Waste pickling liquid recycling system and method

Abstract

The present invention relates to the technical field of industrial wastewater treatment. Disclosed are a waste pickling liquid recycling system and method. The system comprises: a treatment unit used for separately measuring the concentrations of various heavy metal ions in a waste pickling liquid, separately adding corresponding chelating resins to the waste pickling liquid on the basis of the concentrations of the various heavy metal ions, and collecting the resulting recycled acid liquid; a monitoring unit used for collecting the pH value and conductivity data of the recycled acid liquid in real time, inputting the pH value, conductivity data, and heavy metal ion concentration data into a control unit as monitoring data, and establishing a monitoring database; and the control unit for calculating optimal control parameters in real time on the basis of the monitoring data in the monitoring database, automatically adjusting the treatment unit on the basis of the optimal control parameters, and conveying the recycled acid liquid to a pickling process for reuse. By means of multi-parameter collaborative monitoring and intelligent adaptive control, the present invention realizes precise measurement of heavy metal ions in the pickling waste liquid, dynamic optimization treatment, and efficient recycling.

Description

A pickling waste liquid reuse system and method Technical Field

[0001] This invention relates to the field of industrial wastewater treatment technology, specifically to an acid pickling waste liquid reuse system and method. Background Technology

[0002] With the rapid development of new materials industries such as graphite and carbon nanotubes, pickling, as a key surface treatment process, has been widely used, but it has also generated a large amount of acidic wastewater containing heavy metal ions. Currently, industrial technologies for treating pickling wastewater mainly include neutralization precipitation, spray roasting, evaporation crystallization, ion exchange, membrane separation, and extraction. Neutralization precipitation involves adding alkaline agents to the wastewater to precipitate heavy metal ions; spray roasting and evaporation crystallization use heating to recover acid; ion exchange utilizes ion exchange resins to adsorb and separate heavy metal ions; membrane separation uses specific membrane materials to filter the pickling wastewater; and extraction uses organic extractants to separate heavy metal ions from the aqueous phase. Chelating resins, by forming stable complexes with heavy metal ions through chelating groups, achieve separation and show promising application prospects in the field of heavy metal wastewater treatment.

[0003] However, existing treatment technologies face numerous technical challenges in the reuse of pickling wastewater that urgently need to be addressed. While neutralization precipitation can remove heavy metals, it disrupts the composition of the acid, rendering it unusable. Spray roasting and evaporation crystallization methods can recover acid, but they are energy-intensive, costly, and produce acid with insufficient purity, making direct reuse in the pickling process difficult. Ion exchange and membrane separation methods still leave some heavy metal ions in the treated acid, resulting in unsatisfactory reuse. Extraction methods are prone to introducing organic extractants into the recovered acid, affecting its quality. The main problems in the reuse of pickling wastewater are as follows: First, the lack of online monitoring methods for the concentration of various heavy metal ions makes it impossible to monitor the water quality of the reused acid in real time, leading to unstable quality. Second, existing technologies lack a comprehensive process monitoring system, failing to obtain key indicators such as pH and conductivity in real time to ensure the quality of the reused acid. Third, the treatment processes are too simplistic, lacking targeted pretreatment steps for heavy metal removal. Therefore, there is an urgent need to develop an efficient pickling waste liquid recycling system. This system can add a preliminary recycling procedure with heavy metal removal agent before the recycling process. Then, through online analysis of the concentration of each heavy metal ion, real-time monitoring of multiple parameters, and dosing control, it can ensure that the quality of the recycled acid liquid meets the requirements of the pickling process and realize the recycling of pickling waste liquid. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the present invention provides a pickling waste liquid reuse system and method, which can solve the problems mentioned in the background art.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a pickling waste liquid reuse system, comprising: a processing unit, used to detect the concentration of each heavy metal ion in the pickling waste liquid, add corresponding chelating resins to the pickling waste liquid according to the concentration of each heavy metal ion, and collect the reused acid liquid; a monitoring unit, used to collect the pH value and conductivity data of the reused acid liquid in real time, input the pH value, conductivity data and heavy metal ion concentration data as monitoring data into a control unit, and establish a monitoring database; a control unit, which calculates the optimal control parameters in real time based on the monitoring data in the monitoring database, and automatically adjusts the processing unit according to the optimal control parameters to transport the reused acid liquid to the pickling process for reuse; wherein, the control parameters include the rotation speed and dosing ratio of the multi-channel variable frequency metering pump.

[0007] As a preferred embodiment of the pickling waste liquid reuse system of the present invention, the processing unit includes a heavy metal analyzer, a multi-channel variable frequency metering pump, and a recovery module; the heavy metal analyzer is equipped with ion detection modules for various types of heavy metals, and the ion detection modules are equipped with ion-selective electrodes; the ion-selective electrodes include a reference electrode and a measuring electrode, the reference electrode and the measuring electrode forming a potential difference, and the concentration of heavy metal ions is detected based on the potential difference; the multi-channel variable frequency metering pump includes multiple independently controlled metering pump channels, divided into pretreatment channels and posttreatment channels, wherein the pretreatment channel is used to add heavy metal removal agents to pre-treat the pickling waste liquid to obtain initial reuse acid liquid, and the posttreatment channel is used to quantitatively add chelating resins for treating various heavy metal ions to the initial reuse acid liquid, and a reactor is provided between the outlet of the pretreatment channel and the inlet of the posttreatment channel; each metering pump channel is communicatively connected to the control unit; the recovery module collects the pickling waste liquid after the addition of chelating resin and separates the reuse acid liquid; the recovery module includes a collection pump and a storage tank.

[0008] As a preferred embodiment of the pickling waste liquid reuse system of the present invention, the heavy metal analyzer further includes a data processor, which receives the detection signal from the ion-selective electrode, converts it into heavy metal concentration data, and transmits it to the data acquisition module in real time. Each metering pump channel includes a variable frequency motor, a peristaltic pump head, and a chelating resin storage tank. When the control unit detects that the rate of change of heavy metal ion concentration exceeds a first preset threshold, a graded response mechanism is activated. The graded response mechanism includes a first-level response, a second-level response, and a third-level response. The first-level response is to activate monitoring and early warning: the heavy metal analyzer, along with sampling points A and B located at the inlet and outlet of the reactor, synchronously shortens the sampling cycle and increases the data acquisition frequency. If multiple consecutive sampling data show a continuous change in heavy metal ion concentration, the second-level response is activated. If multiple consecutive sampling data show that the heavy metal ion concentration fluctuates within a stable range, the normal sampling cycle is restored.

[0009] The second-level response is to adjust the influent conditions: reduce the feed rate of the collection pump and increase the residence time of the pickling waste liquid in the treatment unit; if the treatment effect is improved after adjusting the residence time, the current influent conditions are maintained; if the treatment effect is not improved, the third-level response is initiated.

[0010] The third-level response is to optimize reagent dosing: the variable frequency motor gradually adjusts its speed according to the buffer curve, prioritizing the adjustment of the pretreatment channel. After the speed stabilizes, the speed of the corresponding chelating resin channel is adjusted sequentially. If the treatment effect is improved, the current process parameters are recorded. If the treatment effect of a certain heavy metal ion still does not meet the requirements, the residence time of the treatment unit is increased.

[0011] As a preferred embodiment of the pickling waste liquid reuse system of the present invention, the monitoring unit includes a pH sensor, a conductivity sensor, and a data acquisition module; the control unit includes an industrial computer, a database server module, and a transmission module; wherein, the data acquisition module collects the pH value, conductivity data, and heavy metal ion concentration data, organizes them into monitoring data, and inputs them into the database server module, which then establishes a monitoring database.

[0012] As a preferred embodiment of the pickling waste liquid reuse system of the present invention, the industrial control computer constructs an adaptive optimization control system; the adaptive optimization control system includes a working condition analysis module, a control strategy module, and an operation optimization module.

[0013] The operating condition analysis module receives monitoring data from the data acquisition module, establishes an interaction matrix R between heavy metal ions, and generates a feature spectrum reflecting the concentration changes of each component; the calculation formula for the interaction matrix R is:

[0014] In the formula, R ij α is an element in the interaction matrix R, representing the degree of interaction between the i-th heavy metal ion and the j-th heavy metal ion; ij β is the chelation competition coefficient between heavy metal ions i and j; ij γ is the concentration fluctuation coupling coefficient; ij C represents the efficiency impact coefficient. i C j These represent the real-time concentrations of the i-th and j-th heavy metal ions, respectively. These are the corresponding target concentrations; K e For processing efficiency characteristic factors; K i K j The chelation equilibrium constants for the i-th and j-th heavy metal ions, respectively; ΔC i ΔC j η represents the concentration changes of the i-th and j-th heavy metal ions, respectively; i η j These are the real-time processing efficiencies for the i-th and j-th heavy metal ions, respectively. The target treatment efficiencies for the i-th and j-th heavy metal ions are respectively; w i δ represents the weighting coefficient of the i-th heavy metal ion; n is the number of heavy metal ions; ij ε is the coefficient of influence of pH value on the interaction between the i-th and j-th heavy metal ions; ij denoted as the coefficient of influence of conductivity on the interaction between the i-th and j-th heavy metal ions; pH* is the target pH value; ΔpH is the allowable pH fluctuation range; EC is the current conductivity; EC* is the target conductivity; ΔEC is the allowable conductivity fluctuation range.

[0015] The control strategy module calculates the optimal combination of speed and dosing ratio parameters for the multi-channel variable frequency metering pump based on the feature map and the interaction influence matrix R. The specific optimization objective function is expressed as follows:

[0016] In the formula, F(v, r) is the optimization objective function, and the input variables are the rotational speed vector v and the ratio vector r; v represents the target concentration of the i-th heavy metal ion; i v is the rotational speed of the i-th metering pump channel; j r is the rotational speed of the j-th metering pump channel. i The target ratio for the i-th metering pump channel; r j The target ratio for the j-th metering pump channel.

[0017] As a preferred embodiment of the pickling waste liquid reuse system of the present invention, the control strategy module determines the priority order of speed adjustment based on the ratio of the elements of the interaction influence matrix of each heavy metal ion with other heavy metal ions, and calculates the ratio of the elements of the interaction influence matrix of the i-th heavy metal ion with other heavy metal ions j; determines the adjustment priority of each heavy metal ion based on the magnitude of the ratio of the elements of the interaction influence matrix; for heavy metal ions with large ratios of the elements of the interaction influence matrix, increases the speed adjustment weight coefficient of the corresponding metering pump, while decreasing the speed adjustment weight coefficient of the metering pumps corresponding to other heavy metal ions; the speed adjustment weight coefficient is equal to the ratio of the metering pump speed.

[0018] The control strategy module also optimizes the dosage by adjusting the pump speed while maintaining the overall dosage. Simultaneously, when the concentration of heavy metal ions, pH value, and conductivity shown in the characteristic spectrum exceed their respective target ranges, the module gradually adjusts the metering pump speed according to a preset step size. When these parameters return to the target range, the metering pump speed is directly adjusted to the target value. Furthermore, based on the real-time fluctuations in heavy metal ion concentration, pH value, and conductivity, the module calculates and adjusts the step size of the metering pump speed proportionally. When multiple heavy metal ions simultaneously exhibit abnormal fluctuations, the control strategy module identifies key heavy metal ions through eigenvalue decomposition of the interaction matrix, sets the metering pump adjustment priority according to the eigenvalue magnitude, and maintains the balance of the dosage between adjacent priority heavy metal ions.

[0019] The operation optimization module evaluates the treatment effect of the optimal combination of rotation speed and dosage ratio parameters in real time. If the heavy metal ion removal rate and chelation reaction rate fluctuate, the interaction influence matrix is ​​automatically updated according to the preset calculation cycle, and the optimal combination of rotation speed and dosage ratio parameters is recalculated and the treatment effect is evaluated based on the updated interaction influence matrix. If the concentration-ratio-efficiency state space analysis value exceeds the second preset threshold, the metering pump speed and ratio parameters are adjusted step by step according to the preset control rules, and the change in heavy metal ion concentration during the adjustment process is recorded. If it is necessary to determine the optimization order of control parameters, key control variables are identified based on the influence weight values ​​and response times of each control parameter, and an optimization adjustment sequence is established according to the importance.

[0020] As a preferred embodiment of the pickling waste liquid reuse system of the present invention, the collecting pump pumps the pickling waste liquid treated with chelating resin by the multi-channel variable frequency metering pump to the storage tank for solid-liquid separation; the storage tank settles and separates the chelating resin-heavy metal complex and the reused acid liquid, and transports the separated reused acid liquid to the monitoring unit; the conveying module includes a conveying pump and a pipeline system.

[0021] To further address the aforementioned technical problems, this invention provides the following technical solution: a method for reusing pickling waste liquid, comprising the following steps: detecting the concentration of each heavy metal ion in the pickling waste liquid using an online heavy metal analyzer in a treatment unit; adding corresponding chelating resins to the pickling waste liquid according to the concentration of each heavy metal ion using a multi-channel variable frequency metering pump to obtain a reused acid solution; collecting the pH value and conductivity data of the reused acid solution in real time using a pH sensor and a conductivity sensor in a monitoring unit, and inputting the pH value, conductivity data, and heavy metal ion concentration data as monitoring data into a control unit through a data acquisition module to establish a monitoring database; and calculating the optimal control parameters in real time based on the monitoring data in the monitoring database using an industrial control computer in the control unit, and automatically adjusting the multi-channel variable frequency metering pump according to the optimal control parameters to deliver the reused acid solution to the pickling process for reuse.

[0022] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the pickling waste liquid reuse system as described above.

[0023] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the pickling waste liquid reuse system as described above.

[0024] The beneficial effects of this invention are as follows: By organically combining a treatment unit, a monitoring unit, and a control unit, this invention achieves efficient treatment and reuse of pickling wastewater. The treatment unit employs a combination of a heavy metal analyzer and a multi-channel variable frequency metering pump to achieve precise detection and quantitative dosing of heavy metal ions. The monitoring unit constructs a multi-dimensional monitoring system for pH value, conductivity, and heavy metal ion concentration, providing real-time and complete data support for system control. The control unit, by establishing an interaction influence matrix and feature spectrum, combined with an adaptive optimization algorithm, achieves intelligent regulation of the heavy metal ion treatment process. This systematic technical solution overcomes the problems of detection lag, coarse control, and poor adaptability in traditional treatment methods, significantly improving chelation treatment efficiency and reagent utilization. Especially when facing fluctuations in influent water quality, the system can quickly respond and automatically optimize control parameters, ensuring the stability of the treatment effect and providing reliable technical support for the resource utilization of industrial pickling wastewater. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 is a schematic diagram of the overall structure of an acid pickling waste liquid reuse system proposed in this invention.

[0027] Figure 2 is an overall flow chart of a method for reusing pickling waste liquid proposed in this invention;

[0028] Figure 3 is a diagram of the computer equipment used in a method for reusing pickling waste liquid proposed in this invention. Detailed Implementation

[0029] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0030] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0031] Example 1, referring to Figure 1, is an embodiment of the present invention, providing an acid pickling waste liquid reuse system.

[0032] Existing pickling wastewater treatment technologies have the following key technical problems: First, there is a lack of online monitoring methods for the concentration of various heavy metal ions, making it impossible to monitor the water quality of the recycled acid in real time, resulting in unstable quality of the recycled acid; second, existing technologies have not established a complete process monitoring system, making it impossible to ensure the quality of the recycled acid by obtaining key indicators such as pH value and conductivity in real time; third, the treatment process is too simple and lacks targeted pretreatment steps for heavy metal removal.

[0033] This application provides a solution to the problems mentioned above. The following will describe in detail how to implement the pickling waste liquid reuse system using multiple embodiments.

[0034] Figure 1 shows a schematic diagram of the overall structure of an acid pickling waste liquid reuse system, including: a treatment unit 100, a monitoring unit 200, and a control unit 300.

[0035] First, the concentration of each heavy metal ion in the pickling waste liquid is detected by the processing unit 100. The corresponding chelating resin is added to the pickling waste liquid according to the concentration of each heavy metal ion, and the recycled acid liquid is collected.

[0036] Furthermore, the processing unit 100 includes a heavy metal analyzer 101, a multi-channel variable frequency metering pump 102, and a recovery module 103. The recovery module 103 includes a collection pump 103a and a storage tank 103b.

[0037] Specifically, in this invention, heavy metal ions include copper ions, nickel ions, and chromium ions. The heavy metal analyzer 101 is equipped with multiple types of heavy metal ion detection modules, mainly including a copper ion detection module, a nickel ion detection module, and a chromium ion detection module (all three are equipped with ion-selective electrodes), as well as a data processor. The ion-selective electrodes include a reference electrode and a measuring electrode. The measuring electrode for the copper ion detection module is a copper ion-selective membrane electrode, the measuring electrode for the nickel ion detection module is a nickel ion-selective membrane electrode, and the measuring electrode for the chromium ion detection module is a chromium ion-selective membrane electrode. The reference electrode is a silver chloride electrode. The concentration of heavy metal ions is detected by measuring the potential difference between the reference electrode and the measuring electrode, where the magnitude of the potential difference is linearly related to the logarithm of the heavy metal ion concentration in the solution, thereby achieving quantitative detection of the concentrations of copper, nickel, and chromium ions in the pickling waste liquid. The data processor receives the detection signal from the ion-selective electrode, converts it into heavy metal concentration data, and transmits it to the data acquisition module 203 in real time.

[0038] The characteristic spectrum is a multi-curve graph with time as the x-axis and the concentration of each heavy metal ion, pH value, and conductivity as the y-axis. It is used to characterize the concentration change trend, fluctuation period, and interaction relationship of each component during the treatment process. The multi-channel variable frequency metering pump 102 includes four independently controlled metering pump channels, divided into pretreatment channels and posttreatment channels. There is one pretreatment channel for adding heavy metal removal agent to pre-treat the pickling waste liquid to obtain initial reuse acid solution. The three posttreatment channels are used to quantitatively add chelating resins for treating copper, nickel, and chromium ions to the initial reuse acid solution. A reactor is set between the outlet of the pretreatment channel and the inlet of the posttreatment channel to ensure the reaction effect of the heavy metal removal agent. Sampling points A and B are set at the inlet and outlet of the reactor, respectively, forming a complete monitoring network in conjunction with the heavy metal analyzer. Each metering pump channel includes a variable frequency motor, a peristaltic pump head, and a corresponding reagent storage tank. The variable frequency motor has a speed range of 0-100 rpm (this range matches the rheological characteristics of the chelating resin and heavy metal removal agent, ensuring both the accuracy of micro-dosing at low concentrations of heavy metal ions and the need for large-dose dosing at high concentrations. Simultaneously, in conjunction with the three-roller peristaltic pump head structure, the metering accuracy can be controlled within ±1%, which is crucial for maintaining the stoichiometric ratio of the treatment reaction). It is regulated by a control signal output from the control unit 300. The peristaltic pump head adopts a three-roller structure to ensure metering accuracy within ±1%. Each storage tank is equipped with a level sensor to monitor the reagent level and transmit the data to the data acquisition module 203.

[0039] Each metering pump channel in the multi-channel variable frequency metering pump 102 is communicatively connected to the control unit 300. When the industrial control computer 301 in the control unit 300 detects that the rate of change of heavy metal ion concentration exceeds a first preset threshold, a graded response mechanism is activated. The graded response mechanism includes a first-level response, a second-level response, and a third-level response.

[0040] The first-level response initiates monitoring and early warning: the heavy metal analyzer 101, in sync with sampling points A and B, shortens the sampling cycle and increases the data acquisition frequency to intensively monitor the processing. If multiple consecutive sampling data show a continuous change in heavy metal ion concentration, the second-level response is initiated; if multiple consecutive sampling data show that the heavy metal ion concentration fluctuates within a stable range, the normal sampling cycle is restored.

[0041] The second-level response involves adjusting the influent conditions: reducing the feed rate of the collection pump 103a in the recovery module 103 and increasing the residence time of the pickling waste liquid in the treatment unit 100. If the treatment effect improves after adjusting the residence time, the current influent conditions are maintained; if the treatment effect does not improve significantly, the third-level response is initiated.

[0042] The third-level response optimizes reagent dosing: the variable frequency motor of the multi-channel variable frequency metering pump 102 gradually adjusts its speed according to the buffer curve, prioritizing the adjustment of the pretreatment channel. After its speed stabilizes, the corresponding posttreatment channels are adjusted sequentially to avoid instantaneous fluctuations in reagent speed. If the treatment effect improves, the current process parameters are recorded; if the treatment effect of a certain heavy metal ion still does not meet the requirements, the residence time of the treatment unit 100 is increased.

[0043] It is important to note that this tiered response mechanism creates a closed-loop control system. Monitoring and early warning ensure timely detection of anomalies; adjusting the influent operating conditions provides sufficient reaction time for subsequent treatment; and optimizing reagent dosing avoids fluctuations through orderly rate adjustment. Throughout the process, three monitoring points continuously collect data, providing data support for optimizing control parameters until the concentrations of each heavy metal ion meet the treatment requirements.

[0044] Preferably, in this embodiment, the three post-processing channels of the multi-channel variable frequency metering pump 102 are respectively designed to treat one type of heavy metal ion. The rationale for this design is as follows: First, different heavy metal ions have different optimal reaction stoichiometric ratios and reaction kinetic characteristics with their corresponding chelating resins, and independent channels can achieve precise quantitative addition. Second, since the concentration fluctuations of various heavy metal ions in pickling waste liquid are often asynchronous, the independent channel design can achieve differentiated control, avoiding the problem of excessive or insufficient chelating resin caused by the traditional single addition method.

[0045] It should be noted that the principle behind "dosing-specific" is based on the stoichiometry and reaction kinetics of heavy metal ions and chelating resins: each heavy metal ion has its specific chelating resin and an optimal dosing ratio. The industrial control computer calculates the required amount of chelating resin based on the preset stoichiometry, using real-time concentration data of each ion from the heavy metal analyzer, and then converts this into the speed command of the corresponding channel's variable frequency motor. For example, if the detected copper ion concentration is 100 mg / L, and the optimal chelating resin dosing ratio is 1:2 (by weight), the industrial control computer will convert this ratio into the corresponding chelating resin volumetric flow rate, and then into the speed command of the variable frequency motor in the copper ion processing channel, achieving precise dosing. This dynamic dosing method based on real-time concentration ensures treatment effectiveness while avoiding excessive use of chelating resin.

[0046] The recovery module 103 collects the pickling waste liquid after the addition of chelating resin and separates the reused acid liquid. The recovery module 103 includes a collection pump 103a and a storage tank 103b. The collection pump 103a pumps the pickling waste liquid treated with chelating resin by the multi-channel variable frequency metering pump 102 to the storage tank 103b for solid-liquid separation; the storage tank 103b separates the chelating resin-heavy metal complex and the reused acid liquid by sedimentation, and the separated reused acid liquid is transported to the monitoring unit 200.

[0047] It should be noted that the rationale for using sedimentation separation in storage tank 103b is that the chelating resin-heavy metal complex has a large molecular weight and density, and the density difference with the acid is significant. Gravity sedimentation can achieve good separation effect and avoid the problems of high equipment cost and easy clogging that exist in other methods such as membrane separation.

[0048] Preferably, the treatment unit 100 primarily addresses the technical problems of inaccurate chelating resin dosing and unstable treatment effects caused by fluctuations in the concentration of various heavy metal ions in pickling wastewater. By using a heavy metal analyzer 101 to monitor the concentration of each ion in real time, and in conjunction with the independent channel design of the multi-channel variable frequency metering pump 102, precise quantitative dosing based on stoichiometry is achieved, avoiding the waste of chelating resin caused by traditional single-dosing methods. Especially when the concentration of heavy metal ions fluctuates drastically, the graded response mechanism ensures the stability of the treatment process by synergistically adjusting the feed rate, dosing rate, and sampling frequency. This integrated technical solution, combining real-time detection, differentiated control, and adaptive response, overcomes the limitations of existing technologies where simple superimposed detection and feeding devices are insufficient to handle complex operating conditions, achieving efficient and economical treatment of pickling wastewater.

[0049] Secondly, the pH value and conductivity data of the recycled acid solution are collected in real time by the monitoring unit 200, and the pH value, conductivity data and heavy metal ion concentration data are input into the control unit 300 as monitoring data to establish a monitoring database.

[0050] Furthermore, the monitoring unit 200 includes a pH sensor 201, a conductivity sensor 202, and a data acquisition module 203.

[0051] Specifically, the present invention collects the pH value of the recycled acid solution in real time through pH sensor 201, collects the conductivity data of the recycled acid solution in real time through conductivity sensor 202, and inputs the pH value, conductivity data and heavy metal ion concentration data as monitoring data into the database server module 302 in the control unit 300 through data acquisition module 203, and the database server module 302 establishes a monitoring database.

[0052] Preferably, in this invention, the monitoring unit 200 not only collects pH and conductivity data, but also integrates these data with heavy metal ion concentration data, and inputs them uniformly into the control unit 300 through the data acquisition module 203. This data integration method enables the system to simultaneously grasp the acid-base characteristics, ionic conductivity, and heavy metal residue of the recycled acid solution, providing multi-dimensional data support for subsequent control decisions. In particular, when the heavy metal ion concentration fluctuates, the changing trends of pH and conductivity can serve as early warning indicators, helping the system to adjust the control parameters of the multi-channel variable frequency metering pump 102 in a timely manner, avoiding lag in treatment effect. This early warning-control mechanism, in conjunction with the graded response mechanism of the processing unit 100, jointly improves the system's response speed and processing accuracy to fluctuations in heavy metal ion concentration.

[0053] Finally, the control unit 300 calculates the optimal control parameters in real time based on the monitoring data in the monitoring database, and automatically adjusts the processing unit 100 according to the optimal control parameters to transport the recycled acid solution to the pickling process for reuse.

[0054] Furthermore, the control unit 300 includes an industrial computer 301, a database server module 302, and a conveying module 303. The conveying module 303 includes a conveying pump 303a and a piping system 303b. The conveying module 303 transports the recycled acid to the pickling process for reuse, and is a key actuator for the control unit 300 to perform the reuse process. The conveying pump 303a adjusts the flow rate according to the control parameters issued by the control unit 300 to ensure that the recycled acid is delivered to the pickling process at a suitable rate. The piping system 303b connects the storage tank 103b and the pickling process, constructing a conveying channel for the recycled acid, including a main conveying pipeline, distribution branch pipes, and corresponding valve control devices to achieve directional conveying and flow regulation of the recycled acid.

[0055] Specifically, the control parameters include the speed and dosing ratio of the multi-channel variable frequency metering pump.

[0056] The industrial control computer 301 constructs an adaptive optimization control system, which includes a condition analysis module, a control strategy module, and an operation optimization module.

[0057] The operating condition analysis module receives monitoring data from the data acquisition module 203, establishes an interaction matrix R between heavy metal ions, and generates a characteristic graph reflecting the concentration changes of each component. It is important to note that the characteristic graph is a multi-curve graph with time on the x-axis and the concentration of each heavy metal ion, pH value, and conductivity on the y-axis. It is used to characterize the concentration change trend, fluctuation period, and interaction relationship of each component during the treatment process. The fluctuation characteristics and intersection positions of each curve reflect the degree of mutual influence between different heavy metal ions, providing a basis for formulating the metering pump speed adjustment strategy. The formula for calculating the interaction matrix R is:

[0058] In the formula, R ij α is an element in the interaction matrix R, representing the degree of interaction between the i-th heavy metal ion and the j-th heavy metal ion; ij β is the chelation competition coefficient between heavy metal ions i and j; ij γ is the concentration fluctuation coupling coefficient; ij C represents the efficiency impact coefficient. i C j These are the real-time concentrations of the i-th and j-th heavy metal ions, respectively; These are the corresponding target concentrations; K e δ is a characteristic factor for processing efficiency. ij ε is the coefficient of influence of pH value on the interaction between the i-th and j-th heavy metal ions; ij is the coefficient of influence of conductivity on the interaction between the i-th and j-th heavy metal ions; pH * Target pH value; ΔpH is the allowable pH fluctuation range; EC is the current conductivity; EC * ΔEC represents the target conductivity; ΔEC represents the allowable fluctuation range of conductivity.

[0059] Chelation competition coefficient α ij The formula used to characterize the competition for chelation sites among different heavy metal ions is as follows:

[0060] Chelation competition coefficient α ij This reflects the ratio of the competitive abilities of the i-th and j-th heavy metal ions for the binding sites of the chelating agent. Where K... i K j , respectively, are the chelation equilibrium constants of the i-th and j-th heavy metal ions; The ratio of equilibrium constants reflects intrinsic competitiveness. This indicates the degree of deviation between the actual concentration and the target concentration, when α ij A value greater than 1 indicates that the i-th type of heavy metal ion has a competitive advantage.

[0061] Concentration fluctuation coupling coefficient β ij The interaction strength used to characterize the treatment efficiency between heavy metal ions is calculated using the following formula:

[0062] In the formula, ΔC i ΔC j These represent the concentration changes of the i-th and j-th heavy metal ions, respectively. The concentration fluctuation coupling coefficient β ij This describes the correlation between the concentration changes of the i-th and j-th heavy metal ions, expressed as a normalized ratio of concentration changes. To measure the synchronicity of fluctuations in the concentrations of two ions, when β ij A value close to 1 indicates a strong correlation between concentration changes.

[0063] Processing efficiency influence coefficient γ ij The interaction strength used to characterize the treatment efficiency between heavy metal ions is calculated using the following formula:

[0064] In the formula, η i η j These are the real-time processing efficiencies for the i-th and j-th heavy metal ions, respectively. The target treatment efficiencies for the i-th and j-th heavy metal ions are respectively; the influence coefficient γ of the treatment efficiency. ij The mutual constraint relationship between the treatment efficiencies of the i-th and j-th heavy metal ions was characterized, and the normalized efficiency ratio was used. To evaluate the trade-off between the removal effects of the two ions, when γ ij A deviation from 1 indicates a significant efficiency disturbance.

[0065] Processing efficiency characteristic factor K e The formula used to characterize the overall treatment effect of the chelation reaction is as follows:

[0066] In the formula, w i is the weighting coefficient for the i-th heavy metal ion; n is the number of heavy metal ion types. Treatment efficiency characteristic factor K e The treatment effect of n heavy metal ions is comprehensively evaluated by weighted averaging, among which... This represents the deviation between the actual efficiency and the target efficiency for the i-th heavy metal ion; the denominator term is... Used for normalization, K e The smaller the value, the better the overall processing effect.

[0067] The control strategy module calculates the optimal combination of speed and dosing ratio parameters for the multi-channel variable frequency metering pump 102 based on the feature map and the interaction influence matrix; its optimization objective function is:

[0068] In the formula, F(v, r) is the optimization objective function, and the input variables are the rotational speed vector v and the ratio vector r.

[0069] First item middle:

[0070] n represents the number of different types of heavy metal ions; C i is the real-time concentration of the i-th heavy metal ion; The target concentration of the i-th heavy metal ion; This represents the absolute difference between the real-time concentration and the target concentration.

[0071] Second item: middle:

[0072] v i v is the rotational speed of the i-th metering pump channel; j r is the rotational speed of the j-th metering pump channel. i The target ratio for the i-th metering pump channel; r j The target ratio for the j-th metering pump channel; This is the actual speed ratio; The target ratio; This is the absolute value of the deviation between the actual speed ratio and the target ratio.

[0073] Calculate the ratio of the interaction influence matrix elements of the i-th heavy metal ion to all other heavy metal ions j in the interaction influence matrix; determine the adjustment priority of each heavy metal ion based on the magnitude of the interaction influence matrix element ratios; for heavy metal ions with large interaction influence matrix element ratios, increase the speed adjustment weight coefficient of the corresponding metering pump, while decreasing the speed adjustment weight coefficient of the metering pumps corresponding to other heavy metal ions; the speed adjustment weight coefficient is equal to the metering pump speed ratio.

[0074] The control strategy module also optimizes the dosage by adjusting the speed ratio while maintaining a constant overall dosage. Simultaneously, the module determines the timing of metering pump speed adjustments based on the fluctuation periods of heavy metal ion concentration, pH value, and conductivity displayed in the characteristic spectrum. Specifically: based on the fluctuation periods of heavy metal ion concentration, pH value, and conductivity in the characteristic spectrum, the module determines the speed adjustment time interval for each metering pump; when the heavy metal ion concentration exceeds the target range and shows an upward trend, the metering pump speed adjustment amplitude is proportional to the concentration change rate; when the heavy metal ion concentration exceeds the target range and shows a downward trend, the metering pump speed adjustment amplitude is proportional to the concentration change rate, and a larger proportional coefficient is used to achieve a rapid response; when the pH value or conductivity exceeds the target range, the speed of the metering pump corresponding to the affected heavy metal ions is adjusted accordingly, with the adjustment amplitude proportional to the degree of deviation in pH value or conductivity. Furthermore, when multiple heavy metal ions exhibit abnormal fluctuations simultaneously, the control strategy module identifies key heavy metal ions by decomposing the eigenvalues ​​of the interaction matrix that considers the effects of pH and conductivity. It then sets the metering pump adjustment priority based on the eigenvalue magnitude to maintain the balance between adjacent priority heavy metal ions. Specifically, this involves: performing eigenvalue decomposition on the interaction matrix that includes the effects of pH and conductivity; arranging the eigenvalues ​​in descending order, with the eigenvalue magnitude representing the importance of the dominant ion in the corresponding eigenvector; determining the metering pump adjustment priority based on the eigenvalue magnitude; and controlling the difference between the speed ratio of the metering pumps corresponding to adjacent priority heavy metal ions and their ratio parameter ratio within a preset range.

[0075] The optimization module evaluates the treatment effect of the optimal combination of rotation speed and dosing ratio parameters in real time. If the heavy metal ion removal rate and chelation reaction rate fluctuate, the interaction influence matrix is ​​automatically updated according to the preset calculation cycle, and the optimal combination of rotation speed and dosing ratio parameters is recalculated and its treatment effect is evaluated based on the updated matrix. If the concentration-ratio-efficiency state space analysis value exceeds the second preset threshold, the metering pump speed and ratio parameters are adjusted step by step according to the preset control rules, and the change in heavy metal ion concentration during the adjustment process is recorded. If it is necessary to determine the optimization order of control parameters, key control variables are identified based on the influence weight values ​​and response times of each parameter, and an optimization adjustment sequence is established according to importance, in which pH value and conductivity are used as constraints in the optimization process.

[0076] It is important to note that the heavy metal ion removal rate is the ratio of the heavy metal ion concentration in the effluent to that in the influent; the chelation reaction rate is the amount of heavy metal ions adsorbed by the chelating resin per unit time, characterized by the reduction in the heavy metal ion concentration in the wastewater per unit time; the concentration-ratio-efficiency state-space analysis value is the Euclidean distance between the state vector composed of the heavy metal ion concentration vector, the reagent ratio parameter vector, and the treatment efficiency vector, and the target state vector. The preset control rules refer to determining the priority order of adjustments based on the heavy metal ion removal effect; determining the metering pump speed adjustment step size based on the state vector deviation; and adjusting the operating parameters of each metering pump sequentially according to the priority order.

[0077] It should be noted that the industrial control computer 301 calculates the interaction influence matrix R based on the monitoring data through the operating condition analysis module and analyzes the dynamic relationship between heavy metal ions. Then, through the control strategy module, it uses the interaction influence matrix to optimize the calculation of pump group parameters and outputs the optimal speed and ratio combination. Finally, through the operation optimization module, it evaluates the treatment effect and feeds back the results to achieve dynamic optimization of control parameters. The system continuously executes the above process to maintain the optimal operating state of the treatment system.

[0078] Preferably, the control unit 300, through the collaborative work of the industrial control computer 301, the database server module 302, and the conveying module 303, solves the technical problems of low control accuracy, slow response, and poor adaptability in traditional pickling waste liquid treatment systems. The core of this unit lies in the construction of an adaptive optimization control system, including a condition analysis module, a control strategy module, and an operation optimization module, realizing intelligent decision-making and dynamic regulation based on multi-dimensional data. In particular, by establishing an interaction influence matrix R between heavy metal ions, the system can accurately describe and quantify complex relationships such as chelation competition and concentration coupling between different ions, overcoming the limitations of traditional control systems in handling multi-component collaborative processing. Based on feature maps and the interaction influence matrix, the control strategy module can calculate the optimal speed and dosing ratio parameter combination of the multi-channel variable frequency metering pump in real time, and achieve ratio optimization by maintaining a constant overall reagent dosage, effectively improving chelation treatment efficiency and reagent utilization. When the system detects abnormal fluctuations, the control unit can automatically adjust the speed change gradient of the metering pump based on the ion concentration change characteristics, achieving rapid response and precise regulation to condition fluctuations. Meanwhile, the operation optimization module continuously evaluates the treatment effect and updates the interaction influence matrix, enabling the system to possess self-learning and adaptive capabilities. It can automatically optimize control parameters based on changes in influent water quality, ensuring the stability of the treatment effect. This control scheme, based on mathematical models and intelligent algorithms, breaks through the limitations of traditional PID control, realizing intelligent and precise control of the pickling waste liquid treatment process, and providing reliable technical support for the efficient reuse of industrial waste liquid.

[0079] In summary, this invention achieves efficient treatment and reuse of pickling wastewater through the organic integration of a treatment unit, a monitoring unit, and a control unit. The treatment unit employs a combination of a heavy metal analyzer and a multi-channel variable frequency metering pump to achieve precise detection and quantitative dosing of heavy metal ions. The monitoring unit constructs a multi-dimensional monitoring system for pH value, conductivity, and heavy metal ion concentration, providing real-time and complete data support for system control. The control unit, by establishing an interaction matrix and feature spectrum, combined with an adaptive optimization algorithm, achieves intelligent regulation of the heavy metal ion treatment process. This systematic technical solution overcomes the problems of detection lag, coarse control, and poor adaptability in traditional treatment methods, significantly improving chelation treatment efficiency and reagent utilization. Especially when facing fluctuations in influent water quality, the system can respond quickly and automatically optimize control parameters, ensuring the stability of the treatment effect and providing reliable technical support for the resource utilization of industrial pickling wastewater.

[0080] Example 2, referring to Figure 2, is an embodiment of the present invention, providing a method for reusing pickling waste liquid.

[0081] Figure 2 shows an overall flow chart of a pickling waste liquid reuse system, including:

[0082] S1: The concentration of each heavy metal ion in the pickling waste liquid is detected by the online heavy metal analyzer 101 in the processing unit 100. The corresponding chelating resin is added to the pickling waste liquid according to the concentration of each heavy metal ion by the multi-channel variable frequency metering pump 102 to obtain the recycled acid liquid.

[0083] S2: The pH value and conductivity data of the recycled acid solution are collected in real time by the pH sensor 201 and conductivity sensor 202 in the monitoring unit 200, and the pH value, conductivity data and heavy metal ion concentration data are input into the control unit 300 as monitoring data through the data acquisition module 203 to establish a monitoring database.

[0084] S3: The industrial computer 301 in the control unit 300 calculates the optimal control parameters in real time based on the monitoring data in the monitoring database, and automatically adjusts the multi-channel variable frequency metering pump 102 according to the optimal control parameters to transport the recycled acid solution to the pickling process for reuse.

[0085] Example 3, referring to Figure 3, is an embodiment of the present invention, which differs from the previous embodiment in that: if the function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0086] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0087] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0088] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0089] Example 4 is an embodiment of the present invention, which provides a pickling waste liquid recycling system. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiment.

[0090] To verify the treatment efficiency of this invention, a comparative experimental scheme was designed in this embodiment. The experiment used pickling wastewater from an electroplating plant as the treatment target, which mainly contains Cu. 2+ Ni 2+ and Cr 3+ Three heavy metal ions were used. The wastewater was treated using the method of this invention (denoted as Scheme A) and a traditional fixed-parameter control system (denoted as Scheme B), respectively. During the experiment, the adaptability of the two schemes to water quality fluctuations was tested by changing the influent water quality conditions; at the same time, key indicators such as heavy metal removal efficiency, chelating resin adsorption capacity, and system operational stability were compared and analyzed.

[0091] During a continuous 120-hour operation cycle, water samples were collected and relevant parameters were measured every 4 hours. This invention employs multi-parameter collaborative monitoring and adaptive optimization algorithms to dynamically adjust the dosage ratio and reaction conditions of different types of chelating resins based on real-time monitoring data; while traditional systems operate with fixed process parameters. A sudden change in influent water quality was specifically designed during the experiment to verify the dynamic response capabilities and treatment stability of both systems.

[0092] Table 1 Comparison of heavy metal removal efficiencies of different treatment schemes

[0093] As can be seen from the comparison data of heavy metal removal efficiency in Table 1, the present invention has a better effect on Cu removal efficiency. 2+ Ni 2+ and Cr 3+ All three heavy metal ions showed excellent removal performance. During 120 hours of continuous operation, the Cu in Scheme A... 2+ Ni 2+ and Cr 3+ The removal rates increased steadily from the initial 92.5%, 91.8%, and 90.6% to 94.8%, 94.2%, and 93.1%, respectively. Meanwhile, the removal efficiency of the traditional scheme B continued to decline, with the removal rates of the three ions dropping to 86.8%, 85.4%, and 84.9% after 120 hours. Particularly after 72 hours, the performance difference between the two schemes widened further, with the removal rate difference between scheme A and scheme B exceeding 7 percentage points. This fully demonstrates that the present invention can improve the treatment effect through continuous parameter optimization, while the traditional fixed-parameter control system suffers from gradual performance degradation due to the lack of an optimization mechanism.

[0094] Table 2 Results of Water Quality Fluctuation Adaptability Test

[0095] The water quality fluctuation adaptability test results in Table 2 allow for an in-depth analysis of the dynamic response characteristics of the two schemes. As the influent concentration fluctuation increased from 10% to 50%, Scheme A demonstrated superior anti-interference capabilities: the treatment efficiency fluctuation increased only from 2.1% to 5.1%, the parameter adjustment time increased from 2.5 minutes to 5.2 minutes, and the system stabilization recovery time increased from 8.5 minutes to 18.5 minutes. In contrast, Scheme B showed significant deterioration in all indicators under the same conditions. When the influent concentration fluctuation reached 50%, the treatment efficiency fluctuation reached as high as 25.4%, the parameter adjustment time reached 20.2 minutes, and the system stabilization recovery time was extended to 52.5 minutes. This set of comparative data clearly demonstrates that the present invention has stronger adjustment capabilities and faster recovery speed in response to water quality fluctuations, effectively maintaining the stable operation of the system, which is of great significance for practical industrial applications.

[0096] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A pickling waste liquid reuse system, characterized in that, include: The processing unit (100) is used to detect the concentration of each heavy metal ion in the pickling waste liquid, add the corresponding chelating resin to the pickling waste liquid according to the concentration of each heavy metal ion, and collect the recycled acid liquid. The monitoring unit (200) is used to collect the pH value and conductivity data of the recycled acid solution in real time, and input the pH value, conductivity data and heavy metal ion concentration data as monitoring data into the control unit (300) to establish a monitoring database; The control unit (300) calculates the optimal control parameters in real time based on the monitoring data in the monitoring database, and automatically adjusts the processing unit (100) according to the optimal control parameters to transport the recycled acid solution to the pickling process for reuse; wherein, the control parameters include the rotation speed and dosing ratio of the multi-channel variable frequency metering pump.

2. The pickling waste liquid reuse system as described in claim 1, characterized in that: The processing unit (100) includes a heavy metal analyzer (101), a multi-channel variable frequency metering pump (102), and a recovery module (103); The heavy metal analyzer (101) is equipped with ion detection modules for various types of heavy metals, and the ion detection modules are equipped with ion-selective electrodes; the ion-selective electrodes include a reference electrode and a measuring electrode, the reference electrode and the measuring electrode form a potential difference, and the concentration of heavy metal ions is detected based on the potential difference; The multi-channel variable frequency metering pump (102) includes multiple independently controlled metering pump channels, which are divided into two categories: pretreatment channels and posttreatment channels. The pretreatment channel is used to add heavy metal removal agents to pre-treat the pickling waste liquid to obtain initial reuse acid liquid. The posttreatment channel is used to quantitatively add chelating resins for treating various heavy metal ions to the initial reuse acid liquid. A reactor is set between the outlet of the pretreatment channel and the inlet of the posttreatment channel. Each metering pump channel is communicatively connected to the control unit (300). The recycling module (103) collects the pickling waste liquid after the addition of chelating resin and separates the reusable acid liquid; the recycling module (103) includes a collection pump (103a) and a storage tank (103b).

3. The pickling waste liquid reuse system as described in claim 2, characterized in that: The heavy metal analyzer (101) also includes a data processor, which is used to receive the detection signal of the ion-selective electrode and convert it into heavy metal concentration data, and transmit it to the data acquisition module (203) in real time. Each metering pump channel includes a variable frequency motor, a peristaltic pump head, and a chelating resin storage tank. When the control unit (300) detects that the rate of change of heavy metal ion concentration exceeds a first preset threshold, a graded response mechanism is activated. The graded response mechanism includes a first-level response, a second-level response, and a third-level response. The first-level response is to initiate monitoring and early warning: the heavy metal analyzer (101) and sampling points A and B set at the inlet and outlet of the reactor synchronously shorten the sampling cycle and increase the data acquisition frequency. If the data of multiple consecutive samplings show that the concentration of heavy metal ions continues to change, the second-level response is initiated; if the data of multiple consecutive samplings show that the concentration of heavy metal ions fluctuates within a stable range, the normal sampling cycle is restored. The second-level response is to adjust the influent conditions: reduce the feed rate of the collection pump (103a) and increase the residence time of the pickling waste liquid in the treatment unit (100); if the treatment effect is improved after adjusting the residence time, the current influent conditions are maintained; if the treatment effect is not improved, the third-level response is initiated. The third-level response is to optimize the dosing of the reagent: the variable frequency motor gradually adjusts the speed according to the buffer curve, and prioritizes the adjustment of the pretreatment channel. After the speed stabilizes, the speed of the corresponding chelating resin channel is adjusted in sequence. If the treatment effect is improved, the current process parameters are recorded. If the treatment effect of a certain heavy metal ion still does not meet the requirements, the residence time of the treatment unit (100) is increased.

4. The pickling waste liquid reuse system as described in claim 3, characterized in that: The monitoring unit (200) includes a pH sensor (201), a conductivity sensor (202), and a data acquisition module (203); The control unit (300) includes an industrial computer (301), a database server module (302), and a conveying module (303); The data acquisition module (203) collects the pH value, conductivity data and heavy metal ion concentration data, organizes them into monitoring data and inputs them into the database server module (302), which then establishes a monitoring database.

5. The pickling waste liquid reuse system as described in claim 4, characterized in that: The industrial control computer (301) constructs an adaptive optimization control system; the adaptive optimization control system includes a working condition analysis module, a control strategy module, and an operation optimization module; The operating condition analysis module receives monitoring data from the data acquisition module (203), establishes an interaction matrix R between heavy metal ions, and generates a feature spectrum reflecting the concentration changes of each component; the calculation formula for the interaction matrix R is: In the formula, R ij α is an element in the interaction matrix R, representing the degree of interaction between the i-th heavy metal ion and the j-th heavy metal ion; ij β is the chelation competition coefficient between heavy metal ions i and j; ij γ is the concentration fluctuation coupling coefficient; ij C represents the efficiency impact coefficient. i C j These represent the real-time concentrations of the i-th and j-th heavy metal ions, respectively. These are the corresponding target concentrations; K e To process efficiency characteristic factors; K i K j The chelation equilibrium constants for the i-th and j-th heavy metal ions, respectively; ΔC i ΔC j These represent the changes in concentration of the i-th and j-th heavy metal ions, respectively. η i η j These are the real-time processing efficiencies for the i-th and j-th heavy metal ions, respectively. The target treatment efficiencies for the i-th and j-th heavy metal ions are respectively; w i δ represents the weighting coefficient of the i-th heavy metal ion; n is the number of heavy metal ions; ij ε is the coefficient of influence of pH value on the interaction between the i-th and j-th heavy metal ions; ij is the coefficient of influence of conductivity on the interaction between the i-th and j-th heavy metal ions; pH * Target pH value; ΔpH is the allowable pH fluctuation range; EC is the current conductivity; EC * Target conductivity; ΔEC represents the allowable fluctuation range of conductivity; The characteristic spectrum is a multi-curve graph with time as the horizontal axis and the concentration of each heavy metal ion, pH value and conductivity as the vertical axis. It is used to characterize the concentration change trend, fluctuation period and interaction relationship of each component during the treatment process. The control strategy module calculates the optimal combination of speed and dosing ratio parameters for the multi-channel variable frequency metering pump (102) based on the feature map and the interaction influence matrix R. The specific optimization objective function is expressed as follows: In the formula, F(v, r) is the optimization objective function, and the input variables are the rotational speed vector v and the ratio vector r; v represents the target concentration of the i-th heavy metal ion; i v is the rotational speed of the i-th metering pump channel; j r is the rotational speed of the j-th metering pump channel. i The target ratio for the i-th metering pump channel; r j The target ratio for the j-th metering pump channel.

6. The pickling waste liquid reuse system as described in claim 5, characterized in that: The control strategy module determines the speed adjustment priority based on the ratio of elements in the interaction matrix of each heavy metal ion with other heavy metal ions. It calculates the ratio of elements in the interaction matrix of the i-th heavy metal ion with each of the other heavy metal ions j. The module then determines the adjustment priority of each heavy metal ion based on the magnitude of these ratios. For heavy metal ions with large interaction matrix ratios, the speed adjustment weight coefficient of the corresponding metering pump is increased, while the speed adjustment weight coefficients of the metering pumps corresponding to other heavy metal ions are decreased. The speed adjustment weight coefficients are equal to the metering pump speed ratios. The control strategy module also optimizes the ratio by maintaining a constant overall dosage and adjusting the pump speed proportionally. Simultaneously, the module determines the timing of metering pump speed adjustments based on the fluctuation cycles of heavy metal ion concentration, pH value, and conductivity displayed in the characteristic spectrum. When heavy metal concentration, pH value, and conductivity exceed their respective target ranges, the metering pump speed is gradually adjusted according to preset step sizes. When these parameters return to the target range, the metering pump speed is directly adjusted to the target value. Furthermore, based on the real-time fluctuation amplitude of heavy metal ion concentration, pH value, and conductivity, the module calculates and adjusts the step size of the metering pump speed change proportionally. When multiple heavy metal ions exhibit abnormal fluctuations simultaneously, the control strategy module identifies key heavy metal ions through eigenvalue decomposition of the interaction influence matrix, sets the metering pump adjustment priority according to the eigenvalue magnitude, and maintains the ratio balance between adjacent priority heavy metal ions. The operation optimization module evaluates the treatment effect of the optimal rotation speed and dosage ratio parameter combination in real time. If the heavy metal ion removal rate and chelation reaction rate fluctuate, the interaction influence matrix is ​​automatically updated according to the preset calculation cycle, and the optimal rotation speed and dosage ratio parameter combination is recalculated and the treatment effect is evaluated based on the updated interaction influence matrix. If the concentration-ratio-efficiency state space analysis value exceeds the second preset threshold, the metering pump speed and ratio parameters are adjusted step by step according to the preset control rules, and the changes in heavy metal ion concentration during the adjustment process are recorded. If it is necessary to determine the optimization order of control parameters, key control variables are identified based on the influence weight values ​​and response times of each control parameter, and optimization adjustment sequences are established according to their importance.

7. The pickling waste liquid reuse system as described in claim 6, characterized in that: The collection pump (103a) pumps the pickling waste liquid treated with chelating resin by the multi-channel variable frequency metering pump (102) to the storage tank (103b) for solid-liquid separation; the storage tank (103b) separates the chelating resin-heavy metal complex and the reused acid liquid by sedimentation, and transports the separated reused acid liquid to the monitoring unit (200). The delivery module (303) includes a delivery pump (303a) and a piping system (303b).

8. A method for reusing pickling waste liquid, based on the pickling waste liquid reuse system according to any one of claims 1 to 7, characterized in that: Includes the following steps, The concentration of each heavy metal ion in the pickling waste liquid is detected by the online heavy metal analyzer (101) in the processing unit (100). The corresponding chelating resin is added to the pickling waste liquid according to the concentration of each heavy metal ion by the multi-channel variable frequency metering pump (102) to obtain the recycled acid liquid. The pH value and conductivity data of the recycled acid solution are collected in real time by the pH sensor (201) and conductivity sensor (202) in the monitoring unit (200), and the pH value, conductivity data and heavy metal ion concentration data are input into the control unit (300) as monitoring data through the data acquisition module (203) to establish a monitoring database. The industrial computer (301) in the control unit (300) calculates the optimal control parameters in real time based on the monitoring data in the monitoring database, and automatically adjusts the multi-channel variable frequency metering pump (102) according to the optimal control parameters to transport the recycled acid solution to the pickling process for reuse.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the pickling waste liquid reuse system according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the pickling waste liquid reuse system as described in any one of claims 1 to 7.

Images