Multi-phase collaborative purification intelligent treatment system for heavy metal wastewater

Abstract

The invention provides a multiphase collaborative purification intelligent treatment system for heavy metal wastewater, which relates to the technical field of wastewater treatment and comprises a pretreatment module, a main treatment module, an advanced treatment module, a post-treatment and recycling module, a sludge treatment module and an intelligent control module. The intelligent control module comprises a data acquisition layer, a control layer and a management layer, and intelligent control on each treatment module is realized by monitoring parameters such as pH value, ORP (oxidation-reduction potential), conductivity and heavy metal ion concentration of the wastewater on line. The data acquisition layer acquires processing parameters in real time through various sensors; the control layer executes control logic based on the PLC / DCS and monitors the operation state through SCADA (Supervisory Control And Data Acquisition); the management layer adopts AI algorithms such as a water quality-energy consumption correlation regression model and a time sequence water quality fluctuation learning model to optimize processing parameters. According to the system, the treatment process can be intelligently adjusted according to the characteristics of the wastewater, the maximization of the heavy metal wastewater treatment efficiency, the minimization of chemical consumption and the minimization of energy consumption are realized, and the treatment effect and the system stability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wastewater treatment, in particular to a multi-phase collaborative purification intelligent treatment system for heavy metal wastewater. BACKGROUND

[0002] With the increasing requirements of industrial pollution prevention and control, efficient treatment of heavy metal wastewater has become a key issue in the field of ecological environmental protection, and the core is to achieve deep removal of heavy metal ions and resource utilization of the treatment process. Currently, heavy metal wastewater treatment technology is gradually developing from single process to multi-process collaborative coupling. Through the combination of chemical precipitation, adsorption, biological transformation and other technologies, combined with intelligent control means to optimize operation parameters, it has become an important path to improve treatment efficiency and resource utilization.

[0003] At present, the related technology has made significant progress. CN120208407A discloses a wastewater treatment system and method, which constructs a "wastewater treatment-energy recovery-resource regeneration" closed-loop system through the cooperation of microbial fuel cell and sludge pyrolysis technology. Among them, the anode electroactive bacteria are used to convert organic matter to produce electricity and simultaneously reduce the concentration of pollutants; for the residual sludge containing heavy metals, Thiobacillus ferrooxidans is used for microbial leaching treatment, which can remove more than 90% of Cu / Pb and other heavy metals; the dewatered sludge is pyrolyzed by two-stage pyrolysis (low-temperature dewatering section uses waste heat, high-temperature section adds FeCl3 catalyst) to prepare biochar, realizing heavy metal solidification and resource regeneration, and the waste heat of pyrolysis products can be used for system temperature control and dewatering pretreatment, significantly improving resource utilization.

[0004] CN120229853A proposes an intelligent wastewater treatment system, which realizes step-by-step purification of wastewater through multi-stage treatment modules (physical, chemical, biological, and deep treatment); relies on water quality detection and sorting modules to accurately identify pollution types such as heavy metals by using correlation degree calculation and feature vector matching algorithm; through the dynamic capacity adjustment formula of the intelligent buffer storage module, the chamber is self-adaptively adjusted to avoid secondary reactions caused by mixed storage; combined with the wastewater backflow judgment module to dynamically match the backflow path, improve the treatment specificity and efficiency.

[0005] However, existing technologies still have significant shortcomings: First, traditional treatment processes are less adaptable to fluctuations in water quality. Although CN120208407A achieves microbial leaching removal of heavy metals, the real-time control mechanism for sudden changes in influent heavy metal concentration is still imperfect, easily leading to unstable treatment results. Second, energy consumption and secondary pollution risks are prominent in the sludge treatment process. Although the two-stage pyrolysis technology reduces energy consumption, the intelligent collaborative optimization of existing concentration and dewatering equipment and the pyrolysis process is insufficient. Third, the integration of intelligent control and deep purification needs to be improved. Although the dynamic reflux strategy of CN120229853A optimizes the treatment path, it lacks full-process dynamic simulation and multi-objective optimization of deep heavy metal removal (such as biochar adsorption capacity) and energy recovery (such as syngas power generation). In addition, most systems have not yet achieved real-time closed-loop control of heavy metal removal efficiency, resource recovery rate, and energy consumption, and the collaborative mechanism of each module still needs to be improved.

[0006] Therefore, there is an urgent need for a multiphase synergistic intelligent treatment system for heavy metal wastewater. By integrating biotransformation, pyrolysis resource recovery and intelligent regulation technologies, it can achieve synergistic effects of deep removal of heavy metals, efficient resource recovery and intelligent optimization of the entire process, and provide more efficient and environmentally friendly technical support for the treatment of heavy metal pollution under complex water quality conditions. Summary of the Invention

[0007] To address the aforementioned technical problems, a multiphase synergistic intelligent treatment system for heavy metal wastewater is provided. This technical solution solves the problems of weak adaptability to water quality fluctuations, unstable treatment or waste of reagents due to manual control; membrane fouling affecting lifespan during membrane separation; backwashing relying on experience and lacking intelligent optimization; high energy consumption and easy secondary pollution in sludge treatment; limited resource utilization pathways; lack of real-time monitoring and closed-loop control throughout the entire process; insufficient module collaboration; and difficulty in achieving multi-objective optimization of efficiency, energy consumption, and reagent consumption.

[0008] The technical solution adopted by this invention to solve its technical problem is: to provide a multiphase synergistic purification intelligent treatment system for heavy metal wastewater, comprising: The pretreatment module is used to pretreat heavy metal wastewater to remove some suspended solids and heavy metals. The pretreatment method includes: adjusting the pH value, adding flocculant, and controlling the stirring speed. The main processing module is used to remove heavy metal ions from wastewater under intelligent control by employing at least one of the following methods: chemical precipitation, adsorption, and membrane separation. The deep treatment module is used to start or stop according to the water quality requirements of the main treatment module. When the water quality of the main treatment module does not meet the standards, it further removes residual heavy metals and organic matter. The post-treatment and reuse module is used to further treat the treated heavy metal wastewater that meets the standards. The sludge treatment module is used to treat the sludge generated in each stage. The treatment methods include: thickening, dewatering and drying. The intelligent control module is used to monitor wastewater treatment-related parameters online, control the actions of each module's actuators, monitor alarms in real time, and optimize operating parameters based on AI algorithms.

[0009] Preferably, the intelligent control module includes a data acquisition layer, a control layer, and a management layer; The data acquisition layer monitors wastewater parameters such as pH, ORP, conductivity, heavy metal ion concentration, suspended solids concentration, flow rate, liquid level, temperature, pressure, and sludge-related parameters online through sensors, and the data is processed and uploaded by the data acquisition unit. The control layer receives data from the data acquisition layer, controls the actions of each module actuator through PLC / DCS, and monitors the operating status and issues alarms in real time through SCADA. The management layer optimizes the operating parameters of each module with the goal of maximizing processing efficiency, minimizing reagent consumption, and minimizing energy consumption.

[0010] Preferably, the sensors in the data acquisition layer include a pH value detection sensor, an ORP sensor, a conductivity sensor, an online heavy metal analyzer, a suspended solids concentration detection sensor, a flow sensor, a liquid level sensor, a temperature sensor, a pressure sensor, and a sludge concentration sensor. A pH sensor monitors the pH values ​​of the influent and effluent from each treatment module in real time. An online heavy metal analyzer continuously detects the concentration of soluble heavy metal ions, and a suspended solids concentration sensor acquires the suspended solids content in the wastewater in real time. The data acquisition unit adopts a modular hardware design, including a signal filtering module, an amplification module, and an analog-to-digital conversion module. After processing the analog or digital signals acquired by the sensors, the data is uploaded to the control layer PLC / DCS according to a preset communication protocol. At the same time, it stores the raw data and the processed data. The storage period is configured through the intelligent control module.

[0011] Preferably, the PLC / DCS of the control layer has built-in preset control logic, which is based on preset parameter thresholds of each processing module. The parameter thresholds include: pH adjustment range of the pretreatment module, basic concentration of reagent addition in the main processing module, and initial operating pressure. After receiving real-time parameters, the PLC / DCS compares them with preset thresholds. When the parameters exceed the limits, it generates control commands to drive the actuators to operate. The actuators include acid and alkali dosing pumps, flocculant dosing pumps, membrane module control valves, and electrochemical power controllers. SCADA displays sensor data, actuator status, and module operating parameters through a human-machine interface. When the monitored parameters exceed the preset alarm threshold, it triggers an audible and visual alarm and records the alarm time, type, and associated parameters, while also pushing the alarm to the management level.

[0012] Preferably, the AI ​​algorithm of the management layer includes a water quality-energy consumption correlation regression model and a time-series water quality fluctuation learning model; the water quality-energy consumption correlation regression model takes influent water quality parameters and operating parameters as inputs, and outputs treated water quality parameters and energy and chemical consumption data, which are used to construct the treatment effect models of the pretreatment module and the main treatment module. The time-series water quality fluctuation learning model learns the nonlinear correlation between parameters of each module by analyzing the water quality fluctuation patterns and parameter adjustment effects in historical data. The multi-objective coordinated optimization model built by the management layer is only applicable to the main processing module and the deep processing module in the startup state. Based on real-time influent water quality data and module operation status, it calculates the optimized values ​​of the operating parameters of each module and sends them to the control layer PLC / DCS.

[0013] Preferably, the preprocessing module is intelligently controlled collaboratively by the management layer and the control layer: The management AI algorithm calculates the optimal pH adjustment value, flocculant dosage, and stirring speed based on real-time influent pH value, suspended solids concentration, and heavy metal concentration data, combined with a flocculation reaction kinetic model. The control layer PLC / DCS controls the start-stop duration and frequency of acid / alkali dosing pumps according to the optimal pH adjustment value, so as to stabilize the pH of wastewater within the preset range. At the same time, the flow rate of the dosing pump is adjusted according to the optimized value of the flocculant dosage, and the operation of the stirring device is controlled according to the calculated stirring speed.

[0014] Preferably, when the main processing module uses chemical precipitation, the heavy metal online analyzer in the data acquisition layer detects the concentration of heavy metal ions in the influent in real time, and the pH value detection sensor monitors the pH value in the reaction tank. The management AI algorithm calculates the theoretical dosage of alkaloid and sulfide precipitants based on the influent heavy metal concentration, target removal rate, and pH value of the reaction tank, combined with the chemical equilibrium model of heavy metal hydroxide and sulfide precipitation, and adjusts the dosage according to historical reagent utilization rate. The control layer PLC / DCS receives the corrected dosage signal to control the operation of the precipitant dosing pump, and at the same time controls the mixing intensity of the reaction tank through the stirring device; The data acquisition layer monitors the concentration of heavy metals in the effluent in real time. When the concentration exceeds the target value, the AI ​​algorithm dynamically adjusts the amount of precipitant added next time based on the deviation, forming a closed-loop control.

[0015] Preferably, when the main processing module uses the adsorption method, the data acquisition layer monitors the concentration of heavy metals in the influent, the flow rate, and the inlet and outlet pressure of the adsorption column; The management AI algorithm selects the appropriate adsorbent type based on the influent water quality parameters and adsorbent performance database, and calculates the theoretical dosage and optimal contact time of adsorbent according to the influent flow rate and heavy metal concentration. The control layer controls the operation of the adsorbent dosing equipment according to the dosage parameters and adjusts the flow rate of wastewater in the adsorption column through the flow control valve. When the outlet pressure of the adsorption column increases or the concentration of heavy metals in the effluent reaches the emission standard limit, the AI ​​algorithm determines that the adsorbent is saturated, generates a regeneration or replacement command, and controls the regeneration equipment to start or issues a replacement reminder.

[0016] Preferably, when the main processing module adopts membrane separation, the intelligent control module monitors the inlet and outlet pressures of the membrane module, membrane flux, transmembrane pressure difference, and effluent water quality in real time. The management AI algorithm builds a membrane fouling prediction model based on historical data. This model is a neural network based on time series analysis. The inputs are real-time pressure, flux, temperature and influent suspended solids concentration, and the output is the predicted value of membrane fouling degree. When the predicted value reaches the preset warning threshold, the AI ​​algorithm calculates the optimal backwashing time, pressure and frequency; the control layer PLC / DCS controls the opening and closing of the backwashing component valves and the operation of the backwashing pump according to the algorithm output parameters, and uses ultrafiltration permeate to clean the membrane module; at the same time, the AI ​​algorithm dynamically adjusts the influent flow rate and operating pressure according to the membrane flux change trend, and extends the service life of the membrane module while ensuring the quality of the effluent.

[0017] Preferably, in the intelligent control of the sludge treatment module, the data acquisition layer monitors the sludge concentration and liquid level in the sludge thickening tank and dewatering equipment in real time through sludge concentration sensors and liquid level sensors; The management AI algorithm uses a linear regression model to predict the sludge production rate based on historical data of sludge production and the current operating status of each processing module. The required retention time for thickening is calculated based on sludge concentration monitoring data, and the sludge inlet valve of the thickening tank is controlled to adjust the sludge inlet volume. When the sludge concentration reaches the start-up threshold of the dewatering equipment, the AI ​​algorithm generates dewatering parameters, and the control layer PLC / DCS controls the start-up of the dewatering equipment according to the parameters. Meanwhile, the AI ​​algorithm controls the temperature of the drying equipment and optimizes its operating time based on the sludge drying requirements and energy consumption model.

[0018] The beneficial effects of this invention are as follows: By adopting a three-layer intelligent control system and a multi-phase synergistic purification mechanism, the system response time is shortened from the traditional 10-15 seconds to within 100ms, improving the system's rapid response capability to water quality fluctuations; overall treatment efficiency is increased by 25%, and energy consumption is reduced by 30%, achieving dual optimization of treatment efficiency and energy consumption; reagent consumption is reduced by 20%, achieving precise control of reagent use through a closed-loop correction mechanism for precipitants and a regeneration trigger mechanism for adsorbents; membrane module lifespan is extended by 30%, and a dynamic backwashing strategy based on membrane fouling prediction effectively extends the equipment's service life; the effluent compliance rate is increased to over 99%, and the intelligent start-stop mechanism of the deep treatment unit ensures the stability of the treated water quality. Compared with traditional heavy metal wastewater treatment systems, this invention achieves comprehensive optimization in treatment efficiency, energy consumption, reagent use, and equipment lifespan. Attached Figure Description

[0019] Figure 1 This is a system framework diagram of the present invention; Figure 2 This is a flowchart of the intelligent control closed loop of the present invention. Detailed Implementation

[0020] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0021] Reference Figure 1 and Figure 2 As shown, a multiphase synergistic intelligent treatment system for heavy metal wastewater is presented. This system comprises multiple modules, including a pretreatment module, a main treatment module, an advanced treatment module, a post-treatment and reuse module, a sludge treatment module, and an intelligent control module. These modules work collaboratively to achieve intelligent control of the wastewater treatment process, improve treatment efficiency, reduce energy and reagent consumption, and ensure stable effluent quality that meets standards.

[0022] The pretreatment module is designed for the initial treatment of heavy metal wastewater, removing some suspended solids and heavy metals. Pretreatment methods include pH adjustment, flocculant addition, and stirring speed control. This module works collaboratively through intelligent control at the management and control layers. The management layer's AI algorithm performs calculations based on real-time influent pH, suspended solids concentration, and heavy metal concentration data, combined with a flocculation reaction kinetic model. This model incorporates core parameters such as flocculant diffusion coefficient, particle collision frequency, and floc growth rate. It establishes a concentration-time correlation equation by simulating the contact reaction process between flocculant and suspended solids. Based on this, it calculates the optimal parameters in three steps: First, using the influent pH as a baseline and considering the pH range for heavy metal hydroxide precipitation, an initial adjustment range is determined. For example, for wastewater containing Cr³⁺, the initial pH adjustment range is set to 7.0-8.5. Second, the initial dosage is calculated based on the linear fitting relationship between suspended solids concentration and flocculant dosage. This relationship is established based on operational data from the past six months, expressed as y = 0.02x + 1.5 (where y is the flocculant dosage kg / h and x is the suspended solids concentration mg / L). Third, the stirring speed parameters are optimized using a response surface model of stirring speed and floc particle size. This model covers a stirring speed range of 100-500 r / min and can output the floc particle size distribution at the corresponding stirring speed. Finally, the stirring speed parameters that result in a particle size ≥ 50 μm are selected. The PLC / DCS in the control layer controls the start-up and shutdown duration and frequency of the acid / alkali dosing pumps according to the optimal pH adjustment value to ensure that the wastewater pH remains stable within the preset range. Simultaneously, it adjusts the dosing pump flow rate based on the optimized flocculant dosage and controls the operation of the agitator according to the calculated stirring speed.

[0023] The main treatment module employs at least one method—chemical precipitation, adsorption, or membrane separation—to remove heavy metal ions from wastewater under intelligent control. When the main treatment module uses chemical precipitation, the online heavy metal analyzer in the data acquisition layer monitors the concentration of heavy metal ions in the influent in real time, and the pH sensor monitors the pH value in the reaction tank. The AI ​​algorithm in the management layer calculates the dosage of reagents based on the influent heavy metal concentration, target removal rate, and pH value of the reaction tank, combined with a chemical equilibrium model for the precipitation of heavy metal hydroxides and sulfides. Taking wastewater containing Cu²⁺ as an example, the model includes the equilibrium equation Ksp = [Cu²⁺][OH⁻]². After calculating the [OH⁻] concentration using the real-time pH value, the total amount of OH⁻ required is substituted into the equation to obtain the dosage of alkali. For wastewater containing Hg²⁺, a sulfide precipitation model is used, based on Ksp(HgS) = 4 × 10⁻ 5³ Calculate the theoretical dosage of sodium sulfide. After calculation, the algorithm will adjust the dosage based on historical reagent utilization rates. The correction coefficient is set to 1 divided by the average utilization rate over the past 30 days. When the actual removal rate after a single dosage deviates from the target value by more than 5%, the system will automatically trigger the coefficient update mechanism. The PLC / DCS in the control layer receives the corrected dosage signal and controls the operation of the precipitant dosing pump. At the same time, it controls the mixing intensity of the reaction tank through the stirring device, keeping the stirring power within the range of 1.5-3.0 W / m³. The data acquisition layer monitors the effluent heavy metal concentration in real time. When the effluent concentration exceeds the target value, the AI ​​algorithm dynamically adjusts the next precipitant dosage based on the deviation. The adjustment amount is the product of the deviation value and the proportional coefficient. The proportional coefficient is optimized in real time through a PID control algorithm, with the proportional band set to 0.1-0.5 and the integral time to 30-60 seconds, forming a complete closed-loop control.

[0024] When the main processing module uses adsorption, the data acquisition layer monitors the influent heavy metal concentration, flow rate, and inlet and outlet pressures of the adsorption column. The management layer's AI algorithm selects the appropriate adsorbent type based on influent water quality parameters and an adsorbent performance database. This database covers 12 commonly used adsorbents, including activated carbon, zeolite, chitosan, and nano-titanium dioxide. Each adsorbent has 20 recorded indicators, such as specific surface area (e.g., activated carbon 800-1200 m² / g), adsorption capacity (e.g., zeolite's adsorption capacity for Pb²⁺ is 150-200 mg / g), selectivity coefficient, and regeneration times. The algorithm sorts different adsorbents by calculating their unit mass removal cost (unit cost = (adsorbent unit price + regeneration cost) / total adsorption capacity) and prioritizes the adsorbent with the lowest cost. Simultaneously, the algorithm calculates the theoretical adsorbent dosage and optimal contact time based on the influent flow rate and heavy metal concentration. The dosage calculation is based on the Langmuir adsorption isotherm q=QbC / (1+bC), where Q is the saturated adsorption capacity and b is the adsorption constant, determined experimentally. After calculating the total amount of heavy metals to be removed per unit time using the influent flow rate, the required adsorbent mass is deduced. The contact time is determined by the ratio of the adsorption column volume to the influent flow rate and must be verified by experimental data to ensure an adsorption efficiency ≥90% within this time. For example, when the influent flow rate is 5 m³ / h and the adsorption column volume is 10 m³, the contact time is set to 2 hours. The control layer controls the operation of the adsorbent dosing equipment according to the dosage parameters and adjusts the flow rate of wastewater within the adsorption column through a flow control valve. When the outlet pressure of the adsorption column rises above 0.3 MPa or the heavy metal concentration in the effluent reaches the emission standard limit, the AI ​​algorithm determines that the adsorbent is saturated, generates a regeneration or replacement command, and controls the regeneration equipment to start (e.g., using a 5% hydrochloric acid solution for regeneration, the regeneration cycle is 1 / 3 of the adsorption cycle) or issues a replacement reminder.

[0025] When the main processing module uses membrane separation, the intelligent control module monitors the inlet and outlet pressures of the membrane module, membrane flux, transmembrane pressure difference, and effluent water quality in real time. The management layer's AI algorithm constructs a membrane fouling prediction model based on historical data. This model is a time-series analysis-based neural network using a 3-layer LSTM structure: the input layer contains 8 neurons, corresponding to 8 parameters including real-time pressure, flux, temperature, and influent suspended solids concentration; the hidden layer has 16 neurons using the ReLU activation function; and the output layer has 1 neuron, outputting the predicted membrane fouling level. Model training uses hourly monitoring data from the past 3 months (a total of 2160 samples), iteratively trained using the Adam optimizer with a learning rate of 0.001 and 5000 iterations until the loss function MSE < 0.01. When the predicted value reaches the preset warning threshold (e.g., pollution level ≥ 60%), the AI ​​algorithm calculates the optimal backwashing time, pressure, and frequency. This process is achieved by establishing a multiple regression model between backwashing parameters and membrane flux recovery rate, aiming for a recovery rate ≥ 95% and the lowest energy consumption. The calculated backwashing time is 1-3 min, pressure is 0.15-0.25 MPa, and frequency is 2-4 times / day. The PLC / DCS in the control layer controls the opening and closing of the backwashing component valves and the operation of the backwashing pump according to the algorithm output parameters, using ultrafiltration permeate to clean the membrane module. Simultaneously, the AI ​​algorithm dynamically adjusts the influent flow rate and operating pressure based on the membrane flux change trend. When the flux decline rate is > 5% / h, the operating pressure is reduced in a 0.05 MPa gradient, and the flow rate is reduced by 10% simultaneously, extending the membrane module's service life while ensuring effluent water quality.

[0026] The advanced treatment module is activated or deactivated based on the effluent quality requirements of the main treatment module. When the effluent from the main treatment module fails to meet standards, it further removes residual heavy metals and organic matter. This module employs a combined electrochemical oxidation and nanomaterial adsorption process: the electrochemical oxidation unit is equipped with three switchable electrodes: titanium-based lead dioxide, iron, and aluminum. An AI algorithm automatically switches between these electrodes based on the real-time COD value monitored by the data acquisition layer (titanium-based lead dioxide is selected when COD > 80 mg / L, and iron is selected when COD is 50-80 mg / L). The nanomaterial adsorption unit is filled with two adsorbents: nano-hydroxyapatite and nano-zinc oxide. The algorithm selects the appropriate adsorbent based on the type of residual heavy metal (e.g., nano-hydroxyapatite is selected when Cd²+ is present, and nano-zinc oxide is selected when Ni²+ is present), ensuring that the effluent COD ≤ 50 mg / L and the heavy metal concentration ≤ 0.01 mg / L.

[0027] The post-treatment and reuse module is used to further treat the treated heavy metal wastewater that meets the standards, including disinfection and water softening. Disinfection is achieved through ultraviolet irradiation. The equipment is equipped with adjustable lamps with a power of 30-150W. The system automatically adjusts the lamp power according to the effluent flow rate (30-150W corresponding to a flow rate of 0-50m³ / h) to ensure that the ultraviolet dose is maintained at 30-40mJ / cm². Softening is achieved through sodium ion exchange resin. The regeneration cycle is controlled based on conductivity monitoring data. When the effluent conductivity is >500μS / cm, the regeneration program is started, using 8-10% sodium chloride solution for countercurrent regeneration, with a regeneration time of 2-3 hours.

[0028] The sludge treatment module is used to treat the sludge generated in each stage of the process, including thickening, dewatering, and drying. In the intelligent control of the sludge treatment module, the data acquisition layer monitors the sludge concentration and liquid level in the sludge thickening tank and dewatering equipment in real time using sludge concentration sensors and liquid level sensors. The management layer's AI algorithm uses a linear regression model to predict the sludge generation rate. The input variables of this model include the flocculant dosage in the pretreatment module, the precipitant dosage in the main treatment module, and the system runtime. The model parameters are obtained by fitting the model using the least squares method (e.g., sludge generation rate = 0.05 × flocculant dosage + 0.12 × precipitant dosage + 0.002 × runtime), and the model parameters are updated every 24 hours with newly generated operating data. The required retention time for thickening is calculated based on sludge concentration monitoring data. This time is determined by the ratio of the effective volume of the thickening tank to (sludge inflow rate - supernatant outflow rate), derived from the concentration balance equation. For example, when the effective volume of the thickening tank is 50 m³, the sludge inflow rate is 2 m³ / h, and the supernatant outflow rate is 1.5 m³ / h, the retention time is set to 100 h. The system regulates the sludge inflow rate by controlling the opening of the sludge inlet valve in the thickening tank to maintain a stable retention time. When the sludge concentration reaches the dewatering equipment start-up threshold (e.g., ≥3%), the AI ​​algorithm generates dewatering parameters. Based on the sludge viscosity data, the filter press pressure (0.6-1.2 MPa for a viscosity of 100-500 cP) and the holding time (15-30 min) are selected. The PLC / DCS in the control layer controls the dewatering equipment to start according to the parameters. Meanwhile, the AI ​​algorithm controls the drying equipment based on the sludge drying requirements and energy consumption model. The model takes sludge moisture content (80-95%) and ambient temperature (5-35℃) as input and outputs the optimal drying temperature (60-80℃) and corresponding running time as output. It is derived by fitting the heat conduction equation and energy consumption data. For example, when the moisture content is 90% and the ambient temperature is 25℃, the drying temperature is set to 70℃ and the running time is 4 hours to achieve a balance between energy consumption and drying efficiency.

[0029] The intelligent control module is used to monitor wastewater treatment-related parameters online, control the actions of various module actuators, monitor alarms in real time, and optimize operating parameters based on AI algorithms. The intelligent control module includes a data acquisition layer, a control layer, and a management layer.

[0030] The data acquisition layer uses sensors to monitor wastewater parameters online, including pH, ORP, conductivity, heavy metal ion concentration, suspended solids concentration, flow rate, liquid level, temperature, pressure, and sludge-related parameters. These parameters are then processed and uploaded by the data acquisition unit. The sensors in the data acquisition layer include a pH sensor, an ORP sensor, a conductivity sensor, an online heavy metal analyzer, a suspended solids concentration sensor, a flow sensor, a liquid level sensor, a temperature sensor, a pressure sensor, and a sludge concentration sensor. Specifically, the pH sensor has a measurement range of 0-14 and an accuracy of ±0.01pH, monitoring the pH of the influent and effluent from each treatment module in real time. The online heavy metal analyzer uses anodic stripping voltammetry, with a detection cycle of 10 minutes per measurement and a detection limit of 0.001mg / L, continuously detecting the concentrations of more than 10 soluble heavy metal ions such as Cu²+, Pb²+, and Zn²+. The suspended solids concentration sensor is based on the laser scattering principle, with a measurement range of 0-5000mg / L and an accuracy of ±1%, acquiring the suspended solids content in wastewater in real time. The data acquisition unit adopts a modular hardware design, including a signal filtering module, an amplification module, and an analog-to-digital converter (ADC). The signal filtering module uses a second-order Butterworth low-pass filter with a cutoff frequency of 5Hz to remove high-frequency noise. The amplification module amplifies the mV-level signal output from the sensor to a standard 0-5V signal, with the amplification factor adjustable from 10 to 1000 times. The ADC module is a 16-bit AD converter with a sampling frequency of 1Hz to ensure data acquisition accuracy. The processed signal is uploaded to the PLC / DCS in the control layer according to the Modbus TCP / IP protocol. Both the raw and processed data are stored simultaneously. The storage period is configured through the intelligent control module, with a default retention period of one year. After this period, the data is automatically compressed and archived to the local server.

[0031] The control layer receives data from the data acquisition layer and controls the actuators of each module via PLC / DCS. SCADA monitors the operating status in real time and issues alarms. The PLC / DCS in the control layer has built-in preset control logic, which is based on preset parameter thresholds for each processing module. These thresholds include the pH adjustment range for the pretreatment module (e.g., 6.5-8.5), the basic concentration of reagents added to the main treatment module (e.g., 5% sodium hydroxide, 10% polyaluminum chloride), and the initial operating pressure (e.g., 0.2 MPa for membrane separation, 0.1 MPa for adsorption column). After receiving real-time parameters, the PLC / DCS compares them with the preset thresholds. When a parameter exceeds the limit, it generates a control command to drive the actuators. For example, when the pH is below 6.5, the alkali dosing pump starts and adjusts its frequency proportionally to the deviation (the frequency increases by 5 Hz for every 0.1 pH increase in deviation); when the membrane module inlet pressure exceeds 0.3 MPa, the bypass valve automatically opens to reduce the pressure. The actuators include acid and alkali dosing pumps (flow range 0-50L / h, adjustment accuracy ±1%), flocculant dosing pumps, membrane module control valves (response time <1s), and electrochemical power controllers (output voltage adjustable 0-50V). SCADA displays sensor data, actuator status, and module operating parameters through a human-machine interface, with a data refresh rate of 1 second. The interface includes real-time curves, historical trends, and equipment status diagrams. When monitored parameters exceed preset alarm thresholds, an audible and visual alarm is triggered (alarm sound level ≥85dB, alarm lights use red, yellow, and green to distinguish urgency levels), and the alarm time, type, and associated parameters (accurate to milliseconds) are recorded. Simultaneously, the alarm information is pushed to the management monitoring terminal via industrial Ethernet.

[0032] The management team optimizes the operating parameters of each module with the goals of maximizing treatment efficiency, minimizing reagent consumption, and reducing energy consumption. The AI ​​algorithms used in the management team include a water quality-energy consumption correlation regression model and a time-series water quality fluctuation learning model. The water quality-energy consumption correlation regression model takes influent water quality parameters (such as Cu²⁺ concentration, Zn²⁺ concentration, suspended solids concentration, and COD value) and operating parameters (such as reagent dosage, stirring speed, and membrane operating pressure) as inputs, and outputs treated water quality parameters and energy and reagent consumption data. This model is constructed using a random forest regression algorithm with a training sample size of ≥1000 groups. Hyperparameters such as tree depth (set to 10-20) and the number of split samples per node (set to 5-10) are optimized through 5-fold cross-validation. The model's R² value is ≥0.9, and it is used to construct treatment effect models for the pretreatment and main treatment modules. The time-series water quality fluctuation learning model analyzes the patterns of water quality fluctuations and the effects of parameter adjustments in historical data to learn the nonlinear correlations between parameters of each module. It employs a sliding time window method (window size set to 24 hours) and uses an association rule mining algorithm (minimum support 20%, minimum confidence 80%) to extract strong correlation rules between parameter adjustments and water quality changes. For example, "when the influent Cu²⁺ concentration suddenly increases by 10%, increasing the sodium sulfide dosage by 5% can make the effluent meet the standards." The multi-objective coordinated optimization model constructed by the management layer is only applicable to the main treatment module and the deep treatment module in the startup state. Based on real-time influent water quality data and module operating status, the NSGA-II algorithm is used to calculate the optimized values ​​of the operating parameters of each module. The algorithm population size is set to 100, the number of iterations is 200, and the objective function weights are set at 40% for treatment efficiency, 30% for chemical consumption, and 30% for energy consumption. The optimization results are verified and then sent to the PLC / DCS of the control layer.

[0033] Through the coordinated operation of the above modules, the heavy metal wastewater treatment system provided in this embodiment can realize intelligent control of the wastewater treatment process, improve treatment efficiency, reduce energy consumption and reagent consumption, and ensure that the effluent water quality meets the standards stably.

[0034] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A multiphase synergistic intelligent treatment system for heavy metal wastewater, characterized in that, include: The pretreatment module is used to pretreat heavy metal wastewater to remove some suspended solids and heavy metals. The pretreatment method includes: adjusting the pH value, adding flocculant, and controlling the stirring speed. The main processing module is used to remove heavy metal ions from wastewater under intelligent control by employing at least one of the following methods: chemical precipitation, adsorption, and membrane separation. The deep treatment module is used to start or stop according to the water quality requirements of the main treatment module. When the water quality of the main treatment module does not meet the standards, it further removes residual heavy metals and organic matter. The post-treatment and reuse module is used to further treat the treated heavy metal wastewater that meets the standards. The sludge treatment module is used to treat the sludge generated in each stage. The treatment methods include: thickening, dewatering and drying. The intelligent control module is used to monitor wastewater treatment-related parameters online, control the actions of each module's actuators, monitor alarms in real time, and optimize operating parameters based on AI algorithms.

2. The intelligent treatment system for multiphase synergistic purification of heavy metal wastewater according to claim 1, characterized in that, The intelligent control module includes a data acquisition layer, a control layer, and a management layer; The data acquisition layer monitors wastewater parameters such as pH, ORP, conductivity, heavy metal ion concentration, suspended solids concentration, flow rate, liquid level, temperature, pressure, and sludge-related parameters online through sensors, and the data is processed and uploaded by the data acquisition unit. The control layer receives data from the data acquisition layer, controls the actions of each module actuator through PLC / DCS, and monitors the operating status and issues alarms in real time through SCADA. The management layer optimizes the operating parameters of each module with the goal of maximizing processing efficiency, minimizing reagent consumption, and minimizing energy consumption.

3. The intelligent treatment system for multiphase synergistic purification of heavy metal wastewater according to claim 2, characterized in that, The sensors in the data acquisition layer include a pH sensor, an ORP sensor, a conductivity sensor, an online heavy metal analyzer, a suspended solids concentration sensor, a flow sensor, a liquid level sensor, a temperature sensor, a pressure sensor, and a sludge concentration sensor. A pH sensor monitors the pH values ​​of the influent and effluent from each treatment module in real time. An online heavy metal analyzer continuously detects the concentration of soluble heavy metal ions, and a suspended solids concentration sensor acquires the suspended solids content in the wastewater in real time. The data acquisition unit adopts a modular hardware design, including a signal filtering module, an amplification module, and an analog-to-digital conversion module. After processing the analog or digital signals acquired by the sensors, the data is uploaded to the control layer PLC / DCS according to a preset communication protocol. At the same time, it stores the raw data and the processed data. The storage period is configured through the intelligent control module.

4. The intelligent treatment system for multiphase synergistic purification of heavy metal wastewater according to claim 2, characterized in that, The PLC / DCS of the control layer has built-in preset control logic, which is based on preset parameter thresholds of each processing module. The parameter thresholds include: pH adjustment range of the pretreatment module, basic concentration of reagent addition in the main processing module, and initial operating pressure. After receiving real-time parameters, the PLC / DCS compares them with preset thresholds. When the parameters exceed the limits, it generates control commands to drive the actuators to operate. The actuators include acid and alkali dosing pumps, flocculant dosing pumps, membrane module control valves, and electrochemical power controllers. SCADA displays sensor data, actuator status, and module operating parameters through a human-machine interface. When the monitored parameters exceed the preset alarm threshold, it triggers an audible and visual alarm and records the alarm time, type, and associated parameters, while also pushing the alarm to the management level.

5. The intelligent treatment system for multiphase synergistic purification of heavy metal wastewater according to claim 2, characterized in that, The AI ​​algorithm of the management system includes a water quality-energy consumption correlation regression model and a time-series water quality fluctuation learning model. The water quality-energy consumption correlation regression model takes influent water quality parameters and operating parameters as inputs and outputs treated water quality parameters and energy and chemical consumption data, which are used to construct the treatment effect models of the pretreatment module and the main treatment module. The time-series water quality fluctuation learning model learns the nonlinear correlation between parameters of each module by analyzing the water quality fluctuation patterns and parameter adjustment effects in historical data. The multi-objective coordinated optimization model built by the management layer is only applicable to the main processing module and the deep processing module in the startup state. Based on real-time influent water quality data and module operation status, it calculates the optimized values ​​of the operating parameters of each module and sends them to the control layer PLC / DCS.

6. The intelligent treatment system for multiphase synergistic purification of heavy metal wastewater according to claim 2, characterized in that, The preprocessing module is intelligently controlled collaboratively by the management and control layers. The management AI algorithm calculates the optimal pH adjustment value, flocculant dosage, and stirring speed based on real-time influent pH value, suspended solids concentration, and heavy metal concentration data, combined with a flocculation reaction kinetic model. The control layer PLC / DCS controls the start-stop duration and frequency of acid / alkali dosing pumps according to the optimal pH adjustment value, so as to stabilize the pH of wastewater within the preset range. At the same time, the flow rate of the dosing pump is adjusted according to the optimized value of the flocculant dosage, and the operation of the stirring device is controlled according to the calculated stirring speed.

7. The intelligent treatment system for multiphase synergistic purification of heavy metal wastewater according to claim 2, characterized in that, When the main processing module uses chemical precipitation, the heavy metal online analyzer in the data acquisition layer detects the concentration of heavy metal ions in the influent in real time, and the pH value detection sensor monitors the pH value in the reaction tank. The management AI algorithm calculates the theoretical dosage of alkaloid and sulfide precipitants based on the influent heavy metal concentration, target removal rate, and pH value of the reaction tank, combined with the chemical equilibrium model of heavy metal hydroxide and sulfide precipitation, and adjusts the dosage according to historical reagent utilization rate. The control layer PLC / DCS receives the corrected dosage signal to control the operation of the precipitant dosing pump, and at the same time controls the mixing intensity of the reaction tank through the stirring device; The data acquisition layer monitors the concentration of heavy metals in the effluent in real time. When the concentration exceeds the target value, the AI ​​algorithm dynamically adjusts the amount of precipitant added next time based on the deviation, forming a closed-loop control.

8. The intelligent treatment system for multiphase synergistic purification of heavy metal wastewater according to claim 2, characterized in that, When the main processing module uses the adsorption method, the data acquisition layer monitors the concentration of heavy metals in the influent, the flow rate, and the inlet and outlet pressures of the adsorption column. The management AI algorithm selects the appropriate adsorbent type based on the influent water quality parameters and adsorbent performance database, and calculates the theoretical dosage and optimal contact time of adsorbent according to the influent flow rate and heavy metal concentration. The control layer controls the operation of the adsorbent dosing equipment according to the dosage parameters and adjusts the flow rate of wastewater in the adsorption column through the flow control valve. When the outlet pressure of the adsorption column increases or the concentration of heavy metals in the effluent reaches the emission standard limit, the AI ​​algorithm determines that the adsorbent is saturated, generates a regeneration or replacement command, and controls the regeneration equipment to start or issues a replacement reminder.

9. The intelligent treatment system for multiphase synergistic purification of heavy metal wastewater according to claim 2, characterized in that, When the main processing module adopts membrane separation, the intelligent control module monitors the inlet and outlet pressures of the membrane module, membrane flux, transmembrane pressure difference, and effluent water quality in real time. The management AI algorithm builds a membrane fouling prediction model based on historical data. This model is a neural network based on time series analysis. The inputs are real-time pressure, flux, temperature and influent suspended solids concentration, and the output is the predicted value of membrane fouling degree. When the predicted value reaches the preset warning threshold, the AI ​​algorithm calculates the optimal backwashing time, pressure and frequency; the control layer PLC / DCS controls the opening and closing of the backwashing component valves and the operation of the backwashing pump according to the algorithm output parameters, and uses ultrafiltration permeate to clean the membrane module; at the same time, the AI ​​algorithm dynamically adjusts the influent flow rate and operating pressure according to the membrane flux change trend, and extends the service life of the membrane module while ensuring the quality of the effluent.

10. The intelligent treatment system for multiphase synergistic purification of heavy metal wastewater according to claim 2, characterized in that, In the intelligent control of the sludge treatment module, the data acquisition layer monitors the sludge concentration and liquid level in the sludge thickening tank and dewatering equipment in real time through sludge concentration sensors and liquid level sensors. The management AI algorithm uses a linear regression model to predict the sludge production rate based on historical data of sludge production and the current operating status of each processing module. The required retention time for thickening is calculated based on sludge concentration monitoring data, and the sludge inlet valve of the thickening tank is controlled to adjust the sludge inlet volume. When the sludge concentration reaches the start-up threshold of the dewatering equipment, the AI ​​algorithm generates dewatering parameters, and the control layer PLC / DCS controls the start-up of the dewatering equipment according to the parameters. Meanwhile, the AI ​​algorithm controls the temperature of the drying equipment and optimizes its operating time based on the sludge drying requirements and energy consumption model.

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