Intelligent cooperative treatment and resource recovery system for mine wastewater

Abstract

The invention relates to the field of mine wastewater treatment in environmental engineering, and discloses an intelligent cooperative treatment and resource recovery system for mine wastewater, which comprises a central controller adopting a double-layer cooperative intelligent control architecture, the bottom layer real-time fuzzy PID control module adjusts control parameters according to a prediction result, the composite biological-electrochemical reactor adopts a vertical layered design, and the central controller dynamically adjusts the duty ratio of pulse voltage of the electrochemical treatment area by monitoring the metabolic state of the biological treatment area, so that the electrochemical treatment effect is improved. And the modularized sludge dewatering-metal recovery unit passes through a high-pressure membrane filter press and a selective adsorption column. Through intelligent predictive control and multi-process cooperation, the self-adaptive capacity and the automation level of the system are improved, and energy conservation and resource closed-loop recovery are completed while efficient removal of pollutants is achieved.

Description

Technical Field

[0001] This invention relates to the field of mine wastewater treatment in environmental engineering, and in particular to an intelligent collaborative treatment and resource recovery system for mine wastewater. Background Technology

[0002] Mine wastewater is one of the main sources of pollution generated during mineral resource development. Its quality and quantity exhibit significant dynamic fluctuations and typically contain a variety of complex pollutants, including suspended solids, organic matter, and heavy metal ions. Direct discharge without treatment poses a serious threat to the ecological environment.

[0003] Currently, the main methods for treating mine wastewater include biological treatment and physiochemical treatment. Biological treatment has a good degradation effect on biodegradable organic matter, but its ability to treat recalcitrant organic matter and heavy metal ions is limited. Physicochemical treatment methods, such as coagulation sedimentation, adsorption, and advanced oxidation technologies such as electrochemistry, can effectively remove specific pollutants, but they are often accompanied by problems such as high reagent consumption, high operating costs, or secondary pollution. Therefore, combined processes that integrate biological and physiochemical treatment have become a research direction in this field.

[0004] Existing systems generally employ control methods based on fixed parameters or simple feedback, resulting in a significant lag in control strategy response compared to actual changes in pollution load. This makes it difficult to achieve precise control of energy consumption and reagent dosage while ensuring stable effluent quality compliance. Furthermore, regarding the internal synergy of composite processes, existing combinations are mostly simple series connections of different treatment units, with each unit's operating parameters operating independently. This fails to establish a dynamic correlation and functional coupling between the biodegradation and physicochemical treatment processes. When the biological treatment system is inhibited by the accumulation of recalcitrant intermediates, subsequent treatment units cannot provide timely reinforcement, thus limiting the overall treatment efficiency and operational stability of the system. Simultaneously, existing technologies do not adequately address the byproducts generated during treatment. Large amounts of sludge with high water content and difficult disposal, as well as valuable metals dissolved in the filtrate, fail to be effectively utilized, resulting in resource waste and environmental pressure from secondary treatment. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent collaborative treatment and resource recovery system for mine wastewater, which solves the problems of poor system adaptability and high energy consumption caused by lagging control strategies and lack of predictive ability for water quality fluctuations in the prior art, as well as insufficient treatment efficiency and stability caused by lack of dynamic coordination between treatment units.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] The intelligent collaborative treatment and resource recovery system for mine wastewater includes:

[0008] Distributed sensor networks are used to collect process parameters of the system.

[0009] A composite bio-electrochemical reactor is used to purify mine wastewater and generate a water-sludge mixture.

[0010] A modular sludge dewatering-metal recovery unit is used for solid-liquid separation and resource recovery of the water-sludge mixture;

[0011] The central controller is connected to the distributed sensor network, the composite bio-electrochemical reactor, and the modular sludge dewatering-metal recovery unit, respectively, and is used to receive the process parameters and generate control commands to drive the actuators in the system based on the process parameters.

[0012] As a further limitation of the technical solution of the present invention, the central controller adopts a two-layer collaborative intelligent control architecture, which includes a high-level predictive optimization layer and a low-level real-time adjustment layer. The high-level predictive optimization layer includes a machine learning prediction module. This module receives influent water quality parameters collected by the distributed sensor network and constructs a feature vector. Furthermore, a water quality prediction model based on support vector machine regression is used to generate predicted values ​​for key water quality parameters after one or more future time steps. The regression prediction function of this model can be expressed as:

[0013] ;

[0014] in:

[0015] The input feature vector;

[0016] The number of support vectors;

[0017] For Lagrange multipliers;

[0018] For kernel functions;

[0019] These are support vectors;

[0020] This is a bias term.

[0021] This predicted value is used to dynamically adjust the baseline values ​​of the control parameters of the underlying real-time adjustment layer.

[0022] The underlying real-time adjustment layer includes a real-time fuzzy PID control module. This module receives the setpoints from the process. Process measurement values ​​fed back from sensors Calculate at discrete time error With error change rate

[0023] ;

[0024] ;

[0025] in, For discrete time The set value; For discrete time Process measurement values; The sampling period is For the previous discrete time... The error.

[0026] The real-time fuzzy PID control module calculates the adjustment amounts of the three parameters—proportional, integral, and derivative—based on a preset fuzzy rule base. The PID parameters will be updated based on this adjustment and the baseline parameter values ​​determined by the machine learning prediction module.

[0027] ;

[0028] ;

[0029] in, The machine learning prediction module determines the outcome based on the prediction results. Real-time adjusted parameter baseline values.

[0030] This module ultimately uses an incremental PID algorithm to calculate the control output. :

[0031] ;

[0032] in: For discrete time Updated proportional, integral, and differential parameters; For discrete time The controller outputs digital signals. For the previous discrete time... The error, For the previous discrete time... The error.

[0033] As a further limitation of the technical solution of the present invention, the composite bio-electrochemical reactor adopts an integrated, vertically layered design, integrating a biological treatment zone and an electrochemical treatment zone from top to bottom.

[0034] The central controller controls the composite bio-electrochemical reactor using a pulse-based synergistic enhancement method based on microbial activity. This method compares the measured values ​​of redox potential and dissolved oxygen, characterizing the macroscopic metabolic activity of the microbial community in the biological treatment zone, obtained from the distributed sensor network, with a preset "high-activity zone." When the measured values ​​deviate from this zone, the duty cycle of the pulse voltage applied to the electrodes in the electrochemical treatment zone is increased; when the measured values ​​are within this zone, the duty cycle of the pulse voltage is decreased. It can be represented as:

[0035] ;

[0036] in:

[0037] The instantaneous voltage applied to the electrode;

[0038] This represents the peak value of the pulse voltage.

[0039] For at any time The pulse duty cycle, the value of which is dynamically adjusted by the central controller 20,

[0040] The pulse period;

[0041] It is a positive integer.

[0042] As a further limitation of the technical solution of the present invention, the process parameters collected by the distributed sensor network include external water quality parameters and internal state parameters. The internal state parameters are obtained by an intermediate layer sensor array located at the interface between the biological treatment zone and the electrochemical treatment zone, and the intermediate layer sensor array includes at least a redox potential sensor and a dissolved oxygen sensor.

[0043] As a further limitation of the technical solution of the present invention, the modular sludge dewatering-metal recovery unit physically includes a high-pressure diaphragm filter press and a selective adsorption column. The selective adsorption column is filled with thiol-functionalized mesoporous silica gel as an adsorbent to selectively capture divalent heavy metal ions in the filtrate.

[0044] In summary, the present invention has at least one of the following beneficial technical effects:

[0045] 1. This invention combines the predictive capabilities of machine learning with the real-time adjustment capabilities of fuzzy PID control by setting up a dual-layer collaborative intelligent control architecture. This enables the system to predict fluctuations in influent water quality and adjust the control strategy in advance, realizing a shift from passive response to proactive prediction. This significantly improves the system's adaptability and automation level, ensures the stability of effluent water quality, and reduces system energy consumption.

[0046] 2. This invention designs an integrated, vertically layered composite bio-electrochemical reactor and adopts a pulse synergistic enhancement control method based on microbial activity to establish a dynamic feedback closed loop between the metabolic state of the biological treatment zone and the oxidation intensity of the electrochemical treatment zone. This achieves deep synergy between biodegradation and electrochemical oxidation processes, effectively improves the removal efficiency of recalcitrant pollutants, and maintains the long-term stable operation of the treatment system.

[0047] 3. By configuring a modular unit including a high-pressure diaphragm filter press and a selective adsorption column, this invention not only achieves deep dewatering of sludge, facilitating subsequent resource utilization, but also realizes efficient recovery of valuable metals in the filtrate through specific adsorption materials, forming a closed-loop path for wastewater treatment and resource recovery, and improving the overall economic benefits of the system. Attached Figure Description

[0048] Figure 1 This is a system framework diagram of the present invention;

[0049] Figure 2 This is a schematic diagram of the method flow of the present invention.

[0050] Among them, 10 is a distributed sensor network; 20 is a central controller; 30 is a composite bio-electrochemical reactor; and 40 is a modular sludge dewatering-metal recovery unit. Detailed Implementation

[0051] The following is in conjunction with the appendix Figure 1 -Appendix Figure 2 The present invention will be further described in detail below.

[0052] This invention provides an intelligent collaborative treatment and resource recovery system for mine wastewater.

[0053] See attached document Figure 1 , Figure 1 This is a structural block diagram of a mine wastewater intelligent collaborative treatment and resource recycling system according to an embodiment of the present invention.

[0054] The intelligent collaborative treatment and resource recovery system for mine wastewater provided in this embodiment of the invention includes the following overall structure: a distributed sensor network 10, a central controller 20, a composite bio-electrochemical reactor 30, and a modular sludge dewatering-metal recovery unit 40.

[0055] In the interconnection of the various parts of the system, a data communication connection is established between the distributed sensor network 10 and the central controller 20. This data communication connection can be wired, such as via Industrial Ethernet, or wireless, such as via Industrial Wireless Network Protocol. Its function is to ensure that all process parameters collected by the distributed sensor network 10 can be transmitted to the central controller 20 without delay and completely.

[0056] The central controller 20 establishes control signal connections with multiple actuators within the composite bio-electrochemical reactor 30 and the modular sludge dewatering-metal recovery unit 40. These actuators are specific devices for process control, and their implementations include, but are not limited to: water pumps for regulating wastewater flow, aerators for controlling dissolved oxygen concentration in the biological treatment zone, pulse power supplies for providing energy to the electrochemical reactions, and electric or pneumatic valves for switching fluid paths. Control commands output by the central controller 20 drive the operation of these actuators through this connection.

[0057] In the system's material flow direction, the mine wastewater to be treated enters through the system inlet, and its flow path is set to sequentially pass through the system's core treatment unit. The wastewater is first pumped into the composite bio-electrochemical reactor 30 for pollutant purification. The resulting water-sludge mixture then enters the modular sludge dewatering-metal recovery unit 40 for final solid-liquid separation and resource recovery. The purified water separated by the modular sludge dewatering-metal recovery unit 40 is discharged from the system outlet, while the separated solids are collected for subsequent resource recovery.

[0058] The complete workflow of this system can be achieved through the following steps:

[0059] S101: The system performs multi-dimensional state perception. The distributed sensor network 10 comprehensively collects data on the system's operating status. This collection is continuous and real-time, and its deployment covers the system's inlet, final outlet, and key process points inside the composite bio-electrochemical reactor 30, thereby obtaining multi-dimensional process parameters including influent water quality, effluent water quality, and the state of the reactor's internal microenvironment.

[0060] S102: The system performs two-layer intelligent decision-making. The central controller 20 receives and processes real-time data transmitted from the distributed sensor network 10. The machine learning prediction module inside the central controller 20 predicts future water quality trends based on historical and current data. Simultaneously, its real-time fuzzy PID control module combines the prediction results with real-time process parameters, and generates and outputs precise control commands for each actuator in the system through internal control algorithms.

[0061] S103: The system performs pulsed synergistic treatment. The composite bio-electrochemical reactor 30 receives the wastewater to be treated and performs purification operations according to the control commands issued by the central controller 20. Inside the reactor, through the dynamic synergistic effect of the upper biological treatment and the lower electrochemical pulse treatment, the effective degradation of various pollutants in the wastewater is achieved.

[0062] S104: The system implements modular resource recovery. The water-sludge mixture generated after treatment by the composite bio-electrochemical reactor 30 is transported to the modular sludge dewatering-metal recovery unit 40. This unit separates the mixture into filtrate and dewatered sludge through high-pressure solid-liquid separation. The filtrate flows through an internal selective adsorption column to recover dissolved valuable metals, while the dewatered sludge is collected as a solid product and used to prepare building materials, thereby achieving closed-loop resource utilization of system waste.

[0063] The function of the distributed sensor network 10 is to acquire comprehensive process parameters for the entire intelligent collaborative treatment and resource recovery system for mine wastewater. In practice, these process parameters are divided into two categories: external water quality parameters and internal state parameters.

[0064] To acquire external water quality parameters, the network includes a set of inlet sensors at the system inlet and a set of outlet sensors at the system outlet. Each set of sensors specifically includes: a pH sensor for measuring the acidity or alkalinity of the water, a turbidity sensor for measuring the turbidity, a heavy metal concentration sensor for measuring the concentration of specific heavy metal ions, and an electromagnetic flowmeter for measuring wastewater flow rate. Data collected by the inlet sensor set characterizes the quality of the raw wastewater and serves as input for predictive control by the central controller 20; data collected by the outlet sensor set is used to monitor the final treatment effect of the system in real time. One embodiment of the heavy metal concentration sensor is a sensor based on an ion-selective electrode.

[0065] To acquire internal state parameters, the network specifically places an intermediate sensor array at the interface between the upper biological treatment zone and the lower electrochemical treatment zone inside the composite bio-electrochemical reactor 30. This sensor array includes at least one oxidation-reduction potential (ORP) sensor for measuring the oxidation-reduction potential of the microenvironment and one dissolved oxygen (DO) sensor for measuring the dissolved oxygen concentration in the water. This deployment location is crucial for realizing the synergistic control method of this invention. The ORP and DO values ​​measured by this sensor array directly reflect the macroscopic metabolic activity state of the microbial community in the biological treatment zone. Therefore, these measurements are used as core indicators for determining the efficiency and state of the biodegradation process and as direct inputs to subsequent pulsed synergistic control strategies.

[0066] In the data transmission implementation, all the sensors collect analog signals. These analog signals are first processed by an analog-to-digital (A / D) conversion module to convert them into digital signals. Subsequently, these digital signals are uniformly formatted and packaged into data frames containing the unique identifier of each sensor, the precise measurement value, and the acquisition timestamp. Finally, these data frames are transmitted to the central controller 20 in real time and continuously through the communication module, providing a data foundation for subsequent intelligent decision-making.

[0067] The central controller 20 is the core of decision-making and control in this embodiment of the invention. Its function is to receive and process process parameters from the distributed sensor network 10, and generate control commands to drive each actuator based on these parameters. In one specific embodiment, the hardware entity of the central controller 20 can be an industrial personal computer (IPC) or a programmable logic controller (PLC), and its internal components include at least: a microprocessor for running control algorithms, a memory for storing data and programs, an analog-to-digital (A / D) conversion module for connecting to the sensor network, a digital-to-analog (D / A) conversion module for connecting to the actuators, and a communication interface module for data interaction.

[0068] The core of the central controller 20 lies in its control system, which employs a two-layer collaborative intelligent control architecture. Functionally, this architecture is divided into a high-level predictive optimization layer and a low-level real-time adjustment layer. The high-level predictive optimization layer is implemented by a machine learning prediction module, whose role is to provide the system with forward-looking predictions of operating conditions; the low-level real-time adjustment layer is implemented by a real-time fuzzy PID control module, whose role is to perform precise dynamic adjustments based on real-time feedback.

[0069] The specific working steps of this two-layer collaborative intelligent control architecture are as follows:

[0070] S201: The machine learning prediction module uses historical operational data stored in memory to generate a water quality prediction model through offline training. In this embodiment, the model is a Support Vector Machine (SVM). Regression Model. During system operation, this module uses the influent water quality parameters collected in real time by the distributed sensor network 10, such as pH value, turbidity, heavy metal concentration, and flow rate, to construct a feature vector. This is used as the input to the model. The model's output is... These are predicted values ​​of key water quality parameters after one or more future time steps. The regression prediction function of this model can be expressed as:

[0071] ;

[0072] in:

[0073] The input feature vector;

[0074] The number of support vectors;

[0075] For Lagrange multipliers;

[0076] In one specific embodiment of this technical solution, a radial basis function kernel function is used, the expression of which is: , where γ is the kernel parameter, which is a positive number pre-set through methods such as cross-validation;

[0077] These are support vectors;

[0078] This is a bias term.

[0079] This prediction result will be used to dynamically adjust the baseline values ​​of the control parameters of the underlying real-time fuzzy PID control module.

[0080] S202: The real-time fuzzy PID control module receives the setpoints from the process. Process measurement values ​​fed back from sensors And calculate at discrete time error With error change rate As its input:

[0081] ;

[0082] ;

[0083] in, For discrete time The set value; For discrete time Process measurement values; The sampling period is For the previous discrete time... The error.

[0084] S203: The central controller internally performs fuzzification, fuzzy inference, and defuzzification processes, calculating the adjustment amounts of the three parameters—proportional, integral, and derivative—based on a preset fuzzy rule base. The PID parameters will be updated based on this adjustment and the baseline parameter values ​​determined by the upper-level machine learning prediction module.

[0085] ;

[0086] ;

[0087] in, The machine learning prediction module determines the outcome based on the prediction results. The parameter reference value is adjusted in real time. This structure enables PID control to have predictive feedforward regulation capability.

[0088] S204: The machine learning prediction module ultimately uses an incremental PID algorithm to calculate the control output. :

[0089] ;

[0090] in: For discrete time Updated proportional, integral, and differential parameters; For discrete time The controller outputs digital signals. For the previous discrete time... The error, For the previous discrete time... The error is measured. After being processed by a digital-to-analog converter module, the signal is converted into a standard industrial signal, which is used to drive the corresponding actuator.

[0091] See attached document Figure 1 -Appendix Figure 2 The composite bio-electrochemical reactor 30 is described in detail. This reactor is the core unit for wastewater purification treatment in the embodiments of the present invention.

[0092] The composite bio-electrochemical reactor 30 adopts an integrated, vertically layered design in its physical structure. This design integrates the biological treatment zone and the electrochemical treatment zone from top to bottom within a single reactor shell. To effectively prevent the upward diffusion of hydroxyl radicals generated in the electrochemical treatment zone into the biological treatment zone, which may cause activity inhibition, a physical isolation-quenching layer is specifically provided between the biological treatment zone and the electrochemical treatment zone in this technical solution. The provision of this layer ensures the close spatial coupling and independent, stable operation of the two treatment methods: biodegradation and electrochemical oxidation.

[0093] The biological treatment zone is located in the upper layer of the reactor. This zone is filled with a polyvinyl alcohol-activated carbon composite carrier (PVA-AC). One method of preparing this composite carrier involves mixing a polyvinyl alcohol solution with activated carbon powder and then physically cross-linking it using a freeze-thaw process. This carrier has a large specific surface area due to its porous structure, and its hydrophilic groups facilitate the attachment, biofilm formation, and growth of specialized microbial communities, resulting in a highly active biofilm.

[0094] The physical isolation-quenching layer is located below the biological treatment zone and above the electrochemical treatment zone. In one embodiment, this layer consists of a porous support plate made of inert material and a layer of granular activated carbon filled thereon. The porous support plate maintains hydraulic connectivity from top to bottom while forming a physical barrier, significantly reducing the intensity of molecular diffusion and micro-eddies. The granular activated carbon layer acts as a free radical quencher, rapidly consuming any residual hydroxyl radicals that may penetrate the physical barrier.

[0095] The electrochemical treatment zone is located in the lower layer of the reactor, below the physical isolation-quenching layer. A three-dimensional graphene electrode is disposed within this zone as both the anode and cathode. One method of preparing this electrode involves growing a graphene layer on a nickel foam framework using chemical vapor deposition, followed by the removal of the nickel framework. This electrode exhibits high conductivity and a large electrochemically active surface area; under an applied electric field, its anode surface can efficiently catalyze the generation of highly oxidizing hydroxyl radicals (·OH) from water molecules.

[0096] Given the extremely short lifespan of hydroxyl radicals under the pulsed electrochemical treatment mode, the physical isolation-quenching layer ensures that the electrochemical treatment process does not adversely affect the upper biological treatment zone. This guarantees the efficient and stable synergy of the two treatment methods, allowing the products of the upper biological treatment to directly become the target of the lower electrochemical treatment, thus forming a synergistic treatment system.

[0097] The biological treatment zone is located in the upper layer of the reactor. This zone is filled with a polyvinyl alcohol-activated carbon composite carrier (PVA-AC). One method of preparing this composite carrier involves mixing a polyvinyl alcohol solution with activated carbon powder and then physically cross-linking it using a freeze-thaw process. This carrier has a large specific surface area due to its porous structure, and its hydrophilic groups facilitate the attachment, biofilm formation, and growth of specialized microbial communities, resulting in a highly active biofilm.

[0098] The electrochemical treatment zone is located in the lower layer of the reactor. A three-dimensional graphene electrode is disposed within this zone as both the anode and cathode. One method of preparing this electrode involves growing a graphene layer on a nickel foam framework using chemical vapor deposition, followed by the removal of the nickel framework. This electrode exhibits high conductivity and a large electrochemically active surface area; under an applied electric field, its anode surface can efficiently catalyze the generation of highly oxidizing hydroxyl radicals (·OH) from water molecules.

[0099] The core operating mechanism of the composite bio-electrochemical reactor 30 is a pulsed synergistic enhancement method based on microbial activity. This method is executed by the central controller 20 and is implemented through the following steps:

[0100] S301: The wastewater to be treated flows from top to bottom through the composite bio-electrochemical reactor 30. In the biological treatment zone, biodegradable organic matter in the wastewater is degraded by microorganisms on the biofilm. The central controller 20 continuously and in real time receives and analyzes the measured values ​​of oxidation-reduction potential (ORP) and dissolved oxygen (DO) from the intermediate layer sensor array deployed at the interface between the biological and electrochemical treatment zones.

[0101] S302: The central controller 20 compares the real-time ORP and DO measurements with a pre-set "high-activity state interval" that characterizes the microbial community in a highly efficient degradation state to determine the current macro-metabolic state of the biological treatment zone.

[0102] S303: When both ORP and DO measurements are within the "high-activity state range," the central controller 20 determines that the current biodegradation process is in a highly efficient and stable state. At this time, the controller executes an energy-saving operation strategy, i.e., outputs control commands to reduce the duty cycle of the pulse voltage applied to the three-dimensional graphene electrode in the lower electrochemical treatment zone. .

[0103] S304: When the measured values ​​of ORP or DO deviate from the "high activity state range," the central controller 20 determines that the biodegradation process is inhibited or that recalcitrant intermediate metabolites have accumulated. At this time, the controller executes an enhanced oxidation strategy, initiating a high-intensity electrochemical oxidation shock. To achieve the required duty cycle... For precise dynamic adjustment, the central controller 20 adopts a proportional-integral (PI) control algorithm.

[0104] First, define a comprehensive state error. This error is used to quantify the degree to which the current system state deviates from the high-activity state range. The error can be the maximum of the normalized errors of the ORP and DO measurements deviating from their respective preset ranges.

[0105] Subsequently, the controller calculates the duty cycle according to the following PI control law. :

[0106] ;

[0107] pulse voltage It can be represented as:

[0108] ;

[0109] in:

[0110] The instantaneous voltage applied to the electrode;

[0111] This represents the peak value of the pulse voltage.

[0112] For at any time The pulse duty cycle, the value of which is dynamically calculated by the PI control algorithm.

[0113] This is the baseline minimum duty cycle under the energy-saving operation strategy;

[0114] and The pre-set proportional and integral gain coefficients;

[0115] For at any time The overall state error;

[0116] This is the cumulative integral term of the overall state error over time;

[0117] The pulse period;

[0118] It is a positive integer.

[0119] The central controller 20 will calculate Value limited to Within the interval, The preset maximum duty cycle (its value is no greater than 1) is used to prevent the output of the central controller 20 from saturating. Proportional term. This ensures that the increment of the duty cycle is proportional to the degree of state deviation, achieving rapid response; the integral term This is used to eliminate steady-state errors, ensuring that the system can eventually be driven back to the high-activity state range. Through this algorithm, precise, closed-loop feedback control of the electrochemical oxidation intensity is achieved, creating a favorable microenvironment for the recovery of microbial activity in the upper biological treatment zone, and realizing dynamic stability and rapid self-adjustment of the overall efficiency of the treatment system.

[0120] By increasing the duty cycle This increases the instantaneous generation of hydroxyl radicals, thereby rapidly decomposing inhibitory substances or recalcitrant intermediates, creating a favorable microenvironment for the recovery of microbial activity in the upper biological treatment zone, and achieving dynamic stability and rapid self-regulation of the overall efficiency of the treatment system.

[0121] The modular sludge dewatering-metal recovery unit 40 is described in detail. This modular sludge dewatering-metal recovery unit is the terminal processing module in this embodiment of the invention responsible for solid-liquid separation and resource recovery of the purified product.

[0122] The modular sludge dewatering-metal recovery unit 40 physically comprises a high-pressure diaphragm filter press and a selective adsorption column. Its overall function is to receive a water-sludge mixture from the composite bio-electrochemical reactor 30, separate it into dewatered sludge and filtrate, and selectively recover valuable metals from the filtrate.

[0123] The specific working steps of this unit are as follows:

[0124] S401: The water-sludge mixture generated after treatment by the composite bio-electrochemical reactor 30 is pumped into a high-pressure diaphragm filter press.

[0125] S402: High-pressure diaphragm filter press performs solid-liquid separation. This operation first involves pumping a mixture into the filter chamber and applying physical pressure, causing the liquid to pass through the filter cloth to form a filtrate, while solid particles are trapped to form a filter cake.

[0126] S403: After the initial pressing, the diaphragm of the high-pressure diaphragm filter press expands by injecting a high-pressure fluid (such as water or air) to perform a secondary pressing on the formed filter cake. This step further removes residual moisture from the filter cake, achieving deep dewatering of the sludge and obtaining dewatered sludge with low moisture content.

[0127] S404: The high-pressure diaphragm filter press performs a discharge operation, releasing dewatered sludge. This dewatered sludge can be used as a resource product, for example, mixed with cement, fly ash, or other solidifying agents, for the preparation of roadbed materials or building bricks.

[0128] S405: The filtrate produced during the pressure filtration process is collected and directed to the selective adsorption column at a controlled flow rate.

[0129] S406: Inside the selective adsorption column, the filtrate flows through a packed adsorbent. In this embodiment, the adsorbent is a thiol-functionalized mesoporous silica gel. The thiol functional groups (-SH) on the surface of this adsorbent interact with specific divalent heavy metal ions (e.g., Cu). 2+ Zn 2+ It has high chemical affinity and can selectively capture and fix these valuable metal ions from the filtrate onto the surface of the adsorbent by forming stable chemical coordination bonds.

[0130] S407: Once the adsorption capacity of the adsorbent reaches saturation, it can be eluted and regenerated. One specific implementation involves passing an acidic solution (e.g., dilute nitric acid solution) through the adsorption column to break the coordination bonds between metal ions and thiol groups, eluting the enriched metal ions to form a metal-rich eluent. This eluent can then be further purified to recover pure metals or their salts.

[0131] S408: The filtrate after treatment by the selective adsorption column, in which most of the valuable metals have been removed, is discharged from the system outlet as the final purified water.

[0132] Example 1:

[0133] This embodiment provides an intelligent collaborative treatment and resource recovery system for mine wastewater generated during coal mining. (See attached diagram.) Figure 1 The system includes a distributed sensor network 10, a central controller 20, a composite bio-electrochemical reactor 30, and a modular sludge dewatering-metal recovery unit 40.

[0134] The mine wastewater to be treated first enters through the system inlet. A distributed sensor network 10 deployed at the inlet monitors the influent in real time. After collecting water quality parameters, the pH sensor, turbidity sensor, heavy metal concentration sensor and flow meter in the network transmit the data to the central controller 20 wirelessly.

[0135] After receiving data, the central controller 20 uses its internal fuzzy PID control algorithm to calculate based on real-time water quality parameters and preset target values, generating control commands. These commands are used to dynamically adjust the pump speed, the aeration rate of the aerator in the composite bio-electrochemical reactor 30, and the dosage of chemicals by the dosing pump. The control accuracy can achieve: ±1% for pump speed adjustment and a 0.1m step size for aeration rate adjustment. 3 The rate of increase is ±0.2 mL / s, and the control error for the dosage of the drug is ±0.2 mL / s.

[0136] Wastewater enters the composite bio-electrochemical reactor 30 under controlled conditions. The upper biological treatment zone of this reactor is filled with a specific surface area of ​​not less than 800 m². 3 A polyvinyl alcohol-activated carbon composite carrier ( / g) is used to cultivate a community of specialized microorganisms that degrade organic matter in the wastewater. Subsequently, the wastewater enters the lower electrochemical treatment zone, where a three-dimensional graphene electrode, under an applied voltage of 1.2V, catalyzes the generation of highly oxidizing hydroxyl radicals (·OH) from water molecules, deeply oxidizing the recalcitrant pollutants remaining from the biological treatment stage. Through this synergistic treatment, the system achieves a chemical oxygen demand (COD) removal rate of over 95%.

[0137] The water-sludge mixture treated in the reactor is then transported to the modular sludge dewatering-metal recovery unit 40. In this unit, a high-pressure diaphragm filter press presses the sludge at a working pressure of 1.5 MPa, reducing its final moisture content to below 60%. The filtrate produced during the filtration process flows through a selective adsorption column filled with thiol-modified silica gel adsorbent, achieving the capture of valuable metals such as copper and zinc, with a recovery rate of no less than 90%. The deeply dewatered and dried sludge is then used to prepare roadbed materials, with the final product having a compressive strength of no less than 15 MPa.

[0138] This embodiment achieves efficient purification and closed-loop utilization of mine wastewater through precise configuration and collaborative operation of each module. Compared with traditional technologies, it significantly improves treatment efficiency and resource recovery rate, and reduces system energy consumption.

[0139] Example 2:

[0140] The overall system structure of this embodiment is basically the same as that of Embodiment 1. The main difference is that the function of the central controller 20 has been deepened. Specifically, a two-layer collaborative intelligent control architecture is adopted to further improve the system's adaptability and automation level, and to cope with the technical challenges of dynamic fluctuations in the quality and quantity of mine wastewater.

[0141] In this embodiment, the control system inside the central controller 20 is constructed at the software level as a hierarchical structure consisting of a high-level predictive optimization layer and a low-level real-time adjustment layer.

[0142] The high-level prediction and optimization layer consists of a machine learning prediction module based on support vector machine (SVM). The training process of this module is completed offline before the system is run. The training dataset comes from a large amount of historical operating data, which includes influent water quality parameters (such as pH, turbidity, COD, flow rate, etc.) under different operating conditions, as well as the corresponding optimal system control parameter combinations (such as aeration rate, pulse voltage duty cycle, etc.).

[0143] After the system enters online operation, the machine learning prediction module continuously receives real-time influent water quality parameters collected by the distributed sensor network 10. Using these parameters as input, the module performs calculations using a pre-trained model. Its core task is no longer simply outputting a numerical value, but rather predicting and determining the "pollution load" level the system will face within a set time window (e.g., the next 30 minutes). For example, if a significant increase in influent COD concentration is predicted, the module will output a warning signal that a "high pollution load is imminent."

[0144] The warning signal is transmitted to the underlying real-time adjustment layer. This layer consists of a real-time fuzzy PID control module, whose core function is to dynamically adjust based on real-time process feedback. Unlike Embodiment 1, in this embodiment, the PID parameter reference value of the real-time fuzzy PID control module is no longer a fixed value, but is dynamically adjusted by the warning signal output from the higher-level predictive optimization layer.

[0145] Specifically, when a signal indicating an impending high pollution load is received, the central controller 20 will proactively increase... and The reference value provides a stronger responsiveness to the entire PID controller. Subsequently, when water quality fluctuations actually occur, the real-time fuzzy PID control module can, based on a more forward-looking and proactive set of parameters, incorporate real-time error... With error change rate Perform precise online tuning.

[0146] Through this two-tiered collaborative control model of "high-level macro-prediction and low-level micro-adjustment," the system achieves a transformation from "passive response" to "active prediction." Compared to traditional control that relies solely on real-time error feedback, this method effectively overcomes the inherent time lag effect of the system, avoids significant overshooting of control quantities or slow response due to sudden changes in water quality, thereby resulting in more stable final effluent quality and minimizing ineffective energy consumption while ensuring treatment effectiveness.

[0147] Example 3:

[0148] The system in this embodiment can adopt the configuration of Embodiment 1 or Embodiment 2 in the upstream processing section (distributed sensor network 10, central controller 20, and composite bio-electrochemical reactor 30). The core improvement of this embodiment lies in the enhanced structural and functional design of the modular sludge dewatering-metal recovery unit 40, which introduces a multi-stage, multi-objective recovery process to achieve more thorough and diversified resource recovery.

[0149] In this embodiment, the water-sludge mixture from the composite bio-electrochemical reactor 30 undergoes a pretreatment step before entering the high-pressure diaphragm filter press: pre-filtration using a ceramic nanofiber membrane. The purpose of this step is to trap colloidal particles and fine suspended solids in the mixture, substances that are difficult to remove effectively by conventional pressure filtration. This pretreatment step offers two significant advantages: firstly, it reduces the turbidity of the filtrate entering the selective adsorption column, preventing physical clogging or contamination of the adsorbent micropores at the source, thereby significantly extending the regeneration cycle and overall lifespan of the adsorbent; secondly, it improves the thoroughness of solid-liquid separation, providing a higher-purity liquid-phase feedstock for subsequent metal recovery.

[0150] The filtrate obtained after pre-filtration and high-pressure diaphragm filtration enters a reinforced dual-layer tandem selective adsorption column system. Following a pre-defined flow path, the filtrate passes sequentially through the first and second adsorption columns.

[0151] The first adsorption column functions the same as in Example 1, specifically for the recovery of valuable heavy metals. Its internal adsorbent is thiol-functionalized mesoporous silica gel. Cu in the filtrate... 2+ Zn 2+ Plasma is preferentially and efficiently captured here. Prioritizing heavy metal recovery is to avoid the potential poisoning or catalytic degradation of materials that subsequently adsorb organic matter by heavy metal ions.

[0152] The liquid flowing out of the first adsorption column then enters the second adsorption column. The second adsorption column is specifically designed to recover certain high-value organic compounds. In this embodiment, targeting phenolic compounds that may be present in coal chemical wastewater, the adsorbent filled in this layer is a macroporous adsorption resin. This resin exhibits excellent adsorption selectivity for phenolic molecules through van der Waals forces and surface physical interactions.

[0153] The regeneration process of this dual-layer adsorption column system also employs a differentiated design. Once the first adsorption column is saturated, it can be switched offline and eluted with a dilute acid solution to obtain a regenerated solution rich in heavy metals. When the second adsorption column is saturated, it can be eluted with an organic solvent such as methanol to obtain a regenerated solution rich in organic matter. Both regenerated solutions can then be used in subsequent purification processes.

[0154] Through this multi-stage filtration and tiered resource recovery design, this embodiment expands the concept of resource recycling from single metal recovery to the dual recovery of metals and organic matter. This not only greatly improves the overall economic efficiency of the system but also results in higher purity of the final effluent, maximizing the utilization of wastewater value and minimizing environmental impact.

Claims

1. A smart collaborative treatment and resource recovery system for mine wastewater, characterized in that, include: Distributed sensor networks are used to collect process parameters of the system. A composite bio-electrochemical reactor is used to purify mine wastewater and generate a water-sludge mixture. A modular sludge dewatering-metal recovery unit is used for solid-liquid separation and resource recovery of the water-sludge mixture; The central controller is connected to the distributed sensor network, the composite bio-electrochemical reactor, and the modular sludge dewatering-metal recovery unit, respectively, and is used to receive the process parameters and generate control commands to drive the actuators in the system based on the process parameters.

2. The intelligent collaborative treatment and resource recovery system for mine wastewater according to claim 1, characterized in that, The central controller adopts a two-layer collaborative intelligent control architecture, which includes a high-level predictive optimization layer and a low-level real-time adjustment layer.

3. The intelligent collaborative treatment and resource recovery system for mine wastewater according to claim 2, characterized in that, The high-level prediction and optimization layer includes a machine learning prediction module, which receives influent water quality parameters collected by the distributed sensor network and generates predicted values ​​for key water quality parameters in the future, so as to dynamically adjust the control parameter benchmark values ​​of the bottom real-time adjustment layer based on the predicted values.

4. The intelligent collaborative treatment and resource recovery system for mine wastewater according to claim 3, characterized in that, The underlying real-time adjustment layer includes a real-time fuzzy PID control module. The fuzzy PID control module is used to calculate and output the control command by combining the control parameter baseline value determined by the machine learning prediction module and the real-time process parameters fed back by the distributed sensor network.

5. The intelligent collaborative treatment and resource recovery system for mine wastewater according to claim 1, characterized in that, The composite bio-electrochemical reactor adopts an integrated, vertically layered design, integrating biological treatment zones and electrochemical treatment zones from top to bottom.

6. The intelligent collaborative treatment and resource recovery system for mine wastewater according to claim 1, characterized in that, The central controller controls the composite bio-electrochemical reactor through a pulse synergistic enhancement method based on microbial activity. The method includes: comparing the measured values ​​of redox potential and dissolved oxygen, which characterize the macroscopic metabolic activity state of the microbial community in the biological treatment zone, as measured by the distributed sensor network, with a preset high-activity state range, and dynamically adjusting the duty cycle of the pulse voltage applied to the electrodes of the electrochemical treatment zone according to the comparison results.

7. The intelligent collaborative treatment and resource recovery system for mine wastewater according to claim 1, characterized in that, The process parameters collected by the distributed sensor network include external water quality parameters and internal state parameters.

8. The intelligent collaborative treatment and resource recovery system for mine wastewater according to claim 7, characterized in that, The internal state parameters are obtained by an intermediate layer sensor array located at the interface between the biological treatment zone and the electrochemical treatment zone. The intermediate layer sensor array includes at least a redox potential sensor and a dissolved oxygen sensor.

9. The intelligent collaborative treatment and resource recovery system for mine wastewater according to claim 1, characterized in that, The modular sludge dewatering-metal recovery unit physically comprises a high-pressure diaphragm filter press and a selective adsorption column.

10. The intelligent collaborative treatment and resource recovery system for mine wastewater according to claim 9, characterized in that, The selective adsorption column is filled with thiol-functionalized mesoporous silica gel as an adsorbent to selectively capture divalent heavy metal ions in the filtrate.

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