Method for recycling alkali liquor for chitosan preparation

Abstract

The present application relates to the technical field of resource recycling of industrial waste liquid, and particularly relates to a method for recycling and reusing lye for chitosan preparation, comprising the following steps: step 1, on-line monitoring and shunting of lye state; step 2, multi-stage separation and impurity removal; step 3, dynamic regulation of ion components: adjusting flow rate based on the change rate of the inlet conductivity value of the ion exchange resin column, and triggering resin regeneration when the outlet pH value reaches the critical point of regeneration; step 4, lye activity compensation: dynamically supplementing lye according to the effective alkali concentration of regenerated lye and the demand of the target reuse process section; and step 5, time sequence control reuse: injecting fresh lye when the initial stage deacetylation reaction starts, and pumping regenerated lye in proportion when the temperature peak of the middle stage reaction is reached. The method not only effectively controls the quality of lye and the reaction efficiency, but also optimizes various parameters in the production process through precise dynamic regulation, improves the recycling rate of lye, reduces the production cost, and realizes the dual effects of environmental protection and energy saving.

Description

Technical Field

[0001] This invention relates to the field of industrial waste liquid recycling technology, and in particular to a method for recycling and reusing alkaline solution used in chitosan preparation. Background Technology

[0002] In the process of preparing chitosan by deacetylation of chitin, a high-concentration sodium hydroxide solution (typically 40-50 wt%) is the key reaction medium. To reduce production costs, an alkali recycling strategy is commonly adopted industrially. However, existing recycling technologies have the following inherent drawbacks:

[0003] During the deacetylation reaction, proteins, lipids, and colloidal substances in chitin are continuously dissolved. Traditional recovery technologies rely solely on plate and frame filtration or centrifugation, which cannot remove soluble proteins (molecular weight 5-100 kDa) and colloidal particles (particle size <10 μm).

[0004] Experiments show that after five cycles of alkaline solution, the viscosity increases by more than 300% (from the initial 15 cP to 60 cP), resulting in a 40%-50% decrease in the mass transfer coefficient of the reaction system.

[0005] High-viscosity alkaline solutions hinder the diffusion of sodium hydroxide into the chitin microcrystal region, significantly prolonging the time required for complete deacetylation (from 8 hours to 12 hours), while increasing energy consumption by more than 30%.

[0006] In addition, deacetylation consumes free base (NaOH→CH3COONa+H2O), and traditional alkali replenishment methods simply add sodium hydroxide without simultaneously removing the byproduct sodium acetate.

[0007] After 10 cycles, the sodium acetate concentration in the alkaline solution can reach 12 wt%, accounting for 35% of the total sodium ion molar ratio, resulting in an artificially high effective alkaline concentration (the measured pH is 13.5, but the deacetylation activity is only equivalent to 65% of that of the fresh alkaline solution).

[0008] Mechanism analysis: The ionization of sodium acetate competitively inhibits OH- - The ion activity was adjusted, and the ionic strength of the reaction system was changed, causing the activation energy of the deacetylation reaction to increase from 42 kJ / mol to 58 kJ / mol.

[0009] Therefore, there is an urgent need for a method for recycling and reusing the alkali solution used in chitosan preparation to solve the above problems. Summary of the Invention

[0010] To achieve the above objectives, the present invention provides a method for recycling and reusing alkaline solutions used in chitosan preparation, comprising the following steps:

[0011] Step 1: Online monitoring and diversion of alkali solution status:

[0012] The dynamic viscosity, conductivity, and turbidity values ​​of the alkaline solution after the deacetylation reaction are collected in real time, and the alkaline solution is diverted to the primary or deep treatment channel according to the preset viscosity-turbidity correlation threshold.

[0013] Step 2: Multi-stage separation and impurity removal:

[0014] The alkaline solution is sequentially passed through a solid residue interception device, a colloidal adsorption device, and a nano-protein capture device to further separate dissolved organic matter from the alkaline solution in the deep treatment channel.

[0015] Step 3: Dynamic regulation of ionic components:

[0016] The flow rate is adjusted based on the rate of change of the inlet conductivity of the ion exchange resin column, and resin regeneration is triggered when the outlet pH value reaches the regeneration critical point.

[0017] Step 4: Alkali Activity Compensation:

[0018] Alkali replenishment is dynamically based on the effective alkali concentration of the regenerated alkali solution and the requirements of the target reuse process section.

[0019] Step 5: Timing control reuse:

[0020] Fresh alkali solution is injected when the initial deacetylation reaction is started, and regenerated alkali solution is pumped in proportionally when the intermediate reaction reaches the peak temperature.

[0021] Preferably, the process of determining the viscosity-turbidity correlation threshold in step 1 includes:

[0022] A negative correlation model between the viscosity of alkaline solution and the mass transfer efficiency of deacetylation reaction was established through orthogonal experiments, with the viscosity value at which the mass transfer efficiency drops to a set percentage as the viscosity threshold.

[0023] The linear relationship between colloidal concentration and turbidity in historical circulating alkaline solutions was analyzed, and the turbidity value corresponding to the critical value of viscosity change when the colloidal concentration reached the critical value was used as the turbidity threshold.

[0024] A two-dimensional decision matrix is ​​formed by correlating viscosity threshold and turbidity threshold. When a real-time data point is located in a high-risk area of ​​the matrix, a deep processing channel is activated.

[0025] Preferably, the operation of separating soluble organic matter in step 2 includes:

[0026] The alkaline solution is flowed through a tubular ceramic membrane device, and molecular weight fractionation is achieved by transmembrane pressure drive.

[0027] The method for setting the transmembrane pressure is as follows: within the membrane pressure resistance limit range, select the minimum pressure value that allows the target organic matter rejection rate to reach the set percentage;

[0028] When the membrane flux decays to a set percentage of its initial value, reverse pulse cleaning is initiated, with the pulse frequency dynamically adjusted according to the flux decay rate.

[0029] Preferably, the method of adjusting the flow rate based on the rate of change of the inlet conductivity value of the ion exchange resin column in step 3 includes:

[0030] Establish a mapping relationship between the rate of decrease in conductivity and the resin adsorption efficiency: when the rate exceeds the first set rate, it indicates that the resin is not saturated, and the flow rate is increased to avoid overloading the equipment; when the rate is lower than the second set rate, it indicates that the resin is nearly saturated, and the flow rate is reduced to prolong the contact time.

[0031] The method for determining the set rate is as follows: by conducting a resin column penetration experiment, the critical conductivity change rate value at which the adsorption efficiency suddenly drops is obtained.

[0032] Preferably, the determination of the regeneration critical point in step 3 includes:

[0033] The decrease in pH value at the resin column outlet per unit time is calculated in real time, and the critical point is determined when the decrease exceeds the set decrease for three consecutive sampling cycles.

[0034] The method for determining the set decrease amount is as follows: the resin adsorption capacity decay curve is determined by titration, and the pH change rate when the adsorption capacity decreases to the set percentage is used as the benchmark.

[0035] Preferably, obtaining the effective alkali concentration in step 4 includes:

[0036] The density-conductivity combined detection method is adopted: the density value of the alkaline solution is obtained by an online density meter, and the data of the conductivity sensor is combined with the data and input into a pre-established sodium hydroxide concentration calculation model;

[0037] The calculation model is constructed as follows: simulated alkaline solution samples with different impurity contents are prepared, and the correspondence between their density, conductivity and actual sodium hydroxide concentration is determined. A compensation equation is then established through multiple regression analysis.

[0038] Preferably, the dynamic alkali replenishment in step 4, based on the effective alkali concentration of the regenerated alkali solution and the requirements of the target reuse process section, includes alkali concentration control, specifically:

[0039] Method for setting the target concentration of alkali supplementation in the initial stage of deacetylation: Based on chitin swelling kinetics experiments, determine the minimum alkali concentration required to achieve the set range for the swelling rate;

[0040] Method for setting the target concentration of alkali supplementation in mid-stage deacetylation: Calculate the alkali concentration compensation value required to maintain a constant reaction rate based on the activation energy model of the deacetylation reaction.

[0041] Preferably, the identification of the temperature peak in step 5 includes:

[0042] Real-time monitoring of the temperature change curve of the reactor; the peak value is determined when the following conditions are met simultaneously:

[0043] (a) The rate of temperature rise drops below a set rate threshold;

[0044] (b) The temperature fluctuation range is less than the set tolerance value;

[0045] The rate threshold is determined by inflection point analysis of the reaction exothermic curve.

[0046] Preferably, determining the reuse ratio of the regenerated alkali solution in step 5 includes:

[0047] By monitoring the free radical concentration of the regenerated alkali solution using a redox potential sensor, an inverse relationship model between the potential value and the activity coefficient of the alkali solution was established.

[0048] The maximum reusable ratio is dynamically calculated based on the ratio of the real-time activity coefficient to the fresh alkali solution.

[0049] When the temperature of the reaction system exceeds the set temperature, the upper limit of the recycling ratio is increased by a proportional coefficient.

[0050] Preferably, the activation conditions for the deep processing channel further include:

[0051] Deep processing is forcibly initiated when the number of alkali cycles reaches a set threshold.

[0052] The method for determining the number of cycles threshold is as follows: statistically analyze the average number of cycles before viscosity mutation in multiple batches of production, deduct the safety margin, and round down.

[0053] The beneficial effects of this invention are:

[0054] 1. This invention utilizes a multi-stage separation device, including a solid residue interception device, a colloid adsorption device, and a nano-protein capture device, to effectively remove dissolved organic matter, colloids, and nano-sized proteins. This step not only improves the quality of the alkaline solution but also maintains the mass transfer efficiency of the reaction system, thereby reducing the negative impact of viscosity increase and significantly improving reaction efficiency.

[0055] 2. This invention monitors the viscosity, conductivity, and turbidity of the alkaline solution in real time and uses a viscosity-turbidity correlation threshold for diversion control, allowing the alkaline solution to enter different processing channels according to actual conditions. Particularly in the deep processing channel, a tubular ceramic membrane separation device is used to separate dissolved organic matter, effectively reducing the viscosity of the alkaline solution, ensuring the diffusion efficiency of sodium hydroxide in the reaction system, significantly shortening the deacetylation reaction time, and reducing energy consumption.

[0056] 3. This invention employs a dynamic alkali replenishment mechanism, combining the effective alkali concentration of the regenerated alkali solution with the requirements of the target reuse process section to dynamically replenish sodium hydroxide. Simultaneously, the sodium hydroxide concentration is accurately calculated using a density-conductivity coupled detection method, preventing excessive accumulation of sodium acetate. Furthermore, through regeneration control of the ion exchange resin column, when the resin's adsorption capacity approaches saturation, the system automatically adjusts the flow rate and regeneration conditions, ensuring the stability of the effective alkali concentration in the alkali solution and avoiding the influence of sodium acetate on the alkali solution concentration.

[0057] 4. This invention achieves precise control of resin regeneration by dynamically regulating the ionic components, particularly when using an ion exchange resin column, by adjusting the flow rate through the rate of change in conductivity. When the sodium acetate concentration rises to a certain level, the system can reduce the inhibitory effect of sodium acetate through regenerating the resin column and compensating with the effective alkali concentration, restoring the activity of the reaction system and ensuring the smooth progress of the deacetylation reaction.

[0058] 5. This invention achieves precise management of the alkali solution by real-time monitoring of multiple parameters such as viscosity, conductivity, turbidity, and pH value, combined with a time-series control and dynamic adjustment mechanism. When parameters such as temperature and viscosity of the alkali solution change during the reaction process, the system can automatically adjust the inflow ratio and replenishment amount of alkali solution, thereby ensuring the stable progress of the deacetylation reaction and avoiding errors from manual operation and unnecessary waste of resources. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0060] Figure 1 This is a flowchart of the steps of the method of the present invention;

[0061] Figure 2 This is a flowchart illustrating the process of determining the viscosity-turbidity correlation threshold in step 1 of the method of the present invention.

[0062] Figure 3 This is a flowchart illustrating the steps for controlling the alkali concentration in the target reuse process section as described in step 4 of the method of the present invention. Detailed Implementation

[0063] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0064] Please see Figures 1-3 This invention provides a method for recycling and reusing alkaline solution used in chitosan preparation. By precisely controlling the state of the alkaline solution, removing harmful impurities, dynamically regulating ion concentration, and compensating for alkaline activity, the method achieves efficient recovery and reuse of the alkaline solution, reduces production costs, and improves the efficiency of the deacetylation reaction.

[0065] In step 1, the current state of the alkaline solution is first obtained by real-time monitoring of key parameters such as dynamic viscosity, conductivity, and turbidity after the deacetylation reaction. By setting a threshold for the correlation between viscosity and turbidity, the purity of the alkaline solution can be determined in a timely manner. When the viscosity and turbidity of the alkaline solution exceed preset ranges, the solution is diverted to either primary or advanced treatment channels. Primary treatment is suitable for lighter contamination, while advanced treatment is suitable for treating dissolved organic matter and fine impurities, ensuring that the recovered alkaline solution has higher purity and reducing the increase in viscosity and the decrease in reaction efficiency.

[0066] In step 2, after entering the deep processing channel, the alkaline solution sequentially passes through a solid residue interception device, a colloidal adsorption device, and a nano-protein capture device. This multi-stage separation process effectively removes proteins, lipids, colloidal particles, and other organic matter dissolved in the alkaline solution. In particular, the nano-protein capture device is effective in removing impurities from proteins with larger molecular weights (such as 5-100 kDa), preventing their accumulation in the alkaline solution, reducing viscosity increases, and ensuring good mass transfer conditions in the reaction system.

[0067] In step 3, the flow rate is adjusted to optimize the resin's adsorption performance by monitoring the rate of change in conductivity at the inlet of the ion exchange resin column in the alkaline solution. The rate of change in conductivity reflects the concentration change of soluble salts, and adjusting the flow rate maintains the ion exchange resin in its optimal operating state. When the pH value in the alkaline solution reaches a preset critical value, the resin regeneration process is triggered. This process effectively removes accumulated impurities, increases the resin's adsorption capacity, extends its service life, and ensures the stability of the alkaline solution during the reaction.

[0068] In step 4, during the regeneration of the alkali solution, the system dynamically replenishes sodium hydroxide based on the effective alkali concentration and the requirements of the target reuse process section. This ensures that the sodium hydroxide concentration is appropriate during the reaction, preventing incomplete deacetylation or a slower reaction rate due to excessively low alkali concentration.

[0069] In step 5, at the initial stage of the deacetylation reaction, fresh alkali solution is injected into the system to ensure the reaction starts. As the reaction proceeds, if the reaction temperature reaches its peak, regenerated alkali solution is pumped in proportionally. This timing control method allows for flexible adjustment of the amount of alkali solution added according to the different needs of the reaction stage, ensuring the stability and efficiency of the reaction process.

[0070] Through multi-stage alkali treatment and precise timing control, impurities in the alkali solution are effectively removed, viscosity increases are reduced, and mass transfer efficiency of the reaction system is maintained. Furthermore, by dynamically adjusting the composition and flow rate of the alkali solution, reaction conditions are optimized, reaction time is shortened, and energy consumption is reduced. Simultaneously, a precise alkali replenishment and resin regeneration mechanism effectively avoids the inhibitory effect of sodium acetate on the reaction, improves the alkali solution reuse efficiency, and reduces production costs. Ultimately, this method makes the recycling and reuse of alkali solution in chitosan preparation possible, meeting both economic and environmental requirements.

[0071] In one possible implementation, firstly, an orthogonal experimental design is used to establish a negative correlation model between viscosity and mass transfer efficiency by combining the mass transfer efficiency of deacetylation reactions under different concentrations of alkali and different conditions. Specifically, during the experiment, by adjusting variables such as the concentration of the alkali, temperature, and time, the mass transfer efficiency and corresponding viscosity values ​​under different conditions are recorded. When the mass transfer efficiency drops to a set percentage, the viscosity value can be used as a viscosity threshold.

[0072] Understandably, this viscosity threshold is used to determine whether the alkali solution has entered the deep processing channel. If the viscosity exceeds this value, it indicates that the alkali solution has exceeded the ideal reaction range, which may affect the reaction rate or effect, thus requiring further processing.

[0073] By analyzing historical circulating alkaline solution turbidity and colloid concentration data, a linear relationship between them was established. Turbidity is an indicator of the concentration of suspended particles in the alkaline solution, while colloid concentration directly affects the settling properties and reaction efficiency of the alkaline solution. Experimental analysis showed that when the colloid concentration reaches a certain critical value, the turbidity of the alkaline solution changes significantly. This critical turbidity value is used as the turbidity threshold to determine whether there is excessive colloid or impurities in the alkaline solution.

[0074] By combining the viscosity and turbidity thresholds mentioned above, a two-dimensional decision matrix is ​​formed. Each region in the matrix corresponds to a different alkali solution state, including normal, warning, and high-risk regions. Specifically, when the real-time monitoring data (viscosity and turbidity values) are located in the high-risk region of the matrix, it indicates that the alkali solution has exceeded the preset safety threshold, which may affect the efficiency and stability of subsequent reactions. At this time, the system will automatically activate the deep processing channel to further remove impurities and purify the alkali solution, ensuring the stability of the reaction conditions.

[0075] By combining viscosity and turbidity as dual control indicators, the treatment of alkali solution becomes more precise and effective. Establishing a negative correlation model and linear relationship allows for a more scientific assessment of alkali solution quality trends, avoiding errors associated with traditional single-parameter judgments and improving processing efficiency. The application of a two-dimensional decision matrix provides a clear reference for real-time decision-making, reducing human error and ensuring timely intervention when alkali solution quality abnormalities occur, preventing reduced reaction efficiency or equipment damage. Simultaneously, the optimized alkali solution reuse mechanism ensures efficient production while also improving resource recovery rates and economic benefits, aligning with the needs of sustainable development.

[0076] In one possible implementation, the pre-treated alkaline solution first flows into a tubular ceramic membrane device. The ceramic membrane possesses high mechanical strength and chemical stability, making it suitable for treating dissolved organic matter in the alkaline solution. Inside the membrane device, a specific transmembrane pressure is set to propel the alkaline solution across the membrane surface. The membrane's pore size and properties classify and retain organic matter according to its molecular weight. Through the difference in transmembrane pressure, large organic molecules are retained on one side of the membrane, while smaller molecules can pass through the membrane pores to the other side, thus achieving the separation of organic matter.

[0077] When setting the transmembrane pressure, it is necessary to ensure that the membrane device operates within its pressure tolerance limits and to select a minimum pressure value that allows the retention rate of the target organic compound to reach the set percentage. While excessively high pressure can improve separation efficiency, it will exacerbate membrane fouling and wear; conversely, excessively low pressure may lead to unsatisfactory separation results. Therefore, during the setting process, the transmembrane pressure is optimized based on the molecular weight of different types of organic compounds, the membrane flux characteristics, and the required retention efficiency to ensure effective retention of the target organic compound while extending the membrane's lifespan.

[0078] Over time, trapped organic matter and other impurities accumulate on the membrane surface, causing a gradual decline in membrane flux. When the membrane flux declines to a predetermined percentage of its initial value, a reverse pulse cleaning process is triggered. During cleaning, the pulse frequency is dynamically optimized based on the rate of flux decline. Pulse cleaning removes organic matter and impurities adhering to the membrane surface by changing the direction of the fluid and using a short burst of high-pressure water to impact the membrane surface, thereby restoring membrane flux. The dynamic adjustment of the pulse cleaning frequency can be precisely controlled according to the degree of membrane fouling, preventing over-cleaning and damage to the membrane while improving cleaning efficiency.

[0079] Firstly, ceramic membrane separation precisely removes large organic molecules from alkaline solutions, effectively reducing contaminants and ensuring the stability of subsequent processes. Secondly, precise setting of transmembrane pressure minimizes membrane damage and energy waste while maintaining separation efficiency, extending membrane lifespan. Furthermore, the dynamic adjustment function of reverse pulse cleaning makes the membrane cleaning process more efficient, allowing adjustments based on the actual degree of fouling. This avoids over-cleaning and its impact on membrane performance, while also improving the adaptability of cleaning frequency and reducing production interruptions caused by membrane fouling. This technology not only improves the efficiency of alkaline solution reuse but also effectively reduces production costs and aligns with sustainable development requirements.

[0080] In one possible implementation, the conductivity at the resin column inlet is first monitored and its change over time is analyzed. Conductivity is an indicator of ion concentration in a solution, and resin adsorption efficiency is closely related to changes in conductivity. As the resin adsorbs ions from the solution, the solution's conductivity gradually decreases. Therefore, the rate of change in conductivity (i.e., the amount of change in conductivity per unit time) can reflect the resin adsorption efficiency.

[0081] When the rate of decrease in conductivity is too rapid, it indicates that the resin has not yet reached saturation and the ion exchange process remains highly efficient. In this case, the flow rate should be appropriately increased to avoid over-utilization of the resin and prevent equipment overload. Conversely, when the rate of decrease in conductivity is slow, it indicates that the resin is nearing saturation and the adsorption efficiency is gradually decreasing. In this case, the flow rate should be reduced to prolong the contact time between the fluid and the resin, ensuring that the adsorption process continues and achieves optimal results.

[0082] The set rate was determined through a breakthrough experiment of the resin column. The purpose of the breakthrough experiment is to simulate the adsorption behavior of the resin column in actual use, and to observe the adsorption performance of the resin by adjusting the flow rate and conductivity. The experimental data can be used to determine the rate of change of conductivity (i.e., the critical conductivity rate) when the resin adsorption efficiency suddenly drops.

[0083] This critical rate value serves as a reference for setting two rate thresholds: a first set rate and a second set rate. When the rate of decrease in conductivity exceeds the first set rate, the flow rate can be increased; conversely, when the rate of decrease in conductivity falls below the second set rate, the flow rate should be decreased. This setting method optimizes the resin's operating state, ensuring full utilization of the resin when it is unsaturated and avoiding waste when it is close to saturation, thereby maximizing the resin's adsorption effect.

[0084] By monitoring changes in conductivity in real time and adjusting the flow rate, the adsorption efficiency of the resin can be precisely controlled, avoiding the impact of excessively fast or slow flow rates on the treatment effect. Increasing the flow rate helps avoid inefficient operation when the resin is unsaturated, while reducing the flow rate when the resin is close to saturation prolongs the contact time with the resin, ensuring the full progress of the ion exchange process.

[0085] By determining the rate of change of critical conductivity through breakthrough experiments, the set flow rate adjustment can be made more scientific and reasonable, avoiding the previous reliance on a single mode with a fixed flow rate. Finally, this adjustment method can improve the service life and adsorption efficiency of the resin, reduce resource waste, and improve the economy and sustainability of the entire alkali solution recycling process.

[0086] In one possible implementation, the pH value at the resin column outlet is first monitored and its changes are recorded. Changes in pH directly reflect the resin's adsorption of ions in the solution. When the resin adsorbs ions from the solution, the pH value changes, typically showing a gradual decrease. Therefore, calculating the decrease in pH value per unit time in real time can effectively reflect changes in the resin's adsorption capacity. By continuously tracking pH changes, the degradation of resin performance can be detected promptly.

[0087] To accurately determine the resin's regeneration critical point, data from three consecutive sampling periods were used. When the pH value decreased by more than a preset threshold within three consecutive sampling periods, the resin was considered to have reached its regeneration critical point. This threshold was a value determined through practical experience and experiments, and it is closely related to the resin's adsorption capacity and operating status. Once the pH value changes over three consecutive periods reach this threshold, it indicates that the resin's adsorption capacity has significantly decreased, approaching saturation, and regeneration is required.

[0088] The method for determining the set decrease rate is to measure the resin's adsorption capacity decay curve using titration. Titration measures the resin's ability to adsorb ions from the solution during treatment, plotting a curve showing the decrease in adsorption capacity over time. When the resin's adsorption capacity decreases to a set percentage, the rate of pH change changes significantly. This rate of change serves as a benchmark for setting the decrease rate, ensuring accurate and timely determination of the critical point when the resin's adsorption capacity approaches saturation.

[0089] By monitoring the pH changes at the resin column outlet in real time, the adsorption performance of the resin can be dynamically tracked, allowing for timely detection of whether the resin's adsorption capacity has decayed to a critical point. Compared to traditional static detection methods, this real-time calculation approach is more accurate, enabling continuous monitoring of resin performance and preventing performance degradation due to untimely resin regeneration.

[0090] By setting a threshold based on a decrease in resin adsorption capacity exceeding a predetermined amount over three consecutive sampling periods, false alarms can be effectively avoided, ensuring that regeneration operations are performed only when the resin's adsorption capacity has significantly decreased, thereby improving regeneration efficiency. Finally, the method for determining the threshold decrease involves a scientific experimental analysis of the resin's adsorption capacity decay using titration, resulting in a more accurate judgment standard. This avoids regeneration operations being performed too early or too late, optimizes the regeneration cycle, improves resin utilization efficiency, and reduces operating costs.

[0091] In one possible implementation, the density of the alkaline solution is first obtained using an online density meter. The density value reflects the concentration of the solute in the alkaline solution, but using the density value alone may not accurately reflect the sodium hydroxide concentration, especially when the alkaline solution contains other impurities. To address this issue, combining data from a conductivity sensor can further improve the accuracy of the measurement. Conductivity reflects the concentration of ions in a solution, and the conductivity of a sodium hydroxide solution is typically proportional to its concentration. Therefore, by simultaneously monitoring both density and conductivity, more comprehensive and accurate data on the sodium hydroxide concentration can be obtained.

[0092] To achieve accurate calculation of sodium hydroxide concentration, simulated alkaline solutions with varying impurity levels must first be prepared experimentally. In the experiment, the density, conductivity, and actual sodium hydroxide concentration of each sample are measured. Using these data, a relationship between density, conductivity, and actual sodium hydroxide concentration is established. This process requires the use of multiple alkaline solution samples containing various impurities to ensure the model's universality and accuracy. Then, multiple regression analysis is used to fit these data and establish a compensation equation, thereby eliminating the influence of impurities on the measurement results.

[0093] The compensation equation, by taking into account the influence of impurities, can effectively adjust the calculated sodium hydroxide concentration, ensuring its accuracy. By inputting real-time density and conductivity data and utilizing a pre-established calculation model, the sodium hydroxide concentration in the alkaline solution can be calculated accurately and in real time.

[0094] By combining density and conductivity measurements, two physical properties can be used simultaneously to monitor sodium hydroxide concentration, offering higher accuracy and reliability compared to traditional single-method approaches. Especially when the alkaline solution contains various impurities, density or conductivity alone may not accurately reflect the true concentration of sodium hydroxide. However, by establishing a multiple regression analysis model and using a compensation equation, errors introduced by impurities can be effectively eliminated, ensuring the accuracy of concentration calculations.

[0095] This method is applicable to alkaline solutions with various complex compositions and allows for real-time monitoring during actual production, reducing human intervention and errors, and improving the automation and control precision of the production process. Furthermore, the real-time sodium hydroxide concentration data can provide a basis for the recycling of alkaline solutions, optimizing their utilization efficiency, reducing resource waste, and enhancing the economic benefits and environmental sustainability of production.

[0096] In one possible implementation, the swelling of chitosan is a crucial step in the initial deacetylation process, and the swelling rate affects the efficiency of acetyl group removal. To ensure that the swelling rate is within a predetermined range, a suitable alkali concentration needs to be determined through chitin swelling kinetics experiments. Swelling kinetics experiments can identify a minimum alkali concentration that allows chitosan to swell sufficiently while maintaining the swelling rate within a predetermined range by observing the swelling rate of chitosan in an alkaline solution at different alkali concentrations. This minimum alkali concentration will serve as the target alkali concentration for initial deacetylation, thereby ensuring the smooth progress of the reaction.

[0097] The rate of the mid-stage deacetylation reaction is affected by the activation energy, and maintaining a constant reaction rate is crucial for improving yield and product quality. Based on the activation energy model of the deacetylation reaction, the required base concentration compensation value to maintain a stable reaction rate under different reaction conditions is calculated. The core of this method is to understand the energy changes in the deacetylation reaction and calculate how to adjust the base concentration during the mid-stage process when external conditions change to ensure that the reaction rate is maintained at a constant level, thereby avoiding efficiency losses caused by an excessively fast or slow reaction.

[0098] The initial deacetylation stage, based on chitosan swelling kinetics and alkali concentration settings, ensures sufficient swelling of chitosan, improving acetyl group removal efficiency and thus enhancing subsequent reaction effects. Secondly, the intermediate deacetylation stage, by considering the activation energy model for alkali concentration compensation, effectively maintains a constant reaction rate, avoiding fluctuations during the reaction process and ensuring the stability and controllability of the entire process, thereby improving the quality and consistency of the final product. This refined control improves alkali utilization efficiency and avoids resource waste and reaction anomalies caused by excessive or insufficient alkali use, thus optimizing the production process and improving economic efficiency and environmental friendliness.

[0099] In one possible implementation, the temperature within the reactor needs to be monitored in real time throughout the reaction process. Temperature sensors continuously acquire temperature data from the reactor and plot temperature change curves. Continuous temperature monitoring clearly shows the temperature rise, fluctuations, and eventual stabilization. Real-time data helps identify potential temperature peaks during the reaction, providing a precise basis for subsequent operations.

[0100] During temperature changes, when the rate of temperature increase gradually slows down and falls below a set rate threshold, the reaction process can be considered to have approached its peak thermal reaction. The rate threshold is determined by analyzing the exothermic curve of the reaction. The exothermic curve reveals changes in the heat released during the reaction; typically, at the inflection point of the reaction, the rate of heat release changes significantly, which is also a sign that the rate of temperature increase has slowed down. Therefore, setting the rate threshold using the inflection point of the exothermic curve helps to accurately capture key points in temperature changes.

[0101] When the rate of temperature rise drops below a set threshold, it is necessary to further confirm the stability of temperature fluctuations. By setting a tolerance value for a temperature fluctuation range, if the temperature fluctuation is small within this tolerance range, it indicates that the temperature has stabilized and the reaction is nearing completion. At this point, the temperature peak can be identified.

[0102] This method can accurately identify temperature peaks and thus infer the reaction progress status. By monitoring temperature changes in real time and combining this with a set temperature rise rate threshold and fluctuation tolerance value, it can accurately capture key temperature change nodes in the reaction, avoiding reaction control errors caused by human factors or monitoring delays. Setting the temperature rise rate threshold and temperature fluctuation tolerance value helps ensure that the temperature during the reaction process is controlled within a reasonable range, preventing adverse effects on the reaction rate and product quality due to excessively high or low temperatures.

[0103] Meanwhile, determining the rate threshold through inflection point analysis of the exothermic curve makes this method more scientific and accurate. This method helps improve the controllability of the entire reaction process, reduces production fluctuations or defective products caused by temperature instability, ultimately improving production efficiency and product quality, and effectively saving energy and raw materials, thus reducing production costs.

[0104] In one possible implementation, a redox potential sensor is used to monitor the concentration of free radicals in the regenerated alkali solution in real time during the reaction. The concentration of free radicals is a key indicator of the reactive substances in the reaction, directly affecting the reaction rate and efficiency. By monitoring the changes in the concentration of these free radicals in real time, the activity information of the alkali solution can be obtained. There is a certain relationship between redox potential and free radical concentration, which helps to determine the activity level of the alkali solution.

[0105] Based on the relationship between redox potential and free radical concentration, an inverse proportional relationship model is further established to describe the change between potential value and alkaline solution activity coefficient. This model provides a basis for dynamically calculating the alkaline solution activity coefficient. By monitoring the potential value, the activity of the alkaline solution can be assessed in real time, thereby determining the proportion suitable for reuse.

[0106] Within each reaction cycle, the maximum reusable ratio is calculated based on the ratio of the monitored alkali activity coefficient to that of fresh alkali. This ratio is dynamically adjusted, taking into account changes in alkali activity during the reaction process. This ensures that each reused alkali meets reaction requirements while minimizing the use of fresh alkali, thereby improving resource utilization efficiency.

[0107] When the temperature of the reaction system exceeds the set temperature, the reaction rate will accelerate, and the activity of the alkali solution may also increase. At this time, the upper limit of the reuse ratio can be appropriately increased according to the preset ratio coefficient to ensure that the reused alkali solution can still maintain sufficient activity when the temperature rises, thereby ensuring the stable progress of the reaction process.

[0108] Real-time monitoring and dynamic adjustment allow for precise control of the reuse ratio of regenerated alkali solution, maximizing resource utilization. By monitoring free radical concentration using a redox potential sensor and assessing the alkali solution's activity based on the inverse relationship between potential value and alkali solution activity coefficient, the determination of the reuse ratio becomes more scientific and accurate. This dynamic adjustment effectively avoids the negative impact of insufficient alkali solution activity on reaction results, while reducing the consumption of fresh alkali solution and lowering production costs.

[0109] Furthermore, by appropriately increasing the upper limit of the recycling ratio under high-temperature reaction conditions, it is possible to better adapt to changes in reaction conditions, ensuring the stability and efficiency of the reaction process when the temperature rises, and further improving the overall economic and environmental benefits of the production process. Therefore, this method not only improves the efficiency and stability of the reaction but also plays a positive role in reducing resource consumption and environmental burden.

[0110] In one possible implementation, during multi-batch production, it is necessary to record the viscosity change of the alkali solution after each cycle. Specifically, the viscosity change is monitored after each cycle, and when a significant abrupt change in viscosity occurs, this point is recorded as the "change point." The average number of cycles before the viscosity change is calculated. This step is based on a large amount of production data to ensure that the selected number of cycles accurately reflects the usage of the alkali solution during the reaction process.

[0111] To ensure stability in the production process, a safety margin is set. This safety margin is a reserve value set to account for potential fluctuations in the production process, aiming to prevent premature or delayed initiation of deep processing due to unforeseen factors. Therefore, the safety margin is subtracted from the statistically obtained average number of cycles to ensure that deep processing does not start prematurely or late due to external interference.

[0112] By determining a reasonable threshold for the number of cycles using the method described above, the system will automatically trigger a deep treatment operation when the number of cycles for the alkali solution reaches this threshold. This condition not only considers viscosity variations in actual production but also ensures treatment stability through the setting of a safety margin.

[0113] By precisely setting the threshold for the number of alkali cycles, the reduction in reaction efficiency due to a decline in alkali quality is effectively avoided. By statistically analyzing the average number of cycles before viscosity abrupt changes in multiple batches of production, the timing of the deep treatment initiation can be ensured to be optimal, thereby maximizing the alkali's lifespan. The practice of deducting a safety margin ensures that unforeseen factors can be addressed during production, guaranteeing that the deep treatment will not be initiated prematurely or delayed due to external factors, thus ensuring production stability.

[0114] Furthermore, setting a threshold for the number of cycles based on actual data makes this method highly practical and adaptable. It can be flexibly adjusted according to the production conditions of different batches, avoiding excessive or insufficient deep processing, thereby maximizing resource utilization, reducing production costs, and improving the efficiency and quality of chitosan preparation.

[0115] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0116] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for recycling an alkali solution used for chitosan production, characterized by, The method comprises the following steps: Step 1: online monitoring of alkaline solution and diversion: Real-time acquisition of the dynamic viscosity value, conductivity value and turbidity value of the alkaline solution after deacetylation reaction, diversion of the alkaline solution to a primary or deep treatment channel according to a preset viscosity-turbidity correlation threshold value; Step 2: multi-stage separation and impurity removal: Making the alkaline solution flow through a solid residue interception device, a colloid adsorption device and a nano protein capture device in sequence, and additionally separating dissolved organic matter from the alkaline solution in the deep treatment channel; Step 3: dynamic regulation of ion components: Adjusting the flow rate based on the change rate of the conductivity value at the inlet of the ion exchange resin column, and triggering resin regeneration when the outlet pH value reaches the critical point of regeneration; Through the regeneration control of the ion exchange resin column, when the adsorption capacity of the resin approaches saturation, the system automatically adjusts the flow rate and the regeneration conditions, ensuring the stability of the effective alkali concentration in the alkaline solution and avoiding the influence of sodium acetate on the concentration of the alkaline solution; Step 4: alkali activity compensation: Dynamic alkali compensation according to the effective alkali concentration of the regenerated alkaline solution and the requirements of the target reuse process section; The acquisition of the effective alkali concentration comprises: Using a density-conductivity combined detection method: obtaining the density value of the alkaline solution through an online densimeter, combining the data of the conductivity sensor, and inputting a pre-established sodium hydroxide concentration calculation model; The construction method of the calculation model is: preparing simulated alkaline solution samples with different impurity contents, determining the corresponding relationship between their density, conductivity and true sodium hydroxide concentration, and establishing a compensation equation through multiple regression analysis; In the dynamic alkali compensation according to the effective alkali concentration of the regenerated alkaline solution and the requirements of the target reuse process section, it comprises alkali concentration control, specifically: The alkali concentration target concentration setting method for the initial deacetylation: based on the chitin swelling kinetics experiment, the minimum alkali concentration required to make the swelling rate reach the set range is determined; The alkali concentration target concentration setting method for the middle deacetylation: according to the deacetylation reaction activation energy model, the alkali concentration compensation value required to maintain a constant reaction rate is calculated; Step 5: time sequence control reuse: When the initial deacetylation reaction starts, fresh alkaline solution is injected, and when the middle reaction reaches the temperature peak, regenerated alkaline solution is pumped in proportion.

2. The method according to claim 1, wherein the alkali solution is recycled and reused. The determination process of the viscosity-turbidity correlation threshold value in step 1 comprises: Establishing a negative correlation model between the viscosity of the alkaline solution and the mass transfer efficiency of the deacetylation reaction through orthogonal experiment, and taking the viscosity value when the mass transfer efficiency decreases to a set percentage as the viscosity threshold value; Analyzing the linear relationship between the colloid concentration and the turbidity in the historical circulating alkaline solution, and taking the turbidity value corresponding to the viscosity sudden change critical value of the colloid concentration as the turbidity threshold value; Correlate the viscosity threshold value and the turbidity threshold value to form a two-dimensional decision matrix, and start the deep treatment channel when the real-time data point is located in the high-risk area of the matrix.

3. The method according to claim 1, wherein the alkali solution is recycled and reused. The operation of the dissolved organic matter separation in step 2 comprises: Making the alkaline solution flow through a tubular ceramic membrane device, and realizing molecular weight fractionation interception through transmembrane pressure driving; The setting method of the transmembrane pressure is: within the pressure limit range of the membrane, the minimum pressure value that makes the target organic matter interception rate reach a set percentage is selected; When the membrane flux decays to a set proportion of the initial value, reverse pulse cleaning is started, and the pulse frequency is dynamically adjusted according to the flux decay rate.

4. The method according to claim 1, wherein the alkali solution is recycled and reused. The method for adjusting the flow rate based on the change rate of the ion exchange resin column inlet conductivity value in step 3 comprises: Establishing a mapping relationship between the conductivity drop rate and the resin adsorption efficiency: when the rate exceeds the first set rate, it indicates that the resin is not saturated, and the flow rate is increased to avoid equipment overload; when the rate is lower than the second set rate, it indicates that the resin is nearly saturated, and the flow rate is reduced to prolong the contact time; The determination method of the set rate is: through the resin column breakthrough experiment, the critical conductivity change rate value when the adsorption efficiency drops sharply is obtained.

5. The method according to claim 1, wherein the alkali solution is recycled and reused. The determination of the regeneration critical point in step 3 comprises: Real-time calculation of the unit time drop of the resin column outlet pH value, and when the drop of three consecutive sampling periods exceeds the set drop, it is determined as the critical point; The determination method of the set drop is: the titration method is used to determine the resin adsorption capacity decay curve, and the pH change rate when the adsorption capacity drops to the set percentage is taken as the benchmark.

6. The method according to claim 1, wherein the alkali solution is recycled and reused. The identification of the temperature peak in step 5 comprises: Real-time monitoring of the reaction kettle temperature change curve, and when the following conditions are met simultaneously, the peak is determined: (a) The temperature rise rate drops below the set rate threshold; (b) The temperature fluctuation range is less than the set tolerance value; The rate threshold is determined by the inflection point analysis of the reaction heat release curve.

7. The method according to claim 2, wherein the alkali solution is recycled and reused. The start-up conditions of the advanced treatment channel further comprise: Forcing to start the advanced treatment when the alkali liquor circulation times reach the set times threshold; The determination method of the times threshold is: counting the average circulation times before viscosity mutation in multiple batches of production, and taking the integer after deducting the safety margin.

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