A wet garbage biogas slurry np prediction-pulse electrolysis-membrane cleaning process

Abstract

This invention discloses a wet waste biogas slurry NP prediction-pulse electrolysis-membrane cleaning process, comprising the following steps: Step S1. Nitrogen / Phosphorus content prediction: Real-time sampling of wet waste biogas slurry samples is collected using online sensors to determine the concentrations of total nitrogen (TN) and total phosphorus (TP); Step S2. Pulse electrolysis pretreatment: Based on the treatment strategy suggestions output by the prediction model, the parameters of the pulse electrolysis reactor are adjusted; Step S3. Membrane cleaning process optimization: In the biogas slurry after pulse electrolysis pretreatment, solid-liquid separation is performed using porous ceramic membranes and ultrafiltration membranes. In this invention, by combining the synergistic control of pulse time and electrolysis voltage, the current intensity and frequency are optimized in response to changes in nitrogen concentration, achieving efficient removal of low-concentration nitrogen, providing a key foundation for reducing pollutant load and membrane fouling in subsequent membrane separation processes.

Description

Technical Field

[0001] This invention relates to the fields of wet waste resource utilization and environmental wastewater treatment technology, and in particular to a wet waste biogas slurry NP prediction-pulse electrolysis-membrane cleaning process. Background Technology

[0002] Wet waste biogas slurry is a high-concentration organic wastewater produced during the anaerobic fermentation of wet waste. Its composition is complex and rich in pollutants such as total nitrogen (TN) and total phosphorus (TP). Direct discharge will cause eutrophication of water bodies and damage the ecological balance, thus becoming a key challenge in the field of environmental protection.

[0003] Currently, traditional technologies for nitrogen and phosphorus removal from wet waste biogas slurry mainly include biological treatment, chemical precipitation, and single electrolysis / membrane separation processes. Biological treatment decomposes nitrogen and phosphorus through microbial metabolism, requiring the construction of a complex microbial community and stringent requirements for environmental conditions such as biogas slurry temperature and pH. Chemical precipitation separates nitrogen and phosphorus by adding chemicals to form precipitates, but suffers from difficulties in precisely controlling the dosage and the potential for secondary sludge pollution. Single electrolysis processes often operate with fixed parameters, failing to adapt to dynamic changes in nitrogen and phosphorus concentrations in the biogas slurry. Traditional membrane separation processes lack targeted pollution control strategies, relying solely on fixed-cycle cleaning without integrating with the upstream treatment process.

[0004] Traditional technologies lack the ability to predict nitrogen and phosphorus concentrations in biogas slurry, making it impossible to formulate adaptive treatment strategies in advance, resulting in insufficient targeting of the treatment process. The electrolysis process does not employ multi-stage differentiated parameter design, making it difficult to simultaneously achieve efficient removal of high-concentration phosphorus and low-concentration nitrogen, resulting in low nitrogen and phosphorus removal rates and overall efficiency. The membrane cleaning process does not dynamically adjust based on the residual pollutants after front-end treatment, easily leading to incomplete or over-cleaning, causing rapid membrane flux decline and shortened lifespan. Furthermore, traditional technologies lack a complete feedback and iteration mechanism, making it unable to cope with dynamic changes in biogas slurry composition, ultimately affecting the stability of treatment effects and the long-term economic viability of the process.

[0005] Based on the above, we propose a wet waste biogas slurry NP prediction-pulse electrolysis-membrane cleaning process to solve the above problems. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of existing technologies by proposing a wet waste biogas slurry NP prediction-pulse electrolysis-membrane cleaning process.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a wet waste biogas slurry NP prediction-pulse electrolysis-membrane cleaning process, comprising the following steps:

[0008] Step S1, Nitrogen / Phosphorus Content Prediction: Samples of wet waste biogas slurry are collected in real time using online sensors to determine the concentrations of total nitrogen (TN) and total phosphorus (TP). A dynamic prediction model for nitrogen / phosphorus content is established based on the detection data. The prediction model combines historical data, biogas slurry generation conditions, and real-time monitoring parameters to output the trend of nitrogen / phosphorus concentration changes in biogas slurry over a future period and generate corresponding treatment strategy suggestions.

[0009] Step S2, Pulse Electrolysis Pretreatment: Adjust the parameters of the pulse electrolysis reactor according to the treatment suggestions output by the prediction model; the pulse electrolysis reactor includes a primary electrolysis unit and a secondary electrolysis unit connected in sequence. The primary electrolysis unit treats the high concentration of phosphorus in the biogas slurry by rapidly migrating and removing it through low-frequency pulse current; the secondary electrolysis unit treats the low concentration of nitrogen through high-frequency pulse current.

[0010] The current frequency and pulse duration of the pulse electrolysis reactor are adaptively adjusted by real-time monitoring of nitrogen and phosphorus concentrations, pH values, and temperature parameters in the biogas slurry. The adaptive adjustment system is trained on historical data using machine learning algorithms to predict the changing trends of biogas slurry components over a period of time.

[0011] The wet waste biogas slurry is sequentially introduced into the primary electrolysis unit and the secondary electrolysis unit for treatment, and the nitrogen / phosphorus in the biogas slurry is directionally migrated and initially removed using pulsed current;

[0012] Step S3, Membrane Cleaning Process Optimization: In the biogas slurry after pulse electrolysis pretreatment, a porous ceramic membrane and an ultrafiltration membrane are used for solid-liquid separation. The parameters of the membrane cleaning process are dynamically adjusted according to the nitrogen / phosphorus residual concentration and membrane fouling of the biogas slurry after pulse electrolysis.

[0013] Preferably, the dynamic prediction model in step S1 adopts an adaptive model based on machine learning, which outputs strategy suggestions by analyzing the biogas slurry generation conditions, historical treatment data, and real-time monitored nitrogen / phosphorus concentrations, pH values, and temperatures.

[0014] Preferably, in step S2, the primary electrolysis unit is specifically designed to treat high concentrations of phosphorus in the biogas slurry, utilizing low-frequency pulsed current for directional migration and removal. Key parameters set include the pulsed current frequency. Current intensity Phosphorus removal rate during electrolysis This can be described by the following formula:

[0015] ,

[0016] in:

[0017] Phosphorus removal rate;

[0018] It is a constant related to the efficiency of the electrolysis reaction;

[0019] It is a reaction kinetic index;

[0020] This represents the initial phosphorus concentration. This represents the residual concentration of phosphorus during the electrolysis process.

[0021] The above formulas show that the phosphorus removal rate is related to the current intensity. Current frequency A positive correlation was observed, which slowed down as phosphorus concentration decreased, through real-time monitoring of phosphorus concentration. The electrolyzer automatically adjusts the current intensity. and pulse frequency .

[0022] Preferably, in step S2, the secondary electrolysis unit is used to treat the low concentration of nitrogen in the biogas slurry, employing a high-frequency pulsed current. Key parameters set include the pulsed current frequency. Current intensity Pulse time and electrolysis voltage ;

[0023] Nitrogen removal rate Described by the following formula:

[0024] ,

[0025] in:

[0026] The nitrogen removal rate;

[0027] It is a constant related to the efficiency of nitrogen removal reaction;

[0028] It is the kinetic index of the nitrogen removal reaction;

[0029] and These represent the initial and post-treatment nitrogen concentrations, respectively.

[0030] The above formulas show that the nitrogen removal rate is also related to the current intensity. ,frequency Positively correlated, and with nitrogen concentration The reduction is slowed down by real-time monitoring of nitrogen concentration. The electrolyzer can adaptively adjust the current intensity according to the actual situation. and frequency .

[0031] Preferably, in step S2, the parameters of the electrolysis unit are dynamically adjusted based on the actual composition of the biogas slurry, pH value, and temperature. The parameter adjustment follows these rules:

[0032] During the phosphorus removal stage, when the concentration When the current intensity exceeds the set threshold, and frequency Automatically increases current to accelerate removal; when the concentration drops to the set target, the system automatically reduces the current intensity and frequency.

[0033] For nitrogen treatment, when the concentration When the current exceeds a set threshold, the system increases the current intensity. and frequency And adjust the pulse time To increase migration rate; when the concentration drops to a lower level, the system reduces electrolysis intensity.

[0034] Preferably, in step S3, the membrane cleaning process includes dynamic adjustment based on real-time fouling monitoring. The membrane fouling monitoring system detects the degree of fouling of the membrane in real time through sensors, uses electrical impedance tomography (EIT) technology to monitor the fouling status and distribution on the membrane surface, and automatically adjusts the concentration of the cleaning agent and the alternation time cycle according to the fouling status.

[0035] Preferably, in step S3, the membrane cleaning process adopts an alternating acid-base cleaning method. The concentration of the cleaning solution and the alternation cycle are dynamically optimized by real-time monitoring of the membrane fouling degree. The acid and base concentrations of the cleaning solution are adjusted according to the nature of membrane fouling and membrane flux.

[0036] In addition, the wet waste biogas slurry NP prediction-pulse electrolysis-membrane cleaning process also includes step S4: treatment effect verification and process adaptive iteration. After the membrane cleaning process is optimized and solid-liquid separation of biogas slurry is achieved in step S3, the total nitrogen (TN) and total phosphorus (TP) concentrations of the separated slurry are sampled and tested. The test results are compared with the nitrogen / phosphorus concentration target values ​​output by the dynamic prediction model in step S1 and the phosphorus removal rate formula of the pulse electrolysis pretreatment in step S2. and nitrogen removal rate formula The calculated theoretical residual concentrations are compared to verify whether the actual nitrogen / phosphorus removal efficiency of the entire process meets the preset emission standards; if the nitrogen / phosphorus concentrations in the clarified liquid do not meet the standards, the system backtracks and analyzes the current intensity of the pulse electrolysis reactor in step S2. Pulse frequency The adaptability to actual biogas slurry treatment needs was analyzed, and the effects of cleaning agent concentration and acid-base alternation cycle on pollutant interception effect in the membrane cleaning process of step S3 were also analyzed. The parameters of the dynamic prediction model in step S1 were corrected by machine learning algorithm, and the operating parameters of the primary electrolysis unit and the secondary electrolysis unit in step S2, the membrane cleaning cycle and the acid-base concentration of the cleaning agent in step S3 were adjusted accordingly.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] This invention significantly improves the removal efficiency of target pollutants through differentiated parameter design and adaptive control of multi-stage electrolysis units. The primary electrolysis unit targets high-concentration phosphorus, based on the formula... Using low-frequency pulsed current as the core parameter, the phosphorus residual concentration is monitored in real time. Dynamically adjust current intensity With frequency When phosphorus concentration is high, increasing parameter values ​​accelerates its directional migration and removal, ensuring rapid separation of high-concentration phosphorus; the secondary electrolysis unit targets low-concentration nitrogen, based on the formula... It uses high-frequency pulsed current, combined with pulse time. With electrolysis voltage Synergistic regulation, synchronous response to nitrogen concentration Optimize the change in current intensity With frequency This technology achieves efficient removal of low-concentration nitrogen. Ultimately, through precise coordination and adaptive parameter adjustment between the two-stage units, the initial nitrogen / phosphorus removal rate is improved compared to traditional electrolysis processes, providing a key foundation for subsequent membrane separation processes to reduce pollutant load and membrane fouling.

[0039] In this invention, based on the nitrogen / phosphorus residual concentration and membrane fouling monitoring data output from each step, and combined with the phosphorus removal rate calculation and nitrogen removal rate calculation in step S2 to infer the pollutant residual characteristics, the degree of pollutant on the membrane surface is determined through monitoring technology. Then, the cleaning agent concentration, acid-base concentration and alternation cycle are dynamically adjusted. For salt fouling caused by incomplete phosphorus removal in step S2, the acid cleaning agent concentration is automatically increased and the alternation cycle is shortened. For nitrogen-containing organic residues, the proportion of alkaline cleaning agent is optimized. This avoids the problems of incomplete cleaning or membrane damage caused by traditional fixed cleaning modes, and ultimately reduces the fouling rate of the membrane module, significantly improving the long-term operational stability and economy of the membrane separation system.

[0040] After the membrane cleaning process in step S3 of this invention is optimized and solid-liquid separation of biogas slurry is achieved, the total nitrogen (TN) and total phosphorus (TP) concentrations of the separated clarified liquid are sampled and tested. The test results are compared with the target values ​​of nitrogen / phosphorus concentrations output by the dynamic prediction model and the theoretical residual concentrations calculated based on the phosphorus removal rate and nitrogen removal rate of the pulse electrolysis pretreatment, respectively, to verify whether the actual nitrogen / phosphorus removal efficiency of the entire process meets the preset emission standards. If the nitrogen / phosphorus concentrations in the clarified liquid do not meet the standards, the system retrospectively analyzes the adaptability of parameters such as the current intensity and pulse frequency of the pulse electrolysis reactor in step S2 to the actual biogas slurry treatment requirements, as well as the impact of the cleaning agent concentration and acid-base alternation cycle in the membrane cleaning process in step S3 on the pollutant interception effect. The parameters of the dynamic prediction model in step S1 are corrected through machine learning algorithms, and the operating parameters of the primary and secondary electrolysis units in step S2, the membrane cleaning cycle in step S3, and the acid-base concentration of the cleaning agent are adjusted accordingly to ensure the stability of nitrogen / phosphorus removal effect and the adaptability of the process to the dynamic changes of biogas slurry composition in the subsequent wet waste biogas slurry treatment process. Attached Figure Description

[0041] Figure 1 Flowchart of wet waste biogas slurry NP prediction-pulse electrolysis-membrane cleaning process;

[0042] Figure 2 This is a schematic diagram of the structure of the pulse electrolysis reactor of the present invention;

[0043] Figure 3 This is a closed-loop feedback diagram of the entire process of this invention. Detailed Implementation

[0044] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0045] like Figure 1 and Figure 3 The diagram shows a flowchart of the wet waste biogas slurry NP prediction-pulse electrolysis-membrane cleaning process provided in this embodiment, including the following steps:

[0046] Step S1, Phosphorus content prediction: Samples of wet waste biogas slurry are collected in real time using online sensors to determine the concentrations of total nitrogen (TN) and total phosphorus (TP). A dynamic prediction model for nitrogen / phosphorus content is established based on the detection data. The prediction model combines historical data, biogas slurry generation conditions, and real-time monitoring parameters to output the trend of nitrogen / phosphorus concentration changes in biogas slurry over a future period and generate corresponding treatment recommendations.

[0047] Step S2, Pulse Electrolysis Pretreatment: Based on the treatment strategy suggestions output by the prediction model, adjust the parameters of the pulse electrolysis reactor, introduce the wet waste biogas slurry into the pulse electrolysis reactor, and use the pulse current to directionally migrate and initially remove nitrogen / phosphorus in the biogas slurry;

[0048] The current frequency and pulse duration of the pulse electrolysis reactor are adaptively adjusted by real-time monitoring of nitrogen and phosphorus concentrations, pH values, and temperature parameters in the biogas slurry. The adaptive adjustment system is trained on historical data using machine learning algorithms to predict the changing trends of biogas slurry composition over a future period.

[0049] like Figure 2 As shown, the pulse electrolysis reactor has multiple electrolysis units, each of which is used to treat pollutants of different concentrations and compositions. The primary electrolysis unit treats the higher concentration of phosphorus in the biogas slurry by rapidly migrating and removing it through low-frequency pulsed current; the secondary electrolysis unit treats the lower concentration of nitrogen through high-frequency pulsed current.

[0050] The primary electrolysis unit is specifically designed to treat high concentrations of phosphorus in biogas slurry, utilizing low-frequency pulsed current for targeted migration and removal. Key parameters set include the pulsed current frequency. Current intensity Phosphorus removal rate during electrolysis This can be described by the following formula:

[0051] ,

[0052] in:

[0053] Phosphorus removal rate;

[0054] It is a constant related to the efficiency of the electrolysis reaction;

[0055] It is a reaction kinetic index;

[0056] This represents the initial phosphorus concentration. This represents the residual concentration of phosphorus during the electrolysis process.

[0057] The above formulas show that the phosphorus removal rate is related to the current intensity. Current frequency A positive correlation was observed, which slowed down as phosphorus concentration decreased, through real-time monitoring of phosphorus concentration. The electrolyzer automatically adjusts the current intensity. and pulse frequency ;

[0058] The secondary electrolysis unit is used to treat the lower concentrations of nitrogen in the biogas slurry. It employs a high-frequency pulsed current, and key parameters include the pulsed current frequency. Current intensity Pulse time and electrolysis voltage ;

[0059] nitrogen removal rate Described by the following formula:

[0060] ,

[0061] in:

[0062] The nitrogen removal rate;

[0063] It is a constant related to the efficiency of nitrogen removal reaction;

[0064] It is the kinetic index of the nitrogen removal reaction;

[0065] and These represent the initial and post-treatment nitrogen concentrations, respectively.

[0066] The above formulas show that the nitrogen removal rate is also related to the current intensity. ,frequency Positively correlated, and with nitrogen concentration The reduction is slowed down by real-time monitoring of nitrogen concentration. The electrolyzer can adaptively adjust the current intensity according to the actual situation. and frequency ;

[0067] The parameters of the electrolysis unit are dynamically adjusted based on the actual composition of the biogas slurry, pH value, and temperature. The parameter adjustment follows these rules:

[0068] During the phosphorus removal stage, when the concentration When the current intensity exceeds the set threshold, and frequency Automatically increases current to accelerate removal; when the concentration drops to the set target, the system automatically reduces the current intensity and frequency.

[0069] For nitrogen treatment, when the concentration When the current exceeds a set threshold, the system increases the current intensity. and frequency And adjust the pulse time To increase migration rate; when the concentration drops to a lower level, the system reduces electrolysis intensity;

[0070] Based on the NP concentration prediction results and treatment suggestions in step S1, the core objectives and parameter adjustment directions of the pulse electrolysis pretreatment can be accurately identified. The distribution characteristics of high-concentration phosphorus and low-concentration nitrogen, future concentration change trends, and key parameters such as biogas slurry generation conditions, real-time monitoring of pH and temperature are synchronously transmitted to the adaptive control system of the pulse electrolysis reactor. This provides data support for the parameter initialization and dynamic adjustment of multi-stage electrolysis units, ensuring the pretreatment effect and the adaptability to subsequent processes.

[0071] By adjusting the parameters of the pulse electrolysis reactor, wet waste biogas slurry is introduced into the pulse electrolysis reactor. With the help of the directional effect of the pulse current, nitrogen and phosphorus in the biogas slurry are targeted for migration and preliminary removal.

[0072] Step S3, Membrane Cleaning Process Optimization: In the biogas slurry after pulse electrolysis pretreatment, a porous ceramic membrane and an ultrafiltration membrane are used for solid-liquid separation. Based on the nitrogen / phosphorus residual concentration and membrane fouling characteristics of the biogas slurry after pulse electrolysis, the parameters of the membrane cleaning process are dynamically adjusted. The cleaning agent is an alternating use of acidic and alkaline solutions.

[0073] After step S2, although nitrogen and phosphorus in the biogas slurry undergo directional migration and preliminary removal, a certain residual concentration still exists, namely, residual phosphorus concentration. Nitrogen residual concentration Furthermore, the electrolysis process may be accompanied by the generation of contaminants such as tiny flocs and colloids. If these substances directly enter the membrane separation system, they can easily cause pore blockage or surface adsorption contamination of the porous ceramic membrane and ultrafiltration membrane. Therefore, step S3 is based on the output of step S2. , The data, combined with the initial filtration efficiency parameters of the membrane module, determines the initial operating threshold for solid-liquid separation;

[0074] When the phosphorus removal rate in step S2 Below the set value, nitrogen residual concentration When the flow rate exceeds the membrane tolerance threshold, the membrane filtration pressure and flow rate will be adjusted first to reduce the contact time between the pollutants and the membrane surface. At the same time, the basic concentration parameters of the pollutants will be set for the subsequent cleaning process to achieve precise connection between pretreatment and membrane separation.

[0075] Based on the real-time sensors of the membrane fouling monitoring system, data such as membrane flux decay rate and transmembrane pressure difference are continuously collected. These data are implicitly related to the electrolysis parameters in step S2.

[0076] For example, if the primary electrolysis unit in step S2 is affected by low-frequency pulse current... Insufficient phosphorus removal leads to incomplete removal of phosphorus. Residual phosphates in biogas slurry easily form insoluble salts with calcium and magnesium ions. Such pollutants can cause a rapid decline in membrane flux. Sensors can use EIT technology to reflect the membrane fouling situation. On the other hand, combining the monitoring results of EIT technology with flux can more accurately and in real time determine the distribution of membrane fouling, and then call the preset cleaning strategy library accordingly.

[0077] To address the salt contamination generated by residual phosphorus in step S2, the concentration of acidic cleaning agent is automatically increased, and the acid-base alternation cycle is shortened. To address the organic amine contaminants that may be generated during nitrogen removal, the proportion of alkaline cleaning agent is increased, achieving intelligent matching between the degree of contamination, membrane flux, and cleaning agent parameters, thus avoiding membrane damage and reagent waste caused by blind cleaning.

[0078] The membrane cleaning process adopts an alternating acid and alkali cleaning method. The concentration and alternation cycle of the cleaning solution are dynamically optimized by real-time monitoring of the membrane fouling level. The acid and alkali concentrations of the cleaning solution are adjusted according to the membrane flux during cleaning. Step S3, based on the alternating acid and alkali cleaning, combines the electrolysis effect and real-time fouling data from step S2 to achieve dynamic iterative optimization of cleaning parameters, taking into account both cleaning efficiency and membrane module lifespan. First, the concentration and alternation cycle of the cleaning solution are not fixed values, but are adaptively adjusted by real-time monitoring of the membrane fouling level.

[0079] When the nitrogen removal rate in step S2 When the temperature is low and there is a lot of nitrogenous organic matter residue in the biogas slurry, an organic fouling layer is easily formed on the membrane surface. At this time, the system will increase the concentration of alkaline cleaning agent and extend the soaking time of the alkaline cleaning stage to enhance the degradation and stripping effect of organic pollutants. Secondly, the acid and alkali concentration of the cleaning solution will be dynamically adjusted according to the membrane fouling situation and membrane flux.

[0080] In this embodiment, when the EIT (Electrical Ingress Testing) determines that the membrane fouling level is high and cleaning is required, acid cleaning is performed first. When the membrane flux no longer changes but the EIT still determines that the membrane is fouled, alkaline cleaning is started. Alkaline cleaning is stopped when the membrane flux recovers and the EIT no longer determines that the membrane needs cleaning. At the same time, the pH sensor provides real-time feedback on the acidity or alkalinity of the cleaning solution to ensure that the cleaning process is always within the optimal reaction range. This dynamic optimization mode can not only greatly improve the membrane cleaning efficiency, but also reduce membrane pore size expansion or surface oxidation problems caused by over-cleaning, extend the replacement cycle of the membrane module, and reduce the overall operating cost of the process.

[0081] Step S4: Verification of Treatment Effect and Adaptive Iteration of Process: After the membrane cleaning process optimization is completed and solid-liquid separation of biogas slurry is achieved in Step S3, the total nitrogen (TN) and total phosphorus (TP) concentrations of the separated clarified liquid are sampled and tested. The test results are compared with the nitrogen / phosphorus concentration target values ​​output by the prediction model in Step S1 and the removal benchmark set in the pulse electrolysis pretreatment in Step S2 to verify whether the actual nitrogen / phosphorus removal efficiency of the entire process meets the standards. If the nitrogen / phosphorus concentrations in the clarified liquid do not meet the emission standards, the system will backtrack and analyze the pulse electrolysis parameters in Step S2, including current intensity. Pulse frequency The adaptability of pulse electrolysis parameters and the impact of membrane cleaning in step S3 on pollutant interception were investigated. The parameters of the dynamic prediction model in step S1 were corrected by machine learning algorithm to ensure that the prediction model always fits the actual working conditions of wet waste biogas slurry treatment, thereby improving the accuracy of nitrogen / phosphorus concentration prediction and process adaptability. At the same time, the operating parameters of the electrolysis unit in step S2 and the membrane cleaning cycle in step S3 were adjusted to ensure that the subsequent wet waste biogas slurry treatment always maintains a high efficiency and stable nitrogen / phosphorus removal effect and can adapt to the dynamic changes of biogas slurry composition.

[0082] This invention significantly improves the removal efficiency of target pollutants through differentiated parameter design and adaptive control of multi-stage electrolysis units. The primary electrolysis unit targets high-concentration phosphorus, based on the formula... Using low-frequency pulsed current as the core parameter, the phosphorus residual concentration is monitored in real time. Dynamically adjust current intensity With frequency When phosphorus concentration is high, increasing parameter values ​​accelerates its directional migration and removal, ensuring rapid separation of high-concentration phosphorus; the secondary electrolysis unit targets low-concentration nitrogen, based on the formula... It uses high-frequency pulsed current, combined with pulse time. With electrolysis voltage Synergistic regulation, synchronous response to nitrogen concentration Optimize the change in current intensity With frequency This technology achieves efficient removal of low-concentration nitrogen. Ultimately, through precise coordination and adaptive parameter adjustment between the two-stage units, the initial nitrogen / phosphorus removal rate is improved compared to traditional electrolysis processes, providing a key foundation for subsequent membrane separation processes to reduce pollutant load and membrane fouling.

[0083] This invention is based on the nitrogen / phosphorus residual concentration and membrane fouling monitoring data output from the steps. It combines the phosphorus removal rate calculation and nitrogen removal rate calculation in step S2 to infer the pollutant residual characteristics. After classifying the membrane surface fouling through EIT identification technology, it dynamically adjusts the cleaning agent concentration and alternation cycle. Based on the membrane fouling situation and distribution, it automatically increases the cleaning agent concentration and shortens the alternation cycle, avoiding the problems of incomplete cleaning or membrane damage caused by traditional fixed cleaning modes. Ultimately, it reduces the fouling rate of the membrane module and significantly improves the long-term operational stability and economy of the membrane separation system.

[0084] After the membrane cleaning process in step S3 of this invention is optimized and solid-liquid separation of biogas slurry is achieved, the total nitrogen (TN) and total phosphorus (TP) concentrations of the separated clarified liquid are sampled and tested. The test results are compared with the target values ​​of nitrogen / phosphorus concentrations output by the dynamic prediction model and the theoretical residual concentrations calculated based on the phosphorus removal rate and nitrogen removal rate of the pulse electrolysis pretreatment, respectively, to verify whether the actual nitrogen / phosphorus removal efficiency of the entire process meets the preset emission standards. If the nitrogen / phosphorus concentrations in the clarified liquid do not meet the standards, the system retrospectively analyzes the adaptability of parameters such as the current intensity and pulse frequency of the pulse electrolysis reactor in step S2 to the actual biogas slurry treatment requirements, as well as the impact of the cleaning agent concentration and acid-base alternation cycle on the pollutant interception effect in the membrane cleaning process in step S3. The parameters of the dynamic prediction model in step S1 are corrected through machine learning algorithms, and the operating parameters of the primary electrolysis unit and the secondary electrolysis unit in step S2, the membrane cleaning cycle and the cleaning agent concentration in step S3 are adjusted accordingly to ensure the stability of nitrogen / phosphorus removal effect and the adaptability of the process to the dynamic changes of biogas slurry composition in the subsequent wet waste biogas slurry treatment process.

[0085] 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, and various changes and modifications can be made without departing from the spirit and scope of the invention, all of which fall within the scope of protection claimed by the present invention. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A wet waste biogas slurry NP prediction-pulse electrolysis-membrane cleaning process, characterized in that, Includes the following steps: Step S1, Nitrogen / Phosphorus Content Prediction: Samples of wet waste biogas slurry are collected in real time using online sensors to determine the concentrations of total nitrogen (TN) and total phosphorus (TP). A dynamic prediction model for nitrogen / phosphorus content is established based on the detection data. The prediction model combines historical data, biogas slurry generation conditions, and real-time monitoring parameters to output the trend of nitrogen / phosphorus concentration changes in biogas slurry over a future period and generate corresponding treatment recommendations. Step S2, Pulse Electrolysis Pretreatment: Adjust the parameters of the pulse electrolysis reactor according to the treatment suggestions output by the prediction model; The pulse electrolysis reactor includes a primary electrolysis unit and a secondary electrolysis unit connected in sequence. The primary electrolysis unit treats the high concentration of phosphorus in the biogas slurry by rapidly migrating and removing it through low-frequency pulse current. The secondary electrolysis unit processes low-concentration nitrogen using high-frequency pulsed current; In step S2, the primary electrolysis unit is used to treat the high concentration of phosphorus in the biogas slurry by using low-frequency pulsed current for directional migration and removal. The key parameters set include the pulsed current frequency. Current intensity Phosphorus removal rate during electrolysis This can be described by the following formula: in: Phosphorus removal rate; It is a constant related to the efficiency of the electrolysis reaction; It is a reaction kinetic index; This represents the initial phosphorus concentration. This represents the residual concentration of phosphorus during the electrolysis process. The above formulas show that the phosphorus removal rate is related to the current intensity. Current frequency A positive correlation was observed, which slowed down as phosphorus concentration decreased, through real-time monitoring of phosphorus concentration. The pulse electrolysis reactor automatically adjusts the current intensity. and pulse frequency ; The current frequency and pulse duration of the pulse electrolysis reactor are adaptively adjusted by real-time monitoring of nitrogen and phosphorus concentrations, pH values, and temperature parameters in the biogas slurry. The adaptive adjustment system is trained on historical data using machine learning algorithms to predict the changing trends of biogas slurry components over a period of time. The wet waste biogas slurry is sequentially introduced into the primary electrolysis unit and the secondary electrolysis unit for treatment, and the nitrogen / phosphorus in the biogas slurry is directionally migrated and initially removed using pulsed current; Step S3, Membrane Cleaning Process Optimization: In the biogas slurry after pulse electrolysis pretreatment, a porous ceramic membrane and an ultrafiltration membrane are used for solid-liquid separation. The parameters of the membrane cleaning process are dynamically adjusted according to the nitrogen / phosphorus residual concentration and membrane fouling of the biogas slurry after pulse electrolysis.

2. The wet waste biogas slurry NP prediction-pulse electrolysis-membrane cleaning process according to claim 1, characterized in that, In step S1, the dynamic prediction model adopts an adaptive model based on machine learning, which outputs strategy suggestions by analyzing the biogas slurry generation conditions, historical treatment data, and real-time monitored nitrogen / phosphorus concentrations, pH values, and temperatures.

3. The wet waste biogas slurry NP prediction-pulse electrolysis-membrane cleaning process according to claim 1, characterized in that, In step S2, the secondary electrolysis unit is used to treat the low concentration of nitrogen in the biogas slurry, employing a high-frequency pulsed current. Key parameters set include the pulsed current frequency. Current intensity Pulse time and electrolysis voltage Nitrogen removal rate Described by the following formula: in: The nitrogen removal rate; It is a constant related to the efficiency of nitrogen removal reaction; It is the kinetic index of the nitrogen removal reaction; and These represent the initial and post-treatment nitrogen concentrations, respectively. The above formulas show that the nitrogen removal rate is also related to the current intensity. ,frequency Positively correlated, and with nitrogen concentration The reduction is slowed down by real-time monitoring of nitrogen concentration. The pulse electrolysis reactor can adaptively adjust the current intensity according to the actual situation. and frequency .

4. The wet waste biogas slurry NP prediction-pulse electrolysis-membrane cleaning process according to claim 1, characterized in that, In step S2, the parameters of the electrolysis unit are dynamically adjusted based on the actual composition of the biogas slurry, pH value, and temperature. The parameter adjustment follows these rules: During the phosphorus removal stage, when the concentration When the current intensity exceeds the set threshold, and frequency Automatically increases current to accelerate removal; when the concentration drops to the set target, the system automatically reduces the current intensity and frequency. For nitrogen treatment, when the concentration When the current exceeds a set threshold, the system increases the current intensity. and frequency And adjust the pulse time To increase migration rate; when the concentration drops to a lower level, the system reduces electrolysis intensity.

5. The wet waste biogas slurry NP prediction-pulse electrolysis-membrane cleaning process according to claim 1, characterized in that, In step S3, the membrane cleaning process includes dynamic adjustment based on real-time fouling monitoring. The membrane fouling monitoring system detects the fouling of the membrane in real time through sensors, uses electrical impedance tomography to monitor the fouling and distribution on the membrane surface, and automatically adjusts the concentration of the cleaning agent and the alternation time period according to the fouling situation.

6. The wet waste biogas slurry NP prediction-pulse electrolysis-membrane cleaning process according to claim 1, characterized in that, In step S3, the membrane cleaning process adopts an alternating acid-base cleaning method. The concentration of the cleaning solution and the alternation cycle are dynamically optimized by real-time monitoring of the membrane fouling degree. The acid and base concentrations of the cleaning solution are adjusted according to the nature of membrane fouling and membrane flux.

7. The wet waste biogas slurry NP prediction-pulse electrolysis-membrane cleaning process according to claim 1, characterized in that, The process also includes step S4: verification of treatment effect and adaptive iteration of the process. After the membrane cleaning process is optimized and solid-liquid separation of biogas slurry is achieved in step S3, the total nitrogen (TN) and total phosphorus (TP) concentrations of the separated slurry are sampled and tested. The test results are compared with the nitrogen / phosphorus concentration target values ​​output by the dynamic prediction model in step S1 and the phosphorus removal rate formula of the pulse electrolysis pretreatment in step S2. and nitrogen removal rate formula The calculated theoretical residual concentrations are compared to verify whether the actual nitrogen / phosphorus removal efficiency of the entire process meets the preset emission standards; if the nitrogen / phosphorus concentrations in the clarified liquid do not meet the standards, the system backtracks and analyzes the current intensity of the pulse electrolysis reactor in step S2. Pulse frequency The adaptability to actual biogas slurry treatment needs was analyzed, and the effects of cleaning agent concentration and acid-base alternation cycle on pollutant interception effect in the membrane cleaning process of step S3 were also analyzed. The parameters of the dynamic prediction model in step S1 were corrected by machine learning algorithm, and the operating parameters of the primary electrolysis unit and the secondary electrolysis unit in step S2, the membrane cleaning cycle and the acid-base concentration of the cleaning agent in step S3 were adjusted accordingly.

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