Intelligent wastewater recycling and zero discharge control system

Abstract

The present invention relates to the field of wastewater treatment technology, and in particular to an intelligent wastewater recovery and zero-emission control system, which is suitable for efficient separation and resource utilization of high-TDS and high-COD wastewater. The present invention combines a dynamic Bayesian network and a deep reinforcement learning algorithm to realize intelligent control and resource utilization of the wastewater treatment process. The system monitors wastewater characteristics in real time, dynamically optimizes operating parameters, and adopts a cross-flow PON pollution-resistant design and a POM wide flow channel design to treat high-TDS (total dissolved solids concentration) and high-COD (chemical oxygen demand) wastewater, thereby achieving the goals of efficient separation, deep concentration and zero emission.

Description

Technical Field

[0001] The present invention relates to the technical field of wastewater treatment, in particular to an intelligent wastewater recovery and zero-emission control system, which is suitable for the efficient separation and resource utilization of high TDS (total dissolved solids) and high COD (chemical oxygen demand) wastewater. Background Art

[0002] In recent years, with the acceleration of industrialization, the amount of high-salt and highly polluted wastewater has increased significantly. Especially in the new energy materials, electronic semiconductors, petrochemical and other industries, wastewater often contains high concentrations of dissolved solids (TDS) and chemical oxygen demand (COD). The discharge of such wastewater not only causes serious pollution to the environment, but also wastes the potential recyclable resources in the wastewater. Traditional wastewater treatment technologies mainly focus on physical, chemical or biological treatment processes, but there are the following main problems when dealing with high TDS and high COD wastewater:

[0003] Low separation efficiency: Traditional membrane separation technology is easily clogged by pollutants in high-salt and high-pollution environments, making it difficult to achieve efficient separation and resource reuse, and the recovery rate is usually less than 50%.

[0004] High energy consumption and operating costs: The deep treatment of high-concentration wastewater usually requires complex multi-stage treatment equipment, and the process energy consumption and operating costs are high, which makes it difficult to meet the needs of enterprises for energy conservation and emission reduction.

[0005] Lack of intelligent regulation: Existing wastewater treatment systems mostly adopt fixed parameter operation modes and are unable to adjust the operating parameters in real time according to the characteristics of the wastewater, resulting in low system efficiency and poor operating stability.

[0006] Insufficient resource utilization: Recyclable substances such as metal ions or inorganic salts contained in wastewater are often directly discarded. There is a lack of effective resource recovery mechanism, resulting in the economic value not being fully tapped.

[0007] In view of the above problems, intelligent, modular and resource-based wastewater treatment technology has become a hot topic in current research and a trend in industrial application. The present invention combines dynamic Bayesian networks and deep reinforcement learning algorithms to improve the efficiency of wastewater treatment and resource recovery rate by real-time analysis and optimization control of wastewater characteristics, and uses cross-flow PON pollution-resistant design and POM wide flow channel design to achieve deep concentration and zero discharge of high TDS and high COD wastewater, overcoming the shortcomings of traditional technologies. Summary of the invention

[0008] The present invention discloses an intelligent wastewater recovery and zero-emission control system, which combines a dynamic Bayesian network and a deep reinforcement learning algorithm to realize intelligent control and resource utilization of the wastewater treatment process. The system monitors wastewater characteristics in real time, dynamically optimizes operating parameters, and uses a cross-flow PON pollution-resistant design and a POM wide flow channel design to treat high TDS (total dissolved solids concentration) and high COD (chemical oxygen demand) wastewater, achieving efficient separation, deep concentration and zero-emission goals.

[0009] The technical solution of the present invention is as follows: an intelligent wastewater recovery and zero-emission control system, comprising:

[0010] A multi-parameter sensor group, used to collect flow, TDS, COD concentration, and pH parameters during wastewater treatment;

[0011] B. Data processing and modeling module, based on dynamic Bayesian network, performs dynamic causal analysis on the collected parameters to predict the system operation status; the dynamic Bayesian network predicts the dynamic operation status of the system, including but not limited to pollutant concentration, flow rate and separation efficiency, through the following formula, and realizes forward-looking regulation of system performance by capturing the causal relationship between parameters:

[0012] ;

[0013] in:

[0014] : The system state at the current time t Conditional probability, based on the state at the previous moment and its constituent state variables The causal relationship determines

[0015] : represents the system status at time t, including:

[0016] Flow rate: measured by flow sensor, unit: m³ / h;

[0017] TDS concentration: measured by TDS sensor, unit: ppm;

[0018] COD concentration: measured by COD sensor, unit: mg / L;

[0019] : Indicates parameters The parent node set of What other variables affect:

[0020] Parent node of traffic : Traffic volume at the last moment ;

[0021] Parent node of TDS : Current traffic and the TDS concentration at the previous moment ;

[0022] COD's parent node : Current traffic and COD concentration at the previous moment ;

[0023] : Represents the conditional probability of pollutant concentration to flow rate and COD, obtained from historical data or training model.

[0024] C. Control optimization module, which uses DDPG deep reinforcement learning algorithm to generate optimization strategies based on the prediction results of dynamic Bayesian network, and dynamically adjusts the membrane separation pressure, transmembrane flow rate and dosage in the wastewater treatment process;

[0025] D. Concentration and separation module, including cross-flow PON pollution-resistant design and POM wide flow channel design, is used to treat high TDS and high COD wastewater. It predicts flow fluctuations and pollutant concentrations through dynamic Bayesian networks, and combines deep reinforcement learning to dynamically optimize operating resistance and separation efficiency to achieve efficient concentration and pollutant removal;

[0026] E. Resource recovery module, based on multi-frequency modified adsorption technology and ion exchange system, selectively recovers metal ions and inorganic salts in concentrated wastewater;

[0027] F. Cleaning and maintenance module, used for automated chemical cleaning and physical cleaning to maintain long-term stable operation of the system.

[0028] Preferably, the parameters of the dynamic Bayesian network are updated by the following formula:

[0029] ;

[0030] Function: This formula is used to dynamically adjust the parameters of the Bayesian network so that the prediction model can adapt to changes in wastewater characteristics in real time and improve the accuracy of the prediction.

[0031] : Updated Bayesian network parameters for optimizing pollutant concentration prediction;

[0032] : Bayesian network parameters before updating;

[0033] η: learning rate, which controls the amplitude of parameter update and is a fixed value in the system, such as 0.01;

[0034] : Log-likelihood gradient, calculated from current flow and pollutant concentration measurements.

[0035] Preferably, the DDPG deep reinforcement learning algorithm generates a control strategy through the following formula, optimizes the strategy of the Actor network, and outputs the optimal control parameters (such as membrane separation pressure and flow rate) by learning system operation data:

[0036] ;

[0037] : The performance objective function of strategy μ, which measures the degree of optimization of the current strategy for system operation;

[0038] : The policy function output by the Actor network generates the optimal action a for the current state s, including but not limited to membrane separation pressure and flow rate;

[0039] s: current system status, including flow rate, TDS concentration, COD concentration;

[0040] a: Control actions generated by the Actor network, including membrane separation pressure and flow rate;

[0041] : The current state and action value evaluated by the Critic network are used to guide the Actor network optimization strategy;

[0042] : The gradient of the Actor network, used to optimize the network weights .

[0043] Preferably, the value of the Critic network is updated by the following formula:

[0044] ;

[0045] Function: This formula is used to train the Critic network and optimize the system operation strategy by evaluating the immediate reward and future reward of the current control action.

[0046] : The loss function of the Critic network, used to measure the error of value estimation;

[0047] r: Instant reward, representing the direct contribution of the current control action to the system performance, unit: %

[0048] Separation efficiency improvement: +50 points;

[0049] COD removal rate improvement: +30 points;

[0050] Energy consumption reduction: +20 points;

[0051] γ: discount factor, usually 0.9, used to balance short-term and long-term benefits;

[0052] : The Critic network evaluates the value of the next state s′ and action μ(s′);

[0053] : The value of the current state and action, used to guide the next step of optimization;

[0054] : Parameters of the Critic network, used to update the value function.

[0055] Preferably, the concentration and separation module is combined with the cross-flow PON pollution-resistant design to reduce membrane surface pollution, and is combined with the POM wide flow channel design to reduce operating resistance.

[0056] Preferably, the control optimization module predicts pollutant concentrations through a dynamic Bayesian network, and a deep reinforcement learning algorithm adjusts the following operating parameters:

[0057] 1. Membrane separation pressure;

[0058] 2. Transmembrane flow rate;

[0059] 3. Wide flow channel flux distribution.

[0060] Preferably, the system automates chemical cleaning and physical cleaning through a cleaning and maintenance module to maintain the long-term operational stability of the membrane assembly.

[0061] Preferably, the combination of the dynamic Bayesian network and the deep reinforcement learning algorithm can achieve real-time optimization of the following operating parameters:

[0062] The membrane separation pressure is dynamically adjusted within the range of 0-83 bar;

[0063] The transmembrane flow rate is dynamically optimized in the range of 0.1-0.5 m / s;

[0064] The dosage is adjusted in real time within the range of 10-100 ppm.

[0065] The present invention improves the intelligent level of wastewater treatment and resource recovery capability by combining dynamic Bayesian network and deep reinforcement learning (DDPG) algorithm. The specific technical effects are as follows:

[0066] 1. Dynamic Bayesian network constructs a dynamic causal relationship model between parameters in the wastewater treatment system (such as the correlation between flow rate, TDS concentration and COD concentration), and predicts the system operation status in real time, including pollutant concentration, separation efficiency and equipment load. It realizes real-time state prediction of the wastewater treatment system, identifies abnormal conditions (such as membrane fouling or flow rate mutation) in advance, and improves the operational reliability of the system. Through dynamic prediction, it provides forward-looking input for the deep reinforcement learning algorithm, making the optimization control more accurate. It improves the system's adaptability to changes in wastewater characteristics and is suitable for the complex treatment needs of high TDS and high COD wastewater.

[0067] 2. The deep reinforcement learning algorithm uses the prediction results of the dynamic Bayesian network as input to optimize the key control parameters (such as membrane separation pressure, transmembrane flow rate and dosage) in the wastewater treatment process in real time and dynamically adjust the operation strategy. Under high TDS (10,000-50,000 ppm) conditions, the wastewater separation efficiency is improved by 15%-20%, and the COD concentration is reduced from 1,500 mg / L to 95 mg / L; The system operating pressure is dynamically adjusted to the range of 0-83 bar and remains stable according to the actual operating conditions; Energy consumption is reduced by 15%-30%, the transmembrane flow rate is optimized to 0.3 m / s during the test operation, and the dosage is adjusted within the range of 50 ppm; The system operates automatically to avoid human intervention and significantly improve stability.

[0068] 3. The combination of dynamic Bayesian network and deep reinforcement learning algorithm constitutes a closed-loop control mechanism of real-time prediction and dynamic optimization. The dynamic Bayesian network constructs a dynamic causal relationship model of wastewater characteristic parameters based on real-time data collected by sensors (such as flow rate, TDS concentration and COD concentration). The network can control the prediction error within ±5% during the prediction process. The deep reinforcement learning algorithm uses the prediction results of the dynamic Bayesian network as input to optimize key parameters such as membrane separation pressure, flow rate and dosage in real time. In the event of sudden fluctuations in wastewater characteristics (such as TDS concentration increased by 20% and flow fluctuations of ±10%), the system can still maintain a separation efficiency of ≥90% and a COD removal rate of ≥93%, and avoid the efficiency reduction caused by the traditional fixed parameter method. The synergy of the two algorithms forms a closed-loop optimization mechanism, which has significant performance in system adaptability and stability: energy consumption is reduced by 15%-20%; the recovery purity of metal ions and inorganic salts in the resource recovery module is increased by more than 10% respectively.

[0069] 4. Combination of cross-flow PON pollution-resistant design and algorithm: Dynamic Bayesian network is used to predict pollutant concentration and flow fluctuations, and deep reinforcement learning algorithm is used to adjust membrane separation pressure and flow rate, reducing membrane surface pollution while maintaining efficient separation. It reduces membrane pollution rate, extends equipment cleaning cycle by 30%, and reduces operation and maintenance costs.

[0070] 5. Combination of POM wide channel design and algorithm: Combined with the separation efficiency and equipment load predicted by the dynamic Bayesian network, deep reinforcement learning adjusts the flux distribution of the wide channel and optimizes the operating resistance. The operating resistance is reduced by 15%, achieving significant energy savings.

[0071] 6. Algorithm combination brings unexpected technical effects:

[0072] Significantly enhanced adaptive capabilities: The combination of dynamic Bayesian networks and deep reinforcement learning algorithms enables the system to quickly adapt to sudden changes in wastewater characteristics (such as TDS and COD concentrations), avoiding sudden drops in treatment efficiency or equipment damage caused by sudden changes in characteristics in traditional systems.

[0073] Comprehensive improvement of wastewater treatment efficiency and resource recovery rate: The synergistic effect of the two algorithms not only improved the separation efficiency during the dynamic optimization process, but also unexpectedly improved the separation purity of metal ions and inorganic salts, increasing the resource recovery rate by more than 10%.

[0074] The combined effect of energy saving and consumption reduction and economic benefits: intelligent control is used to optimize operating conditions, reduce the operating load and energy consumption of equipment, and at the same time, precise control of resource recovery is used to increase economic benefits, achieving the goal of both energy saving and economic benefits.

[0075] The present invention combines dynamic Bayesian networks and deep reinforcement learning algorithms to achieve deep wastewater treatment, resource recovery and zero emission goals through intelligent control and optimization, and is widely used in new energy, electronic semiconductors, petrochemicals, pharmaceuticals and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0077] Embodiment 1:

[0078] An intelligent wastewater recovery and zero-discharge control system, comprising:

[0079] A. Multi-parameter sensor group, used to collect flow, TDS, COD concentration, and pH parameters during wastewater treatment;

[0080] B. Data processing and modeling module, based on dynamic Bayesian network, performs dynamic causal analysis on the collected parameters to predict the system operation status; the dynamic Bayesian network predicts the dynamic operation status of the system, including but not limited to pollutant concentration, flow rate and separation efficiency, through the following formula, and realizes forward-looking regulation of system performance by capturing the causal relationship between parameters:

[0081] ;

[0082] in:

[0083] : The system state at the current time t Conditional probability, based on the state at the previous moment and its constituent state variables The causal relationship determines

[0084] : represents the system status at time t, including:

[0085] Flow rate: measured by flow sensor, unit: m³ / h;

[0086] TDS concentration: measured by TDS sensor, unit: ppm;

[0087] COD concentration: measured by COD sensor, unit: mg / L;

[0088] : Indicates parameters The parent node set of What other variables affect:

[0089] Parent node of traffic : Traffic volume at the last moment ;

[0090] Parent node of TDS : Current traffic and the TDS concentration at the previous moment ;

[0091] COD's parent node : Current traffic and COD concentration at the previous moment ;

[0092] : Represents the conditional probability of pollutant concentration to flow rate and COD, obtained from historical data or training model.

[0093] C. Control optimization module, which uses DDPG deep reinforcement learning algorithm to generate optimization strategies based on the prediction results of dynamic Bayesian network, and dynamically adjusts the membrane separation pressure, transmembrane flow rate and dosage in the wastewater treatment process;

[0094] D. Concentration and separation module, including cross-flow PON pollution-resistant design and POM wide flow channel design, is used to treat high TDS and high COD wastewater. It predicts flow fluctuations and pollutant concentrations through dynamic Bayesian networks, and combines deep reinforcement learning to dynamically optimize operating resistance and separation efficiency to achieve efficient concentration and pollutant removal;

[0095] E. Resource recovery module, based on multi-frequency modified adsorption technology and ion exchange system, selectively recovers metal ions and inorganic salts in concentrated wastewater;

[0096] F. Cleaning and maintenance module, used for automated chemical cleaning and physical cleaning to maintain long-term stable operation of the system.

[0097] Embodiment 2:

[0098] The difference between this embodiment and embodiment 1 is that this embodiment also includes the following technical features:

[0099] The parameters of the dynamic Bayesian network are updated by the following formula:

[0100] ;

[0101] Function: This formula is used to dynamically adjust the parameters of the Bayesian network so that the prediction model can adapt to changes in wastewater characteristics in real time and improve the accuracy of the prediction.

[0102] : Updated Bayesian network parameters for optimizing pollutant concentration prediction;

[0103] : Bayesian network parameters before updating;

[0104] η: learning rate, which controls the amplitude of parameter update and is a fixed value in the system, such as 0.01;

[0105] : Log-likelihood gradient, calculated from current flow and pollutant concentration measurements.

[0106] Preferably, the DDPG deep reinforcement learning algorithm generates a control strategy through the following formula, optimizes the strategy of the Actor network, and outputs the optimal control parameters (such as membrane separation pressure and flow rate) by learning system operation data:

[0107] ;

[0108] : The performance objective function of strategy μ, which measures the degree of optimization of the current strategy for system operation;

[0109] : The policy function output by the Actor network generates the optimal action a for the current state s, including but not limited to membrane separation pressure and flow rate;

[0110] s: current system status, including flow rate, TDS concentration, COD concentration;

[0111] a: Control actions generated by the Actor network, including membrane separation pressure and flow rate;

[0112] : The current state and action value evaluated by the Critic network are used to guide the Actor network optimization strategy;

[0113] : The gradient of the Actor network, used to optimize the network weights .

[0114] Preferably, the value of the Critic network is updated by the following formula:

[0115] ;

[0116] Function: This formula is used to train the Critic network and optimize the system operation strategy by evaluating the immediate reward and future reward of the current control action.

[0117] : The loss function of the Critic network, used to measure the error of value estimation;

[0118] r: Instant reward, representing the direct contribution of the current control action to the system performance, unit: %

[0119] Separation efficiency improvement: +50 points;

[0120] COD removal rate improvement: +30 points;

[0121] Energy consumption reduction: +20 points;

[0122] γ: discount factor, usually 0.9, used to balance short-term and long-term benefits;

[0123] : The Critic network evaluates the value of the next state s′ and action μ(s′);

[0124] : The value of the current state and action, used to guide the next step of optimization;

[0125] : Parameters of the Critic network, used to update the value function.

[0126] Preferably, the concentration and separation module is combined with the cross-flow PON pollution-resistant design to reduce membrane surface pollution, and is combined with the POM wide flow channel design to reduce operating resistance.

[0127] Preferably, the control optimization module predicts pollutant concentrations through a dynamic Bayesian network, and a deep reinforcement learning algorithm adjusts the following operating parameters:

[0128] 1. Membrane separation pressure;

[0129] 2. Transmembrane flow rate;

[0130] 3. Wide flow channel flux distribution.

[0131] Preferably, the system automates chemical cleaning and physical cleaning through a cleaning and maintenance module to maintain the long-term operational stability of the membrane assembly.

[0132] Preferably, the combination of the dynamic Bayesian network and the deep reinforcement learning algorithm can achieve real-time optimization of the following operating parameters:

[0133] The membrane separation pressure is dynamically adjusted within the range of 0-83 bar;

[0134] The transmembrane flow rate is dynamically optimized in the range of 0.1-0.5 m / s;

[0135] The dosage is adjusted in real time within the range of 10-100 ppm.

[0136] Embodiment 3:

[0137] Example: Intelligent treatment and resource recovery of high TDS and high COD wastewater

[0138] Wastewater source: electronic semiconductor production process.

[0139] Initial wastewater characteristics:

[0140] Flow rate: 10 m³ / h;

[0141] TDS concentration: 10,000 ppm;

[0142] COD concentration: 1,500 mg / L.

[0143] Processing target:

[0144] 1. Reduce the TDS concentration of wastewater after treatment to below 500 ppm;

[0145] 2.COD concentration drops below 100 mg / L;

[0146] 3. Achieve a separation efficiency of more than 90%;

[0147] 4. The purity of metal ions (such as sodium, calcium, magnesium, etc.) and inorganic salts (such as sodium chloride) recovered in wastewater shall not be less than 80%.

[0148] Processing process:

[0149] 1. Dynamic Bayesian Network Prediction

[0150] Input Data

[0151] Last moment ( ) status:

[0152] Flow rate: 10 m³ / h;

[0153] TDS concentration: 10,000 ppm;

[0154] COD concentration: 1,500 mg / L.

[0155] Prediction formula

[0156] ;

[0157] Parameter Conditional Probability

[0158] 1. Traffic prediction:

[0159] Conditional probability: P =0.95.

[0160] 2.TDS prediction:

[0161] Conditional probability: P( )=0.85.

[0162] 3.COD prediction:

[0163] Conditional probability: P( )=0.80.

[0164] Calculate predicted probabilities

[0165] ;

[0166] Substitute the data:

[0167] P( )=0.95⋅0.85⋅0.80=0.646

[0168] Prediction results:

[0169] Flow rate: maintained at 10.2 m³ / h;

[0170] TDS concentration: reduced to 9,800 ppm;

[0171] COD concentration: reduced to 1,450 mg / L.

[0172] Forecast adjustment in case of emergencies

[0173] Assuming that the TDS concentration of wastewater suddenly increases from 10,000 ppm to 12,000 ppm (an increase of 20%), the prediction error of the dynamic Bayesian network for the changes in TDS concentration and COD concentration is controlled within ±5%;

[0174] Flow rate fluctuation range: 10.2±0.3 m³ / h.

[0175] 2. Deep reinforcement learning optimization

[0176] Input Data

[0177] State input: prediction result ={flow=10.2, TDS=12000, COD=1,450};

[0178] System control objectives:

[0179] Optimize membrane separation pressure, flow rate and dosage to make TDS and COD meet the standards.

[0180] Optimization formula

[0181] Actor network generation control parameters:

[0182] ;

[0183] Control parameter output:

[0184] Membrane separation pressure: dynamically adjusted to within the range of 0-83 bar according to the working conditions. For example, when treating high TDS wastewater, the pressure can be stabilized at 70 bar.

[0185] Flow rate: 0.3 m / s;

[0186] Dosage (coagulant): 60 ppm

[0187] Critic network value assessment:

[0188] Instant Rewards:

[0189] TDS concentration dropped from 12000 ppm to 490 ppm, contribution reward +50;

[0190] COD concentration dropped from 1,450 mg / L to 95 mg / L, contribution reward +30;

[0191] System energy consumption is reduced by 15%, and contribution reward is +20.

[0192] Future value Q: discount factor γ=0.9.

[0193] Value function calculation:

[0194] ;

[0195] Substitute the data:

[0196] ;

[0197] Optimization results:

[0198] Comprehensive value of control strategy Q: 100.

[0199] 3. Hardware module dynamic control

[0200] Dynamic Adjustment of Cross-Flow PON Design:

[0201] The flow rate fluctuation (e.g. 10.2 ± 0.3 m³ / h) is monitored by a flow sensor to dynamically optimize the membrane separation pressure.

[0202] Adjust the pressure range: adjust the pressure range to 70 bar through real-time flow sensor data to reduce membrane fouling, gradually optimize from 75 bar to 70 bar, reduce membrane fouling rate by 30%, and extend the cleaning cycle to 200 hours.

[0203] Flux distribution of wide channel design:

[0204] According to real-time flow data, dynamically adjust flux distribution to reduce operating resistance:

[0205] The operating resistance is reduced from 10 bar to 8.5 bar;

[0206] System energy consumption is reduced by 15%.

[0207] 4. Actual operation calculation

[0208] Concentration and separation module

[0209] Optimized separation efficiency through cross-flow PON anti-fouling design and POM wide flow channel:

[0210] Separation efficiency calculation:

[0211] ;

[0212] Known:

[0213] Inlet flow: 10.2 m³ / h;

[0214] Concentrate flow rate: 1 m³ / h.

[0215] ;

[0216] Calculation of outlet water TDS concentration:

[0217] ;

[0218] ;

[0219] Calculation of effluent COD concentration:

[0220] ;

[0221] ;

[0222] Resource recovery module

[0223] Concentrate TDS concentration: 50,000 ppm;

[0224] Using multi-frequency modified adsorption technology and ion exchange system:

[0225] Recovered metal ion purity: 85%;

[0226] Recovered inorganic salt purity: 82%.

[0227] Processing results

[0228] The prediction error of the dynamic Bayesian network was controlled within ±5% when the TDS concentration fluctuated suddenly (from 10,000 ppm to 12,000 ppm), ensuring the optimization accuracy of the control parameters of deep reinforcement learning.

[0229] Cross-flow PON design with wide flow channel design operating at 70 bar pressure:

[0230] Membrane fouling rate reduced by 30%;

[0231] The cleaning cycle is extended to 200 hours;

[0232] The operating resistance is reduced from 10 bar to 8.5 bar.

[0233] The system energy consumption was reduced by 18% and the separation efficiency reached 90.2%.

[0234] This embodiment predicts and optimizes the wastewater treatment process through the combination of dynamic Bayesian networks and deep reinforcement learning, achieving the goals of efficient separation, resource recovery, and energy conservation and consumption reduction.

Claims

1. An intelligent wastewater recovery and zero emission control system, characterized in that: include: A. Multi-parameter sensor group, used to collect flow, TDS, COD concentration, and pH parameters during wastewater treatment; B. Data processing and modeling module, based on dynamic Bayesian network, performs dynamic causal analysis on the collected parameters and predicts the system operation status; the dynamic Bayesian network predicts the dynamic operation status of the system, including but not limited to pollutant concentration and flow rate, through the following formula, and realizes forward-looking regulation of system performance by capturing the causal relationship between parameters: ； in: :The system state S at the current time t t Conditional probability, based on the state S at the previous moment t-1 and its constituent state variables The causal relationship determines S t : Indicates the system status at time t, including: Flow rate: measured by flow sensor, unit: m³ / h; TDS concentration: measured by TDS sensor, unit: ppm; COD concentration: measured by COD sensor, unit: mg / L; : Indicates parameters The parent node set of What other variables affect: The parent node P of the flow a (F t ): The flow rate F at the last moment t-1 ; The parent node P of TDS a (T t ): Current flow F t and the TDS concentration F at the previous moment t-1 ; COD's parent node P a (C t ): Current flow F t and the COD concentration C at the previous moment t-1 ; : represents the conditional probability of the parameter, obtained from historical data or training model; C. The control optimization module uses the DDPG deep reinforcement learning algorithm to generate optimization strategies based on the prediction results of the dynamic Bayesian network to dynamically adjust the membrane separation pressure, transmembrane flow rate and dosage during the wastewater treatment process; D. Concentration and separation module, including cross-flow PON pollution-resistant design and POM wide flow channel design, is used to treat high TDS and high COD wastewater. It predicts flow fluctuations and pollutant concentrations through dynamic Bayesian networks, and combines deep reinforcement learning to dynamically optimize operating resistance and separation efficiency to achieve efficient concentration and pollutant removal; E. Resource recovery module, based on multi-frequency modified adsorption technology and ion exchange system, selectively recovers metal ions and inorganic salts in concentrated wastewater; F. Cleaning and maintenance module, used for automated chemical cleaning and physical cleaning to maintain long-term stable operation of the system.

2. The intelligent wastewater recovery and zero-emission control system according to claim 1 is characterized in that: The parameters of the dynamic Bayesian network are updated by the following formula, so that the prediction model can adapt to the changes in wastewater characteristics in real time: ； θ new : Updated Bayesian network parameters for optimizing pollutant concentration prediction; θ old : Bayesian network parameters before updating; η: learning rate, which controls the amplitude of parameter update and is a fixed value in the system; : Log-likelihood gradient, calculated from current flow and pollutant concentration measurements.

3. The intelligent wastewater recovery and zero emission control system according to claim 1 is characterized in that: The DDPG deep reinforcement learning algorithm generates a control strategy through the following formula, optimizes the strategy of the Actor network, and outputs the optimal control parameters of membrane separation pressure and flow rate by learning system operation data: ； J(μ): performance objective function of strategy μ, which measures the degree of optimization of the current strategy for system operation; : The policy function output by the Actor network generates the optimal action a for the current state s, including but not limited to membrane separation pressure and flow rate; s: current system status, including flow rate, TDS concentration, COD concentration; a: Control actions generated by the Actor network, including membrane separation pressure and flow rate; : The current state and action value evaluated by the Critic network are used to guide the Actor network optimization strategy; : The gradient of the Actor network, used to optimize the network weight θ μ .

4. The intelligent wastewater recovery and zero-emission control system according to claim 3 is characterized in that: The value of the Critic network is updated by the following formula, which optimizes the system operation strategy by evaluating the immediate reward and future reward of the current control action: ； L(θ Q ): The loss function of the Critic network, used to measure the error of value estimation; r: immediate reward, representing the direct contribution of the current control action to the system performance, unit: %; γ: discount factor, which is 0.9 and is used to balance short-term and long-term benefits; : The Critic network evaluates the value of the next state s' and action μ(s'); : The value of the current state and action, used to guide the next step of optimization; θ Q : Parameters of the Critic network, used to update the value function.

5. The intelligent wastewater recovery and zero-emission control system according to claim 1 is characterized in that: The concentration and separation module combines the cross-flow PON pollution-resistant design to reduce membrane surface pollution, and combines the POM wide flow channel design to reduce operating resistance.

6. The intelligent wastewater recovery and zero-emission control system according to claim 1 is characterized in that: The control optimization module predicts pollutant concentrations through a dynamic Bayesian network, and a deep reinforcement learning algorithm adjusts the following operating parameters: membrane separation pressure, transmembrane flow rate, and wide channel flux distribution.

7. The intelligent wastewater recovery and zero-emission control system according to claim 1 is characterized in that: The system automates chemical cleaning and physical cleaning through a cleaning and maintenance module to maintain the long-term operational stability of the membrane assembly.

8. The intelligent wastewater recovery and zero-emission control system according to claim 1 is characterized in that: The combination of the dynamic Bayesian network and deep reinforcement learning algorithm can achieve real-time optimization of the following operating parameters: The membrane separation pressure is dynamically adjusted within the range of 0-83 bar; The transmembrane flow rate is dynamically optimized within the range of 0.1-0.5 m / s; The dosage is adjusted in real time within the range of 10-100ppm.

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