Advanced treatment and recycling method for cold-rolled silicon steel magnesium oxide wastewater

Abstract

The invention discloses an advanced treatment and recycling method for cold-rolled silicon steel magnesium oxide wastewater, and particularly relates to the technical field of wastewater treatment. Through pH adjustment, coagulation pretreatment, pre-membrane fine filtration and medicament stabilization of wastewater, stable control of inlet water quality is realized; a two-stage reverse osmosis system is combined with a dynamic flow and cross flow structure, so that the risks of concentration polarization and local stress corrosion are remarkably inhibited; by combining real-time monitoring of membrane surface pressure difference and recovery rate and an intelligent algorithm, self-adaptive optimization regulation and control of operation parameters of the system are realized; according to the method, the problems of serious membrane pollution, poor system stability, low recycling efficiency and the like in the prior art are effectively solved, and the wastewater resource utilization level and the system operation safety are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wastewater treatment, and particularly to a method for deep treatment and reuse of magnesium oxide wastewater from cold-rolled silicon steel. Background Art

[0002] The deep treatment and reuse of magnesium oxide wastewater from cold-rolled silicon steel refers to the wastewater generated during the production of cold-rolled silicon steel by using magnesium oxide as a neutralizing agent or treatment agent. Through advanced deep treatment technologies (such as membrane separation, chemical precipitation, adsorption, etc.), impurities, heavy metals and other harmful substances in the wastewater are removed, so that the treated water quality meets the reuse standard, thereby realizing the recycling of wastewater. In the prior art, the reverse osmosis (RO) system is used to remove dissolved salts and trace heavy metals in water, which is the core equipment to achieve the standard of recycled water. In the natural state, water molecules will flow from a low concentration to a high concentration through a semi-permeable membrane, which is "osmosis". In reverse osmosis, by applying an external high pressure (higher than the osmotic pressure of the solution), water molecules are forced to flow reversely from the high-concentration side (contaminated water) to the low-concentration side (pure water), while dissolved salts, heavy metal ions, organic substances, etc. are intercepted by the membrane.

[0003] The prior art has the following deficiencies:

[0004] During the operation of the reverse osmosis system, if there is uneven water flow distribution or defects in the membrane module design, it is easy to form a concentration polarization zone on the membrane surface, resulting in an abnormal increase in the solute concentration in a local area, exceeding its solubility limit. This local high-concentration environment may cause rapid deposition of salts or the formation of chemical stress, leading to local stress corrosion of the membrane material, and "etching points" will be formed on the membrane surface after long-term operation, destroying the integrity of the membrane, and finally perforation or leakage will occur, seriously affecting the system stability and the quality of the effluent. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for deep treatment and reuse of magnesium oxide wastewater from cold-rolled silicon steel to solve the deficiencies in the background art.

[0006] In order to achieve the above purpose, the present invention provides the following technical solutions: A method for deep treatment and reuse of magnesium oxide wastewater from cold-rolled silicon steel, comprising:

[0007] Preliminarily treating the wastewater containing magnesium oxide during the production of cold-rolled silicon steel, and the treatment includes adjusting the pH and adding a coagulant or a flocculant;

[0008] Adding an anti-scaling agent and a membrane protection agent to the wastewater, and removing colloids and high-molecular organic substances through a multi-media filter or an ultrafiltration device to adjust the inlet water parameters;

[0009] Sequentially feeding the wastewater stabilized before the membrane into at least two-stage reverse osmosis systems for membrane separation treatment, and the reverse osmosis system adopts a dynamic flow regulation and cross-flow distribution structure;

[0010] Real-time monitor the membrane surface differential pressure and recovery rate parameters, and adaptively optimize and control the system operation parameters through intelligent algorithms;

[0011] Collect the reverse osmosis produced water and reuse it according to the production process requirements, and send the concentrated liquid to the concentration or treatment unit for further disposal.

[0012] Preferably, the dynamic flow regulation includes setting up an online flow monitoring and feedback system to adjust the inlet pressure, flow rate and recovery rate of each stage of the reverse osmosis system in real time according to the changes in the influent water quality, conductivity and temperature.

[0013] Preferably, the method for obtaining the membrane surface differential pressure is as follows: Install an online pressure sensor, record the instantaneous pressure values at the inlet and outlet of the first-stage membrane module respectively, and calculate the membrane surface differential pressure of the first-stage membrane module: ΔP = P in -P out ; ΔP is the membrane surface differential pressure of the first-stage membrane module, P in is the instantaneous pressure value at the inlet of the membrane module, and P out is the instantaneous pressure value at the outlet of the membrane module; for a reverse osmosis system composed of multiple stages of membrane shells connected in series, the differential pressure of each stage of membrane shell should be calculated separately, and then the average differential pressure should be summarized as the membrane surface differential pressure. The expression is: In the formula, n is the total number of membrane shells, P in，i , P out，i are the inlet and outlet pressures of the i-th stage membrane shell, and SA is the membrane surface differential pressure.

[0014] Preferably, the method for obtaining the recovery rate is as follows: Select the key parameters affecting the recovery rate to form a feature sequence input: Q feed (t) is the influent water flow rate, Q permeate (t) is the permeate water flow rate, and T(t) is the water temperature; the input is a multi-variable sequence within a time window, and the prediction target is the predicted value of the recovery rate in the future for a period of time Calculate the actual recovery rate R(t) under the condition of interference: However, under the condition of severe fluctuations or sensor errors, it will be predicted and corrected through the LSTM model to achieve SR = LSTM(X t-k+1 ,..., X t ); where: X t is all the input feature vectors at time t, k is the time step, and SR is the recovery rate.

[0015] Preferably, the intelligent algorithm includes using the LSTM neural network to train the historical operation data to predict the future recovery rate fluctuation trend and optimize the system parameter settings.

[0016] Preferably, the membrane surface differential pressure and the recovery rate are converted into a comprehensive feature vector, and the comprehensive feature vector is used as the input of a machine learning model. The machine learning model takes predicting the membrane fouling risk value label for each group of comprehensive feature vectors as the prediction target, and takes minimizing the sum of the prediction errors for all membrane fouling risk value labels as the training target. The machine learning model is trained until the sum of the prediction errors reaches convergence and then the model training is stopped. The membrane fouling risk value is determined according to the model output result, wherein the machine learning model is a polynomial regression model.

[0017] Preferably, the obtained membrane fouling risk value is compared with a gradient risk threshold. The gradient risk threshold includes a first risk threshold and a second risk threshold, and the first risk threshold is less than the second risk threshold. The membrane fouling risk value is compared with the first risk threshold and the second risk threshold respectively;

[0018] If the membrane fouling risk value is greater than the second risk threshold, it is determined as a high-risk pollution state, and the system immediately issues an alarm and adaptively optimizes and regulates the system operation parameters;

[0019] If the membrane fouling risk value is greater than or equal to the first risk threshold and less than or equal to the second risk threshold, it is determined as a medium-risk state, and the system automatically starts low-intensity backwashing and increases the pre-membrane treatment load;

[0020] If the membrane fouling risk value is less than the first risk threshold, it is determined as a low-risk operation state, and the system maintains the current operation mode and only records the state for trend analysis.

[0021] Preferably, the concentrated liquid is reduced in volume and discharged or recycled through membrane concentration, evaporation crystallization or chemical neutralization precipitation. The distilled water can be reused, and the solid residue enters the solid waste treatment system.

[0022] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0023] 1. The present invention forms a complete and efficient deep purification and recycling method through multi-stage treatment and intelligent control of the wastewater containing magnesium oxide in cold-rolled silicon steel. This method realizes precise control of pH, suspended solids, and heavy metal ions in the pretreatment stage. In the pre-membrane treatment stage, by adding anti-scaling agents and high-precision filtration devices, the risk of reverse osmosis membrane fouling is effectively reduced. The reverse osmosis system adopts dynamic flow regulation and cross-flow inlet structure, effectively solving the problems of concentration polarization and local erosion points on the membrane surface caused by uneven flow fields in traditional systems, and significantly improving the operation stability and flux efficiency of the membrane system.

[0024] 2. The present invention combines the membrane surface pressure difference and the recovery rate parameter, constructs a membrane fouling risk prediction mechanism through the LSTM neural network and the polynomial regression model, and realizes the operation status recognition and adaptive optimization control based on real-time data. The system can automatically adjust the operation strategy according to the membrane fouling risk level, such as pressure regulation, load reduction, backwashing, etc., so as to extend the service life of the membrane module and reduce the frequency of chemical cleaning. The final product water can meet the water demand of various processes, and the concentrated liquid is reduced and resourcefully treated through membrane concentration, evaporation or precipitation, effectively reducing the environmental protection pressure. The overall solution has the remarkable advantages of high intelligence, high water resource reuse rate and low operation risk, and is applicable to the high-pollution industrial wastewater reuse scenarios in industries such as steel and metallurgy. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0026] Figure 1 It is a flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] In order to make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0028] Embodiment, please refer to Figure 1 As shown, the method for deep treatment and reuse of magnesium oxide wastewater in cold-rolled silicon steel of this embodiment includes:

[0029] Preliminarily treat the wastewater containing magnesium oxide in the production process of cold-rolled silicon steel, and the treatment includes adjusting the pH and adding a coagulant or a flocculant;

[0030] Add an anti-scaling agent and a membrane protection agent to the wastewater, and remove colloids and high-molecular organic substances through a multi-media filter or an ultrafiltration device to adjust the inlet water parameters;

[0031] Sequentially feed the wastewater stabilized before the membrane into at least two-stage reverse osmosis systems for membrane separation treatment, and the reverse osmosis system adopts a dynamic flow regulation and cross-flow distribution structure;

[0032] Real-time monitor the membrane surface pressure difference and the recovery rate parameter, and adaptively optimize and control the system operation parameters through intelligent algorithms;

[0033] Collect the reverse osmosis produced water and reuse it according to the requirements of the production process. The concentrated liquid is sent to the concentration or treatment unit for further disposal.

[0034] In the production of cold-rolled silicon steel, magnesium oxide is often used as a neutralizing agent for the protective coating during the annealing process. A large amount of magnesium oxide (MgO), magnesium hydroxide (Mg(OH)2) particles, suspended impurities, some heavy metal ions (such as Fe 2+ / Fe 3+ ) and oil organic pollutants remain in the subsequent cleaning or annealing wastewater. Therefore, the goal of the preliminary treatment stage is to stabilize the water quality, reduce the pollution load, and ensure the stable operation of the subsequent membrane system.

[0035] pH adjustment: Adjust the pH value of the water body to the appropriate range (usually pH 6.5 - 7.5) to promote the conversion of magnesium oxide and heavy metal ions into precipitable forms.

[0036] Method: If the wastewater is alkaline (pH > 9), dilute sulfuric acid, hydrochloric acid or citric acid solution can be added for neutralization. If the wastewater is acidic, appropriate amount of sodium hydroxide or lime milk can be added to adjust it to slightly alkaline.

[0037] Control requirement: Adopt an on-line pH monitoring and automatic dosing system to accurately control the dosing amount of the regulator and prevent drastic pH fluctuations.

[0038] Add coagulant: Form precipitable flocs from the fine colloidal particles, magnesium oxide microparticles, organic matter and heavy metals in the wastewater.

[0039] Operation control: Set up a stirring device in the coagulation reaction tank. First, stir quickly to promote the uniform dispersion of the reagent, and then stir slowly to promote the flocculation reaction. Control the dosing amount and reaction time. Generally, the fast stirring time is 1 - 3 min, and the slow stirring time is 10 - 20 min.

[0040] Precipitation and solid-liquid separation: After the flocculation reaction, the wastewater enters the sedimentation tank (which can be an inclined tube sedimentation tank or a vertical flow sedimentation tank), and the flocs, suspended solids and part of magnesium oxide are removed by gravity. The sedimented sludge is regularly removed through the sludge discharge system and enters the sludge treatment unit. Monitoring parameters: suspended solid concentration (SS), turbidity, magnesium oxide concentration, heavy metal residue.

[0041] After the preliminary sedimentation treatment of the wastewater, there may still be trace amounts of soluble salts, colloids, organic matter, oils and suspended solids with smaller particle sizes. If these components directly enter the reverse osmosis (RO) system, it will cause membrane surface scaling, fouling and even irreversible damage. Therefore, before entering the membrane system, further water quality stabilization treatment of the wastewater is required, including reagent addition and purification by the filtration system.

[0042] Adding antiscalants and membrane protectants: Antiscalants: Inhibit the formation of scale on the membrane surface by dissolved salts in water.

[0043] Common types: Organophosphorus compounds (such as ATMP, HEDP), sulfonic acids, organic polymer antiscalants.

[0044] Adding method: Set up an on-line dosing pump, and automatically adjust the dosing amount according to the concentrations of calcium, magnesium, silicon, carbonate and other components in the influent water.

[0045] The recommended dosing concentration range is 2 - 8 mg / L, calculated based on the concentration on the concentrate side.

[0046] Key control parameters: LSI (Langelier Saturation Index) < 0.3 to prevent calcium carbonate scale formation; silicon concentration controlled below 120 mg / L.

[0047] Membrane protectants: Inhibit the growth and reproduction of microorganisms on the membrane surface and delay biofouling.

[0048] Optional chemicals: Non-oxidizing biocides (such as isothiazolinone, DBNPA), slow-release protectants. Use chemicals compatible with the membrane material to avoid damaging the membrane structure; if the system is out of service for a long time, it is necessary to add protectants for sealed circulation to maintain the wet state of the membrane system.

[0049] Removal of colloids and organic matter:

[0050] Multi-media filter: Usually composed of a layered filter bed of quartz sand, anthracite, ceramsite, etc. Function: Remove particulate matter with a particle size > 10 μm, colloidal impurities, and some organic matter. Operating parameters: The filtration rate is controlled at 8 - 12 m / h; backwash regularly (every 6 - 12 hours) to restore the filtration rate.

[0051] Ultrafiltration device: Use hollow fiber or spiral wound membrane modules with pore sizes of 0.01 - 0.1 μm to further remove small molecule colloids, emulsified oil, organic matter and some bacteria. Advantages: High membrane retention accuracy, ensuring that the SDI (Silt Density Index) of the RO influent < 3; can be used as an excellent pretreatment barrier for RO, significantly extending the membrane life.

[0052] Adjustment of influent parameters, the main parameters include: pH control: Maintain in the range of 6.5 - 7.5 to meet the requirements of the RO membrane; temperature adjustment: Generally controlled at 20 - 30 °C to prevent membrane damage at high temperatures; conductivity monitoring: Ensure that the conductivity is within the design tolerance of the membrane to avoid overloading; flow rate and pressure: Precisely adjust the pump speed and influent pressure through a variable frequency control system to ensure stable flow velocity on the membrane surface and avoid concentration polarization.

[0053] After completing the pre-treatment for membrane stability, the wastewater enters a multi-stage reverse osmosis membrane separation system with optimized structure, including the following specific steps:

[0054] Set up a two - stage series reverse osmosis system:

[0055] The first - stage reverse osmosis unit (RO - 1): Receives the wastewater after pretreatment and pre - membrane stabilization; The main goal is to remove most soluble salts, heavy metal ions, and organic residues; Usually designed to operate at a high recovery rate (the recovery rate is generally 60% - 70%); The produced water enters the second - stage system, and the concentrated water enters the concentration treatment module.

[0056] The second - stage reverse osmosis unit (RO - 2): Receives the produced water from the first stage and further improves the water quality purity; Usually adopts a low - pressure operation strategy to reduce energy consumption and control the conductivity of the produced water; The produced water can be directly reused in the process flow, and the concentrated water can be reused as reclaimed water or recharged.

[0057] Adopt a dynamic flow regulation system: Set up an online flow monitoring and feedback control system to respond to the changes in the influent water quality, pressure, and temperature at different time periods; Dynamically adjust the influent flow rate and pressure of the first - stage and second - stage systems to avoid overloading of the membrane surface load or exacerbation of concentration polarization; The system adaptively adjusts the pump speed and the opening degree of the pressure valve based on an algorithm model (which can be embedded in the PLC or SCADA system).

[0058] Set up a cross - flow distribution structure: Each stage of the reverse osmosis membrane module adopts a cross - flow influent design, that is, the influent water passes through the membrane surface at a certain tangential angle; By adding a flow - guiding device or a variable cross - section flow channel structure, the water flow disturbance on the membrane surface is enhanced to prevent concentration polarization in the edge area; A multi - stage flow distribution plate is set inside the membrane shell to make the influent water evenly distributed along the membrane length direction and reduce the formation of dead zones. The advantages are: Compared with the direct - flow influent, the cross - flow design can effectively inhibit the deposition on the membrane surface, improve the stability of the membrane flux; Reduce the membrane fouling rate, extend the cleaning cycle and improve the overall operation efficiency of the system.

[0059] Real - time monitor the membrane surface pressure difference and recovery rate parameters, and adaptively optimize and control the system operation parameters through intelligent algorithms. Specifically:

[0060] Membrane surface pressure difference: Reflects the membrane fouling trend; Recovery rate: Calculated in real - time by the influent and produced water flow meters, reflecting the system efficiency; The data is transmitted to the central control unit at a second - level frequency and trend analysis is performed in the background.

[0061] Among them, the method for obtaining the membrane surface pressure difference is: Install an online pressure sensor (pressure transmitter) to record the instantaneous pressure values at the inlet (influent end) and outlet (concentrated water end) of a section of the membrane module respectively, and calculate the membrane surface pressure difference of a section of the membrane module: ΔP = P in -P out ; ΔP is the membrane surface pressure difference of a section of the membrane module, P in is the instantaneous pressure value at the inlet of the membrane module, P outis the instantaneous pressure value at the outlet of the membrane module; for a reverse osmosis system composed of multiple stages of membrane shells connected in series (such as three-stage series connection), the pressure difference of each stage of membrane shell should be calculated separately, and then the average pressure difference should be summarized as the membrane surface pressure difference. The expression is: In the formula, n is the total number of membrane shells, P in，i , P out，i are the inlet and outlet pressures of the i-th stage membrane shell, and SA is the membrane surface pressure difference.

[0062] In the integrated control system, the membrane surface pressure difference is an important index jointly monitored with the water production flow rate, conductivity, etc. The threshold value can be set through the PLC system. Once ΔP exceeds the preset range, the pump speed will be automatically interlocked and adjusted, an alarm will be triggered, or the flushing mode will be entered.

[0063] The method for obtaining the recovery rate is as follows: Select the key parameters affecting the recovery rate to form a characteristic sequence input: Q feed (t) is the influent flow rate (changing with time), Q permeate (t) is the permeate flow rate and T(t) is the water temperature; the input is a multi-variable sequence within a time window, such as historical data of the past 60 minutes or the past N sampling points. The prediction target is the predicted value of the recovery rate for a future period of time Calculate the actual recovery rate R(t) under the condition of interference: However, under severe fluctuations or sensor errors, this value will be predicted and corrected through the LSTM model to achieve: SR = LSTM(X t-k+1 ,..., X t ); where: X t is all the input feature vectors at time t, k is the time step, and SR is the recovery rate.

[0064] Among them, the LSTM model training steps include: collecting multi-parameter time series data during continuous operation; normalizing the input features (such as Min-Max or Z-score); constructing the training set and test set in a sliding window manner. Construct the LSTM model structure, training target and loss function, and the loss function uses the mean square error (MSE); optimizer: Adam or RMSprop; use historical operation data to train the model; verify the generalization ability of the model under different water quality and load conditions; if the error is large, adjust by means such as adding Dropout, batch normalization, and more LSTM layers.

[0065] Convert the membrane surface differential pressure and recovery rate into a comprehensive feature vector, use the comprehensive feature vector as the input of the machine learning model. The machine learning model takes predicting the membrane fouling risk value label for each group of comprehensive feature vectors as the prediction target, and takes minimizing the sum of the prediction errors for all membrane fouling risk value labels as the training target, and trains the machine learning model until the sum of the prediction errors reaches convergence and then stops the model training. Determine the membrane fouling risk value according to the model output result, where the machine learning model is a polynomial regression model.

[0066] Compare the obtained membrane fouling risk value with the gradient risk thresholds. The gradient risk thresholds include a first risk threshold and a second risk threshold, and the first risk threshold is less than the second risk threshold. Compare the membrane fouling risk value with the first risk threshold and the second risk threshold respectively;

[0067] If the membrane fouling risk value is greater than the second risk threshold, it is determined as a high-risk pollution state, and the system immediately issues an alarm and adaptively optimizes and regulates the system operation parameters;

[0068] If the membrane fouling risk value is greater than or equal to the first risk threshold and less than or equal to the second risk threshold, it is determined as a medium-risk state, and the system automatically starts low-intensity backwashing, increases the pretreatment load before the membrane, and improves stability;

[0069] If the membrane fouling risk value is less than the first risk threshold, it is determined as a low-risk operation state, and the system maintains the current operation mode and only records the state for trend analysis.

[0070] The adaptive optimization and regulation of the system operation parameters are specifically as follows:

[0071] During the system operation process, through the sensor network and the data acquisition module, key operation parameters are obtained in real time, including but not limited to:

[0072] Inlet water flow rate, permeate water flow rate, inlet and concentrate end pressures, membrane surface differential pressure, recovery rate, and water quality indicators such as conductivity, temperature, pH, etc. All parameters are continuously recorded at the set sampling frequency to form a multi-dimensional time series.

[0073] The system inputs the above data into the analysis module to diagnose the current operation state:

[0074] If the membrane surface differential pressure continues to increase and the recovery rate decreases → it is identified as a membrane fouling trend; if the inlet water flow rate changes greatly and the water production is unstable → it is identified as a load fluctuation; if the concentrate pressure rises abnormally → it can be identified as a risk of reflux blockage or membrane element pressure loss. And use regression models, prediction algorithms (such as LSTM, linear regression or empirical curves) to judge whether the subsequent trend is controllable or requires intervention.

[0075] The system sets the control trigger conditions, such as: the decline rate of the recovery rate > 10% / hour; the fluctuations of the flow rate and conductivity exceed the set deviation; the model prediction result shows that the membrane fouling risk value > the risk threshold. Once any condition is met, the optimization control process will be entered.

[0076] The system dynamically selects the control mode according to different working conditions and objectives, including:

[0077] Increase the influent flow rate or the concentrate reflux flow rate to improve the cross-flow rate and reduce the concentration polarization on the membrane surface. When the high recovery rate causes fouling, automatically reduce the recovery rate to the recommended value (e.g., 70% → 65%).

[0078] Adjust the frequency of the high-pressure pump to control the influent pressure and prevent the membrane pressure from exceeding the limit or the pressure difference from being too large. When the pressure difference increases, the system can automatically reduce the pressure to the membrane design safety threshold.

[0079] If the membrane fouling trend is related to abnormal pH, adjust the acid-base dosing amount; if the temperature is too low, reduce the recovery rate or enable heating to optimize the water flux.

[0080] Feed the feedback control signal to units such as multimedia filtration and ultrafiltration to increase the backwashing frequency; if the SDI value (fouling index) before the membrane increases, trigger the ultrafiltration flushing action.

[0081] The system adjusts the pump speed, valve opening, chemical dosing rate, etc. through the automatic control unit (such as PLC, frequency converter, automatic valve); all operations are incorporated into the control log to ensure traceability and the possibility of manual intervention.

[0082] After the control is executed, the system re-evaluates the changes in key parameters within a short operation cycle (such as 5 minutes): if the pressure difference decreases and the recovery rate tends to be stable → judge that the control is effective; if the problem persists or worsens → trigger the secondary strategy or alarm for manual intervention. The system records the control actions and response effects as data accumulation for subsequent algorithm optimization.

[0083] During the operation of the reverse osmosis system, the influent is divided into two parts: Product water: The clean water after desalination treatment by the RO membrane; Concentrate: The high-concentration concentrated liquid containing the intercepted dissolved salts, magnesium oxide residues, heavy metals, etc.

[0084] The recycling of the product water includes:

[0085] After the product water is metered by the online flow meter, it enters the product water buffer tank or the intermediate storage tank; the storage tank is equipped with a liquid level control system, which can automatically supply water according to the water demand or switch to the standby water source.

[0086] Set up equipment such as online conductivity meters, pH meters, thermometers, etc. to ensure that the product water quality meets the standards; if the fluctuations are abnormal, the system can switch the discharge or bypass pipeline through the three-way valve.

[0087] Possible links for reuse in the cold-rolled silicon steel production process: used as cleaning water after annealing; used as cleaning or spraying dilution water for magnesium-based coatings; supplement the circulating cooling water system; used as a diluent for scale removers or pickling processes; cooling water for non-contact equipment (such as hydraulic systems).

[0088] If the hardness and conductivity of the produced water are too low or too high, water mixing can be carried out according to process requirements, such as mixing with fresh water or recycled water before use; set a proportional mixing valve to achieve automatic proportioning according to the preset ratio.

[0089] Further treatment of the concentrate: Enter the concentration treatment unit. After the concentrated water is collected, it enters the following possible paths: a. Multiple-effect evaporation or mechanical vapor recompression (MVR) system; further concentrate the volume of the wastewater to achieve the recovery of salt or magnesium ions; the distilled water can be returned to the system for reuse, and the solid residue enters the solid waste treatment. b. Membrane concentration (such as NF / DTRO): Applied to concentrated water with a large volume and relatively light pollution, recover some valuable substances such as Mg 2+ ; The NF concentrate can be further evaporated or used as a resource raw material. c. Chemical neutralization + precipitation treatment: Add neutralizing agents and flocculants to settle and remove pollutants such as magnesium oxide and heavy metals; the clear liquid meets the discharge standards, and the sludge is dehydrated and then handed over to a hazardous waste or resource treatment plant for treatment.

[0090] If the concentrated water cannot be treated further, on the premise of meeting environmental protection standards, after monitoring and meeting the standards, it can be discharged to the industrial wastewater pipe network or evaporation pond; an on-line monitoring device (COD, ammonia nitrogen, conductivity, etc.) must be set before discharge to achieve data retention and alarm control.

[0091] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain a formula that is closest to the actual situation. The preset parameters in the formulas are set by technicians in this field according to the actual situation.

[0092] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired or wireless (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that contains one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0093] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood by referring to the context. Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this document can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0094] As described above, the above are only specific implementation manners of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application.

Claims

1. A method for deep treatment and reuse of magnesium oxide wastewater from cold-rolled silicon steel, characterized in that: Including: Preliminarily treating the wastewater containing magnesium oxide in the production process of cold-rolled silicon steel, and the treatment includes adjusting the pH and adding a coagulant or a flocculant; Adding an anti-scaling agent and a membrane protection agent to the wastewater, and removing colloids and high-molecular organic matters through a multi-media filter or an ultrafiltration device to adjust the inlet parameters; Sequentially feeding the wastewater stabilized before the membrane into at least two-stage reverse osmosis systems for membrane separation treatment, and the reverse osmosis systems adopt a dynamic flow regulation and cross-flow distribution structure; Real-time monitoring the membrane surface pressure difference and the recovery rate parameters, and adaptively optimizing and controlling the system operation parameters through an intelligent algorithm; Collecting the reverse osmosis produced water and reusing it according to the requirements of the production process, and sending the concentrated liquid to a concentration or treatment unit for further disposal.

2. The method for deep treatment and reuse of magnesium oxide wastewater from cold-rolled silicon steel according to claim 1, characterized in that: The dynamic flow regulation includes setting an on-line flow monitoring and feedback system, and adjusting the inlet pressure, flow rate and recovery rate of each stage of the reverse osmosis system in real time according to the changes of the influent water quality, conductivity and temperature.

3. The deep treatment and reuse method of magnesium oxide wastewater from cold-rolled silicon steel according to claim 1, characterized in that: Wherein, The method for obtaining the transmembrane pressure difference is as follows: Install an online pressure sensor to record the instantaneous pressure values at the inlet and outlet of a single membrane module respectively, and calculate the transmembrane pressure difference of the single membrane module: ΔP = P in - P out ; ΔP is the transmembrane pressure difference of the single membrane module, P in is the instantaneous pressure value at the inlet of the membrane module, and P out is the instantaneous pressure value at the outlet of the membrane module; for a reverse osmosis system composed of multiple membrane shells connected in series, the pressure difference of each membrane shell should be calculated separately, and then the average pressure difference should be summarized as the transmembrane pressure difference. The expression is: In the formula, n is the total number of membrane shells, P in，i , P out，i are the inlet and outlet pressures of the i-th membrane shell, and SA is the transmembrane pressure difference.

4. The method for deep treatment and reuse of magnesium oxide wastewater from cold-rolled silicon steel according to claim 3, characterized in that: The method for obtaining the recovery rate is as follows: Select the key parameters affecting the recovery rate to form a feature sequence as input: Q feed (t) is the influent flow rate, Q permeate (t) is the permeate flow rate and T(t) is the water temperature; the input is a multivariate sequence within a time window, and the prediction target is the predicted value of the recovery rate for a future period of time Calculate the actual recovery rate R(t) under the condition of interference: However, under severe fluctuations or sensor errors, it will be predicted and corrected through the LSTM model to achieve SR = LSTM(X t-k+1 ,..., X t ); where: X t is all input feature vectors at time t, k is the time step, and SR is the recovery rate 5. The method for deep treatment and reuse of magnesium oxide wastewater from cold-rolled silicon steel according to claim 4, characterized in that: The intelligent algorithm includes using an LSTM neural network to train historical operation data to predict the future recovery rate fluctuation trend and optimize the system parameter settings.

6. The method for deep treatment and reuse of magnesium oxide wastewater from cold-rolled silicon steel according to claim 5, characterized in that: Converting the membrane surface pressure difference and the recovery rate into a comprehensive feature vector, using the comprehensive feature vector as the input of a machine learning model, taking the prediction of the membrane pollution risk value label for each group of comprehensive feature vectors as the prediction target, taking minimizing the sum of the prediction errors of all membrane pollution risk value labels as the training target, training the machine learning model until the sum of the prediction errors reaches convergence and then stopping the model training, and determining the membrane pollution risk value according to the model output result, wherein the machine learning model is a polynomial regression model.

7. The method for deep treatment and reuse of magnesium oxide wastewater from cold-rolled silicon steel according to claim 6, characterized in that: Comparing the obtained membrane pollution risk value with a gradient risk threshold, the gradient risk threshold includes a first risk threshold and a second risk threshold, and the first risk threshold is less than the second risk threshold, and comparing the membrane pollution risk value with the first risk threshold and the second risk threshold respectively; If the membrane pollution risk value is greater than the second risk threshold, it is determined as a high-risk pollution state, the system immediately issues an alarm, and adaptively optimizes and controls the system operation parameters; If the membrane pollution risk value is greater than or equal to the first risk threshold and less than or equal to the second risk threshold, it is determined as a medium-risk state, and the system automatically starts low-intensity backwashing and increases the pre-membrane treatment load; If the membrane pollution risk value is less than the first risk threshold, it is determined as a low-risk operation state, and the system maintains the current operation mode and only records the state for trend analysis.

8. The deep treatment and reuse method of cold-rolled silicon steel magnesium oxide wastewater according to claim 7, characterized in that: The concentrated liquid realizes reduced discharge or resource utilization after membrane concentration, evaporation crystallization or chemical neutralization precipitation, the distilled water can be reused, and the solid residue enters the solid waste treatment system.

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