A smart control method and system for neutralizing and treating laboratory acid and alkali waste liquids

Abstract

This application discloses an intelligent control method and system for the neutralization treatment of laboratory acid and alkali waste liquid. The method involves introducing laboratory waste liquid into a reaction tank for trial dosing, acquiring pH response data and dosing execution data during the trial dosing, and determining initial response characteristics. Using the initial buffer characteristics as initial values, the waste liquid buffer characteristics are identified online in conjunction with the pH response data and dosing execution data. Based on the buffer characteristics, it is determined whether the waste liquid meets the preset buffer plateau judgment conditions; if it does, a smaller first dosing rate upper limit is adopted; if not, a larger second dosing rate upper limit is adopted. Then, based on the target pH range, buffer characteristics, and dosing rate upper limit, the dosing control amount of acid or alkali is determined, and the buffer characteristics and dosing control amount are continuously updated and corrected based on the pH response data after dosing.

Description

Technical Field

[0001] This invention relates to the field of laboratory waste liquid treatment technology, specifically to an intelligent control method and system for neutralizing and treating laboratory acid and alkali waste liquids. Background Technology

[0002] Laboratories generate acidic, alkaline, and mixed acid-base waste liquids during chemical, biological, pharmaceutical, and materials analysis experiments. These waste liquids typically originate from reagent preparation, container cleaning, instrument rinsing, sample pretreatment, and reaction residue collection. Their composition is significantly influenced by the experimental procedures, reagent types, cleaning frequency, and mixing ratios. Laboratory acid and alkaline waste liquids usually require neutralization before discharge to bring their pH value within the permitted range for discharge standards, before proceeding with subsequent flocculation, sedimentation, filtration, adsorption, or discharge processes. Because laboratory waste liquids are produced in small batches, from diverse sources, and with frequent compositional variations, waste liquids treated by the same equipment may exhibit significant differences in initial pH value, buffering capacity, ionic strength, temperature, and reaction response rate. Therefore, the precision of acid or alkali dosing control directly affects the stability of neutralization treatment, reagent consumption, and operational safety.

[0003] Existing methods for neutralizing laboratory acid and alkali wastewater mainly include manual dosing, on / off dosing control based on pH sensors, proportional-integral-derivative (PID) control, and automatic dosing control based on multi-parameter sensors. Manual dosing typically relies on operator experience, adding small amounts of acid or alkali solution multiple times based on pH test strips or pH meter readings. On / off dosing control usually starts the dosing pump when the pH value exceeds a preset range and stops dosing once the pH value returns to the target range. PID continuously adjusts the dosing pump output based on the current pH deviation. Some automatic control devices further collect parameters such as temperature, conductivity, and wastewater flow rate, and correct the dosing process using preset rules or empirical parameters. These methods can achieve basic neutralization control under conditions of relatively stable wastewater composition, minimal reaction hysteresis, and normal sensor status.

[0004] However, compared to continuous industrial wastewater, laboratory acid and alkali wastewater exhibits characteristics such as frequent changes in wastewater type, uncertain buffer systems, and significant differences in reaction responses. Existing control methods typically rely solely on the current pH value or pH deviation as the primary control basis, failing to identify the buffering characteristics of the wastewater online. Consequently, they cannot determine the actual pH response of the wastewater to a unit dosage. When the wastewater contains buffering components such as phosphates, acetates, and carbonates, the system may continue to add chemicals due to insignificant pH changes, resulting in significant overshoot after traversing the buffer zone. When the wastewater's buffering capacity is weak, excessive dosage can easily cause the pH value to repeatedly exceed the target range. Furthermore, there are lags in the response of the dosing pump, the mixing and diffusion of the chemicals, and the stabilization of pH sensor readings. Existing methods struggle to dynamically adjust the response detection threshold and predictive control window based on the current buffering state of the wastewater, easily leading to continued dosing before the sensor reflects the true reaction results, causing oscillations and reagent waste. Furthermore, laboratory waste liquids are corrosive and polluting, and pH, conductivity, and temperature sensors are prone to drift, jumps, or failures. Existing control methods lack unified handling of sensor reliability, prior knowledge of historical waste liquid types, and degradation control, resulting in insufficient stability of control strategies under complex waste liquids and abnormal operating conditions. Therefore, a laboratory waste liquid neutralization control method and system is needed that can identify the buffer response characteristics of waste liquids online and adaptively determine the control dosage of acid or alkali solutions by combining reaction hysteresis, sensor reliability, and historical treatment experience. Summary of the Invention

[0005] The main objective of this invention is to provide an intelligent control method and system for the neutralization treatment of laboratory acid and alkali waste liquids, in order to solve the technical problems of existing laboratory acid and alkali waste liquid neutralization control methods, which are difficult to dynamically adjust the dosing strategy according to the buffer characteristics and reaction lag of different waste liquids, resulting in overshoot, oscillation, long adjustment time and large reagent consumption during the pH adjustment process.

[0006] To achieve the above objectives, the present invention provides an intelligent control method for the neutralization treatment of laboratory acid and alkali waste liquid, comprising the following steps: S1. The laboratory waste liquid is introduced into the reaction tank for trial dosing, and the acid-base response data and dosing execution data during the trial dosing are obtained. The initial response characteristics are determined based on the acid-base response data and the dosing execution data. The initial response characteristics include initial buffer characteristics. S2. Using the initial buffer characteristics as the initial value, identify the buffer characteristics of the laboratory waste liquid online based on the pH response data and the dosing execution data. S3. Obtain the preset buffer zone determination conditions, and determine whether the laboratory waste liquid meets the preset buffer zone determination conditions based on the buffer characteristics; if yes, then the upper limit of the dosing rate is determined as the first upper limit of the dosing rate; if no, then the upper limit of the dosing rate is determined as the second upper limit of the dosing rate; wherein, the first upper limit of the dosing rate is less than the second upper limit of the dosing rate. S4. Obtain the target pH range, and determine the dosage control amount of acid or alkali solution based on the target pH range, the buffer characteristics, and the upper limit of the dosing rate. S5. Add acid or alkali solution to the reaction tank according to the dosing control amount, and obtain the pH response data after dosing. Update the buffer characteristics according to the pH response data after dosing, and correct the dosing control amount according to the updated buffer characteristics until the pH value of the laboratory waste liquid enters the target pH range.

[0007] Furthermore, step S1 specifically includes the following steps: The initial pH value of the laboratory waste liquid in the reaction tank, the test dosing data of the dosing device during the test dosing period, and the response change data of the pH value over time during the test dosing period are obtained. The initial response characteristics are determined based on the initial pH value, the response change data, and the trial dosing data; wherein the initial response characteristics include the initial buffer characteristics and the initial reaction hysteresis characteristics.

[0008] More preferably, step S2 specifically includes the following steps: The dosage and reagent concentration of the dosing execution data, the liquid volume in the reaction tank, and the change in pH caused by the dosage during the current identification period are obtained from the dosing execution data during the current identification period. The instantaneous buffer capacity for the current identification cycle is determined based on the dosage, reagent concentration, liquid volume in the tank, and pH change. Determine whether the current identification cycle is the first cycle; When the current identification period is the first identification period, the buffer capacity state in the initial buffer feature is determined as the previous buffer capacity state. When the current identification period is not the first identification period, the buffer capacity state obtained from the previous identification period is determined as the previous buffer capacity state. Update the buffer capacity state of the current identification cycle based on the instantaneous buffer capacity and the previous buffer capacity state. The buffering characteristics of the laboratory waste liquid are determined based on the buffer capacity status of the current identification cycle.

[0009] More preferably, the following steps are included before step S2: Obtain a historical waste liquid type library, which includes the prior values ​​of the joint state variables of each historical waste liquid type, the initial adjustment range of the joint state variables, and the historical response characteristics corresponding to each historical waste liquid type. The initial pH value, the response change data, the initial buffer characteristics, and the initial response hysteresis characteristics are used as the current response characteristics; Obtain preset similarity conditions, compare the current response features with the historical response features corresponding to each historical waste liquid type, and determine the historical waste liquid type that meets the preset similarity conditions; Obtain the prior value of the joint state variable corresponding to the historical waste liquid type that meets the preset similarity conditions, and use it as the initial state value of the joint state variable; Obtain the initial adjustment range of the joint state variable corresponding to the historical waste liquid type that meets the preset similarity conditions; Obtain the similarity between the current response feature and the historical response feature corresponding to the historical waste liquid type that meets the preset similarity conditions, and correct the initial adjustment range of the joint state variable according to the similarity to obtain the target initial adjustment range for online identification of the buffer feature.

[0010] More preferably, after step S5, the following steps are also included: Obtain the convergence quality of the joint state variables after the neutralization process is completed; wherein, the convergence quality of the joint state variables is used to characterize the stability of the update of the joint state variables during the neutralization process; The reaction lag characteristics are obtained, and a comprehensive response characteristic for this neutralization process is generated based on the pH response data, the dosing execution data, the buffer characteristics, and the reaction lag characteristics in this neutralization process; wherein, the reaction lag characteristics are updated based on the initial reaction lag characteristics and the online estimation results of the reaction lag during the neutralization process; Obtain the preset update conditions and the convergence value of the joint state variable, and determine whether the convergence quality of the joint state variable meets the preset update conditions; If the convergence quality of the joint state variable meets the preset update condition, and the comprehensive response feature meets the preset similarity condition of the historical waste liquid type, then based on the comprehensive response feature and the convergence value of the joint state variable, the historical response feature, the prior value of the joint state variable, and the initial adjustment range of the joint state variable for the corresponding historical waste liquid type are updated. When the convergence quality of the joint state variable meets the preset update condition, and the comprehensive response feature does not meet the preset similarity condition of the historical waste liquid type, a new historical waste liquid type is added based on the comprehensive response feature and the convergence value of the joint state variable. If the convergence quality of the joint state variables does not meet the preset update conditions, then writing the neutralization processing data into the historical waste liquid type library is prohibited.

[0011] Furthermore, step S4 also includes the following steps: Obtain the pump response hysteresis value of the dosing device and the mixing and diffusion hysteresis value between the dosing point in the reaction tank and the sensor detection position; Obtain a preset mapping relationship between the buffer feature and the sensor response hysteresis, and determine the sensor response hysteresis value based on the buffer feature and the preset mapping relationship; The total reaction hysteresis value is determined based on the pump response hysteresis value, the mixing diffusion hysteresis value, and the sensor response hysteresis value. Obtain the current pH value, and based on the current pH value, the buffer characteristics, and the total reaction hysteresis value, predict the pH value after issuing the dosing control command; The dosage control amount is determined based on the degree of deviation between the predicted pH value and the target pH range.

[0012] Furthermore, the pH response data is obtained through the following steps: Acquire pH values ​​collected by multiple sensors; Determine whether the pH readings of each sensor meet the preset anomaly detection criteria; If so, the corresponding sensor is determined to be in an abnormal state, and the confidence weight of the corresponding sensor is set to zero. If not, the historical observation residuals of each sensor are obtained, and the confidence weight of the corresponding sensor is determined based on the historical observation residuals; wherein, the historical observation residuals are the differences between the historical pH observation values ​​and the historical fused pH values ​​of the corresponding sensor. The pH observations are fused according to the confidence weights to generate the pH response data.

[0013] More preferably, after step S4, the following steps are also included: The sensors include a pH sensor, a conductivity sensor, and a temperature sensor, used to determine whether the pH sensor, conductivity sensor, and / or temperature sensor are in an abnormal state. When the conductivity sensor is determined to be in an abnormal state, the system enters the first degraded control mode, disabling the correction of the buffer feature by the conductivity data of the abnormal conductivity sensor. When the temperature sensor is determined to be in an abnormal state, it enters the second degraded control mode, and uses preset temperature parameters to replace the measured temperature value of the abnormal temperature sensor. When at least one pH sensor is determined to be in an abnormal state, and the remaining pH sensors can still output observation values, the third degraded control mode is entered. The pH observation values ​​of the pH sensors in the abnormal state are filtered by median value and then fused to generate the pH response data. When all pH sensors can no longer output observation values, the dosing is stopped. During the duration of the first degraded control mode, the second degraded control mode, and the third degraded control mode, the upper limit of the dosing rate is adjusted to the corresponding degraded dosing rate upper limit, and the interval between two adjacent dosings is extended to the degraded dosing interval; wherein, the upper limit of the degraded dosing rate is lower than the upper limit of the first dosing rate, and the degraded dosing interval is greater than the interval between two adjacent dosings in the normal mode.

[0014] The present invention also provides an intelligent control system for the neutralization treatment of laboratory acid and alkali waste liquid, including an on-site treatment unit, an intelligent control unit and a remote interaction unit; The on-site treatment unit includes a waste liquid collection tank, a neutralization reactor, an acid storage tank, an alkali storage tank, a precision dosing pump, a multi-parameter sensor array, a stirring device, and a discharge valve. The neutralization reactor contains a reaction tank. The outlet of the waste liquid collection tank is connected to the inlet of the neutralization reactor. The acid and alkali storage tanks are respectively connected to the dosing ports of the neutralization reactor via the precision dosing pump. The sensing probes of the multi-parameter sensor array are located inside the neutralization reactor. The multi-parameter sensor array includes at least a pH sensor, a temperature sensor, a conductivity sensor, and a flow meter. The stirring device is located inside the neutralization reactor, and the discharge valve is located at the discharge port of the neutralization reactor. The intelligent control unit includes an edge computing controller, which is deployed on-site. The signal input terminal of the edge computing controller is connected to the signal output terminal of the multi-parameter sensor array, and is used to collect the pH response data, temperature data, conductivity data and waste liquid flow data of the multi-parameter sensor array in real time. The control signal output terminal of the edge computing controller is connected to the control signal input terminal of the precision dosing pump, and is used to output the dosing control amount to the precision dosing pump. The remote interaction unit includes a cloud server and a user terminal. The cloud server is communicatively connected to the edge computing controller and is used to receive neutralization processing data, degradation control events and historical waste liquid type library update results uploaded by the edge computing controller, and output monitoring information and maintenance prompts to the user terminal. The edge computing controller is used to execute the method described above.

[0015] Furthermore, the edge computing controller includes a trial dosing control module, a buffer feature identification module, a history type matching module, a response hysteresis estimation module, a sensor reliability assessment module, a dosing control module, and a degradation control module; The trial dosing control module is used to control the precision dosing pump to perform trial dosing, acquire the acid-base response data and dosing execution data collected by the multi-parameter sensor array during the trial dosing, and determine the initial response characteristics, which include initial buffer characteristics and initial reaction hysteresis characteristics. The input of the historical type matching module is connected to the output of the trial dosing control module and the output of the remote interaction unit, respectively. It is used to obtain the initial response features from the trial dosing control module, the prior values ​​of the joint state variables stored in the historical waste liquid type library and the initial adjustment range of the joint state variables from the remote interaction unit, determine the historical waste liquid types that meet the preset similarity conditions, take the prior values ​​of the joint state variables corresponding to the historical waste liquid types that meet the preset similarity conditions as the initial state values ​​of the joint state variables, and correct the initial adjustment range of the joint state variables according to the similarity between the initial response features and the historical response features corresponding to the historical waste liquid types to obtain the target initial adjustment range. The first input terminal of the buffer feature identification module is connected to the output terminal of the trial dosing control module, the second input terminal is connected to the output terminal of the historical type matching module, and the third input terminal is connected to the signal output terminal of the multi-parameter sensor array. It is used to update the buffer feature online based on the initial buffer feature as the initial value and the target initial adjustment range as the initial adjustment range, according to the subsequent pH response data and the dosing execution data, and to determine whether the preset buffer plateau determination conditions are met and to determine the upper limit of the dosing rate based on the buffer feature. The input of the reaction hysteresis estimation module is connected to the output of the buffer feature identification module and the signal output of the multi-parameter sensor array, respectively. It is used to determine the total reaction hysteresis value based on the pump response hysteresis value, the mixing diffusion hysteresis value, the sensor response hysteresis value and the buffer feature, and to predict the pH value based on the current pH value, the buffer feature and the total reaction hysteresis value. The input terminal of the sensor reliability assessment module is connected to the signal output terminal of the multi-parameter sensor array, and is used to determine whether each sensor in the multi-parameter sensor array meets the preset anomaly judgment conditions, and to determine the reliability weight of each sensor based on the judgment result and the historical observation residual of each sensor. The input terminal of the dosing control module is connected to the output terminal of the buffer feature identification module, the output terminal of the reaction hysteresis estimation module, and the output terminal of the sensor reliability assessment module, respectively. The output terminal of the dosing control module is connected to the control signal input terminal of the precision dosing pump. It is used to determine the dosing control amount based on the target pH range, the buffer feature, the upper limit of the dosing rate, and the pH prediction value, and output the dosing control amount to the precision dosing pump. The input terminal of the degradation control module is connected to the output terminal of the sensor reliability evaluation module, and the output terminal of the degradation control module is connected to the input terminal of the dosing control module. It is used to output the upper limit of the corresponding degradation dosing rate and the corresponding degradation dosing interval to the dosing control module when the sensor in the multi-parameter sensor array is in an abnormal state. The remote interaction unit is also used to receive the neutralization processing data, the degradation control event, and the update results of the historical waste liquid type library uploaded by the edge computing controller, and output monitoring information and maintenance prompts to the user terminal.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This application obtains pH response and dosing execution data by conducting trial dosing in the reaction tank, thereby identifying the buffering characteristics of laboratory waste liquid online. This changes the lag and blindness of conventional neutralization treatment, which only relies on the current pH deviation for dosing. The system can predict the sensitivity of complex waste liquids to dosing operations in advance. Furthermore, by determining whether the waste liquid is in a preset buffer plateau state based on the identified buffering characteristics, the system actively tightens the upper limit of the dosing rate to a smaller first dosing rate when it is in the buffer plateau state, and adopts a larger second dosing rate when it is not in the buffer plateau state. This breaks the conventional control method of continuously increasing the dosing rate when pH changes are slow. The intuitive logic of high dosage avoids a sharp change in pH caused by the accumulation of reagents in the early stage after the reaction crosses the buffer plateau, and achieves precise dynamic constraint on the dosing boundary of multi-component waste liquid. On this basis, the dosing control amount is comprehensively determined by combining the target pH range, buffer characteristics and the upper limit of the dosing rate. The buffer characteristics and dosing control amount are continuously updated by using the feedback data generated by subsequent dosing, and an adaptive evolution closed loop of feature identification and control command is constructed. In this way, under unexpected circumstances, the large overshoot and repeated oscillation of the waste liquid in the unknown buffer system during the adjustment process are effectively suppressed, the emission compliance time is significantly shortened and the ineffective consumption of neutralizing reagent is greatly reduced. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. 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 the structures shown in these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart of an intelligent control method for neutralizing laboratory acid and alkali waste liquid according to an embodiment of the present invention.

[0019] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described 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 the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0022] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0023] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0024] Please see Figure 1This embodiment provides an intelligent control method for the neutralization and treatment of laboratory acid and alkali waste liquids. This method can be applied to the neutralization stage of laboratory acid and alkali waste liquid treatment. The treated waste liquid can be a combination of waste liquids from processes such as washing pharmaceutical containers, cleaning instruments, sample pretreatment, and collecting reagent residues. This waste liquid can include acidic waste liquids, alkaline waste liquids, and mixed acid-alkali waste liquids. The waste liquid treatment capacity can be around 1 ton per day; the target pH range for neutralization can be set from 6.0 to 9.0. This embodiment is described based on steps S1 to S5 as described above.

[0025] Specifically, the steps include the following: S1. The laboratory waste liquid is introduced into the reaction tank for trial dosing, and the acid-base response data and dosing execution data during the trial dosing are obtained. The initial response characteristics are determined based on the acid-base response data and the dosing execution data. The initial response characteristics include initial buffer characteristics. Specifically, before formal neutralization control, laboratory waste liquid is first introduced into the reaction tank through the waste liquid collection tank, and the stirring device is started to achieve a stable mixing state in the reaction tank. The stirring speed of the stirring device can be set from 60 r / min to 120 r / min; in this embodiment, it is set to 90 r / min. A multi-parameter sensor array begins to collect the pH, temperature, conductivity, and flow rate of the waste liquid in the reaction tank. The pH sampling period is set to 1 second, the pH sensor resolution is 0.01 pH units, the temperature sensor accuracy is ±0.5℃, the conductivity sensor accuracy is ±1%, and the flow meter accuracy is ±1%. During the trial dosing, a precision dosing pump adds acid or alkali to the reaction tank at a trial rate lower than the normal dosing rate. The trial dosing duration can be set to 60 seconds, and the trial dosing rate can be set to 10% of the normal dosing rate; for example, if the normal dosing rate is 10 mL / min, the trial dosing rate is 1 mL / min. During the trial dosing process, acid-base response data and dosing execution data are obtained. The acid-base response data includes at least a data sequence of acid-base values ​​changing over time during the trial dosing period. The dosing execution data includes at least the dosing type, dosing time, dosing duration, dosing amount, and reagent concentration.

[0026] S2. Using the initial buffer characteristics as the initial value, identify the buffer characteristics of the laboratory waste liquid online based on the pH response data and the dosing execution data. S3. Obtain the preset buffer zone determination conditions, and determine whether the laboratory waste liquid meets the preset buffer zone determination conditions based on the buffer characteristics; if yes, then the upper limit of the dosing rate is determined as the first upper limit of the dosing rate; if no, then the upper limit of the dosing rate is determined as the second upper limit of the dosing rate; wherein, the first upper limit of the dosing rate is less than the second upper limit of the dosing rate. Specifically, the preset buffer zone determination condition can be set as a buffer capacity state ≥ 0.03 mol. L -1 This value can be determined using an acetic acid system as a reference, for example, 0.1 mol. L -1 The buffer capacity of acetic acid near its dissociation equilibrium is approximately 0.0576 mol. L -1 Approximately half of the pH value is used as the buffer zone determination threshold to identify buffer zones with slow pH response and prone to abrupt pH changes after crossing. If the buffer characteristics identified online meet the preset buffer zone determination criteria, the upper limit of the dosing rate is determined as the first upper limit of the dosing rate; for example, if the upper limit of the dosing rate of the precision dosing pump is 10 mL / min, the first upper limit of the dosing rate can be set to 3 mL / min. If the buffer characteristics identified online do not meet the preset buffer zone determination criteria, the upper limit of the dosing rate is determined as the second upper limit of the dosing rate; for example, the second upper limit of the dosing rate can be set to 10 mL / min. In this way, when the waste liquid is located in the buffer zone, the system will not continuously increase the dosing amount due to a small temporary change in pH, but will actively tighten the upper limit of the dosing rate to avoid the accumulation of the agent in the buffer zone and the resulting sudden change in pH when crossing the buffer zone.

[0027] S4. Obtain the target pH range, and determine the dosage control amount of acid or alkali solution based on the target pH range, the buffer characteristics, and the upper limit of the dosing rate. For example, the target pH range can be set to 6.0 to 9.0, or adjusted according to emission standards or laboratory management requirements. The system determines the dosing direction based on the positional relationship between the current pH value and the target pH range; when the current pH value is below the lower limit of the target pH range, it determines to add alkali solution; when the current pH value is above the upper limit of the target pH range, it determines to add acid solution; when the current pH value is within the target pH range, it can stop dosing or only maintain monitoring. The controlled dosing amount can be jointly determined by the current pH value, the target pH range, buffer characteristics, and the upper limit of the dosing rate determined in step S3, ensuring that the controlled dosing amount does not exceed the upper limit of the dosing rate.

[0028] S5. Add acid or alkali solution to the reaction tank according to the dosing control amount, and obtain the pH response data after dosing. Update the buffer characteristics according to the pH response data after dosing, and correct the dosing control amount according to the updated buffer characteristics until the pH value of the laboratory waste liquid enters the target pH range.

[0029] Specifically, the edge computing controller converts the dosing control quantity determined in step S4 into a control signal for the precision dosing pump, controlling the operation of the precision dosing pump corresponding to the acid storage tank or the precision dosing pump corresponding to the alkali storage tank. After dosing, the pH response data after dosing is acquired at a 1-second sampling period and fed back to step S2 to update the buffer characteristics. If the updated buffer characteristics still meet the preset buffer plateau determination conditions, the first dosing rate upper limit is continued; if the updated buffer characteristics do not meet the preset buffer plateau determination conditions, the second dosing rate upper limit can be switched. In each control cycle, the dosing control quantity is corrected according to the updated buffer characteristics until the pH value of the laboratory waste liquid enters the target pH range and remains within this range for a stable holding time. The stable holding time can be set to 120 seconds, that is, when the pH value is detected to be in the range of 6.0 to 9.0 for 120 consecutive seconds, the neutralization treatment of this batch is determined to be complete.

[0030] Furthermore, step S1 specifically includes the following steps: The initial pH value of the laboratory waste liquid in the reaction tank, the test dosing data of the dosing device during the test dosing period, and the response change data of the pH value over time during the test dosing period are obtained. The initial response characteristics are determined based on the initial pH value, the response change data, and the trial dosing data; wherein the initial response characteristics include the initial buffer characteristics and the initial reaction hysteresis characteristics.

[0031] Specifically, the initial pH value can be the median of five consecutive pH samples taken before the trial dosing begins, to reduce the impact of noise from a single measurement. Trial dosing data may include the type of reagent, reagent concentration, pump operating time, and cumulative dosage. Response change data may include a pH time series obtained by sampling once per second within 60 seconds after the trial dosing begins, or it may include the pH change and response start time obtained from this time series. The initial buffer feature characterizes the pH response capability of the waste liquid to acid or alkali addition under trial dosing conditions, and the initial reaction hysteresis feature characterizes the time delay between the trial dosing action and the start of an effective change in the pH observation value. By combining the initial buffer feature and the initial reaction hysteresis feature into the initial response feature, this initial response feature can be subsequently matched with historical response features in a historical waste liquid type database.

[0032] More preferably, step S2 specifically includes the following steps: The dosage and reagent concentration of the dosing execution data, the liquid volume in the reaction tank, and the change in pH caused by the dosage during the current identification period are obtained from the dosing execution data during the current identification period. The instantaneous buffer capacity for the current identification cycle is determined based on the dosage, reagent concentration, liquid volume in the tank, and pH change. Determine whether the current identification cycle is the first cycle; When the current identification period is the first identification period, the buffer capacity state in the initial buffer feature is determined as the previous buffer capacity state. When the current identification period is not the first identification period, the buffer capacity state obtained from the previous identification period is determined as the previous buffer capacity state. Update the buffer capacity state of the current identification cycle based on the instantaneous buffer capacity and the previous buffer capacity state. The buffering characteristics of the laboratory waste liquid are determined based on the buffer capacity status of the current identification cycle.

[0033] Specifically, the current identification period can be consistent with the pH sampling period, for example, set to 1 second. The reagent concentration can be the actual prepared concentration of the acid or alkali solution, for example, a sodium hydroxide solution concentration of 0.1 mol. L -1 The hydrochloric acid solution concentration is 0.1 mol. L -1 The liquid volume in the tank can be obtained by a level sensor or by calculating the cumulative flow. The working volume of the reaction tank is 50L, and the real-time variation range of the liquid volume in the tank is 30L to 60L. The pH change caused by the dosage can be determined by the difference between the fused pH values ​​at the beginning and end of the current identification cycle, or by the pH change within a sliding window.

[0034] Specifically, the instantaneous buffer capacity can be determined according to the following formula: in, This indicates the instantaneous buffer capacity for the current identification cycle; This indicates the dosage within the current identification period; Indicates the concentration of the acid or alkali solution; Indicates the volume of liquid in the reaction tank; This indicates the change in pH caused by the dosage during the current identification period. If... If the change is less than the effective judgment threshold for pH change, such as less than 0.03 pH units, the current instantaneous buffer capacity can be temporarily not used for updating to avoid estimation divergence caused by the denominator being close to zero; this 0.03 pH units can be determined by three times the noise margin of the pH sensor's 0.01 resolution.

[0035] Specifically, the buffer capacity state for the current identification period is updated based on the instantaneous buffer capacity and the previous buffer capacity state. The update method can employ weighted filtering, recursive least squares, or extended Kalman filtering. When using extended Kalman filtering, the previous buffer capacity state is the predicted state value, the instantaneous buffer capacity is the observed value, and the target initial adjustment range or the adjustment range of the previous period is used to determine the observation update weights. Based on the buffer capacity state for the current identification period, the buffer characteristics of the laboratory waste liquid are determined. These buffer characteristics describe the acid-base response capability of the batch of waste liquid to a unit dosage of chemical reagent; a larger buffer capacity indicates a slower acid-base response to the addition of acid or alkali solutions.

[0036] More preferably, the following steps are included before step S2: Obtain a historical waste liquid type library, which includes the prior values ​​of the joint state variables of each historical waste liquid type, the initial adjustment range of the joint state variables, and the historical response characteristics corresponding to each historical waste liquid type. The initial pH value, the response change data, the initial buffer characteristics, and the initial response hysteresis characteristics are used as the current response characteristics; Obtain preset similarity conditions, compare the current response features with the historical response features corresponding to each historical waste liquid type, and determine the historical waste liquid type that meets the preset similarity conditions; Obtain the prior value of the joint state variable corresponding to the historical waste liquid type that meets the preset similarity conditions, and use it as the initial state value of the joint state variable; Obtain the initial adjustment range of the joint state variable corresponding to the historical waste liquid type that meets the preset similarity conditions; Obtain the similarity between the current response feature and the historical response feature corresponding to the historical waste liquid type that meets the preset similarity conditions, and correct the initial adjustment range of the joint state variable according to the similarity to obtain the target initial adjustment range for online identification of the buffer feature.

[0037] Specifically, the prior values ​​of the joint state variables are based on the statistical analysis of similar waste liquid treatment batches in history, and may include historical reference values ​​of buffer capacity state, mixing and diffusion hysteresis state, sensor response hysteresis state, control gain state, and sensor confidence weight state. The initial adjustment range of the joint state variables is used to characterize the range within which the prior values ​​of the joint state variables are allowed to be corrected by real-time response data in the early stage of online identification. In engineering implementation, it can be represented by initial covariance, error boundary, or parameter range given by table lookup.

[0038] More specifically, the preset similarity condition can be either a Mahalanobis distance of less than 2.0 between the current response feature and the historical response feature, or a standardized Euclidean distance of less than 2.0. When multiple historical waste liquid types satisfy the preset similarity condition, the historical waste liquid type with the smallest distance can be selected. More specifically, if the Mahalanobis distance between the current response feature and the historical response feature is 1.0, the initial adjustment range of the joint state variable can be maintained at the original range; if the Mahalanobis distance is 1.8, the initial adjustment range of the joint state variable can be increased to 1.8 times the original range, so that the online identification process relies more on the current real-time data, rather than being overly fixed to historical prior values.

[0039] More preferably, after step S5, the following steps are also included: Obtain the convergence quality of the joint state variables after the neutralization process is completed; wherein, the convergence quality of the joint state variables is used to characterize the stability of the update of the joint state variables during the neutralization process; The reaction lag characteristics are obtained, and a comprehensive response characteristic for this neutralization process is generated based on the pH response data, the dosing execution data, the buffer characteristics, and the reaction lag characteristics in this neutralization process; wherein, the reaction lag characteristics are updated based on the initial reaction lag characteristics and the online estimation results of the reaction lag during the neutralization process; Obtain the preset update conditions and the convergence value of the joint state variable, and determine whether the convergence quality of the joint state variable meets the preset update conditions; If the convergence quality of the joint state variable meets the preset update condition, and the comprehensive response feature meets the preset similarity condition of the historical waste liquid type, then based on the comprehensive response feature and the convergence value of the joint state variable, the historical response feature, the prior value of the joint state variable, and the initial adjustment range of the joint state variable for the corresponding historical waste liquid type are updated. When the convergence quality of the joint state variable meets the preset update condition, and the comprehensive response feature does not meet the preset similarity condition of the historical waste liquid type, a new historical waste liquid type is added based on the comprehensive response feature and the convergence value of the joint state variable. If the convergence quality of the joint state variables does not meet the preset update conditions, then writing the neutralization processing data into the historical waste liquid type library is prohibited.

[0040] Specifically, the convergence quality of the joint state variables can be determined based on the state covariance of the extended Kalman filter, the stability of the residuals, or the magnitude of state changes in the most recent identification periods. For example, the convergence quality can be set to a range of 0 to 1. When the convergence quality is greater than or equal to 0.8, it is considered to meet the preset update conditions.

[0041] More specifically in this embodiment, the reaction lag feature is different from the initial reaction lag feature obtained in step S1 during short-term trial dosing. The initial reaction lag feature is only used to describe the coarse delay during the trial phase, while the reaction lag feature is a lag characterization formed after the treatment is completed, based on the pump response lag value, mixing and diffusion lag value, sensor response lag value, and their online update results throughout the entire neutralization process. Preset update conditions and convergence values ​​of the joint state variables are obtained, and it is determined whether the convergence quality of the joint state variables meets the preset update conditions. If the convergence quality of the joint state variables meets the preset update conditions, and the comprehensive response feature meets the preset similarity conditions for the historical waste liquid type, the historical response feature, the prior value of the joint state variables, and the initial adjustment range of the joint state variables for the corresponding historical waste liquid type are updated according to the comprehensive response feature and the convergence value of the joint state variables. For example, an incremental update can be performed using a forgetting factor of 0.1, so that the current batch data accounts for 10% of the update result, and historical data accounts for 90% of the weight. When the convergence quality of the joint state variables meets the preset update condition, and the comprehensive response characteristics do not meet the preset similarity condition for historical waste liquid types, a new historical waste liquid type is added based on the comprehensive response characteristics and the convergence value of the joint state variables. When the convergence quality of the joint state variables does not meet the preset update condition, the data from this neutralization process is prohibited from being written into the historical waste liquid type library to prevent unstable data caused by sensor malfunctions, accidental addition, or external disturbances from contaminating the historical waste liquid type library.

[0042] Furthermore, step S4 also includes the following steps: Obtain the pump response hysteresis value of the dosing device and the mixing and diffusion hysteresis value between the dosing point in the reaction tank and the sensor detection position; Obtain a preset mapping relationship between the buffer feature and the sensor response hysteresis, and determine the sensor response hysteresis value based on the buffer feature and the preset mapping relationship; The total reaction hysteresis value is determined based on the pump response hysteresis value, the mixing diffusion hysteresis value, and the sensor response hysteresis value. Obtain the current pH value, and based on the current pH value, the buffer characteristics, and the total reaction hysteresis value, predict the pH value after issuing the dosing control command; The dosage control amount is determined based on the degree of deviation between the predicted pH value and the target pH range.

[0043] Specifically, the pump response lag value can be determined based on the pipeline volume and pump flow rate between the outlet of the precision dosing pump and the dosing port of the neutralization reactor. For example, when the pipeline volume is 50 mL and the pump flow rate is 10 mL / s, the pump response lag value is approximately 5 s. The mixing and diffusion lag value can be determined based on the distance between the dosing point and the pH sensor detection position, the stirring state, and the flow state within the tank. For example, when the effective mixing distance between the dosing point and the sensor detection position in the reaction tank is 0.2 m and the effective flow velocity within the tank is 0.05 m / s, the convective transport time is approximately 4 s. By adding the diffusion delay caused by the volume of the mixing zone, the mixing and diffusion lag value can be dynamically changed within the range of 4 s to 20 s.

[0044] The preset mapping relationship described in this embodiment can be obtained through standard buffer calibration. For example, in pure water or a low-buffered solution, the reference response time of the pH sensor is 3 seconds, and the buffer capacity reaches 0.01 mol. L -1 The sensor response hysteresis value can be increased by a preset ratio. That is, the reaction hysteresis is not solely determined by hardware, but is coupled with the buffering characteristics of the current waste liquid. For example, when the current pH value is 4.5, the target pH range is 6.0 to 9.0, and the total reaction hysteresis is 12 seconds, this embodiment does not directly and continuously add alkali based on the current pH of 4.5. Instead, it predicts the pH value after 12 seconds and then determines the dosage control based on the deviation between the predicted pH value and the target pH range. This method can reduce overdosing caused by the combined hysteresis of the dosing pump, mixing and diffusion, and pH sensor.

[0045] Furthermore, the pH response data is obtained through the following steps: Acquire pH values ​​collected by multiple sensors; Determine whether the pH readings of each sensor meet the preset anomaly detection criteria; If so, the corresponding sensor is determined to be in an abnormal state, and the confidence weight of the corresponding sensor is set to zero. If not, the historical observation residuals of each sensor are obtained, and the confidence weight of the corresponding sensor is determined based on the historical observation residuals; wherein, the historical observation residuals are the differences between the historical pH observation values ​​and the historical fused pH values ​​of the corresponding sensor. The pH observations are fused according to the confidence weights to generate the pH response data.

[0046] Specifically, in this embodiment, the plurality of sensors are preferably plurality of pH sensors, for example, two to four pH sensors are arranged at different heights or radial positions within the reaction tank. The preset anomaly determination conditions may include at least one of over-range conditions, jump conditions, and drift conditions; the over-range condition can be a pH observation value less than 0 or greater than 14; the jump condition can be a change in pH observation value greater than 2 pH units between two adjacent samplings, with a sampling interval of 1 second; the drift condition can be a continuous change in pH observation value along the same direction within a 30-second period during which no dosing is performed, with a cumulative change greater than 1.5 pH units. The historical observation residual is calculated using the average absolute residual over the most recent 30 sampling periods. The smaller the historical observation residual, the greater the confidence weight of the corresponding sensor; after the weights of all non-abnormal sensors are calculated, each confidence weight is normalized so that the sum of all confidence weights is 1. The pH observations are weighted and summed according to their respective confidence weights to generate the pH response data. Specifically, the pH observation output by each pH sensor is multiplied by its corresponding confidence weight, and all multiplications are summed to obtain the weighted average, which is the final pH response data for this sampling period. By fusing the pH observations using a weighted average, even if individual pH sensors are affected by waste liquid deposition, bubble adhesion, or short-term drift, abnormal readings will not be directly used for subsequent buffer feature identification and dosing control.

[0047] More preferably, after step S4, the following steps are also included: The sensors include a pH sensor, a conductivity sensor, and a temperature sensor, used to determine whether the pH sensor, conductivity sensor, and / or temperature sensor are in an abnormal state. When the conductivity sensor is determined to be in an abnormal state, the system enters the first degraded control mode, disabling the correction of the buffer feature by the conductivity data of the abnormal conductivity sensor. When the temperature sensor is determined to be in an abnormal state, it enters the second degraded control mode, and uses preset temperature parameters to replace the measured temperature value of the abnormal temperature sensor. When at least one pH sensor is determined to be in an abnormal state, and the remaining pH sensors can still output observation values, the third degraded control mode is entered. The pH observation values ​​of the pH sensors in the abnormal state are filtered by median value and then fused to generate the pH response data. When all pH sensors can no longer output observation values, the dosing is stopped. During the duration of the first degraded control mode, the second degraded control mode, and the third degraded control mode, the upper limit of the dosing rate is adjusted to the corresponding degraded dosing rate upper limit, and the interval between two adjacent dosings is extended to the degraded dosing interval; wherein, the upper limit of the degraded dosing rate is lower than the upper limit of the first dosing rate, and the degraded dosing interval is greater than the interval between two adjacent dosings in the normal mode.

[0048] Specifically, this embodiment employs a degradation control logic: for example, under normal conditions, conductivity data can be used to estimate ionic strength and correct the dissociation state of weak acids; in the first degradation control mode, the system no longer uses abnormal conductivity data for this correction, but instead uses the previous effective conductivity data or the default ionic strength for conservative control. In this embodiment, the preset temperature parameter can be 25°C. In this embodiment, a sliding window with a length of 5 sampling points is used for median filtering. When all pH sensors fail to output observed values, dosing is stopped, and the discharge valve remains closed to prevent the discharge of substandard waste liquid. For example, the upper limit of the degraded dosing rate in the first degraded control mode can be set to 60% of the upper limit of the first dosing rate, the upper limit of the degraded dosing rate in the second degraded control mode can be set to 80% of the upper limit of the first dosing rate, and the upper limit of the degraded dosing rate in the third degraded control mode can be set to 50% of the upper limit of the first dosing rate. The degraded dosing interval can be set to twice the interval between two adjacent dosings in the normal mode. For example, if the interval in the normal mode is 5 seconds, the degraded dosing interval is 10 seconds.

[0049] This invention also provides an intelligent control system for the neutralization treatment of laboratory acid and alkali waste liquid, including an on-site treatment unit, an intelligent control unit, and a remote interaction unit. The on-site treatment unit includes a waste liquid collection tank, a neutralization reactor, an acid storage tank, an alkali storage tank, a precision dosing pump, a multi-parameter sensor array, a stirring device, and a discharge valve. The outlet of the waste liquid collection tank is connected to the inlet of the neutralization reactor for transporting laboratory waste liquid into the reactor; the neutralization reactor contains a reaction tank; the acid and alkali storage tanks are respectively connected to the dosing ports of the neutralization reactor via corresponding precision dosing pumps for adding acid or alkali to the reactor according to control commands; the stirring device is located inside the neutralization reactor to promote uniform mixing of the acid or alkali with the laboratory waste liquid; the discharge valve is located at the discharge port of the neutralization reactor for controlled discharge of the treated waste liquid after the pH value reaches the target pH range. The sensing probes of the multi-parameter sensor array are set inside the neutralization reactor and include at least an acid-base sensor, a temperature sensor, a conductivity sensor, and a flow meter; wherein, the acid-base sensor is used to collect acid-base response data, the temperature sensor is used to collect temperature data, the conductivity sensor is used to collect conductivity data, and the flow meter is used to collect waste liquid flow data.

[0050] Specifically, the intelligent control unit includes an edge computing controller deployed on-site. The signal input terminal of the edge computing controller is connected to the signal output terminal of the multi-parameter sensor array for real-time acquisition of pH response data, temperature data, conductivity data, and waste liquid flow data. The control signal output terminal of the edge computing controller is connected to the control signal input terminals of the precision dosing pump, the stirring device, and the discharge valve, respectively, for controlling the dosing rate of the precision dosing pump, the start / stop and rotation speed of the stirring device, and the opening / closing of the discharge valve. The edge computing controller incorporates an adaptive fuzzy predictive control module, which includes a trial dosing control module, a buffer feature identification module, a historical type matching module, a reaction lag estimation module, a sensor reliability assessment module, a dosing control module, and a degradation control module. The trial dosing control module controls the precision dosing pump to perform trial dosing and determine the initial response characteristics; the historical type matching module inherits the prior values ​​of joint state variables from the historical waste liquid type library based on the initial response characteristics and determines the target initial adjustment range; the buffer feature identification module updates the buffer features online and determines whether the preset buffer plateau judgment conditions are met; the reaction hysteresis estimation module determines the total reaction hysteresis value based on the pump response hysteresis value, the mixing and diffusion hysteresis value, and the sensor response hysteresis value; the sensor reliability assessment module determines the sensor reliability weight based on the anomaly judgment conditions and historical observation residuals; the dosing control module determines the dosing control quantity based on the target pH range, buffer characteristics, upper limit of dosing rate, and pH prediction value; the degradation control module adjusts the corresponding degradation dosing rate upper limit and degradation dosing interval time when the sensor is abnormal, and stops dosing and closes the discharge valve when all pH sensors fail to output.

[0051] Specifically, the remote interaction unit includes a cloud server and a user terminal. The cloud server is communicatively connected to the edge computing controller, and the communication method can be wired Ethernet, wireless LAN, 4G mobile communication, or 5G mobile communication. The cloud server is used to receive neutralization processing data, degradation control events, and historical waste liquid type library update results uploaded by the edge computing controller, and to store the prior values ​​of the joint state variables corresponding to the historical waste liquid types, the initial adjustment range of the joint state variables, and historical response characteristics. The user terminal can be a computer, tablet device, or smartphone, used to display real-time pH curves, dosing execution records, sensor abnormal states, degradation control prompts, and maintenance suggestions. Through the above system structure, the on-site treatment unit completes the physical neutralization of the real waste liquid, the intelligent control unit completes the online identification of buffer characteristics and dosing control, and the remote interaction unit completes historical data management and operation status prompts. The three together support the continuous and stable operation of the above control method in laboratory waste liquid treatment.

[0052] This application obtains pH response and dosing execution data by conducting trial dosing in the reaction tank, thereby identifying the buffering characteristics of laboratory waste liquid online. This changes the lag and blindness of conventional neutralization treatment, which only relies on the current pH deviation for dosing. The system can predict the sensitivity of complex waste liquids to dosing operations in advance. Furthermore, by determining whether the waste liquid is in a preset buffer plateau state based on the identified buffering characteristics, the system actively tightens the upper limit of the dosing rate to a smaller first dosing rate when it is in the buffer plateau state, and adopts a larger second dosing rate when it is not in the buffer plateau state. This breaks the conventional control method that is ineffective when pH changes are slow. The intuitive logic of continuously increasing the dosage avoids the abrupt pH change caused by the accumulation of reagents in the early stage after the reaction crosses the buffer plateau, thus achieving precise dynamic constraint on the dosing boundary of multi-component waste liquid. On this basis, the dosing control quantity is comprehensively determined by combining the target range, buffer characteristics, and upper limit of the dosing rate. The buffer characteristics and control quantity are continuously updated using the feedback data generated by subsequent dosing, thus constructing an adaptive evolution closed loop of feature identification and control commands. In this way, under unexpected circumstances, it effectively suppresses the large overshoot and repeated oscillation of the waste liquid in the unknown buffer system during the adjustment process, significantly shortens the emission compliance time, and greatly reduces the ineffective consumption of neutralizing reagent.

[0053] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A smart control method for neutralizing and treating laboratory acid-base waste liquid, characterized in that, Includes the following steps: S1. The laboratory waste liquid is introduced into the reaction tank for trial dosing, and the acid-base response data and dosing execution data during the trial dosing are obtained. The initial response characteristics are determined based on the acid-base response data and the dosing execution data. The initial response characteristics include initial buffer characteristics. S2. Using the initial buffer characteristics as the initial value, identify the buffer characteristics of the laboratory waste liquid online based on the pH response data and the dosing execution data. S3. Obtain the preset buffer zone determination conditions, and determine whether the laboratory waste liquid meets the preset buffer zone determination conditions based on the buffer characteristics; if yes, then the upper limit of the dosing rate is determined as the first upper limit of the dosing rate; if no, then the upper limit of the dosing rate is determined as the second upper limit of the dosing rate; wherein, the first upper limit of the dosing rate is less than the second upper limit of the dosing rate. S4. Obtain the target pH range, and determine the dosage control amount of acid or alkali solution based on the target pH range, the buffer characteristics, and the upper limit of the dosing rate. S5. Add acid or alkali solution to the reaction tank according to the dosing control amount, and obtain the pH response data after dosing. Update the buffer characteristics according to the pH response data after dosing, and correct the dosing control amount according to the updated buffer characteristics until the pH value of the laboratory waste liquid enters the target pH range.

2. The intelligent control method for neutralizing and treating laboratory acid and alkali waste liquid according to claim 1, characterized in that, Step S1 specifically includes the following steps: The initial pH value of the laboratory waste liquid in the reaction tank, the experimental dosing data of the dosing device during the experimental dosing period, and the response change data of the pH value over time during the experimental dosing period are obtained. The initial response characteristics are determined based on the initial pH value, the response change data, and the trial dosing data; wherein the initial response characteristics include the initial buffer characteristics and the initial reaction hysteresis characteristics.

3. The intelligent control method for neutralizing laboratory acid and alkali waste liquid according to claim 2, characterized in that, Step S2 specifically includes the following steps: The dosage and reagent concentration of the dosing execution data, the liquid volume in the reaction tank, and the change in pH caused by the dosage during the current identification period are obtained from the dosing execution data during the current identification period. The instantaneous buffer capacity for the current identification cycle is determined based on the dosage, reagent concentration, liquid volume in the tank, and pH change. Determine whether the current identification cycle is the first cycle; When the current identification period is the first identification period, the buffer capacity state in the initial buffer feature is determined as the previous buffer capacity state. When the current identification period is not the first identification period, the buffer capacity state obtained from the previous identification period is determined as the previous buffer capacity state. Update the buffer capacity state of the current identification cycle based on the instantaneous buffer capacity and the previous buffer capacity state. The buffering characteristics of the laboratory waste liquid are determined based on the buffer capacity status of the current identification cycle.

4. The intelligent control method for neutralizing and treating laboratory acid and alkali waste liquid according to claim 2, characterized in that, The following steps are included before step S2: Obtain a historical waste liquid type library, which includes the prior values ​​of the joint state variables of each historical waste liquid type, the initial adjustment range of the joint state variables, and the historical response characteristics corresponding to each historical waste liquid type. The initial pH value, the response change data, the initial buffer characteristics, and the initial response hysteresis characteristics are used as the current response characteristics; Obtain preset similarity conditions, compare the current response features with the historical response features corresponding to each historical waste liquid type, and determine the historical waste liquid type that meets the preset similarity conditions; Obtain the prior value of the joint state variable corresponding to the historical waste liquid type that meets the preset similarity conditions, and use it as the initial state value of the joint state variable; Obtain the initial adjustment range of the joint state variable corresponding to the historical waste liquid type that meets the preset similarity conditions; Obtain the similarity between the current response feature and the historical response feature corresponding to the historical waste liquid type that meets the preset similarity conditions, and correct the initial adjustment range of the joint state variable according to the similarity to obtain the target initial adjustment range for online identification of the buffer feature.

5. The intelligent control method for neutralizing and treating laboratory acid and alkali waste liquid according to claim 4, characterized in that, Step S5 is followed by the following steps: Obtain the convergence quality of the joint state variables after the neutralization process is completed; wherein, the convergence quality of the joint state variables is used to characterize the stability of the update of the joint state variables during the neutralization process; The reaction lag characteristics are obtained, and a comprehensive response characteristic for this neutralization process is generated based on the pH response data, the dosing execution data, the buffer characteristics, and the reaction lag characteristics in this neutralization process; wherein, the reaction lag characteristics are updated based on the initial reaction lag characteristics and the online estimation results of the reaction lag during the neutralization process; Obtain the preset update conditions and the convergence value of the joint state variable, and determine whether the convergence quality of the joint state variable meets the preset update conditions; If the convergence quality of the joint state variable meets the preset update condition, and the comprehensive response feature meets the preset similarity condition of the historical waste liquid type, then based on the comprehensive response feature and the convergence value of the joint state variable, the historical response feature, the prior value of the joint state variable, and the initial adjustment range of the joint state variable for the corresponding historical waste liquid type are updated. When the convergence quality of the joint state variable meets the preset update condition, and the comprehensive response feature does not meet the preset similarity condition of the historical waste liquid type, a new historical waste liquid type is added based on the comprehensive response feature and the convergence value of the joint state variable. If the convergence quality of the joint state variables does not meet the preset update conditions, then writing the neutralization processing data into the historical waste liquid type library is prohibited.

6. The intelligent control method for neutralizing and treating laboratory acid and alkali waste liquid according to claim 1, characterized in that, Step S4 also includes the following steps: Obtain the pump response hysteresis value of the dosing device and the mixing and diffusion hysteresis value between the dosing point in the reaction tank and the sensor detection position; Obtain a preset mapping relationship between the buffer feature and the sensor response hysteresis, and determine the sensor response hysteresis value based on the buffer feature and the preset mapping relationship; The total reaction hysteresis value is determined based on the pump response hysteresis value, the mixing diffusion hysteresis value, and the sensor response hysteresis value. Obtain the current pH value, and based on the current pH value, the buffer characteristics, and the total reaction hysteresis value, predict the pH value after issuing the dosing control command; The dosage control amount is determined based on the degree of deviation between the predicted pH value and the target pH range.

7. The intelligent control method for neutralizing and treating laboratory acid and alkali waste liquid according to claim 1, characterized in that, The pH response data is obtained through the following steps: Acquire pH values ​​collected by multiple sensors; Determine whether the pH readings of each sensor meet the preset anomaly detection criteria; If so, the corresponding sensor is determined to be in an abnormal state, and the confidence weight of the corresponding sensor is set to zero. If not, the historical observation residuals of each sensor are obtained, and the confidence weight of the corresponding sensor is determined based on the historical observation residuals; wherein, the historical observation residuals are the differences between the historical pH observation values ​​and the historical fused pH values ​​of the corresponding sensor. The pH observations are fused according to the confidence weights to generate the pH response data.

8. The intelligent control method for neutralizing laboratory acid and alkali waste liquid according to claim 7, characterized in that, Step S4 is followed by the following steps: The sensors include a pH sensor, a conductivity sensor, and a temperature sensor, and are used to determine whether the pH sensor, conductivity sensor, and / or temperature sensor are in an abnormal state. When the conductivity sensor is determined to be in an abnormal state, the system enters the first degraded control mode, disabling the correction of the buffer feature by the conductivity data of the abnormal conductivity sensor. When the temperature sensor is determined to be in an abnormal state, it enters the second degraded control mode, and uses preset temperature parameters to replace the measured temperature value of the abnormal temperature sensor. When at least one pH sensor is determined to be in an abnormal state, and the other pH sensors can still output observation values, the third degraded control mode is entered. The pH observation values ​​of the pH sensors in the abnormal state are filtered by median value and then fused to generate the pH response data. Stop adding chemicals when all pH sensors fail to output a reading. During the duration of the first degraded control mode, the second degraded control mode, and the third degraded control mode, the upper limit of the dosing rate is adjusted to the corresponding degraded dosing rate upper limit, and the interval between two adjacent dosings is extended to the degraded dosing interval; wherein, the upper limit of the degraded dosing rate is lower than the upper limit of the first dosing rate, and the degraded dosing interval is greater than the interval between two adjacent dosings in the normal mode.

9. An intelligent control system for neutralizing and treating laboratory acid-base waste liquid, characterized in that, It includes an on-site processing unit, an intelligent control unit, and a remote interaction unit; The on-site treatment unit includes a waste liquid collection tank, a neutralization reactor, an acid storage tank, an alkali storage tank, a precision dosing pump, a multi-parameter sensor array, a stirring device, and a discharge valve. The neutralization reactor contains a reaction tank. The outlet of the waste liquid collection tank is connected to the inlet of the neutralization reactor. The acid and alkali storage tanks are respectively connected to the dosing ports of the neutralization reactor via the precision dosing pump. The sensing probes of the multi-parameter sensor array are located inside the neutralization reactor. The multi-parameter sensor array includes at least a pH sensor, a temperature sensor, a conductivity sensor, and a flow meter. The stirring device is located inside the neutralization reactor, and the discharge valve is located at the discharge port of the neutralization reactor. The intelligent control unit includes an edge computing controller, which is deployed on-site. The signal input terminal of the edge computing controller is connected to the signal output terminal of the multi-parameter sensor array, and is used to collect the pH response data, temperature data, conductivity data and waste liquid flow data of the multi-parameter sensor array in real time. The control signal output terminal of the edge computing controller is connected to the control signal input terminal of the precision dosing pump, and is used to output the dosing control amount to the precision dosing pump. The remote interaction unit includes a cloud server and a user terminal. The cloud server is communicatively connected to the edge computing controller and is used to receive neutralization processing data, degradation control events and historical waste liquid type library update results uploaded by the edge computing controller, and output monitoring information and maintenance prompts to the user terminal. The edge computing controller is used to perform the method as described in any one of claims 1 to 8.

10. The intelligent control system for neutralizing laboratory acid and alkali waste liquid according to claim 9, characterized in that, The edge computing controller includes a trial dosing control module, a buffer feature identification module, a historical type matching module, a response hysteresis estimation module, a sensor reliability assessment module, a dosing control module, and a degradation control module. The trial dosing control module is used to control the precision dosing pump to perform trial dosing, acquire the acid-base response data and dosing execution data collected by the multi-parameter sensor array during the trial dosing, and determine the initial response characteristics, which include initial buffer characteristics and initial reaction hysteresis characteristics. The input of the historical type matching module is connected to the output of the trial dosing control module and the output of the remote interaction unit, respectively. It is used to obtain the initial response features from the trial dosing control module, the prior values ​​of the joint state variables stored in the historical waste liquid type library and the initial adjustment range of the joint state variables from the remote interaction unit, determine the historical waste liquid types that meet the preset similarity conditions, take the prior values ​​of the joint state variables corresponding to the historical waste liquid types that meet the preset similarity conditions as the initial state values ​​of the joint state variables, and correct the initial adjustment range of the joint state variables according to the similarity between the initial response features and the historical response features corresponding to the historical waste liquid types to obtain the target initial adjustment range. The first input terminal of the buffer feature identification module is connected to the output terminal of the trial dosing control module, the second input terminal is connected to the output terminal of the historical type matching module, and the third input terminal is connected to the signal output terminal of the multi-parameter sensor array. It is used to update the buffer feature online based on the initial buffer feature as the initial value and the target initial adjustment range as the initial adjustment range, according to the subsequent pH response data and the dosing execution data, and to determine whether the preset buffer plateau determination conditions are met and to determine the upper limit of the dosing rate based on the buffer feature. The input of the reaction hysteresis estimation module is connected to the output of the buffer feature identification module and the signal output of the multi-parameter sensor array, respectively. It is used to determine the total reaction hysteresis value based on the pump response hysteresis value, the mixing diffusion hysteresis value, the sensor response hysteresis value and the buffer feature, and to predict the pH value based on the current pH value, the buffer feature and the total reaction hysteresis value. The input terminal of the sensor reliability assessment module is connected to the signal output terminal of the multi-parameter sensor array, and is used to determine whether each sensor in the multi-parameter sensor array meets the preset anomaly judgment conditions, and to determine the reliability weight of each sensor based on the judgment result and the historical observation residual of each sensor. The input terminal of the dosing control module is connected to the output terminal of the buffer feature identification module, the output terminal of the reaction hysteresis estimation module, and the output terminal of the sensor reliability assessment module, respectively. The output terminal of the dosing control module is connected to the control signal input terminal of the precision dosing pump. It is used to determine the dosing control amount based on the target pH range, the buffer feature, the upper limit of the dosing rate, and the pH prediction value, and output the dosing control amount to the precision dosing pump. The input terminal of the degradation control module is connected to the output terminal of the sensor reliability evaluation module, and the output terminal of the degradation control module is connected to the input terminal of the dosing control module. It is used to output the upper limit of the corresponding degradation dosing rate and the corresponding degradation dosing interval to the dosing control module when the sensor in the multi-parameter sensor array is in an abnormal state. The remote interaction unit is also used to receive the neutralization processing data, the degradation control event, and the update results of the historical waste liquid type library uploaded by the edge computing controller, and output monitoring information and maintenance prompts to the user terminal.

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