Chemical high-concentration organic wastewater treatment method, device and system

Abstract

The application relates to the technical field of wastewater treatment, in particular to a chemical high-concentration organic wastewater treatment method, device and system. The method comprises the following steps: obtaining dynamic response data obtained in the process of performing a symmetric pressure disturbance operation on a membrane system, the symmetric pressure disturbance operation comprises a pressure reduction operation and a pressure recovery operation, and the dynamic response data comprises a pressure reduction response sequence and a pressure recovery response sequence of water production conductivity; determining a response lag index based on the dynamic response data; judging a pollution state of a membrane surface based on the response lag index, the pollution state being inorganic pollution or organic pollution; and performing a membrane treatment operation corresponding to the pollution state. The method can improve the pertinence and effectiveness of membrane treatment, avoid invalid cleaning or cleaning delay caused by misjudgment, and improve the intelligentization of wastewater treatment.

Description

Technical Field

[0001] This application relates to the field of wastewater treatment technology, specifically to a method, apparatus, and system for treating high-concentration organic wastewater from chemical processes. Background Technology

[0002] In the treatment of high-concentration organic wastewater in industries such as chemical, pharmaceutical and printing and dyeing, nanofiltration and reverse osmosis membranes are usually used to separate and concentrate inorganic salts and organic pollutants in the wastewater. However, during long-term operation, pollutants in the wastewater (such as dissolved organic matter, colloids, inorganic salts, etc.) will be adsorbed and deposited on the membrane surface and in the membrane pores, forming an unavoidable membrane fouling phenomenon, which leads to a decrease in membrane flux and reduces the treatment efficiency of organic wastewater. Therefore, it is necessary to clean the membrane surface in a timely manner.

[0003] In the existing technology, the monitoring of membrane fouling status mainly relies on the threshold alarm of transmembrane pressure difference. When the transmembrane pressure difference is detected to rise continuously to a preset critical value, it is determined that the fouling has reached the level that requires cleaning, and the cleaning procedure is triggered.

[0004] However, changes in transmembrane pressure can only reflect the overall increase in resistance caused by fouling, but cannot distinguish the fouling state of the membrane surface. For reversible fouling, excessive use of chemical cleaning will result in waste of reagents, secondary pollution of wastewater, and damage to membrane materials. For irreversible organic fouling, physical cleaning alone or chemical cleaning with insufficient intensity cannot effectively remove it, which may lead to the accumulation and deterioration of fouling, resulting in low effectiveness of membrane treatment and thus affecting the long-term stable operation of wastewater treatment. Summary of the Invention

[0005] To address the technical problem of low membrane treatment effectiveness, which in turn affects the long-term stable operation of wastewater treatment, this application aims to provide a method, apparatus, and system for treating high-concentration organic wastewater from chemical industries. The specific technical solution adopted is as follows: This application provides a method for treating high-concentration organic wastewater from a chemical plant, comprising: acquiring dynamic response data obtained during a symmetrical pressure perturbation operation on a membrane system, the symmetrical pressure perturbation operation including a pressure reduction operation and a pressure recovery operation, the dynamic response data including a pressure-reducing response sequence and a pressure-increasing response sequence of the permeate conductivity, the membrane system being used to filter organic wastewater; determining a response hysteresis index based on the dynamic response data, the response hysteresis index being used to characterize the degree of hysteresis in mass transfer response between the pressure-increasing response sequence and the pressure-reducing response sequence; determining the fouling state of the membrane surface based on the response hysteresis index, the fouling state being inorganic or organic fouling; and performing membrane treatment operations corresponding to the fouling state.

[0006] Optionally, the acquisition of dynamic response data obtained during the symmetrical pressure perturbation operation on the membrane system includes: after a preset blind zone time for the pressure reduction operation, collecting the permeate conductivity for a preset collection time to obtain a first permeate conductivity sequence; after collecting the first permeate conductivity sequence, performing a pressure recovery operation, and after a preset blind zone time, collecting the permeate conductivity for a preset collection time to obtain a second permeate conductivity sequence; performing differential calculation, filtering, and normalization on the first and second permeate conductivity sequences respectively to obtain a pressure reduction response sequence and a pressure increase response sequence. The pressure reduction response sequence includes the weight of the permeate conductivity change at each pressure reduction moment during the entire pressure reduction process, and the pressure increase response sequence includes the weight of the permeate conductivity change at each pressure increase moment during the entire pressure increase process.

[0007] Optionally, determining the response hysteresis index based on the dynamic response data includes: determining the time distance between each depressurization moment in the depressurization response sequence and each boost moment in the boost response sequence; determining the optimal matching relationship between the depressurization response sequence and the boost response sequence, wherein the optimal matching relationship is the matching relationship with the minimum sum of time distances, and the optimal matching relationship includes multiple matching pairs and the weight allocation ratio of each matching pair; and determining the response hysteresis index based on the time distance and weight of each matching pair in the optimal matching relationship.

[0008] Optionally, the above-mentioned determination of the fouling state of the membrane surface based on the response hysteresis index includes: obtaining the baseline response hysteresis index of the membrane system in a clean state; determining the net hysteresis growth rate based on the response hysteresis index and the baseline response hysteresis index; and determining the fouling state of the membrane surface based on the net hysteresis growth rate.

[0009] Optionally, the aforementioned organic pollution includes loose organic pollution and dense organic pollution. The determination of the pollution state of the membrane surface based on the net hysteresis growth rate includes: determining the pollution state of the membrane surface as inorganic pollution when the net hysteresis growth rate is less than or equal to the inorganic pollution threshold; determining the pollution state of the membrane surface as loose organic pollution when the net hysteresis growth rate is greater than the inorganic pollution threshold and less than or equal to the loose organic pollution threshold; and determining the pollution state of the membrane surface as dense organic pollution when the net hysteresis growth rate is greater than the loose organic pollution threshold.

[0010] Optionally, the above-mentioned membrane treatment operation corresponding to the fouling state includes: increasing the opening of the membrane system concentrate regulating valve when the fouling state is inorganic fouling.

[0011] Optionally, the aforementioned organic contamination includes loose organic contamination, and the aforementioned membrane treatment operation corresponding to the contamination state includes: when the contamination state is loose organic contamination, determining the number of flushing cycles based on the net hysteresis growth rate; and performing multiple flushing operations based on the number of flushing cycles, wherein each flushing operation involves pumping a low-pressure alkaline cleaning solution into the membrane system and allowing it to stand for a preset time.

[0012] Optionally, the aforementioned organic contamination includes dense organic contamination, and the aforementioned membrane treatment operation corresponding to the contamination state includes: when the contamination state is dense organic contamination, sequentially pumping at least one chemical cleaning solution into the membrane system for chemical cleaning.

[0013] This application also provides a chemical high-concentration organic wastewater treatment device, including a data acquisition module, a data analysis module, a pollution determination module, and a treatment module. The data acquisition module is used to acquire dynamic response data obtained during a symmetrical pressure perturbation operation on a membrane system. This symmetrical pressure perturbation operation includes a pressure reduction operation and a pressure recovery operation. The dynamic response data includes a pressure-reducing response sequence and a pressure-increasing response sequence of the product water conductivity. The membrane system is used to filter organic wastewater. The data analysis module is used to determine a response hysteresis index based on the dynamic response data. This response hysteresis index characterizes the degree of hysteresis in mass transfer response compared to the pressure-reducing response sequence. The pollution determination module is used to determine the pollution state of the membrane surface based on the response hysteresis index. This pollution state is either inorganic or organic pollution. The treatment module is used to perform membrane treatment operations corresponding to the pollution state.

[0014] This application also provides a chemical high-concentration organic wastewater treatment system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements any of the above-described chemical high-concentration organic wastewater treatment methods.

[0015] This application has the following beneficial effects: By acquiring dynamic response data during symmetrical pressure perturbation operations and determining the fouling state based on the lag in mass transfer response between the pressure-boosting and pressure-reducing response sequences, accurate identification of membrane fouling status is achieved. Compared to existing technologies that rely solely on steady-state parameters such as transmembrane pressure difference, this application introduces dynamic response characteristics, effectively distinguishing between two distinct sources of resistance: inorganic salt concentration polarization and organic fouling layers. This allows for objective assessment of subsequent membrane treatment operations, improving the targeting and effectiveness of membrane treatment, avoiding ineffective or delayed cleaning due to misjudgment, and enhancing the intelligence of wastewater treatment. Attached Figure Description

[0016] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a method for treating high-concentration organic wastewater from a chemical industry, provided in one embodiment of this application. Figure 2 This is a structural diagram of a chemical high-concentration organic wastewater treatment device provided in one embodiment of this application. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation methods, structures, features, and effects of a method, apparatus, and system for treating high-concentration organic wastewater in a chemical industry according to this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0020] A membrane system is a core equipment unit for treating high-concentration organic wastewater from chemical plants. Its main function is to separate pollutants from water molecules in the wastewater through filtration. A membrane system includes at least membrane elements, a high-pressure pump, piping, and sensors. The membrane element is the core component that actually performs the filtration function. Its surface is covered with a selectively permeable polymer membrane material; this surface is called the membrane surface. During wastewater treatment, wastewater flows through the membrane element surface driven by the high-pressure pump. Water molecules permeate through the membrane surface into the product water side, while inorganic salts, organic matter, and other pollutants in the wastewater are retained on the membrane surface, thus achieving purification. Therefore, the cleanliness of the membrane surface directly determines the filtration performance and wastewater treatment effect of the membrane system.

[0021] In the treatment of high-concentration organic wastewater in industries such as chemical, pharmaceutical, and printing and dyeing, nanofiltration and reverse osmosis membrane systems play a crucial role in salt separation and concentration. During long-term operation, two main types of coating layers inevitably form on the membrane surface: one is the concentration polarization layer formed by the retention of high-concentration inorganic salts, and the other is the organic fouling layer formed by the adsorption and deposition of macromolecular organic matter.

[0022] In current engineering practices, the degree of fouling in membrane systems is typically determined by an increase in transmembrane pressure differential (TMP) or a decrease in permeate flux. However, this steady-state-based monitoring method has significant limitations: both the thickening of the inorganic salt polarization layer and the accumulation of organic fouling layers manifest as an increase in macroscopic fluid resistance (i.e., an increase in pressure differential). Existing technologies struggle to distinguish between these two distinct sources of resistance based solely on pressure differential signals.

[0023] This limitation leads to blind cleaning strategies: misjudging reversible inorganic polarization as organic pollution and frequently performing unnecessary chemical cleaning will interrupt production and shorten membrane life; if the initial accumulation of organic pollution layer is not detected in time, it will gradually become dense and caking under continuous high pressure, resulting in permanent degradation of membrane flux.

[0024] In reality, although both lead to increased resistance, their dynamic response characteristics to pressure changes differ. The formation and dissipation of the inorganic concentration polarization layer are mainly controlled by hydrodynamics, and its response to pressure changes is relatively rapid; while the organic fouling layer, as a barrier that hinders the back diffusion of solutes, causes a time lag in changes in local salt concentration on the membrane surface relative to changes in operating pressure. However, this dynamic response characteristic is often masked by the inherent response delay of equipment (such as high-pressure pumps and pipelines), making it difficult to extract and quantify using conventional methods.

[0025] Based on this difference, embodiments of this application provide a method, apparatus and system for treating high-concentration organic wastewater in the chemical industry. By actively applying pressure disturbance and observing the dynamic response of the conductivity of the product water, the fouling state of the membrane surface is identified, and then membrane treatment operation is carried out.

[0026] The following description, in conjunction with the accompanying drawings, details the specific scheme of a method, apparatus, and system for treating high-concentration organic wastewater in the chemical industry provided in this application.

[0027] Please see Figure 1 The diagram illustrates a process flow chart of a method for treating high-concentration organic wastewater from a chemical industry, according to an embodiment of this application.

[0028] like Figure 1 As shown, the method for treating high-concentration organic wastewater from chemical plants includes S101-S104.

[0029] S101. Obtain dynamic response data during the symmetrical pressure perturbation operation on the membrane system.

[0030] The symmetrical pressure disturbance operation includes pressure reduction operation and pressure recovery operation, and the dynamic response data includes pressure reduction response sequence and pressure increase response sequence of product water conductivity. The membrane system is used to filter organic wastewater.

[0031] It should be understood that symmetrical pressure disturbance operation refers to sending a command to the high-pressure pump inverter of the membrane system, causing it to perform a symmetrical process of reducing pressure and then restoring it to the original pressure. Throughout the entire symmetrical pressure disturbance operation, conductivity sensors installed on the permeate pipeline continuously collect data on the change in permeate conductivity over time, with each permeate conductivity reading representing a sampling point, thus obtaining dynamic response data.

[0032] In one optional implementation, after a preset blind period of time for performing the pressure reduction operation, the permeate conductivity for a preset collection time is collected to obtain a first permeate conductivity sequence; after obtaining the first permeate conductivity sequence, a pressure recovery operation is performed, and after a preset blind period of time, the permeate conductivity for a preset collection time is collected to obtain a second permeate conductivity sequence; the first permeate conductivity sequence and the second permeate conductivity sequence are respectively subjected to differential calculation, filtering and normalization processing to obtain a pressure reduction response sequence and a pressure increase response sequence.

[0033] The depressurization response sequence includes the weight of the change in permeate conductivity at each depressurization moment during the entire depressurization process (hereinafter referred to as the weight at each depressurization moment), and the pressurization response sequence includes the weight of the change in permeate conductivity at each pressurization moment during the entire pressurization process (hereinafter referred to as the weight at each pressurization moment).

[0034] It should be understood that the preset blind zone duration is the time between the issuance of a pressure reduction command or a pressure increase command and the execution of the command to reach the pressure indicated by the command. During this time, the pressure is unstable. Therefore, data acquisition should avoid the preset blind zone duration. Data acquisition should be performed after the preset blind zone duration to avoid fluid turbulence and pump vibration noise.

[0035] It is understandable that, since the pressure disturbance operation is a symmetrical operation, the preset blind zone duration, preset acquisition duration, and acquisition frequency are the same after the pressure is reduced and increased.

[0036] For example, assuming the pressure reduction command indicates that the pressure will be adjusted to 85% of the original set value within 2 seconds, the preset blind zone duration is 2 seconds.

[0037] Optionally, when the membrane system is in a stable operating state, a pressure reduction command can be sent to the high-pressure pump frequency converter. After the pressure reduction command is issued, a timer is started to wait for a preset dead zone duration. After the preset dead zone duration ends, data acquisition is triggered to obtain the permeate conductivity at multiple pressure reduction moments within a preset acquisition duration. The permeate conductivity at these multiple pressure reduction moments is sorted in chronological order to obtain a first permeate conductivity sequence. This first permeate conductivity sequence records the continuous change of permeate conductivity over time after the pressure stabilizes at a low value for a period of time.

[0038] After the first product water conductivity sequence is collected, a boost command is sent to the high-pressure pump frequency converter. After waiting for a preset dead zone time, data acquisition is triggered again to obtain the product water conductivity at multiple boost times within the preset acquisition time. The product water conductivity obtained in this acquisition is sorted in time sequence to obtain the second product water conductivity sequence. The second product water conductivity sequence records the continuous change of product water conductivity over time for a period of time after the pressure is restored to the original pressure value.

[0039] For example, the data acquisition frequency can be 10Hz, and the preset acquisition duration can be 60 seconds. The conductivity values ​​of the produced water obtained from this acquisition are arranged in chronological order.

[0040] It should be noted that during the entire data acquisition process, the influent flow rate and the opening of the concentrate regulating valve should be kept constant to ensure that the changes in the acquired data are mainly due to changes in the transmembrane pressure difference, rather than adjustments to other operating parameters.

[0041] After obtaining the first and second product water conductivity sequences, data preprocessing is required to highlight the dynamic characteristics of conductivity changes.

[0042] It should be understood that due to slight fluctuations in the influent water quality and power frequency, the original collected data (i.e., the first and second product water conductivity sequences) are usually superimposed with high-frequency random noise. If this noise is not filtered out, it will affect the accuracy of subsequent feature extraction. Therefore, moving average filtering is performed on the first and second product water conductivity sequences respectively.

[0043] Optionally, the window length used for filtering can be 5, that is, the value of the current point is replaced by the arithmetic mean of 5 consecutive sampling points.

[0044] Optionally, after filtering, the filtered first and second product water conductivity sequences can be differentially calculated separately: For the first product water conductivity sequence, the absolute value of the difference between two adjacent conductivity values ​​in the sequence is calculated sequentially, and the obtained absolute values ​​of the difference are arranged in chronological order to obtain the first rate of change sequence. Each value in the first rate of change sequence represents the instantaneous drastic change in conductivity at the corresponding pressure drop moment; the larger the value, the more drastic the change in conductivity at that pressure drop moment. Similarly, the second product water conductivity sequence is differentially calculated using the same method to obtain the second rate of change sequence.

[0045] It should be understood that since the numerical values ​​of the first and second rate of change sequences are affected by the absolute level of the permeate conductivity, direct comparison would introduce interference unrelated to membrane fouling. Therefore, it is necessary to normalize the two rate of change sequences, converting them into a dimensionless proportional form to eliminate amplitude differences and retain only waveform morphology information.

[0046] Optionally, the normalization process for the first rate of change sequence is as follows: first, calculate the sum of all rates of change in the first rate of change sequence, and then sum this sum to a preset minimum positive number (e.g., 10). -6 The values ​​of each rate of change in the first rate of change sequence are added together and used as the normalized denominator. Each rate of change in the first rate of change sequence is divided by the normalized denominator to obtain the weight of the change in the conductivity of the product water at each depressurization moment in the entire depressurization process. The weights of the change in the conductivity of the product water at each depressurization moment in the entire depressurization process are arranged in time sequence to obtain the depressurization response sequence.

[0047] Similarly, the boost response sequence is obtained.

[0048] It should be noted that the preset minimum positive number is to avoid the situation where the sum of all rates of change is 0, making calculation impossible.

[0049] It should be understood that the sum of the weights of all depressurization moments in the depressurization response sequence (or boost response sequence) is approximately 1, which can be mathematically regarded as a probability distribution.

[0050] Thus, through the above steps, two normalized response sequences were obtained: a buck response sequence and a boost response sequence.

[0051] The above-mentioned method for obtaining dynamic response data effectively filters out invalid data caused by fluid turbulence, pressure wave oscillation, and inverter response dead zone during the high-pressure pump adjustment by setting a preset blind zone duration. It highlights the drastic change in conductivity through differential calculation, thereby eliminating the influence of background values. Through normalization processing, it ensures that the data of the depressurization process and the boost process have the same dimensions and comparable basis, providing high-quality data input for subsequent feature extraction.

[0052] S102. Determine the response lag index based on dynamic response data.

[0053] The response hysteresis index is used to characterize the degree of hysteresis in the mass transfer response of the boost response sequence compared to the depressurization response sequence.

[0054] It should be understood that the presence of organic pollutants slows down the mass transfer process on the membrane surface. This slowdown is manifested in the data as an overall time lag and tail drag in the boost waveform relative to the depressurization waveform. Therefore, by comparing the degree of response lag of the boost response sequence with the depressurization response sequence in the dynamic response data, the fouling status of the membrane surface can be assessed.

[0055] In one implementation of this application, the time distance between each bucking moment in the buck response sequence and each boosting moment in the boost response sequence can be determined first; then the optimal matching relationship between the buck response sequence and the boost response sequence can be determined one-to-one; and then the response hysteresis index can be determined based on the time distance and weight allocation ratio of each matching pair in the optimal matching relationship.

[0056] The optimal matching relationship is the matching relationship with the smallest sum of time distances. The optimal matching relationship includes multiple matching pairs and the weight allocation ratio of each matching pair.

[0057] Optionally, the sequence length of the buck response sequence and the boost response sequence can be determined, i.e., the total number of moments included, and the sequence number of each buck moment and boost moment in the sequence. The ratio between the sequence number of a buck moment (or boost moment) and the sequence length is determined as the normalized time coordinate of that buck moment (or boost moment). The square of the difference between the normalized time coordinates of a buck moment and a boost moment is determined as the time distance between the buck moment and the boost moment.

[0058] It should be understood that the normalized time coordinate of a bucking moment in a bucking response sequence is used to characterize the position of that bucking moment on the bucking time axis, and the normalized time coordinate of a boosting moment in a boosting response sequence is used to characterize the position of that boosting moment on the boosting time axis.

[0059] Optionally, the time interval between a pressure reduction moment and a pressure increase moment satisfies the following formula:

[0060] in, Indicates the first step in the buck response sequence The first step in the sequence of pressure reduction and pressure increase response The time interval between each boost moment This indicates the sequence lengths of the buck response sequence and the boost response sequence. Indicates the first Normalized time coordinates of each pressure drop moment. Indicates the first Normalized time coordinates of each boost moment.

[0061] Subsequently, a time span cost matrix is ​​constructed based on the buck time axis and the boost time axis. This time span cost matrix is ​​a two-dimensional matrix with the rows being the buck time axis and the columns being the boost time axis. Each element in the matrix stores the time distance between a buck moment in the buck time axis and a boost moment in the boost time axis.

[0062] It should be understood that when the normalized time coordinates of the bucking moment and the boosting moment are the same, the time distance between them is 0. The greater the distance between the normalized time coordinates of the bucking moment and the boosting moment, the greater the time distance increases quadratically, thus imposing higher costs on matching pairs that are far apart.

[0063] Next, it is necessary to determine the optimal matching relationship from the buck response sequence to the boost response sequence. In this optimal matching relationship, one buck time point may correspond to multiple boost time points, and one boost time point may correspond to multiple buck time points. The goal of this optimal matching relationship is to find a matching scheme that minimizes the time distance cost from the buck response sequence to the boost response sequence, while satisfying the distribution constraints of the two sequences.

[0064] The distribution constraint means that: First, the sum of the weights allocated from each bucking moment in the buck response sequence must be equal to the weight of that bucking moment, that is, the weight of each bucking moment in the buck response sequence is fully allocated; Second, the sum of the weights received at each boosting moment in the boost response sequence must be equal to the weight of that boosting moment, that is, the weight requirement of each boosting moment in the boost response sequence is fully satisfied.

[0065] For example, assuming the weight of bucking time 1 is 0.5, the weight of bucking time 1 is distributed to boosting time 1, boosting time 2 and boosting time 3. The weights allocated to boosting time 1, boosting time 2 and boosting time 3 should be added together to 0.5. For example, a weight of 0.1 is allocated to boosting time 1, a weight of 0.3 is allocated to boosting time 2 and a weight of 0.1 is allocated to boosting time 3.

[0066] In this embodiment, the Sinkhorn algorithm is used to iteratively solve for the optimal matching relationship.

[0067] First, a time tolerance coefficient is introduced. This coefficient determines the algorithm's tolerance for small local time fluctuations. A larger coefficient means the algorithm focuses more on the overall trend and allows for a certain degree of local deviation; a smaller coefficient means the algorithm focuses more on precise time point matching and is more sensitive to local differences. The value of this coefficient can be adjusted according to the dynamic characteristics of the actual system.

[0068] For example, the value of the time tolerance coefficient should match the magnitude of the normalized time coordinate. The higher the acquisition frequency, the smaller the time tolerance coefficient. When the acquisition frequency is adjusted, the time tolerance coefficient can be slightly adjusted within the range of 0.005 to 0.02. For example, when the acquisition frequency is 10Hz, the time tolerance coefficient can be 0.01. This value can effectively balance the relationship between overall shape matching and local detail preservation.

[0069] Then, based on the time distance matrix and the time tolerance coefficient, the kernel matrix is ​​calculated. The kernel matrix has the same dimension as the time distance matrix. The kernel matrix converts the time distance into similarity weights. The closer the time distance, the larger the corresponding value of the kernel matrix. The farther the time distance, the closer the corresponding value of the kernel matrix is ​​to 0.

[0070] Optionally, the similarity weight of an element in the kernel matrix satisfies the following formula:

[0071] in, Indicates the first The moment of blood pressure reduction and the first Similarity weights between boost moments Indicates the first The moment of blood pressure reduction and the first The time interval between each boost moment Indicates the time tolerance factor. This represents an exponential function.

[0072] In this formula, the smaller the time distance, the greater the similarity weight; the larger the time distance, the smaller the similarity weight, and it approaches 0. Through this transformation, the original time distance is mapped to a similarity scale between 0 and 1, which facilitates subsequent iterative calculations.

[0073] Next, two auxiliary vectors are defined: a row balancing vector and a column balancing vector. Both have the same length as the buck response sequence, meaning their values ​​equal the number of buck moments in the sequence. The row balancing vector adjusts the weight distribution of each buck moment in the buck response sequence, while the column balancing vector adjusts the reception weight of each boost moment in the boost response sequence. At the start of the iteration, the classification of each buck moment in the row balancing vector is initialized to 1, and the component of each boost moment in the column balancing vector is initialized to 1. This means that initially, the weight distribution of each buck moment and each boost moment is assumed to be uniform.

[0074] Subsequently, a fixed number of alternating iterative updates are performed to gradually approximate the optimal match that satisfies the distribution constraints.

[0075] Optionally, in this embodiment of the application, the preset number of iterations is set to 20, which is sufficient to make the row balance vector and column balance vector converge to a stable value.

[0076] Specifically, each iteration includes the following two update steps: Before the iterative update begins, the components of the row balance vector at all depressurization times and the components of the column balance vector at all boost times are initialized to 1. Then, the first and second steps are repeated for the preset number of iterations.

[0077] Step 1: Update the row balance vector.

[0078] Specifically, based on the components of all boost moments in the current column balance vector, update the components of all deboost moments in the row balance vector. Optionally, the update formula for the components of a deboost moment is:

[0079] in, The row balance vector is represented at the th row. The component of each blood pressure drop moment, Indicates the first The weight of each time point of pressure reduction This indicates the number of time points in the buck response sequence. Represents the kernel matrix of the first... The moment of blood pressure reduction and the first Similarity weights between boost moments Indicates the current column balance vector at the th position. The component at each boost moment.

[0080] In this formula, This reflects the current state, the first The first time the pressure was reduced to the first The ease or difficulty of assigning weights at each boost moment; the denominator This indicates that in the current iteration step, from the th... Starting from the first depressurization moment, the total matching potential is matched to all boost moments. The first... The weight of each depressurization moment is divided by the current total matching potential to obtain the updated [weight]. The physical logic is: if the current total matching potential is large (i.e., the denominator is large), it means that the first... There are already many ways to match the pressure reduction timing with the pressure boost timing, so... The value should be appropriately reduced to avoid over-allocation; if the current total matching potential is small (i.e., the denominator is small), it indicates that the first... If there are insufficient opportunities to allocate time for pressure reduction, then... It should be appropriately increased to enhance its matching ability.

[0081] The second step is to update the column balance vector.

[0082] It should be understood that after updating the row balance vector, the newly obtained row balance vector is used to update the components of the column balance vector at all boost times.

[0083] Optionally, the update formula for the column balance vector at a boost moment is:

[0084] in, The column balance vector is represented at the th The component at each boost moment, Indicates the first The weight of each boost moment, This indicates the number of time points in the buck response sequence. Represents the kernel matrix of the first... The moment of blood pressure reduction and the first Similarity weights between boost moments This indicates that the updated row balance vector is at the th position. The component of each blood pressure reduction moment.

[0085] Repeat the two update steps above until the preset number of iterations is reached.

[0086] After multiple alternating updates, the row balance vector and column balance vector gradually converge. At this point, the obtained row balance vector and column balance vector can make the weight distribution in the row direction consistent with the buck response sequence, and the weight reception in the column direction consistent with the boost response sequence.

[0087] After iteration, the mapping weight matrix is ​​calculated based on the converged row and column balance vectors. The mapping weight matrix has the same dimensions as the kernel matrix. Each element in the mapping weight matrix represents the proportion of the weight in the buck response sequence corresponding to the buck time step mapped to the corresponding buck time step in the boost response sequence.

[0088] Optionally, the value of an element in the mapping weight matrix satisfies the following formula:

[0089] in, Indicates the first The weights of the first and second pressure reduction moments are allocated to the first... The weighting ratio for each boost moment. The row balance vector is represented at the th row. The component of each blood pressure drop moment, Represents the kernel matrix of the first... The moment of blood pressure reduction and the first Similarity weights between boost moments The row balance vector is represented at the th row. The component at each boost moment.

[0090] It should be understood that when the value of an element is not 0, the match is considered successful. The bucking moment and the bucking moment are identified as a matching pair, and the value of the element is identified as the weight allocation ratio of the matching pair. By statistically analyzing all the matching pairs, the optimal matching relationship is obtained.

[0091] It should be understood that the mapping weight matrix includes the weight allocation ratio between each bucking time and each boosting time, that is, the optimal matching relationship between the weight of the bucking time and the weight of the boosting time.

[0092] Finally, the response lag index is determined based on the time distance and weight allocation ratio of each matching pair in the optimal matching relationship.

[0093] Optionally, the response lag exponent satisfies the following formula:

[0094] in, The response hysteresis exponent, expressed in seconds, represents the actual mass transfer lag time of the boost response sequence compared to the depressurization response sequence. Indicates the first The weights of the first and second pressure reduction moments are allocated to the first... The weighting ratio for each boost moment. Indicates the first The sequence number of the boost moment is related to the first boost moment. The difference in the sequence number of each voltage drop moment. This represents the time interval between adjacent sampling points, in seconds. The value is 1 / sampling frequency. This represents the total number of moments in the buck response sequence and the boost response sequence.

[0095] In this formula, when When the value is positive and larger, it indicates that the boost response lags behind the buck response, and the response hysteresis exponent should be larger; when The smaller the value of the negative exponent, the earlier the boost response is compared to the buck response, and the smaller the response hysteresis exponent. When the value is zero, it indicates that the two moments are synchronized. This indicates the weight ratio of the matching pair in the overall matching scheme. The product of the two represents the response lag value contributed by the matching pair. Summing the lag values ​​of all matching pairs yields the final response lag index.

[0096] The method provided in S102 above finds the optimal matching relationship with the minimum sum of time distances by quantifying the time distances between each depressurization moment in the depressurization response sequence and each boosting moment in the boosting response sequence. Then, it determines the response hysteresis exponent by combining the time distances of the matching pairs with the weighting ratio. This method transforms the morphological differences between the depressurization and boosting response sequences into quantifiable index indicators. By constructing the optimal matching relationship, it accurately captures the overall mass transfer response hysteresis characteristics of the boosting response sequence relative to the depressurization response sequence. It can effectively quantify nonlinear waveform distortion, achieving a precise and objective characterization of the degree of mass transfer hysteresis on the membrane surface, providing a scientific and effective quantitative basis for accurately judging the fouling state of the membrane surface.

[0097] S103. Determine the fouling status of the membrane surface based on the response hysteresis index.

[0098] The pollution state can be classified as inorganic or organic.

[0099] It should be understood that, in addition to membrane fouling causing response hysteresis, the mechanical inertia of high-pressure pumps, the fluid damping of long-distance pipelines, and the response delay of sensors all introduce inherent system hysteresis. To eliminate the interference of these equipment factors, this module introduces a benchmark correction mechanism to calculate the net hysteresis growth rate after deducting the inherent system hysteresis, and accordingly accurately classifies the fouling state.

[0100] In one alternative implementation, a baseline response hysteresis index of the membrane system in a clean state can be obtained; based on the response hysteresis index and the baseline response hysteresis index, a net hysteresis growth rate is determined; and based on the net hysteresis growth rate, the fouling state of the membrane surface is judged.

[0101] It should be understood that within 24 hours of the initial commissioning of the membrane system or after a thorough chemical cleaning (especially oxidative cleaning) of the membrane surface, the membrane system can be considered to be in a clean state. At this time, based on the methods provided in S101-S102 above, the response hysteresis index is determined and designated as the baseline response hysteresis index. This baseline response hysteresis index is used to characterize the inherent asymmetric hysteresis of the system introduced by hardware facilities such as pumps, pipelines, and sensors under a pollution-free state.

[0102] Alternatively, the net lagged growth rate satisfies the following formula:

[0103] in, Indicates the net lagged growth rate. Indicates the response lag index. Indicates the benchmark response lag index. This represents a preset, extremely small positive number, such as 0.01. express and The maximum value in is used to prevent [the virus] from [being detected] under extremely ideal experimental conditions. Approaching 0, which leads to an excessively small denominator, causes tiny measurement fluctuations to be mistakenly amplified into huge percentage increases (i.e., mathematical divergence), thus ensuring the stability of the numerical value.

[0104] In this formula, This represents the net hysteresis increment caused solely by changes in the properties of the membrane surface coating, after deducting the inherent hysteresis of the equipment. This net hysteresis increment is compared with the baseline response hysteresis index to obtain the growth rate of the net hysteresis increment.

[0105] In one alternative implementation, organic pollution also includes loose organic pollution and dense organic pollution. Two thresholds can be set to distinguish between the three pollution states, such as an inorganic pollution threshold and a loose organic pollution threshold, whereby the inorganic pollution threshold should be less than the loose organic pollution threshold.

[0106] If the net hysteresis growth rate is less than or equal to the inorganic pollution threshold, the pollution state of the membrane surface is determined to be inorganic pollution; if the net hysteresis growth rate is greater than the inorganic pollution threshold and less than or equal to the loose organic pollution threshold, the pollution state of the membrane surface is determined to be loose organic pollution; if the net hysteresis growth rate is greater than the loose organic pollution threshold, the pollution state of the membrane surface is determined to be dense organic pollution.

[0107] It should be understood that the terms "loose organic pollution" and "dense organic pollution" are used to distinguish the degree of organic pollution, and different treatment operations should be adopted for different degrees of pollution.

[0108] Optionally, the inorganic fouling threshold and the loose organic fouling threshold can be adjusted according to the membrane system type (e.g., nanofiltration / reverse osmosis) and the actual wastewater quality.

[0109] For example, the inorganic pollution threshold can be set to 20%, and the loose organic pollution threshold can be set to 100%.

[0110] It is understandable that if the net hysteresis growth rate is less than or equal to the inorganic pollution threshold, it indicates that the system's response hysteresis increment is very small, suggesting that the membrane surface is mainly covered by an inorganic salt concentration polarization layer. In this case, the pollution state of the membrane surface can be determined to be inorganic pollution.

[0111] If the net hysteresis growth rate is greater than the inorganic contamination threshold and less than or equal to the loose organic contamination threshold, it indicates that the hysteresis increment is significant and an organic gel layer of a certain thickness has been deposited on the membrane surface. The gel hinders back diffusion, resulting in a slower response. At this point, the contamination state of the membrane surface can be determined to be loose organic contamination.

[0112] When the net hysteresis growth rate is greater than the loose organic contamination threshold, it indicates that the hysteresis increment is extremely large, the organic gel layer has undergone severe densification or aging and compaction, the fluid permeation resistance is huge and the response is extremely slow. At this time, the contamination state of the membrane surface can be determined to be dense organic contamination.

[0113] S104. Perform membrane treatment operations corresponding to the state of contamination.

[0114] Specifically, for inorganic contamination, fluid optimization operations targeting inorganic salt concentration polarization can be performed; for loose organic contamination, low-pressure flushing operations based on dynamic circulation can be performed; and for dense organic contamination, deep cleaning operations including chemical cleaning can be performed.

[0115] The following describes three membrane treatment operations: 1. When the pollution is inorganic, increase the opening of the concentrate regulating valve of the membrane system.

[0116] It should be understood that when the fouling state of the membrane surface is determined to be inorganic fouling, it indicates that the current flux decline is mainly due to the concentration polarization layer formed by the accumulation of high-concentration salts on the membrane surface, rather than stubborn fouling by organic matter. In this case, the remediation goal is to disrupt the concentration polarization boundary layer, thereby increasing the fluid shear force on the membrane surface to remove the accumulated salts and prevent further formation of inorganic scale.

[0117] The specific actions are as follows: Maintain the water production status unchanged, send a command to the concentrate regulating valve. The command should include a preset opening degree (e.g., 5%) and a preset optimization duration (e.g., 1 hour) so that the opening degree of the concentrate regulating valve increases to the preset opening degree based on the current operating opening degree, and continues to execute the preset optimization duration. After that, the concentrate regulating valve is automatically restored to the original opening degree and re-enters the monitoring cycle, waiting for the next pollution status judgment.

[0118] It is understandable that increasing the opening of the concentrate valve will directly reduce the recovery rate of the membrane system, thereby increasing the cross-flow velocity inside the membrane element. The high-speed fluid generates a stronger scouring and shearing force on the membrane surface, which can effectively remove the high concentration of salt accumulated on the membrane surface and restore the membrane flux.

[0119] 2. When the pollution is loose organic pollution, determine the number of flushing cycles based on the net lag growth rate; and perform multiple flushing operations based on the number of flushing cycles.

[0120] One of the rinsing operations involves pumping a low-pressure alkaline cleaning solution into the membrane system and allowing it to stand for a preset time.

[0121] It should be understood that when the fouling state of the membrane surface is determined to be loose organic fouling, it indicates that an organic gel layer of a certain thickness but with a relatively loose structure has been deposited on the membrane surface. At this time, repeated loading and unloading of hydrostatic pressure can induce compression-expansion deformation of the gel layer, thereby causing it to loosen and detach.

[0122] It is understandable that the larger the net lag growth rate, the more serious the organic pollution, and the more flushing cycles are required. Therefore, the net lag growth rate can be multiplied by a preset proportional coefficient (e.g., 10) and then rounded to obtain the basic number of cycles.

[0123] Optionally, to prevent excessively long cleaning times due to an excessive number of cycles calculated under extreme contamination conditions, a maximum cycle limit can be set, and the smaller value between the base number of cycles and the maximum cycle limit can be taken as the final number of rinsing cycles to be performed.

[0124] For example, in this embodiment of the application, the maximum cycle limit is set to 10 times.

[0125] For example, assuming a net lagged growth rate of 50%, the base number of cycles is 5.

[0126] After determining the number of flushing cycles, the system pauses the high-pressure pump's water production operation, starts the cleaning pump, and begins the cyclic flushing operation. Each flushing cycle consists of two steps: an operating phase and a settling phase. Operation phase: Pump an alkaline cleaning solution with a pH of 10 into the membrane system, control the operating pressure to be below 0.2 MPa, and control the flow rate to 50% of the maximum allowable feed water flow rate of the membrane system, and continue operation for 3 minutes. During this phase, the low-pressure alkaline cleaning solution is used to wet the organic gel layer and initially loosen its structure.

[0127] Settling Phase: Turn off the cleaning pump and allow the membrane system to stand and soak in the cleaning solution for a preset time (e.g., 5 minutes). This phase utilizes the natural swelling properties of the organic gel under low pressure to expand its volume, weakening its adhesion to the membrane surface and creating more favorable conditions for the next flushing cycle.

[0128] The above operation and settling phases are repeated according to the number of flushing cycles. After all cycles are completed, the water production state is automatically restored and the monitoring cycle is re-entered.

[0129] 3. When the fouling state is dense organic fouling, at least one chemical cleaning solution is pumped into the membrane system in sequence for chemical cleaning.

[0130] It should be understood that when the fouling state of the membrane surface is determined to be dense organic fouling, it indicates that the organic gel layer has undergone severe densification or aging and hardening, and physical rinsing is insufficient to remove it. Chemical oxidation must be used to break the chemical bonds of the organic molecules. At this point, the system completely stops production and triggers a deep chemical cleaning procedure.

[0131] The chemical cleaning procedure includes the following steps in sequence: The first step, acid washing step: Pump an acidic cleaning solution with a pH of 2, such as hydrochloric acid solution, into the membrane system, circulate and soak for 60 minutes.

[0132] It should be understood that the purpose of this step is to remove metal oxides or inorganic scale that may coexist with organic contaminants, creating favorable conditions for subsequent oxidative cleaning.

[0133] The second step is the oxidation cleaning process: After draining the acid washing waste liquid, an alkaline cleaning solution with a pH of 11 containing an oxidant is pumped into the membrane system, and the system is circulated and soaked for 60 minutes to ensure that the oxidant and organic pollutants are fully in contact and react.

[0134] Optionally, the oxidant can be 500 ppm sodium hypochlorite. Sodium hypochlorite, as a strong oxidant, can break the chain structure of large organic molecules such as proteins and humic acids, causing them to degrade into smaller molecules and detach from the membrane surface.

[0135] The third step is rinsing: After draining the chemical reagents, repeatedly rinse the membrane system with permeate or permeate water until the pH of the permeate water returns to the neutral range to ensure that there are no chemical residues.

[0136] In one alternative implementation, the baseline waveform distortion index can be updated after chemical cleaning is completed. Specifically, within 24 hours after chemical cleaning, symmetrical pressure perturbation operations are performed multiple times, and the response hysteresis index is calculated. The average value is then used as the new baseline waveform distortion index.

[0137] The method provided in S104 above achieves precise matching between cleaning intensity and pollution type through differentiated treatment operations for different pollution states. This avoids over-cleaning of slight inorganic polarization and prevents organic pollution from developing into irreversible caking due to failure to treat it in time. It significantly extends the chemical cleaning cycle and reduces the operation and maintenance costs of the membrane system.

[0138] The methods provided in S101-S104 above acquire dynamic response data during symmetrical pressure perturbation operations and determine the fouling state based on the lag in mass transfer response between the pressure-boosting response sequence and the pressure-reducing response sequence, thus achieving accurate identification of membrane fouling status. Compared with existing technologies that rely solely on steady-state parameters such as transmembrane pressure difference, this application introduces dynamic response characteristics, which can effectively distinguish between two distinct sources of resistance: inorganic salt concentration polarization and organic fouling layers. This allows for objective judgment of subsequent membrane treatment operations, improving the targeting and effectiveness of membrane treatment, avoiding ineffective cleaning or cleaning delays due to misjudgment, and enhancing the intelligence of wastewater treatment.

[0139] This application also provides a device for treating high-concentration organic wastewater from chemical plants, such as... Figure 2 As shown, the chemical high-concentration organic wastewater treatment device 20 includes a data acquisition module 201, a data analysis module 202, a pollution determination module 203, and a treatment module 204.

[0140] The data acquisition module 201 is used to acquire dynamic response data obtained during the symmetrical pressure perturbation operation on the membrane system.

[0141] The symmetrical pressure disturbance operation includes pressure reduction operation and pressure recovery operation, and the dynamic response data includes pressure reduction response sequence and pressure increase response sequence of product water conductivity. The membrane system is used to filter organic wastewater.

[0142] The data analysis module 202 is used to determine the response lag index based on the dynamic response data.

[0143] The response hysteresis index is used to characterize the degree of hysteresis in the mass transfer response of the boost response sequence compared to the depressurization response sequence.

[0144] The contamination determination module 203 is used to determine the contamination status of the membrane surface based on the response hysteresis index.

[0145] The pollution state can be classified as inorganic or organic.

[0146] The treatment module 204 is used to perform membrane treatment operations corresponding to the pollution state.

[0147] It should be noted that the chemical high-concentration organic wastewater treatment device 20 provided in this application embodiment can also perform any of the above-mentioned optional chemical high-concentration organic wastewater treatment methods.

[0148] This application also provides a chemical high-concentration organic wastewater treatment system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements any of the above-described chemical high-concentration organic wastewater treatment methods.

[0149] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0150] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for treating high-concentration organic wastewater from chemical processes, characterized in that, include: The dynamic response data obtained during the symmetrical pressure perturbation operation on the membrane system includes pressure reduction operation and pressure recovery operation. The dynamic response data includes the pressure reduction response sequence and pressure increase response sequence of the permeate conductivity. The membrane system is used to filter organic wastewater. Based on the dynamic response data, a response hysteresis index is determined, which is used to characterize the degree of hysteresis in mass transfer response of the boost response sequence compared to the depressurization response sequence. Based on the response hysteresis index, the fouling state of the membrane surface is determined, and the fouling state is inorganic or organic. Perform the membrane treatment operation corresponding to the aforementioned contamination state.

2. The method for treating high-concentration organic wastewater from chemical plants according to claim 1, characterized in that, The acquisition of dynamic response data obtained during the symmetrical pressure perturbation operation on the membrane system includes: After a preset blind zone duration during the pressure reduction operation, the conductivity of the produced water is collected for a preset collection duration to obtain the first sequence of produced water conductivity. After obtaining the first product water conductivity sequence, a pressure recovery operation is performed, and after a preset blind zone time, the product water conductivity is collected for a preset collection time to obtain the second product water conductivity sequence. The first product water conductivity sequence and the second product water conductivity sequence are subjected to differential calculation, filtering and normalization to obtain a pressure reduction response sequence and a pressure increase response sequence. The pressure reduction response sequence includes the weight of the product water conductivity change at each pressure reduction moment in the entire pressure reduction process. The pressure increase response sequence includes the weight of the product water conductivity change at each pressure increase moment in the entire pressure increase process.

3. The method for treating high-concentration organic wastewater from chemical plants according to claim 1, characterized in that, The determination of the response lag index based on the dynamic response data includes: Determine the time interval between each pressure drop moment in the pressure drop response sequence and each pressure rise moment in the pressure rise response sequence; Determine the optimal matching relationship between the buck response sequence and the boost response sequence. The optimal matching relationship is the matching relationship with the minimum sum of time distances. The optimal matching relationship includes multiple matching pairs and the weight allocation ratio of each matching pair. The response lag index is determined based on the time distance and weight of each matching pair in the optimal matching relationship.

4. The method for treating high-concentration organic wastewater from chemical processes according to claim 3, characterized in that, The determination of the fouling state of the membrane surface based on the response hysteresis index includes: Obtain the baseline response hysteresis index of the membrane system under clean conditions; Based on the aforementioned response lag index and the benchmark response lag index, the net lag growth rate is determined; The fouling status of the membrane surface is determined based on the net hysteresis growth rate.

5. The method for treating high-concentration organic wastewater from chemical processes according to claim 4, characterized in that, The organic contamination includes loose organic contamination and dense organic contamination. The determination of the contamination status of the membrane surface based on the net hysteresis growth rate includes: If the net hysteresis growth rate is less than or equal to the inorganic contamination threshold, the contamination state of the membrane surface is determined to be inorganic contamination. If the net hysteresis growth rate is greater than the inorganic pollution threshold and less than or equal to the loose organic pollution threshold, the pollution state of the membrane surface is determined to be loose organic pollution. If the net hysteresis growth rate is greater than the loose organic pollution threshold, the pollution state of the membrane surface is determined to be dense organic pollution.

6. The method for treating high-concentration organic wastewater from chemical processes according to claim 1, characterized in that, The membrane treatment operation corresponding to the contamination state includes: When the pollution state is inorganic pollution, increase the opening of the concentrate regulating valve of the membrane system.

7. The method for treating high-concentration organic wastewater from chemical plants according to claim 4, characterized in that, The organic pollution includes loose organic pollution, and the membrane treatment operation corresponding to the pollution state includes: When the pollution state is loose organic pollution, the number of flushing cycles is determined based on the net hysteresis growth rate; Based on the number of rinsing cycles, multiple rinsing operations are performed cyclically. Each rinsing operation involves pumping a low-pressure alkaline cleaning solution into the membrane system and allowing it to stand for a preset time.

8. The method for treating high-concentration organic wastewater from chemical processes according to claim 1, characterized in that, The organic pollution includes dense organic pollution, and the membrane treatment operation corresponding to the pollution state includes: When the contamination state is dense organic contamination, at least one chemical cleaning solution is sequentially pumped into the membrane system for chemical cleaning.

9. A device for treating high-concentration organic wastewater from chemical processes, characterized in that, It includes a data acquisition module, a data analysis module, a pollution assessment module, and a treatment module; The data acquisition module is used to acquire dynamic response data obtained during the symmetrical pressure perturbation operation on the membrane system. The symmetrical pressure perturbation operation includes pressure reduction operation and pressure recovery operation. The dynamic response data includes a pressure reduction response sequence and a pressure increase response sequence of the permeate conductivity. The membrane system is used to filter organic wastewater. The data analysis module is used to determine the response lag index based on the dynamic response data. The response lag index is used to characterize the degree of lag in mass transfer response of the boost response sequence compared to the depressurization response sequence. The contamination determination module is used to determine the contamination status of the membrane surface based on the response hysteresis index, wherein the contamination status is inorganic or organic. The treatment module is used to perform membrane treatment operations corresponding to the pollution state.

10. A chemical high-concentration organic wastewater treatment system, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the chemical high-concentration organic wastewater treatment method as described in any one of claims 1-8.

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