Control method for chemical production copper-containing wastewater treatment system control model

Abstract

The invention relates to the technical field of automatic control, in particular to a control method for a chemical production copper-containing wastewater treatment system control model. The method comprises the following steps: collecting copper-containing wastewater data of a sedimentation tank by using a copper-containing wastewater treatment system; performing preliminary purification simulation based on the copper-containing wastewater data of the sedimentation tank, and removing suspended solids in the purification simulation process by using a sand filter to generate preliminary purification copper-containing wastewater data; removing copper ions in the preliminarily purified copper-containing wastewater data, including chemical precipitation removal, ion exchange removal and membrane separation removal, so as to obtain wastewater data; performing copper ion-containing abnormity judgment on the wastewater data based on a preset wastewater recovery standard to obtain copper ion-containing abnormity wastewater data; detecting the copper ion concentration based on the copper ion-containing abnormal wastewater data; and analyzing a stirring motor fault based on the copper ion concentration to obtain a stirring motor abnormal fault code. The treatment efficiency and energy management of the wastewater recovery system are optimized based on an automatic control technology.

Description

Technical Field

[0001] The invention relates to the technical field of automatic control, and in particular to a control method for a control model of a chemical production copper-containing wastewater treatment system. Background Art

[0002] The control technology of copper-containing wastewater recycling and treatment mainly involves the removal of copper ions in wastewater through physical, chemical and biological methods. The discharge problem of industrial copper-containing wastewater is becoming increasingly prominent, and copper ion removal has become an important research direction in the field of wastewater treatment. The traditional copper-containing wastewater recycling and treatment efficiency is low, usually relying on manual operation and experience judgment, resulting in incomplete copper ion removal or excessive chemical agent administration, poor accuracy and stability, and easy to be affected by changes in the external environment. Lacking real-time data monitoring and feedback mechanism, the traditional system has low integration in data acquisition and feedback control, and cannot flexibly respond to changes in copper ion concentration, flow rate, etc. in wastewater. Energy consumption and resource waste are also a prominent problem. Traditional methods often require a lot of energy and chemical reagents, which increases operating costs, and some methods waste water resources. High equipment maintenance cost is also a disadvantage of traditional methods. Equipment needs to be frequently cleaned and replaced, with high maintenance costs and prone to failure, which increases the operation and maintenance burden of the system. The treatment process of traditional methods lacks flexibility and cannot be accurately adjusted according to the different components and treatment requirements of wastewater, resulting in unstable recovery effects. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a control method for a control model of a chemical production copper-containing wastewater treatment system to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above object, a control method for a control model of a chemical production copper-containing wastewater treatment system comprises the following steps: Step S1: using a copper-containing wastewater treatment system to collect copper-containing wastewater data from a sedimentation tank; performing a preliminary purification simulation based on the copper-containing wastewater data from the sedimentation tank, and using a sand filter to remove suspended matter during the purification simulation process to generate preliminary purified copper-containing wastewater data; Step S2: removing copper ions from the preliminary purified copper-containing wastewater data, including chemical precipitation removal, ion exchange removal and membrane separation removal, to obtain wastewater data; Step S3: Based on the preset wastewater recycling standard, the wastewater data is judged to contain copper ions abnormally, and the wastewater data containing copper ions abnormally is obtained; the copper ion concentration is detected based on the wastewater data containing copper ions abnormally; the stirring motor fault is analyzed based on the copper ion concentration to obtain the stirring motor abnormal fault code; based on the stirring motor abnormal fault code, the treatment process control data of the automatic regulation and removal of copper ions is controlled; Step S4: Push the treatment process control data to the control model of the copper-containing wastewater treatment system in chemical production, and predict the wastewater volume; control the energy of the wastewater recovery equipment based on the wastewater volume to obtain the equipment energy control data.

[0005] The present invention can effectively remove suspended matter in wastewater and improve the subsequent copper ion removal efficiency by using a copper-containing wastewater treatment system to collect copper-containing wastewater data in a sedimentation tank and perform preliminary purification simulation. The use of a sand filter enhances the stability of the purification process and reduces the burden on subsequent treatment links. Through a variety of removal methods such as chemical precipitation, ion exchange and membrane separation, not only can copper ions be efficiently removed, but also the risk of incomplete copper ion removal or excessive agent in traditional methods can be reduced. Abnormal judgment and copper ion concentration detection of wastewater data based on preset standards provide real-time feedback, can accurately identify problems in the treatment, such as stirring motor failure, avoid errors caused by empirical judgment in traditional methods, and further improve system stability and accuracy. The automated control process enables the treatment process to adapt to changes in wastewater concentration, flow, etc. in real time, enhances flexibility, and avoids the limitation that traditional methods cannot cope with environmental changes. In addition, by pushing the treatment process control data to the wastewater recovery system and predicting the amount of wastewater, the energy use of the equipment can be more accurately controlled, thereby reducing energy consumption, reducing resource waste, and reducing operating costs. This method optimizes the wastewater recycling process, reduces manual intervention, improves overall efficiency, reduces equipment maintenance frequency and cost, and improves the system's automation level. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings: Figure 1 It is a schematic flow chart of the steps of the control method of the control model of the chemical production copper-containing wastewater treatment system of the present invention; Figure 2 Detailed step flow diagram of step S3 in the present invention; Figure 3 Detailed step flow diagram of step S33 in the present invention; The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0007] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0008] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0009] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0010] To achieve this, please refer to Figures 1 to 3 The present invention provides a control method for a control model of a chemical production copper-containing wastewater treatment system, the method comprising the following steps: Step S1: using a copper-containing wastewater treatment system to collect copper-containing wastewater data from a sedimentation tank; performing a preliminary purification simulation based on the copper-containing wastewater data from the sedimentation tank, and using a sand filter to remove suspended matter during the purification simulation process to generate preliminary purified copper-containing wastewater data; In the present embodiment, the copper-containing wastewater of the sedimentation tank is collected by the copper-containing wastewater treatment system, and the collected data include key indicators such as the temperature, pH value, copper ion concentration, and turbidity of the water sample. These data are used to analyze the preliminary distribution of copper ions in the water body. After the data collection is completed, the water treatment simulation software is used to perform preliminary purification simulation. In this simulation process, the copper ion concentration change in the wastewater is calculated by simulation for the physicochemical properties of copper ions. In the simulation process, parameters such as water flow and suspended matter concentration are set as simulation inputs, and the specific values ​​are adjusted according to the monitoring data of the actual wastewater source. The aggregation and sedimentation of suspended matter particles occurring in the purification process are determined by simulation. After the purification simulation, the simulated wastewater is filtered using a sand filter to remove suspended matter. The particle filtration accuracy of the sand filter is set to 10 microns, and the filtered wastewater sample will be used as the copper-containing wastewater data after preliminary purification. The copper ion concentration and suspended matter concentration in the preliminary purification data should be compared according to the difference before and after the sand filter is filtered to complete data collection and processing.

[0011] Step S2: removing copper ions from the preliminary purified copper-containing wastewater data, including chemical precipitation removal, ion exchange removal and membrane separation removal, to obtain wastewater data; In the present embodiment, the copper-containing wastewater data after preliminary purification is subjected to the removal of copper ions, mainly by three methods: chemical precipitation method, ion exchange method and membrane separation method. In the chemical precipitation treatment, a certain amount of sodium sulfide solution is first added to the wastewater, and the sodium sulfide reacts with the copper ions in the water to generate copper precipitation, and the precipitate is settled and filtered through a sedimentation tank to obtain wastewater with copper ions removed. The amount of sodium sulfide solution added is determined according to the concentration of copper ions in the wastewater, and the goal is to reduce the copper ion concentration to less than 0.5mg / L. The ion exchange method uses a specific ion exchange resin, which is specifically used for exchange reaction with copper ions in water. The exchange capacity of the resin needs to be adjusted according to the copper ion concentration of the wastewater. The membrane separation method uses a reverse osmosis membrane with a permeability of 98%, which can effectively filter copper ions. In the membrane separation system, the setting of water flow and pressure is crucial, with the flow rate set to 3L / min and the pressure set to 0.8MPa. The wastewater treated by the three methods is merged to obtain the final wastewater data, and the copper ion concentration is controlled to be below 0.2mg / L.

[0012] Step S3: Based on the preset wastewater recycling standard, the wastewater data is judged to contain copper ions abnormally, and the wastewater data containing copper ions abnormally is obtained; the copper ion concentration is detected based on the wastewater data containing copper ions abnormally; the stirring motor fault is analyzed based on the copper ion concentration to obtain the stirring motor abnormal fault code; based on the stirring motor abnormal fault code, the treatment process control data of the automatic regulation and removal of copper ions is controlled; In this embodiment, according to the preset wastewater recovery standard, the copper ion concentration in the wastewater is first judged to be abnormal. The standard setting is that the copper ion concentration exceeds 1.0 mg / L, which is abnormal data. Therefore, in the wastewater data, when the copper ion concentration value exceeds the threshold, the data is judged to be abnormal wastewater data containing copper ions. At this time, the copper ion concentration in the wastewater needs to be further monitored, and the actual concentration of copper ions is detected by a chemical analysis instrument (such as ICP-OES). Through the collected copper ion concentration data, if the concentration exceeds 1.0 mg / L, the analysis of the stirring motor failure is triggered. The stirring motor failure is collected by the monitoring system, mainly monitoring parameters such as motor speed and power consumption. When the motor speed is lower than the set standard value and the power consumption is abnormal, the system automatically generates a fault code for the stirring motor, and the fault code will be sent to the central control system through the communication network. Based on the fault code, the system will automatically adjust the treatment process in the copper ion removal process, including adjusting the amount of chemical agents added, the running time of the ion exchange resin, and the working pressure of the membrane separation system, so as to ensure that the removal process of copper ions in the wastewater can proceed smoothly.

[0013] Step S4: Push the treatment process control data to the control model of the copper-containing wastewater treatment system in chemical production, and predict the wastewater volume; control the energy of the wastewater recovery equipment based on the wastewater volume to obtain the equipment energy control data.

[0014] In this embodiment, the obtained treatment process control data (such as the amount of chemical precipitant added, the ion exchange resin replacement cycle, etc.) is pushed to the wastewater recovery system through the data interface. The wastewater recovery system predicts the treatment volume of wastewater based on these data. The prediction method is calculated based on the relationship between the wastewater flow rate and the treatment time. The flow data is provided by the wastewater flow sensor. Assuming that the flow rate is 5L / min and the treatment time is 2 hours, the estimated wastewater volume is 600L. Based on this wastewater volume, the recovery system will perform energy control and control energy consumption by adjusting the operating power of the recovery equipment. The equipment energy control data is generated by the energy management system, and the system adjusts the energy supply mode according to the energy consumption curve of the wastewater recovery system. At this time, the power consumption should be set according to the power and operating time of the recovery equipment. Assuming that the equipment power is 10kW, the estimated energy consumption is 20kWh. The energy management system will monitor and adjust the energy supply in real time to ensure the efficient operation of the recovery system.

[0015] Preferably, step S1 specifically comprises: Step S11: using the copper-containing wastewater treatment system to collect copper-containing wastewater data from the sedimentation tank; In this embodiment, the copper-containing wastewater treatment system is used to collect data on the copper-containing wastewater in the sedimentation tank. The collected parameters include but are not limited to key water quality indicators such as copper ion concentration, turbidity, pH value, conductivity, temperature and suspended matter concentration of the water sample. The collection process is carried out by an online water quality monitoring instrument installed near the wastewater tank, and the monitoring instrument measures the various water quality parameters of the wastewater in real time at fixed time intervals. The copper ion concentration is determined by electrochemical analysis methods (such as ion selective electrode method), and turbidity and suspended matter concentration are monitored in real time by light scattering method and interference filtration method. The collected data is sent to the central control system and stored in the database for use in subsequent steps.

[0016] Step S12: performing preliminary purification simulation based on the copper-containing wastewater data of the sedimentation tank to obtain preliminary purification simulation data; In the present embodiment, after obtaining the copper-containing wastewater data of the sedimentation tank, preliminary purification simulation is performed based on these data by special water quality simulation software. During the simulation process, according to the water quality parameters (such as copper ion concentration, turbidity, suspended solids concentration, etc.) in the initial data, the change trend in the water quality improvement process is predicted by the reaction kinetic model and the fluid dynamics model. During the simulation, the water flow rate is set to 5L / min, and the simulation time is 30 minutes. According to the known amount of chemical treatment agent input and the physical treatment conditions in the sedimentation tank (such as water flow rate, residence time, etc.), simulation is performed to output the data after preliminary purification. These simulation data include but are not limited to information such as suspended solids concentration, copper ion concentration change, turbidity change in the treated water sample. The simulation process is calculated by the actual wastewater flow, reaction time and the amount of reagent added to calculate the quality of the purified wastewater.

[0017] Step S13: Identify the change trend of suspended matter concentration of the preliminary purification simulation data; In this embodiment, for the change of suspended matter concentration in the preliminary purification simulation data, the data analysis software is first used to identify the trend. In this process, the change curve of the suspended matter concentration is extracted and compared with the preset standard value. The change of suspended matter concentration is achieved by the mass concentration of suspended particles in the wastewater sample collected in real time, with a concentration range of 10mg / L to 200mg / L, and the concentration is measured using a filter paper weight method or a particle analyzer (such as a laser particle size analyzer). In order to accurately identify the changing trend of suspended matter concentration, the analysis software calculates the suspended matter concentration at each time point and performs trend fitting. The trend analysis algorithm uses the least squares method to perform data smoothing to determine whether the suspended matter concentration is gradually rising, falling or stabilizing. Through this method, the dynamic changes in the suspended matter concentration in the wastewater can be clearly judged.

[0018] Step S14: When the trend of the change in the suspended matter concentration meets the following conditions at the same time, it is determined to enter the sand filter filtration stage and generate preliminary purification data of copper-containing wastewater: the suspended matter concentration is greater than 50 mg / L, the turbidity is higher than 20 NTU, the particle size is concentrated in the range of 10-100 μm, and the sedimentation rate is lower than 0.1 mm / s, the pH value is in the range of 6.5-8.5, and the conductivity is less than 5000 μS / cm.

[0019] In this embodiment, according to the identification results of the change trend of the suspended matter concentration, when the suspended matter concentration reaches certain conditions, the system will automatically determine whether to enter the sand filter filtration stage. The specific conditions are set as follows: the suspended matter concentration is greater than 50 mg / L, the turbidity is higher than 20 NTU, the particle size is concentrated in the range of 10-100 μm, and the sedimentation rate is lower than 0.1 mm / s, the pH value is in the range of 6.5-8.5, and the conductivity is less than 5000 μS / cm. When the suspended matter concentration of the wastewater exceeds 50 mg / L, combined with the particle size analysis, if the particle size is mostly distributed in the range of 10-100 μm, and the sedimentation rate is lower than 0.1 mm / s, it indicates that the solid particles in the wastewater are not easy to be removed by sedimentation, and are suitable for entering the sand filter for further treatment. In addition, the monitoring results of pH value and conductivity are measured in real time by pH meter and conductivity meter to ensure compliance with the specified standard range. According to these monitoring results, the system will automatically trigger the start command of the sand filter and guide the wastewater to the sand filter for filtration. During the filtration process, the sand filter treats wastewater according to the set filtration rate (such as 2L / min), thereby generating preliminary purified copper-containing wastewater data. These data include parameters such as suspended solids concentration, turbidity and copper ion concentration in the filtered wastewater, which will be further used in subsequent treatment stages.

[0020] Preferably, the chemical precipitation removal in step S2 includes: When the preliminary purification data of copper-containing wastewater meets the following conditions at the same time, it is determined to enter the chemical precipitation removal stage: The pH value is in the range of 6.5-9.0 and is on an upward trend; In this embodiment, in the wastewater treatment system, the pH value in the wastewater is monitored in real time by an installed online pH sensor. The sensor automatically collects data every 30 seconds and transmits the measurement results to the central control system. The system sets the normal range of pH value to be 6.5 to 9.0. When the pH value enters this range, the system further checks the changing trend of the pH value. If the pH value is on an upward trend and within the set range, it means that the pH value of the wastewater is stable and meets the conditions for entering the chemical precipitation stage. If the pH value exceeds 9.0 or is lower than 6.5, the system will start the adjustment process and guide the wastewater to the adjustment tank for pH adjustment. This process requires the pH of the wastewater to be adjusted by automatically adding acid or alkali to ensure that it meets the requirements. Through the analysis of historical data, the control system can calculate the pH change rate in real time to determine whether it meets the requirements of the upward trend. Only when the pH value is stable within the range and is on an upward trend can it enter the subsequent treatment.

[0021] The ORP value is in the range of -100mV to +100mV, and the change trend is stable; In this embodiment, the oxidation-reduction potential (ORP) is an important indicator reflecting the reduction and oxidation reaction capabilities of wastewater. An online ORP sensor is used for real-time monitoring, and the range of ORP is set to -100mV to +100mV. When the ORP value fluctuates beyond this range, the system will immediately sound an alarm and will not allow the wastewater to enter the chemical precipitation stage. The ORP sensor detects the ORP value every 30 seconds and transmits the data to the control system in real time. The system not only monitors the ORP value, but also analyzes the change trend of the ORP to determine whether it tends to stabilize. If the ORP value is within this range and the variation is less than ±10mV, it indicates that the redox environment in the wastewater is stable. At this time, the system will determine that the wastewater meets the prerequisites for chemical precipitation. If the ORP value fluctuates violently, the system will suspend entering the precipitation stage and perform necessary adjustment operations, such as adjusting the dosage of the oxidant or reductant.

[0022] The copper ion concentration is maintained within the range of 5-50 mg / L for 5 consecutive minutes, and the decreasing rate is less than 1.0 mg / L·min; In this embodiment, the concentration of copper ions in the wastewater is an important indicator for chemical precipitation removal. The system performs real-time monitoring by electrochemical analysis, such as using an ion selective electrode (ISE) or an atomic absorption spectrometer (AAS) device. Every minute, the system automatically records the concentration data of copper ions and analyzes the data for five consecutive minutes. The copper ion concentration is set to be maintained between 5-50 mg / L, and the rate of concentration change shall not exceed 1.0 mg / L min. If the concentration of copper ions is stable within this range for 5 consecutive minutes and the rate of concentration change meets the requirements, it is considered that the copper ion concentration in the wastewater is suitable for entering the chemical precipitation stage. If the concentration exceeds this range, the system will trigger an alarm and suspend the precipitation operation. In addition, the system will judge the rate of decline based on the concentration change trend of copper ions. If the rate of decline is greater than 1.0 mg / L min, the wastewater does not meet the conditions for entering the precipitation stage, and the system will automatically switch to other treatment paths.

[0023] The online turbidity meter detected that the turbidity value of the solution increased by more than 50 NTU within 3 minutes. At the same time, the particle size analyzer detected the presence of precipitated particles with a particle size greater than 10 μm in the solution, and the particle number concentration exceeded 500 / mL. In this embodiment, the turbidity of the solution is directly related to the concentration and particle size of the particulate matter in the wastewater, especially in the chemical precipitation reaction, the presence of particulate matter will affect the precipitation effect. The turbidity of the wastewater is monitored in real time by an online turbidity meter, and the turbidity increase of more than 50NTU is set as the trigger condition for entering chemical precipitation. The turbidity of the wastewater is measured every 30 seconds by the turbidity meter. If the turbidity of the solution is detected to increase by more than 50NTU within 3 minutes, the system will trigger the particle analysis program. The particle analyzer detects the particle size of the particulate matter in the wastewater by laser scattering method and measures its number concentration in real time. The particle size detection threshold set by the system is greater than 10μm, and the particle concentration is required to exceed 500 / mL. If the particle concentration exceeds the threshold, it indicates that there are enough precipitation particles in the wastewater, which meets the treatment conditions of chemical precipitation. The system will automatically enter the next precipitation removal stage.

[0024] Through sedimentation rate analysis, it was detected that the sedimentation rate of the precipitated particles was greater than 0.2 mm / s, and the sedimentation trend was stable.

[0025] In this embodiment, the sedimentation rate is an important indicator to measure whether the particulate matter in the wastewater can be effectively precipitated. Through the sedimentation rate sensor installed in the wastewater tank, the system can monitor the sedimentation rate of the particulate matter in the wastewater in real time. The sensor tracks the sedimentation process of the particulate matter through video image analysis or laser ranging technology. Every 5 minutes, the system records the sedimentation rate of the particulate matter and analyzes the sedimentation trend. If the sedimentation rate of the particulate matter is greater than 0.2mm / s and the sedimentation trend is stable, it means that the particulate matter has reached the appropriate sedimentation conditions and the wastewater meets the requirements for entering the chemical precipitation removal stage. When the sedimentation rate is greater than 0.2mm / s, the system will start the addition of the precipitant (such as sodium hydroxide or aluminum sulfate) and ensure the smooth progress of the sedimentation process through stirring and mixing facilities. Sedimentation rate detection ensures the effective separation of the precipitated particles, thereby providing sufficient precipitation conditions for subsequent wastewater treatment.

[0026] Preferably, the ion exchange removal in step S2 includes: When the preliminary purification data of copper-containing wastewater meets the following conditions at the same time, it is determined to enter the ion exchange removal stage: The pH value is in the range of 4.0-7.5, and the change trend is stable; In this embodiment, the pH value in the wastewater is monitored in real time by an installed online pH sensor. The sensor is set to sample once per minute and upload the data to the control system in real time. The system sets the effective range of the pH value to 4.0 to 7.5. When the monitored pH value is within this range, the system will further analyze the trend of the pH value change. By calculating the rate of change of the pH value in the past three minutes, if the rate of change does not exceed 0.1 units / min, and the pH value remains stable within this range, it is considered that the pH value of the wastewater is stable and meets the conditions for entering the ion exchange removal stage. If the pH value is within the set range and the trend of change is stable, the system confirms that the wastewater meets the requirements for continued treatment and enters the subsequent ion exchange process.

[0027] The ORP value is maintained within the range of -100mV to +100mV without drastic fluctuations; In this embodiment, the redox potential (ORP) in the wastewater is monitored in real time by an online ORP sensor, which automatically samples and records the ORP value every minute. The system sets the effective range of the ORP value to -100mV to +100mV. Within this range, the fluctuation range of the ORP value shall not exceed ±10mV. If the change in the ORP value exceeds this standard during the monitoring period, the system will issue an alarm and stop entering the ion exchange stage. If the ORP value is within the specified range and the fluctuation range remains within ±10mV, it means that the redox environment of the wastewater is stable and meets the conditions for entering the next stage. At this time, the control system will automatically switch to the ion exchange treatment stage.

[0028] The copper ion concentration is maintained within the range of 2-20 mg / L for 3 consecutive minutes, and the decreasing rate is greater than 0.5 mg / L·min. At the same time, the current detection value decreases by more than 10% compared with the maximum value of the previous detection cycle; In this embodiment, the concentration of copper ions is monitored in real time by an ion selective electrode (ISE) or atomic absorption spectrometry (AAS) device. The monitoring frequency is set to sample once per minute, and the copper ion concentration data is recorded for three consecutive minutes. The copper ion concentration should be kept in the range of 2-20 mg / L. If the copper ion concentration remains in this range for three consecutive minutes, and the concentration decrease rate is greater than 0.5 mg / L·min, and the current concentration is more than 10% lower than the maximum concentration of the previous cycle, the wastewater meets the conditions for entering the ion exchange treatment. If the copper ion concentration fluctuates in an unqualified manner, the system will sound an alarm, automatically stop the ion exchange operation, and return to the previous level of wastewater regulation.

[0029] The conductivity sensor detected that the conductivity value dropped by more than 300 μS / cm within 5 minutes, and the saturation monitoring data of the ion exchange resin showed that the exchange capacity was still within the effective range; In this embodiment, the system monitors the conductivity of the wastewater through a conductivity sensor, and sets the conductivity change of more than 300μS / cm as the basis for entering the ion exchange stage. The sensor samples once every 5 minutes and records the conductivity change of the wastewater in real time. If the conductivity value drops by more than 300μS / cm within five minutes, it indicates that the ion concentration of the wastewater has changed significantly, and the conditions for entering the ion exchange stage are met. In addition, the saturation of the ion exchange resin is monitored in real time by the sensor. The monitoring data of the resin saturation should show that the exchange capacity of the resin is still within the effective range, that is, the saturation is less than 85%. If the resin saturation exceeds this range, the system will warn and stop the ion exchange process, and automatically guide it into the resin regeneration stage. The resin saturation detection is judged based on the residual capacity of the resin and the ion exchange reaction in the wastewater, and the specific value is calculated based on the initial exchange capacity of the resin and the concentration change of the ions in the wastewater.

[0030] The online flow meter detected that the water flow rate of the exchange column was stable, and the pressure sensor showed that the inlet and outlet pressure difference was less than 0.05MPa.

[0031] In this embodiment, an online flow meter is used to monitor the water outlet flow rate of the ion exchange column. The system sets the flow rate to be maintained within the set range, i.e., 1.5 to 2.0 L / min per minute. When the flow rate stabilizes within this range, the system confirms that the flow condition meets the requirements for entering the ion exchange stage. The pressure sensor is used to monitor the inlet and outlet pressure difference, and the upper limit of the pressure difference setting is 0.05 MPa. If the sensor detects that the inlet and outlet pressure difference is less than 0.05 MPa, it indicates that the fluid channel in the exchange column is unobstructed and meets the conditions for ion exchange treatment. If the pressure difference exceeds the set value, the system will automatically adjust the flow rate or switch to a spare exchange column to ensure that the pressure remains within the effective range.

[0032] Preferably, the membrane separation and removal in step S2 includes: When the preliminary purification data of copper-containing wastewater meets the following conditions at the same time, it is determined to enter the membrane separation and removal stage: The pH value is in the range of 4.5-8.0, and the fluctuation range is less than 0.5 within 5 minutes; In this embodiment, the pH value in the wastewater is monitored in real time by an installed online pH sensor, and the sensor is set to sample once per minute and upload data to the control system in real time. The system sets the effective range of pH value to 4.5 to 8.0. If the monitored pH value falls within this range, and the fluctuation range of the pH value is less than 0.5 within 5 consecutive minutes, the system will further confirm that the wastewater meets the requirements of membrane separation. Specifically, the fluctuation range is obtained by calculating the maximum and minimum difference of the pH value within 5 minutes. When the difference is less than 0.5, it means that the pH value is stable and meets the pretreatment conditions of membrane separation. If the pH value fluctuates beyond the standard, the system will sound an alarm and stop entering the membrane separation stage to ensure that the wastewater is treated in a suitable pH environment.

[0033] The ORP value is stable between +50mV and +250mV, and there is no drastic change; In this embodiment, the ORP value in the wastewater is monitored in real time by an ORP sensor, and the sampling frequency of the sensor is set to once per minute. The system sets the effective range of the ORP value to be +50mV to +250mV, and the ORP value must not fluctuate violently. The change in the ORP value must not exceed ±10mV in each sampling cycle. If the ORP value is stable within the set range and the amplitude of the change meets the requirements, the system determines that the wastewater meets the requirements of the membrane separation stage. In the control system, the fluctuation amplitude of the ORP value is calculated by comparing the difference between two consecutive monitoring data. If the change is less than ±10mV, it is considered that the ORP is stable and does not affect the membrane separation operation. If the ORP value exceeds the set range, the system will stop processing and adjust the chemical properties of the wastewater.

[0034] The copper ion concentration is maintained within the range of 1-10 mg / L for 3 consecutive minutes, and the rate of decrease is greater than 0.6 mg / L·min. At the same time, the current detection value decreases by more than 12% compared with the maximum value of the previous detection cycle; In this embodiment, the copper ion concentration in the wastewater is monitored in real time by an ion selective electrode (ISE) or atomic absorption spectrometry (AAS) device, with a sampling frequency of once per minute. The copper ion concentration must be maintained within the range of 1-10 mg / L, and the copper ion concentration value for three consecutive minutes must meet the following two conditions: the concentration decrease rate is greater than 0.6 mg / L·min, and the current detection value is more than 12% lower than the maximum value of the previous detection cycle. At this point, the system will calculate the concentration change rate within three minutes and compare it with the threshold. If the decline rate and concentration change conditions are met, the system will confirm that the wastewater meets the membrane separation treatment conditions. If the copper ion concentration fluctuates abnormally or does not meet the standard, the system will suspend the membrane separation operation and adjust the treatment process.

[0035] The transmembrane pressure difference is within 0.1-0.3MPa, and the change is less than 10% within 5 minutes; In this embodiment, during the membrane separation process, the transmembrane pressure difference is monitored in real time by a pressure sensor, and the effective range of the transmembrane pressure difference is set to 0.1-0.3MPa. During the membrane separation stage, the rate of change of the transmembrane pressure difference cannot exceed 10%, and it needs to be monitored for 5 consecutive minutes. If the transmembrane pressure difference detected by the sensor is within this range and the amplitude of change is less than 10%, it is considered that the membrane system is stable and meets the treatment requirements. The amplitude of change of the transmembrane pressure difference is calculated by comparing the maximum and minimum values ​​of the monitoring data within 5 minutes. If the pressure difference exceeds the set range or the amplitude of change is too large, the system will automatically alarm, and adjust the membrane system parameters or replace the membrane element.

[0036] The turbidity of the membrane inlet water was lower than 5NTU for 3 consecutive minutes, and the pollution index was less than 5, indicating that the inlet water quality met the membrane treatment requirements.

[0037] In this embodiment, the turbidity of the membrane separation inlet water is monitored in real time by a turbidity sensor. The sensor is set to sample once per minute and record the inlet water quality. According to the requirements of the control system, the inlet turbidity must be lower than 5NTU, and the pollution index must be lower than 5. The pollution index is comprehensively calculated based on the solid particles, chromaticity and organic matter concentration of the inlet water. If the turbidity value and the pollution index meet the standards, the system confirms that the wastewater meets the membrane separation requirements. Specifically, the pollution index is a comprehensive assessment based on the turbidity value, suspended matter content and organic pollutant concentration. If the pollution index exceeds 5, the system will trigger an alarm, stop the membrane separation process, and perform water quality pretreatment.

[0038] Preferably, step S3 specifically comprises: Step S31: Based on the preset wastewater recycling standard, the wastewater data is judged to contain abnormal copper ions, and the abnormal copper ion wastewater data is obtained; In this embodiment, the copper ion concentration in the wastewater is detected in real time by an online ion selective electrode (ISE) or an atomic absorption spectrometer (AAS) device. The sensor samples once every 1 minute to obtain copper ion concentration data. The preset wastewater recovery standard requires that the copper ion concentration cannot exceed 10 mg / L, otherwise it is considered that the wastewater contains abnormal copper ions. The system judges the results of each sampling, and when the detected copper ion concentration exceeds 10 mg / L, the data is marked as "abnormal wastewater data containing copper ions". At this time, the system will store the data in the database, mark it as an abnormal state, and start the subsequent processing process. The core of the abnormal judgment process lies in the real-time monitoring of copper ion concentration and the comparison with the standard threshold.

[0039] Step S32: Detecting the copper ion concentration based on the abnormal wastewater data containing copper ions; In this embodiment, when the wastewater is judged to contain abnormal copper ions, the copper ion concentration in the wastewater needs to be reconfirmed. The equipment used is an online atomic absorption spectrometer or an ion selective electrode, and the sampling frequency of the equipment is set to once per minute. During the detection of the copper ion concentration, the equipment automatically extracts the wastewater sample according to the preset sampling period, and the copper ion concentration is determined by the calibration curve method. The concentration value is determined by the standard curve method, in which the calibration curve is generated by the measurement data of standard solutions of different concentrations. This process ensures the accuracy of the concentration test results, and all test data will be recorded in the control system for subsequent processing.

[0040] Step S33: Automatically controlling the amount of oxidant added during the copper ion removal process based on the copper ion concentration; In this embodiment, according to the copper ion concentration data, the system will automatically adjust the amount of oxidant to be delivered according to the preset copper ion concentration control threshold. When the threshold is set to copper ion concentration exceeding 10mg / L, the oxidant delivery is automatically started. The amount of oxidant delivered is adjusted by the control system through the PID control algorithm to ensure that the copper ion concentration quickly drops to the standard range. The system first calculates the required amount of oxidant delivered by measuring the copper ion concentration in real time. For example, when the copper ion concentration is 15mg / L, the system automatically calculates the amount of oxidant delivered according to the reaction rate and the amount of copper ions to be removed, usually in mL / min, and controls the precise delivery of the oxidant through a flow meter. The regulation process of the delivery amount is completed based on continuous data feedback to ensure that the predetermined removal effect is achieved.

[0041] Step S34: Automatically regulating the amount of chemical precipitation in the copper ion removal process based on the copper ion concentration; In this embodiment, after confirming the copper ion concentration, the system will automatically adjust the amount of chemical precipitant to be dosed according to the real-time data of the copper ion concentration. Chemical precipitants usually use sodium hydroxide or sodium sulfide to convert copper ions into insoluble precipitates through precipitation reactions. The amount of dosage is determined based on the copper ion concentration and the preset reaction formula, and the system calculates the required amount of precipitation according to the concentration change. For example, when the copper ion concentration is 12 mg / L, the system automatically calculates the required amount of precipitant according to the set theoretical dosage ratio (such as 1:1), usually measured in mL / L, and adjusted by the automated delivery system. The entire delivery process is coordinated with the online monitoring concentration data and the automatic control valve to accurately deliver the chemical precipitant to ensure that the copper ions in the wastewater are completely removed.

[0042] Step S35: Integrate the oxidant dosage and the chemical precipitation dosage, and record the integrated dosage for treatment control to obtain treatment process control data.

[0043] In this embodiment, the amount of oxidant and precipitant is monitored in real time by a flow meter. The system records the amount of each agent and integrates the data. For example, when the copper ion concentration is 15 mg / L, the system calculates that the amount of oxidant is 10 mL / min and the amount of chemical precipitant is 5 mL / min. The combination of the two amounts will form a set of treatment process control data. The system automatically records these data in a database and generates a processing log for the operator to view and analyze. The control data includes the total amount of oxidant and chemical precipitant, the time and flow rate of each delivery, and other information. These data will serve as the basis for subsequent optimization and adjustment to ensure the stability and efficiency of each treatment process.

[0044] Preferably, step S33 is specifically: Step S331: Calculate the concentration fluctuation rate of the copper ion concentration, and draw a concentration period fluctuation diagram based on the concentration fluctuation rate; identify the period of intense fluctuation in the concentration period fluctuation diagram; monitor the operating state of the stirring motor according to the period of intense fluctuation; In this embodiment, an online ion selective electrode (ISE) is used to monitor the copper ion concentration in real time, and the system collects concentration data once a minute to form a time series. These concentration data will be used to calculate the volatility. The volatility reflects the change in copper ion concentration between different time points. Specifically, it is to calculate the concentration difference between the concentration change amplitude at each sampling moment and the concentration at the previous moment. By statistically analyzing the concentration changes in each time period, the system can identify the law of concentration fluctuations. After calculating the concentration fluctuation rate for each time period, tools such as MATLAB are used to draw a fluctuation graph of concentration changes. The horizontal axis of the chart represents time, and the vertical axis represents the copper ion concentration. The change amplitude and fluctuation trend of the concentration can be intuitively seen in the figure. If the fluctuation amplitude exceeds the preset fluctuation threshold (for example, ±5mg / L), it is considered that the concentration fluctuation is large, which means that an abnormality has occurred. When the concentration fluctuation amplitude is large, the system will mark the period and analyze it in combination with the operating status data of the stirring motor monitored in real time. Specifically, the system will analyze whether the period of intense concentration fluctuation corresponds to the fault or abnormal operating state of the stirring motor. Through correlation analysis, the system can determine whether the fluctuation in copper ion concentration is related to the working state of the motor, thereby further determining the root cause of the problem.

[0045] Step S332: Identify abnormal fault codes of the stirring motor operation state, including blade abnormal fault codes and coupling abnormal fault codes; In this embodiment, in order to identify the fault state of the stirring motor, the system needs to collect the operating data of the motor in real time, including current, voltage, temperature and vibration signals. The normal operation of the motor will be maintained within a certain parameter range. By setting a threshold value (such as when the current value exceeds the range of ±5%, it indicates an abnormality), the system will analyze these data and compare them with the preset normal value, and generate a fault code when an abnormality is found. Specifically, the abnormal fault code of the blade is identified by monitoring the load change, speed and current fluctuation of the motor. When the speed or load fluctuation amplitude of the blade exceeds the predetermined range (for example, the speed fluctuation exceeds ±10RPM), the blade fault alarm will be triggered. The coupling abnormal fault code is identified based on the vibration signal and torque fluctuation of the coupling. If the vibration signal frequency exceeds the set frequency range (such as less than 10Hz or higher than 200Hz), and is accompanied by abnormal torque fluctuations (for example, the fluctuation amplitude exceeds ±15Nm), the coupling fault alarm will be triggered. The system analyzes these sensor data in real time and determines whether the operating state of the motor is abnormal according to the set fault diagnosis algorithm and threshold.

[0046] Step S333: collecting multiple blade fault signals based on the blade abnormal fault code; counting the time of sensing and collecting fault signals according to the multiple blade fault signals; calculating the propagation time difference of the time of sensing and collecting fault signals; and estimating the blade angle position based on the propagation time difference; In this embodiment, once the system identifies the abnormal blade fault code, it starts to collect fault signals of multiple blades. Multi-blade fault signals are collected through acceleration sensors and vibration sensors, which are installed on the blades and bearings of the stirring motor. The acceleration sensor can monitor the vibration of the blades during rotation, and the vibration sensor is used to monitor the frequency changes related to the rotation of the blades. Through the data from these sensors, the system can analyze the fault conditions of each blade. Whenever a fault signal is collected, the system records the timestamp of the fault occurrence and calculates the time difference between each fault signal. This time difference represents the time difference required for the fault signal to be transmitted from the point of occurrence to different sensors. The system calculates the propagation time difference based on this time difference (for example, if the distance d between the sensors is 1 meter and the propagation speed v is 340m / s, the propagation time difference can be calculated by the formula ; calculated). Through the propagation time difference and the known propagation speed (such as the speed of sound in air), the system can infer the angular position of the fault signal source and thus determine which blade has failed.

[0047] Step S334: Detect blade fracture based on the blade angle position to obtain blade fracture data; In this embodiment, after the angular position of the blade fault signal is known, the system continues to monitor the state of the blade to detect whether a break has occurred. The system identifies abnormal movement of the blade through vibration analysis. For example, under normal circumstances, the blade should rotate smoothly, and the angle change fluctuates within a certain range. If the blade is broken, it will cause irregular rotation, such as increased vibration amplitude and drastic angle changes. The system monitors the rotation angle and vibration signal of the blade in real time. Once it is found that the amplitude of the vibration signal exceeds the set threshold (such as the vibration amplitude exceeds 20mm / s), or the change in the blade angle exceeds the set angle threshold (for example, ±30°), it is judged that the blade is broken. The system generates fracture data and stores it for subsequent analysis and processing. These data are transmitted to the control system in real time, and subsequent control measures are automatically triggered based on this information.

[0048] Step S335: collecting coupling vibration signals based on the coupling abnormal fault code; collecting torque sensor data based on the coupling abnormal fault code; In this embodiment, the failure of the coupling is determined by the vibration signal and the torque signal. Through the vibration sensor installed on the coupling, the system can detect the vibration of the coupling in real time. Low-frequency vibration signals (usually less than 10Hz) indicate that there is an abnormality in the coupling. If the coupling is loose or rotates unevenly, it will cause a low-frequency enhanced vibration signal. The system also monitors the torque fluctuation of the coupling through a torque sensor, and an increase in torque fluctuation is usually an indication of looseness. When the system detects that the torque fluctuation amplitude exceeds the set threshold (such as ±20Nm) and is accompanied by an increase in the low-frequency vibration signal (such as a vibration frequency of less than 5Hz), it will be considered that the coupling has failed, and a coupling abnormality fault code will be generated and an alarm will be triggered.

[0049] Step S336: Counting low-frequency enhanced vibration signals based on the coupling vibration signals; counting torque fluctuation increase data based on the torque sensor data; determining loose bolts based on the low-frequency enhanced vibration signals and the torque fluctuation increase data to obtain coupling loose data; In this embodiment, loose bolts usually lead to low-frequency enhanced vibration signals and increased torque fluctuations. The system processes the vibration signal of the coupling through vibration analysis technology. If the frequency of the vibration signal is concentrated in the low-frequency range (such as less than 10Hz) and the vibration amplitude exceeds the set threshold (such as more than 30mm / s), it will be judged that there are loose bolts. In addition, the system also analyzes the fluctuation of the torque data. If the torque fluctuation amplitude increases (such as the fluctuation amplitude exceeds ±25Nm), it is more likely to be further judged as loose bolts in combination with the low-frequency vibration signal. Through the signal processing algorithm, the system can combine the vibration frequency and torque fluctuation data to determine in real time whether there are loose bolts in the coupling, generate loose data, and trigger related processing mechanisms.

[0050] Step S337: Automatically control the amount of oxidant added in the copper ion removal process based on the blade breakage data and the coupling looseness data.

[0051] In this embodiment, once the system detects that the blade is broken or the coupling is loose, the amount of oxidant added is automatically adjusted according to these fault data. If the blade is broken or the coupling is loose, resulting in uneven stirring, the copper ion removal effect will decrease, so it is necessary to increase the amount of oxidant added. In the specific implementation process, the system adjusts the amount of oxidant added through the PID control algorithm. According to the fault data, the system automatically adjusts the amount of oxidant added (for example, the amount added is increased from 2L / h to 5L / h) to ensure that the copper ion concentration is maintained within the set standard range. The adjustment of the amount added is controlled in real time by the feedback mechanism to ensure the efficiency of the removal process. During the adjustment process of the oxidant amount added, the system will ensure the balance between the amount added and the removal process through real-time concentration monitoring and motor status monitoring.

[0052] Preferably, step S34 is specifically as follows: Step 341: performing deviation calculation on the copper ion concentration based on a preset copper ion threshold concentration to obtain concentration deviation data; In this embodiment, the copper ion concentration is monitored in real time by an online ion selective electrode (ISE). The ISE sensor continuously collects copper ion concentration data in the water sample in the treatment system, usually once a minute. The ISE sensor uses the principle of ion selectivity to generate electrical signals for copper ions, which are converted into concentration values ​​after calibration, and the data is transmitted to the central control system in real time. After data acquisition, the system compares it with the preset copper ion concentration threshold. The threshold concentration is set in advance and can be adjusted according to the needs of different scenarios. Assume that the set threshold concentration is 2 mg / L. Whenever the actual concentration collected exceeds the threshold, the system calculates the concentration deviation between the current copper ion concentration and the threshold concentration, that is, the deviation value is obtained by the difference formula. For example, when the measured actual concentration is 3 mg / L and the preset threshold concentration is 2 mg / L, the deviation value is 1 mg / L (3 mg / L-2 mg / L=1 mg / L). This deviation value is the amount of excess concentration, which indicates that the actual copper ion concentration exceeds the set allowable range. This calculation process is automatically executed every time the system collects concentration data, and the calculation results will provide a basis for the subsequent adjustment of the precipitant dosage. If the deviation value is positive, it means that the actual copper ion concentration is higher than the set value and needs further processing. This concentration deviation value will be used as a key parameter for adjusting the dosage of the reagent in the subsequent processing process to guide the system on how to accurately control the use of chemical precipitants to keep the copper ion concentration within the set safety range. Therefore, real-time and accurate concentration monitoring and deviation calculation are the basis for the effective operation of the system.

[0053] Step 342: Calculate the compensation precipitation dosage using the concentration deviation data; Calculate the precipitation agent injection acceleration rate using the concentration deviation data; In this embodiment, the system will establish a quantitative relationship between the concentration deviation and the required precipitation dosage according to the historical experimental data or the rules set by the experiment. Assume that it is determined through preliminary experiments that 0.5L of precipitant needs to be added for every 1mg / L of copper ion concentration deviation. Based on this relationship, the system will dynamically calculate the required precipitation dosage based on the concentration deviation data calculated in real time. For example, if the concentration deviation is 3mg / L, the required precipitation dosage is 1.5L (3mg / L×0.5L / mg / L). This precipitation dosage is to adjust the copper ion concentration back to the preset threshold and ensure that the copper ion concentration in the water body drops to an appropriate range. After obtaining the required compensation precipitation dosage, the system will further calculate the precipitation dosage acceleration rate. The calculation of the dosage acceleration rate is based on the required precipitation dosage and the time interval of the dosage. Assuming that the target dosage set by the system is 10L and the dosage time is 5 minutes, the dosage acceleration rate is 2L / min (10L÷5min=2L / min). The dosage acceleration rate indicates that 2L of precipitant needs to be injected per minute, and the system will control the equipment at this rate to achieve a continuous and stable dosage process. The calculation result of the injection acceleration rate is the basis for the subsequent control system to adjust the dosage of the metering pump, ensuring that the precipitant is accurately added at a predetermined rate and amount to maintain the stability and efficiency of the water treatment process.

[0054] Step 343: performing metering pump delivery simulation based on the compensation precipitant dosage and the precipitant delivery acceleration rate to obtain metering pump delivery simulation data; In the present embodiment, the metering pump is a device for accurately controlling the amount of precipitant to be dosed, and the system will simulate the working state of the pump during operation according to the set injection acceleration rate. The core of the simulation is to ensure that the pump stably and accurately dispenses the precipitant according to the calculated injection acceleration rate within the specified time. During the simulation process, the system will set the operation time of the metering pump, and the operation time of the pump is usually calculated according to the set total amount of precipitant to be dosed and the injection acceleration rate. For example, if the amount of precipitant to be dosed is 10L, and the set injection acceleration rate is 2L / min, the operation time of the pump will be 5 minutes. On this basis, the system will record the injection rate of the metering pump to ensure that the dosage is consistent with the preset rate. In addition, the system will also simulate the load of the pump, evaluate the load of the pump at work and whether there is a risk of overload, and excessive load will cause abnormal operation or damage of the pump. Through real-time monitoring data, the system will continuously adjust the simulation parameters to ensure the accuracy of the simulation results. These simulation data not only help optimize the pump's performance, but also identify potential abnormal situations in advance, such as excessive pump load and pump overheating. The system can adjust equipment operation in a timely manner based on the simulation results to ensure that the precipitant delivery process is stable and reliable.

[0055] Step 344: Based on a preset metering pump delivery threshold, abnormal leakage determination is performed on the metering pump delivery simulation data to obtain abnormal leakage data; and the sealing diaphragm leakage position of the abnormal leakage data is extracted; In this embodiment, the system sets the normal delivery range of the metering pump, for example, it stipulates that the delivery rate of the pump shall not exceed 5L / min, and the lower limit rate should be above 0.5L / min. The system collects the delivery rate data of the pump in real time and compares it with the set threshold. If the simulation data shows that the delivery rate exceeds the upper limit or is lower than the lower limit, it is immediately determined to be an abnormal leakage. For example, if the simulation data shows that the delivery rate of the pump at a certain time point is 6L / min, which exceeds the set maximum rate threshold, it is determined to be an abnormal delivery. In addition, the system will use the sensor data of the pump to extract the leakage position, paying special attention to the sealing diaphragm area, because the diaphragm is often the weak link of pump leakage. The extraction of the leakage position analyzes the pressure, flow and temperature signals fed back by the sensor, identifies the data points with large deviations from normal operation, and then infers the location of the leakage source. Through these data, the system can quickly locate the leakage position of the pump, usually the connection part near the diaphragm or the diaphragm itself, and then determine whether a leak has occurred. This judgment can timely discover potential failures or abnormalities of the metering pump, ensure the accuracy of precipitant delivery, avoid inaccurate precipitant dosage due to leakage, and ensure the stability and effectiveness of the wastewater treatment process.

[0056] Step 345: Detecting diaphragm wear based on the sealing diaphragm leakage position and recording diaphragm wear data; In this embodiment, the system accurately locates the leak location through the sensor signal on the sealing diaphragm. The detection of the leak location usually relies on multiple data sources, including pressure sensors, temperature sensors and vibration sensors. These sensors work together to capture the signal of potential leaks in real time. Specifically, the pressure sensor monitors the pressure fluctuations to which the sealing diaphragm is subjected. When a leak occurs, the working pressure of the sealing diaphragm usually changes. If the pressure at the leak location fluctuates abnormally or is unstable, this is usually a signal that the diaphragm is worn or cracked. By comparing the normal working pressure and the abnormal pressure value, the system can preliminarily determine the leak area and potential diaphragm damage. On the other hand, the temperature sensor is used to monitor the temperature change in the sealing diaphragm area. The wear or cracks of the diaphragm lead to a decrease in local sealing, which in turn causes increased friction and generates additional heat. At this time, the temperature sensor will detect an abnormal increase in temperature. The greater the temperature change, the more severe the wear, especially in the area where the leak occurs, the more obvious the temperature fluctuation. The system can judge the potential risk and severity of wear by the amplitude and frequency of temperature changes. In addition, vibration sensors also play a vital role in monitoring diaphragm wear. Irregularity or increased intensity of vibration signals usually means a decrease in the stability of the mechanical system, caused by the wear of the diaphragm. Especially at the leakage location, if the vibration sensor detects an abnormal vibration mode or vibration amplitude, it means that the sealing diaphragm has been deformed or damaged, which in turn affects the normal operation of the pump. The system will conduct a comprehensive analysis based on these sensor data. If local stress concentration, deformation or corrosion signs are found, it can be confirmed that the diaphragm has been worn. At this time, the system will record all relevant data and convert the wear information into a detailed wear data file. These data will serve as an important basis for subsequent operations to help determine whether the diaphragm needs to be replaced or other repair measures need to be taken.

[0057] It is particularly important that step S345 includes the following steps: Divide the leakage area based on the sealing diaphragm leakage position; In this embodiment, in the copper-containing wastewater treatment system for chemical production, in order to accurately monitor the leakage of the sealing diaphragm, it is necessary to divide the leakage area based on the leakage position of the sealing diaphragm. First, an ultrasonic detector or an infrared thermal imager is used to scan the surface of the sealing diaphragm to record the temperature gradient change and the ultrasonic echo signal intensity. For the leakage point, a high-resolution optical imaging system is used to shoot the surface of the diaphragm, and the edge features of the leakage area are extracted by an image processing algorithm (such as the Canny edge detection method). Subsequently, the leakage area is divided according to the leakage aperture size, the diffusion range of the leaking liquid, and the material deformation around the leakage point, and the leakage area data is converted into three-dimensional coordinate point cloud data. For multiple leakage points, the leakage area is classified by the K-means clustering algorithm to ensure that the regional boundaries between different leakage points do not overlap and interfere. Finally, the morphological characteristics, boundary coordinates, and area data of the leakage area are recorded to provide basic data for subsequent stress analysis.

[0058] Detect leakage stress according to the leakage area to obtain leakage stress data; In this embodiment, based on the divided leakage area, the local strain distribution around the leakage area is measured by strain gauges, and the stress of the leakage area is calculated in combination with finite element analysis. First, a strain gauge array is arranged around the leakage area, and the strain of each strain gauge is converted into an electrical signal by a Wheatstone bridge circuit, and the acquisition frequency is set to 1000Hz to ensure the real-time acquisition of high-precision strain data. Subsequently, a finite element calculation model is established based on the material parameters of the leakage area (such as Young's modulus E=200GPa, Poisson's ratio ν=0.3), and the actual working pressure (such as 0.6MPa to 1.2MPa range) and temperature (such as 50℃ to 90℃ range) of chemical wastewater are applied to calculate the stress distribution of the leakage area. The equivalent stress of the leakage area is determined by the Von Mises stress calculation formula σ_v=√(σ_x²+σ_y²-σ_xσ_y+3τ_xy²), and the stress concentration area is further analyzed to screen out the stress peak point, and finally the leakage stress data is obtained.

[0059] Extract high leakage stress data according to leakage stress data; In this embodiment, the leakage stress data is statistically analyzed to extract high leakage stress data and screen out key stress points that may cause rapid failure of the sealing diaphragm. First, based on the distribution of the leakage stress data, the standard deviation method is used to calculate the stress fluctuation range, and the stress threshold σ_th=μ+2σ is set, where μ is the average stress value of the leakage area and σ is the stress standard deviation. For areas where the stress exceeds σ_th, they are defined as high leakage stress areas, and stress data points in these areas are extracted. Subsequently, the high stress points are fitted by an interpolation algorithm (such as the B-spline interpolation method) to generate a high leakage stress distribution surface for subsequent calculation of the diaphragm material deformation.

[0060] Identify the deformation of the diaphragm material based on the high leakage stress data; In this embodiment, the elastic-plastic finite element analysis method is used to simulate the deformation of the diaphragm material in the high leakage stress area. For different stress points, the displacement-stress relationship ΔL=(σ / E)·L is used to calculate the deformation of the material based on the Poisson's ratio and Young's modulus of the diaphragm. Considering multiple pressure cycles in actual operation, the hysteresis curve of the material is determined using the cyclic loading experimental data, and the maximum deformation ΔL_max is calculated. At the same time, a three-dimensional laser displacement sensor is used to scan the surface of the diaphragm, measure the actual displacement of the high leakage stress area, and compare it with the theoretical calculated value to ensure the accuracy of the calculation results. Finally, the deformation data of the diaphragm material is recorded and used to evaluate the degree of fatigue damage of the diaphragm.

[0061] Evaluate diaphragm fatigue damage based on diaphragm material deformation; In this embodiment, the deformation amplitude of the diaphragm material is classified according to the Rainflow Counting Method, the number of different stress cycles is counted, and the fatigue damage accumulation value D=Σ(n_i / N_i) is calculated in combination with the Miner damage accumulation method, where n_i is the actual number of cycles under stress level i, and N_i is the fatigue life of the material at this stress level. For different leakage stress areas, the fatigue life threshold D_th=0.8 is set based on experimental data. When the damage accumulation value exceeds D_th, it is considered that there is severe fatigue damage in the area. In addition, a scanning electron microscope (SEM) is used to observe the microscopic cracks of the diaphragm, record the crack propagation rate, and calculate the fatigue crack growth in combination with the Paris formula da / dN=C(ΔK)^m, so as to further evaluate the degree of fatigue damage of the diaphragm.

[0062] The diaphragm wear is evaluated based on diaphragm fatigue damage, and the diaphragm wear data is recorded.

[0063] In this embodiment, a three-dimensional laser profiler is used to measure the surface roughness Ra value of the worn area of ​​the diaphragm, and an optical interferometer is used to analyze the wear mark depth. Then, the wear volume of the diaphragm is calculated according to the Archard wear formula W=(K·P·V) / H, where K is the wear coefficient, P is the applied load, V is the sliding distance, and H is the material hardness. During the calculation process, a real-time monitoring system is used to record the flushing speed of chemical wastewater (such as 2m / s to 5m / s) and pressure fluctuations, and the wear model parameters are corrected in combination with experimental data. Finally, based on the calculated wear data, the initial thickness of the diaphragm is compared to determine the remaining service life, and the diaphragm wear data is stored in a database for subsequent fault warning systems.

[0064] Step 346: dynamically evolving the pump body piston wear crack based on the diaphragm wear data, and predicting the pump body rupture probability based on the pump body piston wear crack; In this embodiment, the wear of the diaphragm not only affects the sealing, but also causes the pressure inside the pump body to change, thereby affecting the working state of the pump body piston. The system evaluates the wear of the piston part by monitoring the pressure changes and vibration signals inside the pump body. First, the system uses a pressure sensor to monitor the pressure fluctuations inside the pump body in real time. When the diaphragm is worn, the seal is not tight, resulting in unstable pressure. This change will be transmitted to the piston part of the pump body, thereby accelerating the wear of the piston. By analyzing the pressure fluctuations inside the pump body, the system can infer the wear and crack evolution of the piston when it is subjected to unstable pressure. In addition, the data of the vibration sensor is also a key basis. The wear cracks of the piston usually cause changes in the vibration mode of the pump body, especially when cracks occur, the frequency and amplitude of the vibration fluctuate irregularly. By monitoring the vibration signal, the system can identify the crack extension and wear signs of the piston. According to the wear data of the diaphragm, the system combines the physical properties of the pump body material (such as hardness, elasticity, etc.) and working conditions (such as use time, load intensity, etc.) to dynamically infer the wear degree of the piston and the evolution of its cracks. This process takes into account the fatigue strength, wear rate and environmental factors of the material. The system will update the model based on these dynamic change data, continuously track the progress of piston wear, and predict the trend of crack expansion. Ultimately, the system will calculate the probability of pump body rupture, and this probability value will provide an important basis for subsequent equipment maintenance and replacement decisions. Through this prediction, equipment managers can take timely measures to avoid irreversible damage to the pump body and improve the safety and service life of the equipment.

[0065] It is particularly important that step S346 includes the following steps: Identify the pressure fluctuation inside the pump body based on the diaphragm wear data to obtain the pump body pressure fluctuation data; In this embodiment, the operation data of the pump body is collected based on the diaphragm wear data, mainly including the input power of the pump body, the operating temperature of the pump chamber, the working pressure and the pump speed. A high-precision pressure sensor (such as model: Honeywell26PC series, range 0-20MPa, accuracy ±0.05%FS) is used to monitor the pressure changes in real time at various positions in the pump chamber. The sampling frequency of these sensors is set to 100 times per second to ensure that rapid pressure fluctuations can be captured. The data of the sensor is transmitted to the central control unit (such as using the NI PXI platform) through the data acquisition system, and data processing is performed. The pressure signal is spectrally analyzed by Fourier transform, and the main frequency components of the pressure fluctuations are extracted, and screened according to the standard (for example, the pressure fluctuation amplitude exceeds the set threshold of 0.1MPa for significant fluctuations). Finally, the pump body pressure fluctuation data is obtained, including information such as the amplitude, frequency and fluctuation period of the pressure change.

[0066] Estimate the piston stress condition based on the pump body pressure fluctuation data; In this embodiment, the fluid pressure distribution in the pump chamber is calculated by fluid dynamics simulation, and the working state of the pump body is simulated by fluid dynamics software (such as ANSYS Fluent). The fluid mechanics characteristics in the pump chamber are obtained by combining parameters such as pump speed, liquid viscosity, and flow rate. Then, finite element analysis software (such as ABAQUS) is used in combination with the pump body structure model to input the pressure fluctuation data obtained by simulation and perform piston force analysis. In this process, the contact surface, friction coefficient, and material properties between the piston and the pump chamber are set (for example, the piston is made of aluminum alloy, the Young's modulus is 70GPa, and the Poisson's ratio is 0.33). By calculating the instantaneous impact force, deformation, and equivalent stress caused by the pressure fluctuation, the force condition of the piston under different pressure fluctuations is inferred. Finally, the piston force data in each cycle is recorded to further provide a basis for material fatigue damage assessment.

[0067] Evaluate the fatigue damage of the pump body material based on the piston stress condition; In this embodiment, the SN curve (stress-life curve) commonly used in fatigue theory is used to predict the fatigue life of the material. According to the fatigue strength of the material, the stress range is determined. For example, if the piston material is aluminum alloy, the typical SN curve of aluminum alloy is used for analysis. The stress data of each cycle is compared with the SN curve to calculate the corresponding fatigue life. In the fatigue damage accumulation analysis, Miner damage accumulation theory is used to calculate the damage value of each cycle, and the overall damage is accumulated. The calculation formula is D=Σ(n_i / N_i), where n_i is the number of cycles under each stress amplitude, and N_i is the fatigue life under the stress amplitude. By repeatedly calculating the damage of each cycle, the overall fatigue damage data of the pump body material is finally obtained, and the risk of fatigue failure is further judged.

[0068] Predict the starting point of wear crack growth of pump piston based on fatigue damage of pump material; In this embodiment, the fracture mechanics method is used to perform crack initiation analysis based on the stress intensity factor (K) of the material. First, in areas with high fatigue damage, the calculation formula of the stress intensity factor K is used, K=Yσ√πa, where Y is the geometric shape factor, σ is the stress amplitude, and a is the initial length of the crack, to evaluate possible crack initiation areas. Based on the fatigue damage data, high-damage areas in the pump body are identified. The stress intensity factor K in these areas is close to the critical value, and cracks may initiate from these locations. Combined with the critical value of crack initiation (such as the fracture toughness K_IC of the material) and the crack growth law, the starting point of crack growth on the piston surface is determined. The parameter settings in this step include the fracture toughness of the material (such as aluminum alloy K_IC=22MPa·m^0.5) and the crack extension threshold.

[0069] The crack propagation simulation is performed based on the starting point of the wear crack growth of the pump body piston to obtain the crack propagation data of the pump body; In this embodiment, ABAQUS software is used to create a three-dimensional model of the pump body and piston, and the crack starting point and its initial crack length (assuming that the initial crack length is 1 mm) are input. By simulating the stress state of the pump body under different working conditions, the extended finite element method (XFEM) is used to perform crack propagation analysis. Crack propagation follows the Paris formula (da / dN=C(ΔK)^m), where C is the material constant and ΔK is the range of stress intensity factors. Based on the stress distribution on the piston surface, the crack propagation path and propagation rate under different loading conditions are simulated. According to the simulation results, the length, propagation rate and propagation direction of the crack propagation are recorded. Finally, the pump body crack propagation data is obtained, including the current length of the crack, the propagation speed and the possible final fracture position.

[0070] Predict the probability of pump body rupture based on pump body crack growth data.

[0071] In this embodiment, the failure mode and effects analysis (FMEA) is used to evaluate the probability of pump body rupture by calculating the remaining life of crack extension to fracture and combining the working environment of the pump body (such as working pressure, working temperature, cyclic load, etc.). Specifically, the fracture toughness and remaining crack length of the material are used in combination with Frey's fatigue analysis model to calculate the probability of pump body rupture under different working conditions. Assuming that the crack will cause the pump body to rupture when it extends to 10mm, the simulation results predict the time required for the crack to extend to this length. Finally, based on the crack extension data under various working conditions, the probability of pump body rupture is statistically analyzed, and the pump body rupture probability data is recorded to provide a decision-making basis for equipment maintenance and replacement.

[0072] Step 347: Automatically control the dosage of chemical precipitation in the copper ion removal process according to the probability of pump body rupture.

[0073] In this embodiment, the system evaluates the safety of the equipment based on the probability of pump rupture. If the calculated probability of rupture is higher than the set threshold (e.g., 50%), the system will determine that the current pump has a high risk of rupture. In order to prevent the pump from being subjected to excessive pressure and load, the system will reduce the working pressure of the pump by reducing the amount of chemical precipitant. This adjustment is intended to reduce the excessive use of precipitants, thereby avoiding further increase in pressure fluctuations inside the pump. On the other hand, if the probability of rupture is low, the system can return to the normal amount of precipitant to ensure that the effect of removing copper ions is not affected and maintain the efficiency and stability of the operation. The entire control process depends on the system's real-time monitoring and analysis of the probability of pump rupture and the preset rupture probability threshold. The system can flexibly adjust the amount of precipitant based on these data to ensure that the chemical reaction of removing copper ions is still effective while avoiding unnecessary damage to the pump and other key equipment. Through this automated control, the system not only optimizes the copper ion removal process, but also maximizes the service life of the equipment and ensures that the equipment operates efficiently within a safe range.

[0074] Preferably, step S4 is specifically: Step S41: Pushing the processing process control data to the control model of the copper-containing wastewater treatment system of chemical production, and predicting the wastewater volume; dividing the recovery value of the wastewater volume to obtain a high wastewater recovery volume and a low wastewater recovery volume; In this embodiment, the treatment process control system pushes real-time data (such as wastewater flow, temperature, chemical component concentration, etc.) collected by sensors to the wastewater recovery system. These data are transmitted through a wireless communication interface (such as Wi-Fi or Bluetooth) to ensure that the data is updated in real time. The wastewater recovery system predicts the amount of wastewater based on historical data and the current wastewater flow model. Specifically, the wastewater volume prediction model uses measurement tools such as flow meters, thermometers, and chemical analysis instruments, and the real-time data obtained is input into the prediction algorithm. The division of wastewater recovery volume is based on a set threshold. For high wastewater recovery volume, the wastewater volume with a recovery volume greater than a set threshold (such as 1000L / h) is set as a high recovery volume. The low wastewater recovery volume is set to a wastewater volume below this threshold. The system uses flow measurement equipment (such as a mass flow meter) to accurately measure the flow of wastewater and divide the recovery value of wastewater according to the flow value. The recovery system formulates corresponding processing strategies based on the divided value classification to provide a basis for the operation of subsequent steps.

[0075] Step S42: calculating the specific heat capacity of the wastewater based on the high wastewater recovery amount; adjusting the heating power of the heater based on the specific heat capacity of the wastewater; In this embodiment, the quality of wastewater is obtained by multiplying the wastewater flow data obtained by the flow meter by the density of the wastewater. The determination of the wastewater density is based on the temperature and chemical composition of the wastewater. The real-time temperature value of the wastewater is obtained by the temperature sensor during each treatment process. The temperature change of the wastewater is recorded in real time by the temperature sensor, and the system calculates the temperature change, and then combines the wastewater flow and density to obtain the quality of the wastewater. Based on these data, the specific heat capacity of the wastewater can be calculated according to certain heat calculation rules. Subsequently, the calculated specific heat capacity is used to determine the specific needs of wastewater heating. For example, if the specific heat capacity of the wastewater is large, it means that more energy is required to heat the wastewater. At this time, the system will adjust the heating power of the heater according to the value of the specific heat capacity of the wastewater. The adjustment of the heating power is achieved by adjusting the current or voltage of the heater in real time. The control system will dynamically adjust the output power of the heater according to the target temperature (for example, 60°C) and the current specific heat capacity data of the wastewater to ensure that the wastewater reaches the set temperature range during the heating process. This process is closed-loop controlled by real-time feedback from the temperature sensor and power regulation of the heater to ensure that the wastewater is always maintained within a predetermined temperature range when heated.

[0076] Step S43: calculating the recovery rate based on the low wastewater recovery amount; and adjusting the pump running speed by frequency conversion drive based on the recovery rate; In this embodiment, the system continuously tracks the input amount of raw wastewater and the actual amount of wastewater recovered through monitoring tools such as flow meters. The result of the recovery rate calculation is used to reflect the efficiency of wastewater recovery. When the system detects that the recovery rate is lower than a preset threshold (for example, lower than 80%), the system will start the variable frequency drive to adjust the running speed of the pump to increase the recovery amount. In a specific implementation, the variable frequency drive will accurately adjust the speed of the pump according to the calculated recovery rate and wastewater flow data. By changing the frequency of the variable frequency drive and adjusting the running speed of the pump, the system can increase the suction volume of the pump when the recovery efficiency is low, thereby improving the wastewater recovery efficiency. This process realizes real-time feedback and adjustment between the pump speed and the recovery rate, ensuring that the recovery process can be automatically accelerated when the wastewater recovery is insufficient, thereby improving the overall efficiency of the system.

[0077] Step S44: real-time monitoring of the electric energy consumption of the heater heating power and the pump running speed, and recording the electric energy consumption data; In this embodiment, the system obtains the power consumption data of the heater and the pump in real time through the power monitoring instrument. For the heater, the power consumption of the heater is calculated by the current sensor and voltage sensor installed on the power line of the heater. For the pump, the power consumed is monitored by installing a power meter (such as a three-phase power meter) in the power line of the pump motor. The system will record and calculate the specific values ​​of power consumption in real time according to the working status of the heater and the pump. These data are transmitted to the data acquisition system, and the system dynamically adjusts the operating parameters according to the power consumption of the heater and the pump, and provides a basis for subsequent load regulation and energy-saving analysis by recording the power consumption data. The records of power consumption data will be saved in the system database for subsequent analysis and optimization.

[0078] Step S45: adjusting the heater load based on the power consumption data to obtain the heater load data; adjusting the pump load based on the power consumption data to obtain the pump load data; integrating the heater load data and the pump load data to obtain the equipment energy control data.

[0079] In this embodiment, the system performs load regulation by analyzing the power consumption data of the heater and the pump. The heater load regulation is based on the power consumption data of the heater. When the power consumption reaches a preset threshold (such as more than 300W), the system will automatically adjust the workload of the heater, for example, by reducing the heating power or adjusting the running time of the heater to reduce power consumption. For the pump, the system also performs load regulation based on the power consumption data. When the power consumption of the pump exceeds the set standard (such as more than 500W), the system will adjust the running rate or working time of the pump to avoid energy waste. After integrating the load regulation data of the heater and the pump, the system generates equipment energy control data. This data includes indicators such as power consumption, load changes, and energy efficiency of the heater and pump for reference by equipment maintenance personnel, and provides a decision-making basis for optimizing equipment operation. The system uses energy management algorithms to integrate various data and generate energy control plans to ensure efficient and energy-saving use of equipment energy during wastewater recovery.

[0080] Preferably, step S44 is specifically as follows: Step S441: monitor the heating power of the heater in real time, and evaluate the electric energy consumption of the heater based on the heating power of the heater; In this embodiment, a current sensor and a power sensor are installed at the input end of the heater to collect the current and voltage values ​​of the heater in real time. The current sensor detects the current change in the heater circuit and converts it into a digital signal and transmits it to the control system; similarly, the power sensor collects the electric energy information consumed by the heater in real time. These real-time data will be used to calculate the instantaneous power of the heater, and the calculation formula is power (P) = voltage (V) × current (I). Based on the collected current and voltage data, the system calculates the power of the heater in real time, and further uses these data to estimate the electric energy consumption of the heater. In order to more accurately evaluate the electric energy consumption, the system combines the rated power of the heater and the current working status, considers the load condition, operating environment and working efficiency of the heater, and uses a preset energy consumption evaluation algorithm for further calculation. The energy consumption evaluation algorithm combines the real-time power value with the historical operating data of the heater to generate the corresponding electric energy consumption data, continuously monitors the operating status of the heater, and stores these data for subsequent analysis. This process can track and record the heater's electricity usage in real time, providing basic data for analyzing its energy consumption trends, thereby ensuring that the heater always maintains a high-efficiency state during operation and avoids entering a non-efficient operating state, ultimately providing data support for optimizing the heating process and reducing energy waste.

[0081] Step S442: monitor the pump running speed in real time, and evaluate the pump running power consumption based on the pump running speed; In this embodiment, the running speed of the pump is monitored in real time by a speed sensor installed on the pump motor shaft. The speed sensor can accurately detect the rotation speed of the pump motor shaft and transmit the speed signal to the central control system in real time. The central control system estimates the running power consumption of the pump using the power calculation formula based on the received speed data and the working parameters of the pump, such as the pump model, load and working environment. Specifically, the control system collects the current and voltage data of the pump motor, and further calculates the power consumption of the pump using the power formula (power = voltage × current) in combination with the load and operating conditions of the pump. On this basis, the control system can evaluate the actual power consumption of the pump by monitoring the running data of the pump in real time, and compare the actual consumption with the preset standard to determine whether the working state of the pump is in the efficient operation range. In this way, by continuously tracking the running state of the pump, it is ensured that the pump operates within the optimal working efficiency range to avoid energy waste caused by excessive load or unreasonable operation. At the same time, the system can also analyze the long-term energy efficiency performance of the pump by comparing historical data with current energy consumption data, providing a basis for optimizing the operation management and energy efficiency improvement of the pump.

[0082] Step S443: Calculate the total power consumption according to the power consumption of the heater and the power consumption of the pump operation to obtain power consumption data.

[0083] In this embodiment, the electric energy consumption data of the heater and the electric energy consumption data of the pump operation are weighted to obtain the total electric energy consumption of the heater and the pump. This weighted calculation is completed by the integrated control module in the system, which can summarize and analyze the energy consumption data of the heater and the pump in real time. The control module weights the energy consumption data of the heater and the pump according to their actual role in the entire wastewater treatment process, combined with their respective energy efficiency and workload, to ensure that the calculation result can accurately reflect the comprehensive energy consumption of the two. Specifically, the control module optimizes the working state of the heater and the pump by adjusting the operating parameters of the heater and the pump, thereby controlling the overall electric energy consumption within an optimal range to avoid energy waste. This weighted calculation not only takes into account the real-time energy consumption of the heater and the pump, but also combines their contribution to the efficiency of wastewater treatment. By analyzing the energy consumption performance of the heater and the pump under different working conditions, the working strategy of the equipment is automatically adjusted, so that the entire system can maintain efficient operation and reduce unnecessary electric energy consumption during the treatment process, thereby achieving overall energy efficiency optimization.

[0084] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0085] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A control method for a control model of a chemical production copper-containing wastewater treatment system, characterized in that: The following steps are involved: Step S1: using a copper-containing wastewater treatment system to collect copper-containing wastewater data from a sedimentation tank; performing a preliminary purification simulation based on the copper-containing wastewater data from the sedimentation tank, and using a sand filter to remove suspended matter during the purification simulation process to generate preliminary purified copper-containing wastewater data; Step S2: removing copper ions from the preliminary purified copper-containing wastewater data, including chemical precipitation removal, ion exchange removal and membrane separation removal, to obtain wastewater data; Step S3: Based on the preset wastewater recycling standard, the wastewater data is judged to contain abnormal copper ions, and the abnormal wastewater data containing copper ions is obtained; Detect copper ion concentration based on abnormal wastewater data containing copper ions; analyze stirring motor faults based on copper ion concentrations to obtain stirring motor abnormal fault codes; Process control data for removing copper ions based on automatic regulation of abnormal fault codes of stirring motors; Step S4: Push the treatment process control data to the control model of the copper-containing wastewater treatment system in chemical production, and predict the wastewater volume; control the energy of the wastewater recovery equipment based on the wastewater volume to obtain the equipment energy control data.

2. The control method for the control model of the chemical production copper-containing wastewater treatment system according to claim 1 is characterized in that: Step S1 is specifically as follows: Step S11: using the copper-containing wastewater treatment system to collect copper-containing wastewater data from the sedimentation tank; Step S12: performing preliminary purification simulation based on the copper-containing wastewater data of the sedimentation tank to obtain preliminary purification simulation data; Step S13: Identify the change trend of suspended matter concentration of the preliminary purification simulation data; Step S14: When the trend of the change in the suspended matter concentration meets the following conditions at the same time, it is determined to enter the sand filter filtration stage and generate preliminary purification data of copper-containing wastewater: the suspended matter concentration is greater than 50 mg / L, the turbidity is higher than 20 NTU, the particle size is concentrated in the range of 10-100 μm, and the sedimentation rate is lower than 0.1 mm / s, the pH value is in the range of 6.5-8.5, and the conductivity is less than 5000 μS / cm.

3. The control method for the control model of the chemical production copper-containing wastewater treatment system according to claim 1 is characterized in that: The chemical precipitation removal in step S2 includes: When the preliminary purification data of copper-containing wastewater meets the following conditions at the same time, it is determined to enter the chemical precipitation removal stage: The pH value is in the range of 6.5-9.0 and is on an upward trend; The ORP value is in the range of -100mV to +100mV, and the change trend is stable; The copper ion concentration is maintained within the range of 5-50 mg / L for 5 consecutive minutes, and the decreasing rate is less than 1.0 mg / L·min; The online turbidity meter detected that the turbidity value of the solution increased by more than 50 NTU within 3 minutes. At the same time, the particle size analyzer detected the presence of precipitated particles with a particle size greater than 10 μm in the solution, and the particle number concentration exceeded 500 / mL. Through sedimentation rate analysis, it was detected that the sedimentation rate of the precipitated particles was greater than 0.2 mm / s, and the sedimentation trend was stable.

4. The control method for the control model of the chemical production copper-containing wastewater treatment system according to claim 1 is characterized in that: The ion exchange removal in step S2 includes: When the preliminary purification data of copper-containing wastewater meets the following conditions at the same time, it is determined to enter the ion exchange removal stage: The pH value is in the range of 4.0-7.5, and the change trend is stable; The ORP value is maintained within the range of -100mV to +100mV without drastic fluctuations; The copper ion concentration is maintained within the range of 2-20 mg / L for 3 consecutive minutes, and the decreasing rate is greater than 0.5 mg / L·min. At the same time, the current detection value decreases by more than 10% compared with the maximum value of the previous detection cycle; The conductivity sensor detected that the conductivity value dropped by more than 300 μS / cm within 5 minutes, and the saturation monitoring data of the ion exchange resin showed that the exchange capacity was still within the effective range; The online flow meter detected that the water flow rate of the exchange column was stable, and the pressure sensor showed that the inlet and outlet pressure difference was less than 0.05MPa.

5. The control method for the control model of the chemical production copper-containing wastewater treatment system according to claim 1 is characterized in that: The membrane separation and removal in step S2 includes: When the preliminary purification data of copper-containing wastewater meets the following conditions at the same time, it is determined to enter the membrane separation and removal stage: The pH value is in the range of 4.5-8.0, and the fluctuation range is less than 0.5 within 5 minutes; The ORP value is stable between +50mV and +250mV without drastic changes; The copper ion concentration is maintained within the range of 1-10 mg / L for 3 consecutive minutes, and the decreasing rate is greater than 0.6 mg / L·min. At the same time, the current detection value decreases by more than 12% compared with the maximum value of the previous detection cycle; The transmembrane pressure difference is within 0.1-0.3MPa, and the change is less than 10% within 5 minutes; The turbidity of the membrane inlet water was lower than 5NTU for 3 consecutive minutes, and the pollution index was less than 5, indicating that the inlet water quality met the membrane treatment requirements.

6. The control method for the control model of the chemical production copper-containing wastewater treatment system according to claim 1 is characterized in that: Step S3 is specifically as follows: Step S31: Based on the preset wastewater recycling standard, the wastewater data is judged to contain abnormal copper ions, and the abnormal copper ion wastewater data is obtained; Step S32: Detecting the copper ion concentration based on the abnormal wastewater data containing copper ions; Step S33: Automatically controlling the amount of oxidant added during the copper ion removal process based on the copper ion concentration; Step S34: Automatically regulating the amount of chemical precipitation in the copper ion removal process based on the copper ion concentration; Step S35: Integrate the oxidant dosage and the chemical precipitation dosage, and record the integrated dosage for treatment control to obtain treatment process control data.

7. The control method for the control model of the chemical production copper-containing wastewater treatment system according to claim 6 is characterized in that: Step S33 is specifically as follows: Step S331: Calculate the concentration fluctuation rate of the copper ion concentration, and draw a concentration period fluctuation diagram based on the concentration fluctuation rate; identify the period of intense fluctuation in the concentration period fluctuation diagram; monitor the operating state of the stirring motor according to the period of intense fluctuation; Step S332: Identify abnormal fault codes of the stirring motor operation state, including blade abnormal fault codes and coupling abnormal fault codes; Step S333: collecting multiple blade fault signals based on the blade abnormal fault code; and collecting fault signal time based on the multi-blade fault signal statistics sensor; Calculate the propagation time difference of the fault signal collected by the sensor; infer the angle position of the blade based on the propagation time difference; Step S334: Detect blade fracture based on the blade angle position to obtain blade fracture data; Step S335: collecting coupling vibration signals based on the coupling abnormal fault code; collecting torque sensor data based on the coupling abnormal fault code; Step S336: Counting low-frequency enhanced vibration signals based on the coupling vibration signals; and counting torque fluctuation increase data based on the torque sensor data; Loose bolts are determined based on low-frequency enhanced vibration signals and torque fluctuation increase data to obtain coupling looseness data; Step S337: Automatically control the amount of oxidant added in the copper ion removal process based on the blade breakage data and the coupling looseness data.

8. The control method for the control model of the chemical production copper-containing wastewater treatment system according to claim 6 is characterized in that: Step S34 is specifically as follows: Step 341: performing deviation calculation on the copper ion concentration based on a preset copper ion threshold concentration to obtain concentration deviation data; Step 342: Calculate the compensation precipitation dosage using the concentration deviation data; Calculate the precipitation agent injection acceleration rate using the concentration deviation data; Step 343: performing metering pump delivery simulation based on the compensation precipitant dosage and the precipitant delivery acceleration rate to obtain metering pump delivery simulation data; Step 344: performing abnormal leakage determination on the metering pump delivery simulation data based on a preset metering pump delivery threshold to obtain abnormal leakage data; Extract the sealing diaphragm leakage location of abnormal leakage data; Step 345: Detecting diaphragm wear based on the sealing diaphragm leakage position and recording diaphragm wear data; Step 346: dynamically evolving the pump body piston wear crack based on the diaphragm wear data, and predicting the pump body rupture probability based on the pump body piston wear crack; Step 347: Automatically control the dosage of chemical precipitation in the copper ion removal process according to the probability of pump body rupture.

9. The control method for the control model of the chemical production copper-containing wastewater treatment system according to claim 1, characterized in that: Step S4 is specifically as follows: Step S41: Pushing the processing process control data to the control model of the copper-containing wastewater treatment system of chemical production, and predicting the wastewater volume; dividing the recovery value of the wastewater volume to obtain a high wastewater recovery volume and a low wastewater recovery volume; Step S42: Calculating the specific heat capacity of wastewater based on the high wastewater recovery amount; Adjust the heating power of the heater based on the specific heat capacity of the wastewater; Step S43: calculating the recovery rate based on the low wastewater recovery amount; and adjusting the pump running speed by frequency conversion drive based on the recovery rate; Step S44: real-time monitoring of the electric energy consumption of the heater heating power and the pump running speed, and recording the electric energy consumption data; Step S45: adjusting the heater load based on the power consumption data to obtain the heater load data; adjusting the pump load based on the power consumption data to obtain the pump load data; integrating the heater load data and the pump load data to obtain the equipment energy control data.

10. The control method for the control model of the chemical production copper-containing wastewater treatment system according to claim 9, characterized in that: Step S44 is specifically as follows: Step S441: monitor the heating power of the heater in real time, and evaluate the electric energy consumption of the heater based on the heating power of the heater; Step S442: monitor the pump running speed in real time, and evaluate the pump running power consumption based on the pump running speed; Step S443: Calculate the total power consumption according to the power consumption of the heater and the power consumption of the pump operation to obtain power consumption data.

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