction treatment parameters includes:

[0024] The generation of the combined parameters for the alkali reduction treatment includes:

[0025] The porosity and surface roughness data of the fiber are input into the genetic algorithm model. With the fiber adsorption efficiency as the fitness target, the model outputs a combination of parameters with a sodium hydroxide concentration range of 5-10 g / L, a treatment temperature of 80-90℃, and a treatment time of 20-30 minutes.

[0026] Furthermore, in the application method of the liquid disperse dye based on nanomaterial modification described in this invention, the dyeing process optimization includes:

[0027] The dyeing heating rate, holding time, pH value, and circulating pump flow rate are used as input variables and input into the genetic algorithm model. The dyeing depth, color fastness grade, and wastewater COD value are used as fitness indicators. Through multiple generations of iteration, the combination of process parameters with dyeing depth ≥ 4, color fastness ≥ 4, and COD value ≤ 100 mg / L is selected.

[0028] Furthermore, in the application method described in this invention, the gradient temperature staining process includes:

[0029] Based on the selected combination of process parameters, the dye bath was heated from room temperature to 80°C at an initial heating rate of 2°C / min and held for 10 minutes, and then heated to 130°C at a rate of 1°C / min and held for 40 minutes.

[0030] Furthermore, in the application method of the liquid disperse dye modified by nanomaterials described in this invention, the reduction cleaning and color-fixing treatment includes:

[0031] The amount of residual dye on the dyed fibers is detected in real time by a spectrometer. The detection data is input into the genetic algorithm model, which outputs an optimized combination of parameters: sodium hydrosulfite concentration of 2-5 g / L, washing temperature of 70-80℃, and polyquaternary ammonium salt fixing agent dosage of 1-3%.

[0032] Furthermore, in the application method of the liquid disperse dye based on nanomaterial modification described in this invention, the wastewater recycling treatment includes:

[0033] Detection of modified nanoparticles in dyeing wastewater using dynamic light scattering instrument The concentration and particle size distribution data are input into the genetic algorithm model to generate a combination of recovery parameters with a sedimentation time of 2-4 hours, a filtration accuracy of 0.1-0.5μm, and a drying temperature of 80-100℃. The recovered nanomaterials are then reused in the dye formulation.

[0034] The invention has beneficial effects;

[0035] This invention significantly improves the performance and application efficiency of liquid disperse dyes through surface functionalization modification of nanomaterials and multi-parameter synergistic optimization using a genetic algorithm. Modified nanomaterials Surface amino functional groups form directional chemical bonds with dye molecules, enhancing the dye's adsorption strength on the fiber surface and its thermal migration stability under high-temperature conditions. Combined with a gradient heating process to dynamically match fiber adsorption kinetics, this improves the consistency of dyeing depth by 15%-20%, achieving a color fastness grade of 4 or higher. A genetic algorithm model integrates fiber pretreatment parameters, dyeing process variables, and wastewater recovery indicators. Through multi-objective iterative optimization, it generates a globally optimal process combination, overcoming the limitations of low efficiency and poor adaptability of traditional empirical parameter matching methods. This reduces the COD value of dyeing wastewater. The combined design of surfactants and dispersants synergistically inhibits dye aggregation, improving the long-term stability of the dispersion system. Simultaneously, cationic fixing agents strengthen the bonding between dye and fiber interfaces through charge neutralization, improving wet rubbing color fastness to grade 4. The above technical solutions achieve closed-loop control of precise dyeing process parameters, efficient resource utilization, and clean production, balancing dyeing quality, energy consumption optimization, and environmental protection requirements. Attached Figure Description

[0036] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0037] Figure 1 A flowchart illustrating the application method of liquid disperse dyes modified with nanomaterials provided in this embodiment of the invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. The technical solutions provided by various embodiments of this invention will be described in detail below with reference to the accompanying drawings. To better understand the objectives of this invention, it will be described in further detail below.

[0039] The present invention provides a method for applying liquid disperse dyes modified with nanomaterials, comprising: the following components by mass percentage:

[0040] Disperse Violet 93: 10.0%-12.0%;

[0041] Disperse Orange 288: 15.0%-17.0%;

[0042] Disperse Blue 291:1: 25.0%-28.0%;

[0043] Cetyl / octadecylamine polyoxyethylene ether: 0.5%-1.0%;

[0044] Laurylamine polyoxyethylene ether: 0.5%-1.0%;

[0045] Sodium lignosulfonate: 3.0%-5.0%;

[0046] Modified nano 1.5%-2.5%, the modified nano Surface functionalization modification with silane coupling agent;

[0047] Organosilicon defoamer: 0.3%-0.5%;

[0048] The remainder is deionized water.

[0049] The liquid disperse dye based on nanomaterial modification provided by this invention has a component design centered on functional synergy. Through main dye compounding, synergistic interaction between surfactant and dispersant, nanomaterial modification and solvent regulation, a stable nanocomposite dispersion system is formed.

[0050] Disperse Violet 93, Disperse Orange 288, and Disperse Blue 291:1, three main dyes, are blended at mass percentages of 10.0%-12.0%, 15.0%-17.0%, and 25.0%-28.0%, respectively, to achieve hue control in darker color schemes based on the principle of complementary primary colors. Disperse Violet 93 serves as the base color blending component, providing a stable purple base color. Disperse Orange 288 enhances the dye's penetration depth into the fiber and improves dyeing saturation through its conjugated molecular structure. Disperse Blue 291:1, due to its polycyclic aromatic hydrocarbon structure, enhances the van der Waals forces between the dye and the fiber, improving thermal stability and color fastness. The defined mass percentage ranges of the three components, verified through experiments, balance hue accuracy and dyeing depth, avoiding color deviations or uneven penetration caused by excessively high proportions of a single dye.

[0051] Cetyl / octadecylamine polyoxyethylene ether and laurylamine polyoxyethylene ether are used as nonionic surfactants, compounded at a mass percentage of 0.5%-1.0%, respectively. The long carbon chain (C16-C18) of the former molecule can form hydrophobic interactions with the hydrophobic groups of the dye molecules, while the short carbon chain (C12) of the latter can reduce the surface tension of the aqueous phase. Their synergistic effect effectively reduces the interfacial free energy between the dye molecules and nanomaterials, inhibits the nonspecific adsorption of dye molecules and nanoparticles, and promotes the uniform dispersion of the nanocomposite system in the aqueous phase. The choice of nonionic surfactants avoids the charge neutralization problems that may be caused by ionic surfactants, achieving stability of the dispersion system over a wide pH range.

[0052] Sodium lignosulfonate, as an anionic dispersant, is added at a mass percentage of 3.0%-5.0%. Its molecule contains multiple sulfonic acid groups, which can stabilize the composite structure of nanoparticles and dye molecules through electrostatic repulsion. The aromatic ring structure of sodium lignosulfonate can form π-π stacking interactions with the aromatic groups of dye molecules, further enhancing the steric hindrance effect of the composite system. This prevents particle aggregation and sedimentation caused by temperature changes or mechanical shearing during storage or processing, maintaining the long-term dispersion stability of the dye system.

[0053] Modified nano These are silica nanoparticles surface-functionalized with silane coupling agents, comprising 1.5%–2.5% by mass. The silane coupling agents (such as γ-aminopropyltriethoxysilane) are applied to the nanoparticles via a hydrolysis-condensation reaction. Amino functional groups are introduced onto the surface, which can form hydrogen bonds or covalent bonds with polar groups such as hydroxyl and carboxyl groups in dye molecules, creating directional binding sites between dye molecules and nanoparticles. These binding sites enhance the adsorption strength of dye molecules on the fiber surface and inhibit the thermal migration of dye molecules under high-temperature dyeing conditions (130℃), reducing the precipitation of dye from the fiber interior to the surface and improving the thermal migration color fastness after dyeing.

[0054] The silicone defoamer is added at a mass percentage of 0.3%-0.5%. Its polysiloxane segments have low surface tension, allowing it to selectively penetrate the gas-liquid interface of microbubbles within the system, disrupting the elastic balance of the bubble film and promoting bubble rupture. The amount added must be controlled within a specified range: below 0.3%, it cannot effectively eliminate microbubbles generated during high-speed shearing; above 0.5%, it may cause the defoamer to form new interfaces in the system, affecting the uniformity of dye dispersion.

[0055] Deionized water is used as the solvent medium, accounting for the remainder (approximately 50%) of the sum of the mass percentages of all components. The use of deionized water prevents the complexation reaction between calcium and magnesium ions in tap water and dye molecules or dispersants, reducing the formation of insoluble substances in the system. By adjusting the amount of deionized water added, the viscosity and solids content of the system can be controlled, balancing the interactions between the components: excessively high solids content (e.g., exceeding 50%) may increase the system viscosity, affecting the penetration efficiency during dyeing; excessively low solids content (e.g., below 40%) may reduce the effective concentration of the dye, affecting the dyeing depth.

[0056] In the preparation process, the order of addition of each component and the shear conditions must be strictly controlled. First, Disperse Violet 93, Disperse Orange 288, and Disperse Blue 291 are premixed in a ratio of 1:1 in a portion of deionized water. Initial dispersion is achieved by low-speed stirring (100-300 rpm) to avoid damage to the dye molecular structure caused by high-speed shear. Then, hexadecyl / octadecylamine polyoxyethylene ether, laurylamine polyoxyethylene ether, sodium lignosulfonate, and organosilicon defoamer are added sequentially. Under high-speed shear conditions (1500-3000 rpm), the nanoparticles and dye molecules are fully composited. During this process, the shear force can break the agglomerates of nanoparticles and promote their interfacial bonding with dye molecules. Finally, the remaining deionized water is added, and stirring is continued (500-800 rpm) until the system is homogeneous and free of precipitation. The chemical stability is verified by pH testing (pH range 5.0-6.0). Before filtration and packaging, the sample is sieved through a 200-300 mesh screen to remove mechanical impurities and large, undispersed particles, thus avoiding color spot defects caused by particle aggregation during dyeing.

[0057] The selection of each component and the control of process parameters jointly enabled the efficient preparation and performance optimization of nanocomposite dyes. The complementary blending of the main dyes achieved accurate hue and dyeing depth, while the synergistic effect of surfactants and dispersants ensured dispersion stability. Modified nano... The chemical bonding enhances the adsorption and thermal migration stability, and the solvent regulation of deionized water balances the viscosity and solid content of the system, ultimately forming a high-performance liquid disperse dye suitable for synthetic fibers such as polyester and nylon.

[0058] Specifically, the application method of the liquid dispersed dye based on nanomaterial modification according to the present invention includes the following steps:

[0059] Step 1: Collect porosity and surface roughness data of polyester or nylon fibers, input them into the genetic algorithm model, and generate a combination of parameters for sodium hydroxide concentration, treatment temperature and time for alkali reduction treatment.

[0060] Step 2: Perform alkali reduction treatment on the fibers according to the combined parameters to control the surface roughness of the fibers;

[0061] Step 3: Immerse the treated fibers in a dye bath, which is prepared by diluting liquid disperse dye at a bath ratio of 1:5 to 1:8 and adjusting the pH to 5.0 to 5.5;

[0062] Step 4: Input the dyeing heating rate, holding time and dye liquor circulation flow rate into the genetic algorithm model, and use the dyeing depth, color fastness and wastewater COD value as fitness indicators to iteratively screen and obtain the optimized process parameters.

[0063] Step 5: Perform gradient temperature dyeing process based on the optimized process parameters. After dyeing, dynamically adjust the reduction cleaning and cationic color fixing parameters according to the floating color residue data, and optimize the precipitation and filtration parameters by using the modified nano-SiO2 concentration and particle size distribution data in the wastewater.

[0064] The method for applying liquid disperse dyes modified with nanomaterials described in this invention achieves dynamic control of fiber pretreatment, dyeing parameter adaptation, and resource recovery through the coordinated process of data acquisition, model optimization, process execution, and post-processing.

[0065] In step 1, the porosity and surface roughness data of polyester or nylon fibers were acquired using quantitative detection methods. Porosity data were determined by mercury intrusion porosimetry or gas adsorption. The former calculates porosity by utilizing the volume change of mercury permeating the fiber pores under pressure, while the latter analyzes the pore structure using nitrogen adsorption-desorption isotherms. Surface roughness data were acquired using laser confocal microscopy or atomic force microscopy. Laser confocal microscopy acquires the three-dimensional surface morphology through non-contact scanning, while atomic force microscopy measures microscopic undulations through probe contact scanning. The acquired porosity (8%-12%) and roughness (1.5-2.5 μm) data were used as initial input variables and fed into the pre-trained genetic algorithm model. This model is built on a historical experimental database and includes the mapping relationship between fiber characteristics and alkali reduction treatment parameters. With fiber adsorption efficiency as the fitness target, it iteratively screens within a parameter space of sodium hydroxide concentration of 5-10 g / L, treatment temperature of 80-90℃, and treatment time of 20-30 minutes through crossover, mutation, and selection operations. Finally, it outputs the combination of alkali reduction treatment parameters that are adapted to the fiber surface morphology.

[0066] In step 2, based on the parameter combination output from step 1, the fiber undergoes a surface etching reaction with sodium hydroxide solution during the alkali reduction treatment. The sodium hydroxide solution is prepared with deionized water, and its concentration is determined by the model output of 5-10 g / L. The treatment temperature is controlled at 80-90℃ to accelerate the etching reaction kinetics. The treatment time is set to 20-30 minutes to balance the increase in surface roughness with the loss of fiber mechanical properties. The treated fiber is rinsed multiple times with deionized water until pH neutral, and surface moisture is removed by centrifugation or hot air drying, forming a multi-level surface structure with increased micropore density and controllable roughness, providing more binding sites for the subsequent adsorption of nanocomposite dyes.

[0067] In step 3, before immersing the treated fibers in the dye bath, the liquid disperse dye needs to be diluted at a bath ratio of 1:5 to 1:8. The dilution operation involves mixing the liquid dye with deionized water by volume using a metering pump. The bath ratio range is chosen based on a balance between the dye solids content (approximately 50%) and the required dyeing depth, ensuring that the effective dye concentration in the dye bath meets the fiber adsorption requirements. The diluted dye bath is then adjusted to a pH of 5.0-5.5 using an acetate-sodium acetate buffer solution. This pH range promotes the ionization of polar groups (such as hydroxyl and carboxyl groups) in the dye molecules, enhancing their electrostatic bonding with anionic sites on the fiber surface, while avoiding excessive acidity that could lead to protonation of amino groups on the nanomaterial surface (affecting the bonding with dye molecules).

[0068] In step 4, the dyeing process parameters are optimized based on the dye bath prepared in step 3. The dyeing heating rate, holding time, and dye liquor circulation flow rate are input variables into the genetic algorithm model. The range of input variables is: heating rate 1-2℃ / min, holding time 30-50 minutes, and circulation flow rate 5-10L / min. The model uses dyeing depth (≥4 grade), color fastness (≥4 grade), and wastewater COD value (≤100mg / L) as fitness indicators. Through roulette wheel selection and elite retention strategies, iterative screening prioritizes the retention of parameter combinations that simultaneously meet all three indicators. During the optimization process, the model combines the thermal stability of nanomaterials (no decomposition at 130℃) and the diffusion kinetics of dye molecules (penetration rate in the 80-130℃ range) to dynamically adjust the parameter combinations, ultimately outputting optimized process parameters that are suitable for the current fiber characteristics and dye bath concentration.

[0069] In step 5, a gradient temperature dyeing process is executed based on the optimized process parameters from step 4. In the initial heating stage, the dye bath is raised from room temperature to 80°C at a rate of 2°C / min. This rate, determined through model screening, aims to balance the adsorption rate of dye molecules with the thermal expansion effect of the fiber. After reaching 80°C, the temperature is held for 10 minutes, utilizing the dispersion effect of surfactants to form a uniform adsorption layer of nanocomposite dye on the fiber surface. In the subsequent heating stage, the temperature is raised to 130°C at a rate of 1°C / min. This rate is suitable for the modified nanocomposite dyes. The thermal stability threshold is reduced, and the hydrolysis of dye molecules at high temperatures is reduced. After maintaining at 130°C for 40 minutes, the nanomaterial anchors the dye molecules through the surface amino functional groups, which enhances the diffusion and penetration of the dye into the fiber. At the same time, the circulating pump drives the dye liquor to flow to eliminate the concentration gradient and improve the dyeing uniformity.

[0070] After dyeing, the amount of residual dye on the fiber surface (absorbance at a specific wavelength) is detected using a UV-Vis spectrophotometer. The detection data is input into a genetic algorithm model to dynamically adjust the reduction cleaning parameters. During the reduction cleaning stage, the concentration of sodium hydrosulfite is determined by the model output of 2-5 g / L, and the cleaning temperature is controlled at 70-80℃ to decompose unfixed dye molecules through a reduction reaction. The dosage of polyquaternary ammonium salt fixing agent is 1-3%, which enhances the binding between the dye and the fiber through the electrostatic interaction between cationic groups and anionic sites on the fiber surface. In the wastewater treatment stage, a dynamic light scattering instrument is used to detect modified nanoparticles in the wastewater. The concentration and particle size distribution (measured under scattering angle of 90° and wavelength of 532nm) are used to input the detection data into the model to generate recovery parameters with precipitation time of 2-4 hours, filtration accuracy of 0.1-0.5μm, and drying temperature of 80-100℃, so as to realize the efficient recovery and recycling of nanomaterials.

[0071] A closed-loop control is formed through data-driven dynamic correlation between each step: the fiber characteristic data in step 1 provides the basis for the alkali reduction treatment in step 2; the treatment result in step 2 affects the dye bath adsorption efficiency in step 3; the dye bath state in step 3 determines the direction of parameter optimization in step 4; the optimized parameters in step 4 guide the process execution in step 5; and the post-treatment data in step 5 (floating dye residue, nanomaterial concentration) is fed back to the model to further optimize the process parameters for subsequent batches. This process, through the synergy of quantitative detection, algorithm optimization, and process execution, overcomes the limitations of static parameter settings in traditional processes, achieving a comprehensive improvement in dyeing quality, energy consumption control, and environmental protection requirements.

[0072] Specifically, in the application method of liquid disperse dyes based on nanomaterial modification described in this invention, the matching of alkali reduction treatment parameters includes:

[0073] The generation of the combined parameters for the alkali reduction treatment includes:

[0074] The porosity and surface roughness data of the fiber are input into the genetic algorithm model. With the fiber adsorption efficiency as the fitness target, the model outputs a combination of parameters with a sodium hydroxide concentration range of 5-10 g / L, a treatment temperature of 80-90℃, and a treatment time of 20-30 minutes.

[0075] The alkali reduction treatment parameter matching process described in this invention achieves a dynamic correlation between fiber surface morphology and treatment conditions through quantitative detection of fiber characteristics, algorithm model optimization, and parameter screening, providing an interfacial basis for the efficient adsorption of subsequent nanocomposite dyes.

[0076] Standardized testing methods were used to collect fiber porosity and surface roughness data. Porosity data were determined using mercury intrusion porosimetry or gas adsorption: mercury intrusion porosimetry utilizes the volume change of mercury permeating fiber pores under different pressures to calculate porosity (8%-12%) and pore size distribution; gas adsorption uses nitrogen adsorption-desorption isotherm analysis to obtain the specific surface area and pore size distribution of micropores within the fiber. Surface roughness data were collected using non-contact laser confocal microscopy or contact atomic force microscopy: laser confocal microscopy obtains the arithmetic mean deviation of the surface profile (Ra value 1.5-2.5 μm) through three-dimensional scanning, reflecting macroscopic roughness; atomic force microscopy measures the root mean square deviation of microscopic undulations (Rq value 0.5-1.0 μm) through probe scanning, reflecting microscopic roughness. These two types of data together constitute a quantitative index of fiber surface morphology, providing multi-dimensional features for model input.

[0077] The genetic algorithm model was constructed based on a historical experimental database containing data on fiber adsorption efficiency (the ratio of dye adsorption to fiber mass) under different fiber types (polyester, nylon), different alkali concentrations (3-15 g / L), treatment temperatures (70-100℃), and treatment times (10-40 minutes). The model uses porosity and surface roughness as input variables and fiber adsorption efficiency as the fitness function. It performs a global search within the parameter space through crossover, mutation, and selection operations. The crossover operation generates new individuals by exchanging some variables (such as temperature and time) with different parameter combinations. The mutation operation randomly perturbs a single variable (such as concentration) (±1 g / L). The selection operation retains parameter combinations with high adsorption efficiency (≥85%) based on fitness values, avoiding local optima.

[0078] The parameter space is defined based on the chemical properties of the fiber and the process requirements. The sodium hydroxide concentration is set at 5-10 g / L, covering the etching threshold of the amorphous regions on the surface of polyester or nylon fibers: below 5 g / L, the etching reaction rate is too slow to effectively increase micropore density; above 10 g / L, excessive etching of crystalline regions may occur, leading to a decrease in fiber mechanical properties (such as breaking strength). The treatment temperature is set at 80-90℃, which is the activation energy matching temperature for the reaction between sodium hydroxide and the fiber: below 80℃, reaction kinetics are insufficient, resulting in low etching efficiency; above 90℃, solution evaporation intensifies, and concentration fluctuations affect treatment uniformity. The treatment time is set at 20-30 minutes, balancing etching depth and fiber damage: less than 20 minutes results in insignificant improvement in surface roughness; more than 30 minutes results in fiber weight loss (mass loss ratio) exceeding 5%, affecting the final mechanical properties of the textile.

[0079] The model iteratively selects the output parameter combinations, and experimentally verifies their suitability for the fiber surface morphology. For example, when the fiber porosity is 10% and the roughness Ra = 2.0 μm, the model may output a combination of sodium hydroxide concentration of 8 g / L, temperature of 85℃, and time of 25 minutes. Under this combination, the sodium hydroxide solution selectively removes the amorphous regions on the fiber surface through etching, increasing the micropore density by 30%-40% and raising the roughness Ra to 2.5-3.0 μm, forming a multi-level surface structure conducive to the adsorption of nanocomposite dyes. The treated fibers are rinsed with deionized water until the pH is neutral (pH = 7 ± 0.5), and surface moisture is removed by centrifugation (3000-5000 rpm) or hot air drying (60-80℃) to ensure no residual alkali solution interferes with the subsequent dye bath adsorption process.

[0080] The dynamic nature of the parameter matching process is reflected in the model's response to different fiber characteristics. For fibers with low porosity (8%) and low roughness (Ra=1.5μm), the model tends to select a higher sodium hydroxide concentration (9-10g / L) or a longer treatment time (28-30 minutes) to enhance the etching effect; for fibers with high porosity (12%) and high roughness (Ra=2.5μm), the model reduces the concentration (5-6g / L) or shortens the time (20-22 minutes) to avoid over-etching. This adaptation mechanism ensures that the surface morphology of different batches of fibers after treatment meets the adsorption requirements of nanocomposite dyes, thus improving the stability of the dyeing process.

[0081] In summary, the matching of alkali reduction treatment parameters, through quantitative detection, model optimization, and dynamic adaptation, achieved a precise correlation between fiber surface morphology and treatment conditions, providing an interfacial basis for the efficient adsorption of subsequent nanocomposite dyes. At the same time, it balanced fiber mechanical properties and process efficiency, supporting the quality control and repeatability of the dyeing process.

[0082] Specifically, the application method of the liquid disperse dye based on nanomaterial modification according to the present invention includes the following dyeing process optimization:

[0083] The dyeing heating rate, holding time, pH value, and circulating pump flow rate are used as input variables and input into the genetic algorithm model. The dyeing depth, color fastness grade, and wastewater COD value are used as fitness indicators. Through multiple generations of iteration, the combination of process parameters with dyeing depth ≥ 4, color fastness ≥ 4, and COD value ≤ 100 mg / L is selected.

[0084] The dyeing process optimization process described in this invention achieves dynamic adaptation of dyeing parameters with fiber characteristics and dye performance through input variable definition, model construction, multi-objective iterative screening, and parameter verification, thereby supporting the synergistic improvement of dyeing quality and environmental protection requirements.

[0085] The selection of input variables is based on key control factors in the dyeing process. The dyeing heating rate refers to the rate of change of the dye bath temperature over time, set within the range of 1-3℃ / min. This range covers the kinetic requirements for dye molecules to penetrate from the fiber surface to the interior: a rate that is too low (<1℃ / min) may prolong the process time and increase energy consumption; a rate that is too high (>3℃ / min) may lead to uneven dye adsorption, affecting the dyeing depth. The holding time refers to the duration the dye bath is maintained at the target temperature (e.g., 130℃), set within the range of 20-60 minutes. This duration needs to balance dye penetration depth and thermal migration stability: a time that is too short (<20 minutes) may result in insufficient dye fixation; a time that is too long (>60 minutes) may exacerbate dye hydrolysis and reduce color fastness. pH value refers to the acidity or alkalinity of the dye bath, with a range of 4.5-6.0. This range affects adsorption efficiency by adjusting the charge state between dye molecules and the fiber surface: too low a pH (<4.5) may lead to protonation of amino groups on the surface of nanomaterials, weakening the bond with dye molecules; too high a pH (>6.0) may increase the tendency of dye hydrolysis. The circulation pump flow rate refers to the flow rate of the dye liquor within the dyeing equipment, with a range of 3-15 L / min. This flow rate is necessary to achieve uniform dye liquor concentration: too low a flow rate (<3 L / min) may lead to localized concentration gradients, resulting in uneven coloring; too high a flow rate (>15 L / min) may increase equipment energy consumption and the risk of fiber mechanical damage.

[0086] The genetic algorithm model was constructed based on a historical experimental database, which contained correlation data between different combinations of input variables (heating rate, holding time, pH value, and circulating pump flow rate) and corresponding fitness indices (dyeing depth, color fastness, and COD value). Dyeing depth was determined by measuring the reflectance of the fiber surface using a spectrophotometer and quantified according to the K / S value (Kubelka-Munk function). The grade was determined by referring to existing standards. Wastewater COD value was determined using the potassium dichromate method, reflecting the organic matter content in the wastewater. The model used these three indices as fitness functions and, through roulette wheel selection and elite retention strategies, prioritized retaining parameter combinations that simultaneously met the requirements of dyeing depth ≥ 4, color fastness ≥ 4, and COD value ≤ 100 mg / L.

[0087] The iterative selection process is achieved through multi-generational population evolution. The initial population consists of randomly generated combinations of input variables, with each generation containing 50-100 parameter combinations. Fitness values ​​are calculated for each generation through simulated dyeing experiments: dyeing depth is determined by the diffusion degree of dye molecules within the fiber, influenced by the heating rate and holding time; color fastness is related to the anchoring strength of nanomaterials to dye molecules, influenced by pH and circulation pump flow rate; COD value is determined by the amount of unadsorbed dye and residual surfactant, influenced by holding time and circulation pump flow rate. Parameter combinations with fitness values ​​below a threshold are eliminated. High-fitness individuals generate a new generation through crossover (exchanging some variables in different combinations, such as exchanging heating rate and holding time) and mutation (randomly perturbing a single variable, such as pH ±0.2). The number of iterations is set to 20-30 generations to achieve search coverage of the main regions of the parameter space.

[0088] The optimized parameter combination output by the screening needs to be verified through actual dyeing experiments. For example, when the input variables are heating rate of 2℃ / min, holding time of 40 minutes, pH value of 5.2, and circulation pump flow rate of 10L / min, the dyeing depth K / S value can reach 23.0 (corresponding to grade 4 or above), the color fastness grade reaches 4-5 (dry rubbing / wet rubbing), and the wastewater COD value is 88mg / L (≤100mg / L), verifying the effectiveness of this combination. For different fiber types (such as polyester and nylon) or dye loading (such as dark and medium colors), the model will dynamically adjust the parameter combination: due to the high crystallinity of polyester fibers, a longer holding time (45-50 minutes) may be required to promote dye penetration; due to the strong hygroscopicity of nylon fibers, a lower pH value (5.0-5.2) may be required to enhance charge binding.

[0089] The dynamic nature of dyeing process optimization is reflected in the model's balance of multiple objectives. When there is a conflict between dyeing depth and COD value (e.g., extending the holding time may increase dyeing depth but increase COD value), the model prioritizes satisfying key indicators through the weight allocation of the fitness function (e.g., dyeing depth weight 0.4, color fastness weight 0.3, COD value weight 0.3). When there is a contradiction between color fastness and energy consumption (e.g., reducing the heating rate may increase color fastness but increase energy consumption), the model selects the comprehensive optimal solution based on the statistical regularity of historical data. This multi-objective optimization mechanism achieves global optimality of process parameters, rather than local optimality of a single indicator.

[0090] In summary, by precisely defining input variables, iterating the model through multiple objectives, and dynamically verifying parameters, the optimization of the dyeing process achieves the adaptation of dyeing parameters to fiber characteristics and dye performance. This improves dyeing quality while reducing pollutant emissions, thus supporting the achievement of clean production goals.

[0091] Specifically, the application method of the present invention includes a gradient temperature staining process comprising:

[0092] Based on the selected combination of process parameters, the dye bath was heated from room temperature to 80°C at an initial heating rate of 2°C / min and held for 10 minutes, and then heated to 130°C at a rate of 1°C / min and held for 40 minutes.

[0093] The gradient temperature dyeing process described in this invention controls the dye bath temperature in stages, combining the characteristics of nanomaterials with fiber adsorption kinetics, to achieve an orderly process of dye molecules from surface adsorption to internal fixation, thus supporting a synergistic improvement in dyeing depth and color fastness.

[0094] In the initial heating stage, the dye bath is heated from room temperature (25-30℃) to 80℃ at a rate of 2℃ / min. This heating rate is chosen based on the adsorption kinetics of dye molecules and the thermal expansion characteristics of the fiber: if the rate is lower than 2℃ / min, the adsorption rate of dye molecules in the low-temperature region is too slow, potentially prolonging the process time; if the rate is higher than 2℃ / min, the surface micropore size of the fiber changes due to thermal expansion, potentially affecting the initial uniformity of dye adsorption. After reaching 80℃, the temperature is held for 10 minutes. During this stage, the dispersing effect of surfactants (such as hexadecyl / octadecylamine polyoxyethylene ether and laurylamine polyoxyethylene ether) is utilized to promote the formation of a uniform adsorption layer of nanocomposite dyes on the fiber surface. The holding time is set based on the reaction kinetics of the dye molecules and the binding sites on the fiber surface: if it is less than 10 minutes, the dye molecules will not completely cover the fiber surface, potentially leading to color spots in the subsequent high-temperature stage; if it is longer than 10 minutes, the dye molecules will excessively aggregate on the surface, potentially increasing the amount of residual dye.

[0095] During the high-temperature phase, the dye bath was heated from 80℃ to 130℃ at a rate of 1℃ / min. This heating rate was chosen to suit the modified nanomaterials. The thermal stability threshold is as follows: when the rate is below 1℃ / min, the process time is prolonged and energy consumption increases; when the rate is above 1℃ / min, the amino functional groups on the surface of the nanomaterial may dissociate from the dye molecules due to the sudden temperature rise, weakening the anchoring effect. After reaching 130℃ and maintaining it for 40 minutes, this stage utilizes the high-temperature diffusion characteristics of disperse dyes (130℃ is above the glass transition temperature of polyester fibers) to promote the diffusion and penetration of dye molecules from the fiber surface into the internal amorphous region. The holding time is set based on the diffusion kinetics of dye molecules inside the fiber: less than 40 minutes results in insufficient dye penetration into the fiber interior and inadequate dyeing depth; more than 40 minutes may cause dye molecules to hydrolyze at high temperatures, leading to a decrease in color fastness and an increase in the COD value of the wastewater.

[0096] The design of the gradient heating curve is closely related to the fiber surface morphology (roughness after alkali reduction treatment) and the properties of nanomaterials (bonding ability of surface amino functional groups). For fibers with high roughness (Ra=2.5-3.0μm) after alkali reduction treatment, the holding time in the initial heating stage can be appropriately extended (e.g., 12 minutes) to ensure that dye molecules fully cover the surface micropores; for fibers with low roughness (Ra=1.5-2.0μm), the holding time can be shortened (e.g., 8 minutes) to avoid excessive dye accumulation on the surface. Modified nanomaterials The presence of [a specific substance] inhibits the thermal migration of dyes at high temperatures by bonding with surface amino groups to dye molecules, reducing the precipitation of dyes from the fiber interior to the surface, thereby improving thermal migration color fastness.

[0097] The synergistic effect of temperature and time at each stage was optimized using a genetic algorithm. When selecting process parameters, the model comprehensively considered the compatibility of the initial heating rate with fiber thermal expansion, the matching of the high-temperature rate with the thermal stability of the nanomaterials, and the consistency of the holding time with the dye diffusion kinetics. For example, when the model outputs initial parameters of a heating rate of 2℃ / min and a holding time of 10 minutes, the corresponding fiber surface micropore density is moderate (porosity 10%), allowing for rapid and uniform adsorption of dye molecules. When the output parameter is a high-temperature rate of 1℃ / min and a holding time of 40 minutes, the corresponding nanomaterial surface amino density is high (≥2μmol / m²), effectively anchoring dye molecules and supporting diffusion and penetration during the high-temperature stage.

[0098] In summary, the gradient temperature dyeing process, through staged temperature control and combining the characteristics of nanomaterials with the surface morphology of fibers, achieves an orderly process from surface adsorption to internal fixation of dye molecules. This enhances color fastness while increasing dyeing depth, supporting the quality control and stability of the dyeing process.

[0099] Specifically, the application method of liquid disperse dyes based on nanomaterial modification according to the present invention includes the reduction cleaning and color fixing treatment as follows:

[0100] The amount of residual dye on the dyed fibers is detected in real time by a spectrometer. The detection data is input into the genetic algorithm model, which outputs an optimized combination of parameters: sodium hydrosulfite concentration of 2-5 g / L, washing temperature of 70-80℃, and polyquaternary ammonium salt fixing agent dosage of 1-3%.

[0101] The reduction cleaning and color-fixing process described in this invention achieves efficient removal of unfixed dyes and improves color fastness through the detection of residual floating dye, optimization of model parameters, and dynamic correlation of process execution, thus supporting the quality control of dyed fibers.

[0102] Real-time detection of residual dye was achieved using a spectrometer for quantitative analysis. The amount of residual dye on the dyed fibers was measured using a UV-Vis spectrophotometer. The detection wavelength was selected based on the maximum absorption wavelength of the dye molecules (e.g., approximately 570 nm for Disperse Violet 93 and approximately 480 nm for Disperse Orange 288). The concentration of unfixed dye was quantified by measuring the absorbance of light reflected from the fiber surface. The detection data was transmitted in real-time to a genetic algorithm model via a data acquisition card, forming a continuous data stream reflecting the amount of residual dye and providing dynamic input for parameter optimization.

[0103] The genetic algorithm model's parameter optimization is based on a historical cleaning experiment database, which contains correlation data between different residual colorants (0.2%-1.0%) and corresponding sodium hydrosulfite concentrations (1-6 g / L), cleaning temperatures (60-90℃), and polyquaternary ammonium salt fixing agent dosages (0.5-4%). ​​The model primarily optimizes for residual colorants ≤0.5%, while also considering cleaning energy consumption (the product of temperature and time) and fixing agent cost (the ratio of dosage to effect). Through crossover, mutation, and selection operations, the optimal combination is selected within the parameter space. Crossover generates new individuals by exchanging some variables (such as sodium hydrosulfite concentration and cleaning temperature) of different parameter combinations. Mutation randomly perturbs individual variables (such as fixing agent dosage) by ±0.5%. Selection retains the parameter combination with the lowest residual colorant content and the best overall cost.

[0104] The concentration of sodium hydrosulfite is set at 2-5 g / L, covering the reduction requirements of unfixed dyes: below 2 g / L, the reducing capacity is insufficient and cannot effectively decompose floating dye; above 5 g / L, excessive sodium hydrosulfite may react with the fiber, causing yellowing or a decrease in fiber strength. The cleaning temperature is set at 70-80℃, which is the optimal reaction temperature for sodium hydrosulfite: below 70℃, the reduction reaction rate is too slow, prolonging the cleaning time; above 80℃, the decomposition rate of sodium hydrosulfite accelerates, increasing the loss of effective components and affecting the cleaning effect.

[0105] The dosage of polyquaternary ammonium salt fixing agent is set at 1-3%. This range balances charge neutralization and fixing efficiency: when the dosage is below 1%, the cationic groups are insufficient to cover the anionic sites on the fiber surface, resulting in limited fixing effect; when the dosage is above 3%, excessive fixing agent may form a film on the fiber surface, affecting the dyeing depth or causing color deviation. Fixing treatment is usually carried out after reduction cleaning. After cleaning, the fibers are rinsed with deionized water until neutral (pH=6-7), and then immersed in a solution containing polyquaternary ammonium salt fixing agent, which binds to the anionic groups on the fiber surface through electrostatic interaction.

[0106] The synergistic effect of the parameter combination was verified experimentally. For example, when the residual dye content was 0.4%, the model might output a combination of sodium hydrosulfite concentration of 3.5 g / L, cleaning temperature of 75℃, and fixing agent dosage of 2.5%. Under this combination, sodium hydrosulfite decomposes unfixed dye molecules at 75℃, and the circulating cleaning solution (flow rate 5-8 L / min) promotes the diffusion and removal of desorbed products. A polyquaternary ammonium salt fixing agent at 2.5% is used to treat the fiber at 50-60℃, strengthening the bond between the dye and the fiber through the electrostatic interaction between cationic groups and anionic sites on the fiber surface. After treatment, the wet rubbing fastness of the fiber improved to grade 4 or higher, and the residual dye content decreased to 0.3%-0.5%, verifying the effectiveness of the parameter combination.

[0107] The dynamic nature of the reduction cleaning and color-fixing treatment is reflected in the model's response to different levels of residual dye. When the residual dye level is high (e.g., 0.6%), the model tends to select a higher concentration of sodium hydrosulfite (4-5 g / L) or a longer cleaning time (20-25 minutes); when the residual dye level is low (e.g., 0.3%), the model reduces the concentration (2-3 g / L) or shortens the time (15-20 minutes) to avoid fiber damage or increased costs due to over-treatment. This adaptive mechanism ensures that fibers from different dyeing batches can meet color fastness requirements after treatment, improving the stability of the process.

[0108] In summary, the reduction cleaning and fixation treatment, through real-time detection of residual floating dye, dynamic optimization of model parameters, and precise execution of the process, achieves efficient removal of unfixed dyes and improved color fastness, supporting the quality control of dyed fibers and the repeatability of the process.

[0109] Specifically, the wastewater recycling treatment in the application method of the nanomaterial-modified liquid dispersed dye described in this invention includes:

[0110] Detection of modified nanoparticles in dyeing wastewater using dynamic light scattering instrument The concentration and particle size distribution data are input into the genetic algorithm model to generate a combination of recovery parameters with a sedimentation time of 2-4 hours, a filtration accuracy of 0.1-0.5μm, and a drying temperature of 80-100℃. The recovered nanomaterials are then reused in the dye formulation.

[0111] The wastewater recycling and treatment process described in this invention achieves modified nanomaterials in dyeing wastewater through the synergy of nanomaterial state detection, model parameter optimization, and process execution. The efficient recycling and reuse of these technologies support the construction of a closed-loop clean production system.

[0112] Modified nano-materials in dyeing wastewater The state monitoring was performed using dynamic light scattering (DLS) analysis. During testing, wastewater samples were pretreated through a 0.45 μm filter (to remove large particulate impurities) and then injected into the DLS sample cell. The detection wavelength was set to 532 nm and the scattering angle to 90°. By measuring the fluctuations in the scattering intensity of incident light by the nanoparticles, their hydrodynamic diameter and particle size distribution (e.g., the D90 value, indicating that 90% of the particles are smaller than this diameter) were calculated. Simultaneously, the concentration of the nanomaterials (in g / L) was quantified by comparing the scattered light intensity with that of a standard solution. The detection data was transmitted in real-time to a genetic algorithm model, forming multidimensional features reflecting the dispersion state of the nanomaterials, providing a basic input for parameter optimization.

[0113] The genetic algorithm model's parameter optimization is based on a historical recovery experiment database, which contains correlation data between different nanomaterial concentrations (0.5-3.0 g / L), particle size distributions (D90 = 100-300 nm), and corresponding settling times (1-6 hours), filtration precision (0.05-1.0 μm), and drying temperatures (60-120 °C). The model uses nanomaterial recovery rate (≥90%) and post-regeneration performance retention rate (surface amino functional group retention rate ≥85%, specific surface area recovery rate ≥90%) as fitness targets. Through crossover, mutation, and selection operations, the optimal combination is screened within the parameter space. The crossover operation generates new individuals by exchanging some variables (such as settling time and filtration precision) of different parameter combinations. The mutation operation randomly perturbs individual variables (such as drying temperature) (±5 °C). The selection operation retains parameter combinations with high recovery rates and good regeneration performance.

[0114] The precipitation time is set to 2-4 hours, a range that covers the modified nanomaterials. The natural sedimentation kinetics are as follows: If the sedimentation time is less than 2 hours, the nanoparticles do not fully aggregate and settle, and the turbidity of the supernatant (>10 NTU) affects subsequent filtration efficiency; if the time is longer than 4 hours, the sedimentation process tends to reach equilibrium, and extending the time has limited effect on improving the recovery rate. The filtration precision is set at 0.1-0.5 μm, which matches the particle size distribution of the nanomaterials (D90=180 nm): precision below 0.1 μm (e.g., 0.05 μm) may cause filter membrane clogging and reduced flux; precision above 0.5 μm may allow some nanoparticles to pass through, reducing the recovery rate. The drying temperature is set at 80-100℃, which is the evaporation temperature of adsorbed water on the nanomaterial surface: below 80℃, the water evaporation rate is too slow, prolonging the drying time; above 100℃, it may cause thermal decomposition of amino functional groups on the nanomaterial surface (e.g., deammoniation reaction), affecting regeneration performance.

[0115] The recovered nanomaterials need to undergo surface regeneration treatment before being reused in dye formulation. The precipitated nanomaterials are collected by centrifugation (4000-6000 rpm) or pressure filtration (0.2-0.5 MPa). The filter cake is washed with deionized water (3-5 times) to remove residual dyes and surfactants. The filtration stage uses a multi-layer gradient filter membrane (e.g., 0.5 μm pre-filtration + 0.1 μm fine filtration) to achieve efficient retention of nanoparticles. The dried nanomaterials are redispersed in deionized water by ultrasonic dispersion (frequency 20-40 kHz, power 100-300 W) to restore their dispersion stability, and then added to the dye preparation system according to the formulation ratio (1.5%-2.5%) of claim 1. The surface amino functional groups of the regenerated nanomaterials are detected by infrared spectroscopy (characteristic absorption peak approximately 3400 cm⁻¹). - ¹), the specific surface area was determined by nitrogen adsorption method (BET method) to ensure that its properties are consistent with those of the original material.

[0116] The synergistic effect of the parameter combination was verified experimentally. For example, when the concentration of nanomaterials in the wastewater was 1.8 g / L and the particle size D90 = 180 nm, the model might output a combination of a sedimentation time of 3 hours, a filtration accuracy of 0.2 μm, and a drying temperature of 90 °C. Under this combination, after the wastewater was allowed to stand for 3 hours, the turbidity of the supernatant decreased to below 5 NTU, the filter membrane flux during the filtration stage remained at 80 L / (m²·h), and after drying, the specific surface area of ​​the nanomaterials recovered to 93% of the initial value, with a surface amino retention rate of 87%. When the recycled material was reused in dye formulation, the dyeing depth deviation was <2% (compared to the dyeing depth using new nanomaterials), verifying the effectiveness of the parameter combination.

[0117] The dynamic nature of wastewater recycling is reflected in the model's response to different nanomaterial states. When the concentration of nanomaterials in the wastewater is high (e.g., 2.5 g / L) and the particle size is large (D90 = 250 nm), the model tends to select a longer settling time (3.5-4 hours) or a higher filtration precision (0.1-0.2 μm); when the concentration is low (e.g., 1.0 g / L) and the particle size is small (D90 = 120 nm), the model shortens the settling time (2-2.5 hours) or reduces the filtration precision (0.3-0.5 μm). This adaptive mechanism enables the efficient recovery of nanomaterials from different batches of wastewater, improving the economic and environmental benefits of the process.

[0118] In summary, wastewater recycling and treatment, through real-time detection of the state of nanomaterials, dynamic optimization of model parameters, and precise execution of the recycling process, has achieved the goal of modifying nanomaterials in dyeing wastewater. The efficient recycling and reuse of resources form a closed loop of clean production, supporting the synergistic goal of efficient resource utilization and environmental pollution control.

[0119] The specific implementation of this invention takes polyester fiber dyeing as an application scenario. Through the functional modification of nanomaterial surfaces and the multi-parameter synergistic optimization of genetic algorithms, it achieves efficient preparation and application of liquid disperse dyes, and solves the problem of the lack of a dynamic correlation model between nanomaterial surface modification parameters and dye molecule complex state.

[0120] The composition ratio of the liquid disperse dye is: Disperse Violet 93 (11%), Disperse Orange 288 (16%), and Disperse Blue 291:1 (27%), which are compounded according to the principle of complementary primary colors to provide a basic hue for dark colors; hexadecyl / octadecylamine polyoxyethylene ether and laurylamine polyoxyethylene ether each account for 0.8%, which inhibit non-specific adsorption of dyes and nanomaterials by reducing interfacial tension; sodium lignosulfonate accounts for 4%, which stabilizes the nanocomposite structure through electrostatic repulsion; modified nano... (Modified with γ-aminopropyltriethoxysilane) accounts for 2%, and the surface amino functional groups form hydrogen bonds with the hydroxyl groups of dye molecules to enhance adsorption stability; the balance is deionized water (about 50%) to adjust the system viscosity and solid content balance.

[0121] In the preparation process, firstly, disperse violet 93, disperse orange 288, and disperse blue 291 were added to a portion of deionized water in a ratio of 1, and the mixture was stirred at low speed (200 rpm) for 15 minutes to achieve initial dispersion. Then, surfactant, sodium lignosulfonate, and silicone defoamer (0.4%) were added sequentially, and the mixture was sheared at high speed (2000 rpm) for 30 minutes to promote the recombination of nanoparticles and dye molecules. Finally, the remaining deionized water was added, and the mixture was stirred at medium speed (600 rpm) until the system was homogeneous. Impurities were removed by passing the mixture through a 200-mesh sieve to obtain the liquid disperse dye.

[0122] In the application process, the porosity (10%) and surface roughness (Ra=2.0μm) data of polyester fibers were first collected and input into the genetic algorithm model to generate alkali reduction treatment parameters: sodium hydroxide concentration 8g / L, treatment temperature 85℃, and treatment time 25 minutes. After treatment with these parameters, the surface micropore density of the fibers increased by 35%, and the roughness was improved to Ra=2.8μm, forming a multi-level structure that is conducive to dye adsorption.

[0123] During dye bath preparation, the liquid disperse dye was diluted at a bath ratio of 1:6, and an acetate-sodium acetate buffer solution was added to adjust the pH to 5.2, achieving electrostatic binding between dye molecules and anionic sites on the fiber surface. Dyeing process parameters were determined through model optimization: an initial heating rate of 2℃ / min to 80℃, followed by a 10-minute holding period to promote uniform dye adsorption; subsequently, a heating rate of 1℃ / min to 130℃, followed by a 40-minute holding period, was used to enhance dye penetration by utilizing the thermal stability of nanomaterials.

[0124] After dyeing, the residual dye content on the fibers was measured using a UV-Vis spectrophotometer (0.4%). The model was then used to generate reduction cleaning parameters: sodium hydrosulfite concentration 3.5 g / L, cleaning temperature 75℃, and polyquaternary ammonium salt fixing agent dosage 2.5%. After cleaning, the wet rubbing color fastness of the fibers improved to grade 4, and the residual dye content decreased to 0.3%. During wastewater treatment, a dynamic light scattering instrument was used to detect the modified nano-... With a concentration of 1.8 g / L, a particle size of D90 = 180 nm, a model generation precipitation time of 3 hours, a filtration accuracy of 0.2 μm, and a drying temperature of 90 °C, the specific surface area of ​​the recovered nanomaterials was restored to 93% of the initial value. After being reused in dye formulation, the dyeing depth deviation was <2%.

[0125] The above implementation method uses a genetic algorithm model to quantify the mapping relationship between the surface modification parameters of nanomaterials (silane coupling agent concentration 1.0%, modification temperature 70℃, reaction time 1.5 hours) and the dye dispersion stability (Zeta potential -35mV, dispersion stability index 92%) and fiber adsorption efficiency (adsorption amount per unit area 0.8mg / cm²), which solves the problem of missing dynamic correlation models and realizes precise control of dyeing process parameters and a clean production closed loop.

[0126] This invention addresses the lack of dynamic correlation models by constructing a multi-parameter dynamic correlation model based on a genetic algorithm, integrating historical experimental data on nanomaterial surface modification parameters and dye molecule complex states. The model uses surface modification parameters such as silane coupling agent concentration, modification temperature, and reaction time as input variables, and dye dispersion stability indices (e.g., Zeta potential, particle size distribution) and fiber adsorption efficiency (e.g., adsorption amount per unit area) as output indicators. Through iterative training using crossover, mutation, and selection operations, a quantitative mapping relationship between modification parameters and complex states is established, overcoming the limitations of traditional empirical parameter matching methods.

[0127] The model directly quantifies the correlation between key parameters and dispersion stability by screening surface modification parameter combinations through multi-objective optimization. For example, given a parameter space with a silane coupling agent concentration of 0.5%-1.5%, a modification temperature of 60-80℃, and a reaction time of 1-2 hours, the model outputs the optimal bonding conditions between the amino functional group density and the polar groups of the dye molecules, with a dispersion stability index ≥90% as the fitness target, thereby inhibiting nanoparticle aggregation and improving the long-term stability of the dispersion system.

[0128] The model is further embedded in the dyeing process chain. Through dynamic adaptation of fiber characteristic data (porosity, roughness) and process parameters (alkali reduction concentration, heating rate), the mapping between key parameters and fiber adsorption efficiency is quantified. For example, input data of fiber porosity of 8%-12% and roughness of 1.5-2.5μm are optimized by the model to generate alkali reduction parameters of sodium hydroxide concentration of 5-10g / L and treatment temperature of 80-90℃. This regulates the micropore density on the fiber surface, enabling a quantifiable functional relationship between the adsorption efficiency of the nanocomposite dye and the surface modification parameters, thus achieving precise control of the adsorption process.

Claims

1. A method for applying liquid dispersed dyes modified with nanomaterials, characterized in that, Liquid disperse dyes consist of the following components by mass percentage: Disperse Violet 93: 10.0%-12.0%; Disperse Orange 288: 15.0%-17.0%; Disperse Blue 291:1: 25.0%-28.0%; Cetyl / octadecylamine polyoxyethylene ether: 0.5%-1.0%; Laurylamine polyoxyethylene ether: 0.5%-1.0%; Sodium lignosulfonate: 3.0%-5.0%; Modified nano 1.5%-2.5%, the modified nano Surface functionalization modification with silane coupling agent; Organosilicon defoamer: 0.3%-0.5%; The remainder is deionized water; The application method includes the following steps: Data on the porosity and surface roughness of polyester or nylon fibers are collected and input into a genetic algorithm model to generate a combination of parameters for sodium hydroxide concentration, treatment temperature, and time for alkali reduction treatment. The fiber is subjected to alkali reduction treatment according to the combined parameters to control the surface roughness of the fiber. The treated fibers are immersed in a dye bath, which is prepared by diluting liquid disperse dye at a bath ratio of 1:5 to 1:8 and adjusting the pH to 5.0 to 5.

5. The dyeing heating rate, holding time and dye liquor circulation flow rate are input into the genetic algorithm model. The optimal process parameters are obtained by iterative screening using dyeing depth, color fastness and wastewater COD as fitness indicators. A gradient temperature dyeing process is executed based on the optimized process parameters. After dyeing, the reduction cleaning and cationic color-fixing parameters are dynamically adjusted according to the residual color data. Modified nanomaterials in the wastewater are then used to further enhance the dyeing process. Optimize sedimentation and filtration parameters using concentration and particle size distribution data.

2. The application method of liquid disperse dyes based on nanomaterial modification according to claim 1, characterized in that, The porosity and surface roughness data of the collected polyester or nylon fibers are input into the genetic algorithm model to generate a combination of parameters for the sodium hydroxide concentration, treatment temperature, and time for alkali reduction treatment, including: The porosity and surface roughness data of the fiber are input into the genetic algorithm model. With the fiber adsorption efficiency as the fitness target, the model outputs a combination of parameters with a sodium hydroxide concentration range of 5-10 g / L, a treatment temperature of 80-90℃, and a treatment time of 20-30 minutes.

3. The application method of liquid disperse dyes based on nanomaterial modification according to claim 2, characterized in that, The process involves inputting the dyeing heating rate, holding time, and dye liquor circulation flow rate into the genetic algorithm model, and using dyeing depth, color fastness, and wastewater COD as fitness indicators to iteratively screen and obtain optimized process parameters, including: The dyeing heating rate, holding time, pH value, and circulating pump flow rate are used as input variables and input into the genetic algorithm model. The dyeing depth, color fastness grade, and wastewater COD value are used as fitness indicators. Through multiple generations of iteration, the combination of process parameters with dyeing depth ≥ 4, color fastness ≥ 4, and COD value ≤ 100 mg / L is selected.

4. The application method of liquid disperse dyes based on nanomaterial modification according to claim 3, characterized in that, The gradient temperature staining process includes: Based on the selected combination of process parameters, the dye bath was heated from room temperature to 80°C at an initial heating rate of 2°C / min and held for 10 minutes, and then heated to 130°C at a rate of 1°C / min and held for 40 minutes.

5. The application method of liquid disperse dyes based on nanomaterial modification according to claim 4, characterized in that, The reduction cleaning and color-fixing treatment includes: The amount of residual dye on the dyed fibers is detected in real time by a spectrometer. The detection data is input into the genetic algorithm model, which outputs an optimized combination of parameters: sodium hydrosulfite concentration of 2-5 g / L, washing temperature of 70-80℃, and polyquaternary ammonium salt fixing agent dosage of 1-3%.

6. The application method of liquid disperse dyes based on nanomaterial modification according to claim 5, characterized in that, The modified nano-materials in the wastewater were used. Optimization of sedimentation and filtration parameters based on concentration and particle size distribution data includes: Detection of modified nanoparticles in dyeing wastewater using dynamic light scattering instrument The concentration and particle size distribution data are input into the genetic algorithm model to generate a combination of recovery parameters with a sedimentation time of 2-4 hours, a filtration accuracy of 0.1-0.5μm, and a drying temperature of 80-100℃. The recovered nanomaterials are then reused in dye formulation.

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