Control method for wastewater treatment process-oriented reclaimed water reuse

Abstract

This application relates to the field of wastewater treatment technology, specifically to a method for controlling the reuse of recycled water in wastewater treatment processes. The method includes: collecting various parameter data; calculating the transmembrane pressure difference based on pressure, fitting a straight line to obtain the rate of change of the transmembrane pressure difference; constructing a trend sequence using desalination rate, transmembrane pressure difference, and permeate flow rate, and determining the RO membrane quality coefficient based on the variance of the trend sequence, the correlation differences among the data constituting the trend sequence, and the rate of change of the transmembrane pressure difference; fitting the permeate flow rate curve, and determining the pre-RO membrane characteristic coefficient based on the difference in the permeate flow rate change rate, the mean of influent turbidity, the mean of elemental change rate, and the RO membrane quality coefficient; adjusting the membrane cleaning interval after the adjustment in the previous cycle based on the RO membrane quality coefficient and the pre-RO membrane characteristic coefficient to obtain the adjusted membrane cleaning interval for each cycle; and cleaning the RO membrane based on the adjusted membrane cleaning interval for each cycle. This application ensures the stability of water treatment quality.

Description

Technical Field

[0001] This application relates to the field of wastewater treatment technology, specifically to a method for controlling the reuse of recycled water in wastewater treatment processes. Background Technology

[0002] With wastewater resource utilization becoming an important strategy for sustainable development, greywater reuse, as a recycling method that uses treated wastewater for industrial cooling, urban greening, and other applications, can not only effectively alleviate the contradiction between water supply and demand but also significantly reduce pollutant emissions. Against this backdrop, greywater reuse technology has been widely applied. Reverse osmosis (RO) membrane technology, due to its highly efficient desalination and removal of trace pollutants, has become the core process in greywater reuse treatment. However, RO membranes are highly susceptible to fouling and clogging by organic matter, colloids, microorganisms, and inorganic salts during operation, leading to decreased membrane flux, increased operating pressure, and reduced desalination rate. In severe cases, it can even cause irreversible damage, directly affecting the stability of effluent quality and system operating costs. Therefore, effectively controlling the operating status of RO membranes is crucial to ensuring the stable operation of greywater reuse systems.

[0003] Existing technologies typically employ timed membrane cleaning to reduce fouling and clogging of reverse osmosis membranes; however, these methods have significant drawbacks. This timed cleaning method lacks flexibility. Firstly, it cannot be adjusted based on the feed water quality, making it difficult to guarantee the stability of the purified water quality. Secondly, while membrane cleaning helps ensure the filtration quality of the reverse osmosis membrane, frequent cleaning can damage the membrane, reducing its lifespan. Furthermore, membrane cleaning requires system shutdown, and frequent start-ups and shutdowns can damage other equipment in the treatment system. The paper "Design of a PLC-based Reverse Osmosis Water Purification Control System" proposes a dynamic adjustment method for the membrane cleaning interval. This scheme measures the water purification efficiency based on the water tank level and adjusts the membrane cleaning interval accordingly. However, this method ignores the dynamic changes in membrane fouling, and relying solely on water purification efficiency makes it difficult to accurately measure these dynamic changes. Therefore, the membrane cleaning interval still needs optimization, and the RO membrane filtration quality still needs improvement. Thus, a more optimized membrane cleaning control method is urgently needed to further improve the quality of reclaimed water treatment. Summary of the Invention

[0004] To address the technical problem of unstable water treatment quality caused by inconsistent RO membrane cleaning intervals, this application provides a method for controlling the reuse of reclaimed water in wastewater treatment processes. The specific technical solution adopted is as follows: This application proposes a method for controlling the reuse of reclaimed water in wastewater treatment processes, which includes the following steps: Collect various parameters of the RO membrane inlet pipe, concentrate outlet pipe and product water outlet pipe in each cycle, and preprocess the data of each parameter. For any given cycle, the transmembrane pressure difference is calculated based on the pressure parameter in the parameters, and a fitted straight line is obtained by fitting the line. The slope of the fitted straight line is used as the rate of change of the transmembrane pressure difference. The desalination rate is calculated based on the influent conductivity and product conductivity in the parameters. The transmembrane pressure difference, desalination rate, and product flow rate in the parameters are used to construct three correlation sequences according to time series. The three sequences are decomposed into a trend sequence. The first and second correlations are determined based on the magnitude of the correlation between the desalination rate and the transmembrane pressure difference and product flow rate within the cycle. The RO membrane quality coefficient is determined based on the rate of change of the transmembrane pressure difference, the variance of the trend sequence, and the difference between the first and second correlations for each cycle. Curve fitting is performed on all permeate flow rates within the cycle, and the difference in permeate flow rate change rate is determined based on the derivatives of all points in the fitted curve. The RO membrane in-feed characteristic coefficients for each cycle are obtained based on the mean of all influent turbidity, the mean of element change rate in all permeate flow rates, the RO membrane quality coefficient, and the difference in permeate flow rate change rate. The membrane cleaning interval for each cycle is adjusted based on the RO membrane pre-characteristic coefficient and the RO membrane quality coefficient of each cycle and the previous cycle. The RO membrane is cleaned based on the membrane cleaning interval adjusted for each cycle.

[0005] In the aforementioned scheme, this application proposes a dynamic control method for membrane cleaning intervals in the RO membrane treatment process for reclaimed water reuse. First, based on parameter changes during membrane cleaning, an RO membrane quality coefficient is calculated. This coefficient quantifies the performance changes of the RO membrane, helping to enhance the accuracy of RO membrane status assessment. Then, considering the influence of influent water quality in the RO membrane treatment process, an RO membrane pre-treatment characteristic coefficient is calculated. This index assesses the impact of the process before RO membrane treatment on influent water quality, helping to improve the accuracy of RO membrane fouling detection and ensuring the stability of RO membrane filtered water quality. Finally, the cleaning interval is dynamically adjusted. This method, when adjusting the RO membrane cleaning interval, considers not only the current state of the RO membrane but also the impact of its lifespan on performance, while simultaneously taking into account the influence of influent water quality. This further enhances the accuracy and sensitivity of RO membrane fouling detection, allowing for dynamic adjustment of the membrane cleaning time. This achieves a more accurate membrane cleaning control method, ensuring the stability of the reclaimed water treatment system's operation and water treatment quality.

[0006] In one embodiment, the parameters include inlet membrane pressure, concentrate pressure, permeate pressure, permeate flow rate, inlet turbidity, inlet conductivity, and permeate conductivity.

[0007] In one embodiment, the period is the time between two adjacent membrane cleanings.

[0008] In one embodiment, the vertical axis of the fitted straight line is the transmembrane pressure difference at each moment, and the horizontal axis is the acquisition order of each data point within the period.

[0009] In one embodiment, the method for determining the first correlation and the second correlation based on the magnitude of the correlation between the desalination rate within the cycle and the transmembrane pressure difference and the permeate flow rate is as follows: Calculate the correlation between all desalination rates and all transmembrane pressure differences and all permeate flow rates in each cycle. The maximum value of the correlation is recorded as the first correlation, and the minimum value of the correlation is recorded as the second correlation.

[0010] In one embodiment, the RO membrane quality coefficient is negatively correlated with the rate of change of transmembrane pressure difference, the variance of the trend sequence, and the difference between the first correlation and the second correlation.

[0011] In one embodiment, the method for determining the difference in the rate of change of permeate flow rate based on the derivative of all points in the fitted curve is as follows: The derivatives of all points are used as input to the Otsu thresholding algorithm to obtain the segmentation threshold; the absolute value of the difference between the average derivative of all points in the water production flow sequence that is greater than the segmentation threshold and the average derivative of all points that is less than the segmentation threshold is taken as the difference in the rate of change of water production flow.

[0012] In one embodiment, the characteristic coefficient before the RO membrane is positively correlated with the mean of the influent turbidity and the difference in the rate of change of the permeate flow rate, and negatively correlated with the mean of the rate of change of elements in all permeate flows and the RO membrane quality coefficient.

[0013] In one embodiment, the method for adjusting the membrane cleaning interval for the current cycle based on the RO membrane pre-characteristic coefficient and the RO membrane quality coefficient of each cycle and previous cycles to obtain the adjusted membrane cleaning interval for the current cycle is as follows: , This represents the RO membrane quality coefficient for the i-th cycle. This represents the average RO membrane quality coefficient across all cycles prior to the i-th cycle. Indicates the first Characteristic coefficients of the RO membrane in each cycle, Represents the normalization function. This represents an exponential function with the natural constant as its base. This represents the membrane cleaning interval after adjustment in the (i-1)th cycle. This represents the membrane cleaning interval after adjustment in the i-th cycle.

[0014] In one embodiment, the method for cleaning the RO membrane based on the membrane cleaning interval adjusted for each cycle is as follows: The adjusted membrane cleaning interval for each cycle is used as the interval for RO membrane cleaning between that cycle and the next cycle.

[0015] The beneficial effects of this application are as follows: This application proposes a dynamic control method for membrane cleaning intervals in the RO membrane treatment process for reclaimed water reuse. First, based on parameter changes during membrane cleaning, an RO membrane quality coefficient is calculated. This coefficient quantifies the performance changes of the RO membrane, enhancing the accuracy of RO membrane condition assessment. Then, considering the influence of influent water quality in the RO membrane treatment process, a pre-treatment characteristic coefficient is calculated. This index assesses the impact of the process preceding RO membrane treatment on influent water quality, improving the accuracy of RO membrane fouling detection and ensuring the stability of RO membrane filtered water quality. Finally, the cleaning interval is dynamically adjusted. This method, when adjusting the RO membrane cleaning interval, considers not only the current state of the RO membrane but also the impact of its lifespan on performance, while simultaneously taking into account the influence of influent water quality. This further enhances the accuracy and sensitivity of RO membrane fouling detection, allowing for dynamic adjustment of the membrane cleaning time. This achieves a more accurate membrane cleaning control method, ensuring the stability of the reclaimed water treatment system's operation and water treatment quality. 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 wastewater reuse control method for a wastewater treatment process 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, structure, features, and effects of the wastewater reuse control method for wastewater treatment processes proposed in 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] Example of a control method for reclaimed water reuse in wastewater treatment processes: The following description, in conjunction with the accompanying drawings, details the specific scheme of the wastewater reuse control method for the wastewater treatment process provided in this application.

[0021] Please see Figure 1 The diagram illustrates a flow chart of a wastewater reuse control method for a wastewater treatment process according to an embodiment of this application. The method includes the following steps: Step S001: Collect various parameter data and preprocess the data.

[0022] For wastewater reuse technology, fouling and clogging of the RO membrane can affect wastewater treatment efficiency. Therefore, it is necessary to clean the RO membrane. This application provides a method for dynamically adjusting the cleaning time of the RO membrane to alleviate the problem of poor flexibility caused by timed cleaning.

[0023] Pressure transmitters were used to collect inlet pressure, concentrate pressure, and permeate pressure at the RO membrane inlet pipe, concentrate outlet pipe, and permeate outlet pipe. A flow meter was used to collect permeate flow rate at the RO membrane permeate outlet pipe. An online conductivity meter was used to collect inlet and permeate conductivity data. Finally, a turbidity sensor was used to collect inlet turbidity data from the RO membrane inlet pipe. All collected data were normalized. Each type of data was treated as a parameter. In this embodiment, the data collection interval was 5 minutes.

[0024] The time between two consecutive membrane cleaning cycles is defined as a data acquisition period, referred to simply as a period. The acquired data undergoes various preprocessing steps, including missing value handling, outlier handling, and data noise reduction. In this embodiment, missing values ​​are filled using shape-preserving piecewise cubic spline interpolation. Outliers are handled as missing values ​​using the LOF anomaly detection method, and adaptive noise reduction is employed.

[0025] At this point, the parameter data for all preprocessed parameters has been obtained.

[0026] Step S002: Calculate the transmembrane pressure difference based on pressure, and obtain the rate of change of the transmembrane pressure difference after fitting a straight line; construct a trend sequence by desalination rate, transmembrane pressure difference and permeate flow rate, and determine the RO membrane quality coefficient based on the variance of the trend sequence, the correlation difference of the data constituting the trend sequence and the rate of change of the transmembrane pressure difference.

[0027] RO membrane treatment, as a key process in wastewater reuse, is crucial for ensuring water quality. During RO membrane filtration, membrane fouling significantly impacts water filtration, necessitating membrane cleaning to reduce fouling. Current technology typically involves periodic cleaning of the RO membrane. However, due to varying influent water quality and different demand for treated water at different times, periodic cleaning cannot guarantee the stability of the purified water quality. Furthermore, frequent membrane cleaning negatively impacts the RO membrane and other equipment in the wastewater reuse system, affecting its overall stability. Therefore, selecting an appropriate RO membrane cleaning interval is essential for wastewater reuse systems.

[0028] To determine the appropriate RO membrane cleaning interval, the state of the RO membrane first needs to be accurately quantified. Current technologies mostly quantify RO membrane state based on water purification efficiency, but this method struggles to simultaneously account for membrane fouling and doesn't consider the membrane's lifespan. Therefore, the quantification of RO membrane state needs optimization. Firstly, under normal circumstances, RO membrane fouling is a gradual, relatively slow accumulation process. This results in an overall increasing trend in the transmembrane pressure difference, but the growth rate is slow and nearly linear. Water purification efficiency and transmembrane pressure difference show a negative correlation. Secondly, the desalination rate can be used to represent the water purification quality of the RO membrane. For a high-quality RO membrane, even with fouling and blockage, the water purification quality will not significantly decrease; that is, the correlation between the desalination rate, water purification efficiency, and transmembrane pressure difference is relatively small. Finally, these changes will return to their original state after RO membrane cleaning.

[0029] However, as the lifespan of the RO membrane decreases, it will suffer physical or chemical damage. First, it will be difficult to restore the RO membrane to its original state after cleaning. Second, due to the damage to the RO membrane, the quality of purified water will decrease significantly when the RO membrane is fouled. The more severe the RO membrane fouling, the worse the quality of purified water. This will gradually increase the correlation between desalination rate, water purification efficiency, and transmembrane pressure difference.

[0030] For any given cycle, the transmembrane pressure difference is calculated based on the inlet pressure, concentrate pressure, and permeate pressure at each moment of the cycle. Then, the transmembrane pressure difference at each moment is used as the ordinate, and the acquisition sequence within the corresponding cycle is used as the abscissa. Linear fitting is performed, and the fitted linear function for that cycle is output. Linear fitting and transmembrane pressure difference are well-known techniques, and will not be elaborated further.

[0031] In subsequent cycles, the transmembrane pressure difference, permeate flow rate, and desalination rate are arranged in chronological order of data acquisition to form a transmembrane pressure difference sequence, a permeate flow rate sequence, and a desalination rate sequence. The desalination rate is calculated based on the influent and permeate conductivity; the calculation method for the desalination rate is a well-known technique and will not be elaborated upon in this embodiment. The mean value of all transmembrane pressure differences within each cycle is calculated, and the mean value of the transmembrane pressure differences from all previous cycles is used as input to the time-series decomposition algorithm to obtain a trend sequence.

[0032] When RO membranes perform well, although membrane fouling still occurs, the accumulation rate of fouling is relatively slow, resulting in a smaller change in transmembrane pressure. Furthermore, even with severe membrane fouling, high filtration quality can still be maintained, leading to a low correlation between desalination rate, water purification efficiency, and transmembrane pressure. The membrane also recovers well after cleaning, meaning the transmembrane pressure changes are similar across different cycles, resulting in a high RO membrane quality factor. Conversely, as the lifespan of the RO membrane decreases, membrane performance deteriorates, and the corresponding transmembrane pressure changes more rapidly. As membrane fouling worsens, filtration quality becomes difficult to maintain, increasing the correlation between desalination rate, water purification efficiency, and transmembrane pressure. Finally, the difference between the cleaned membrane and its original state is greater, resulting in a lower RO membrane quality factor. Since higher water purification efficiency corresponds to higher permeate flow rate, water purification efficiency can be considered as a measure of permeate flow rate.

[0033] The variance of the trend sequence is used as the degree of performance degradation of the RO membrane, and the slope of the fitted straight line function of all transmembrane pressure differences in each period is used as the rate of change of transmembrane pressure difference in each period. The correlation between the desalination rate sequence and the transmembrane pressure difference sequence and the permeate flow rate sequence are calculated respectively. The maximum value of the correlation is recorded as the first correlation, and the minimum value of the correlation is recorded as the second correlation. In this embodiment, the correlation is calculated using the Pearson correlation coefficient.

[0034] The RO membrane quality coefficient was determined based on the rate of change of transmembrane pressure difference in each cycle, the degree of performance degradation of the RO membrane, and the correlation differences between the desalination rate sequence and the transmembrane pressure difference sequence and the permeate flow rate sequence, respectively.

[0035] The RO membrane quality coefficients showed negative correlations with the rate of change of transmembrane pressure, the degree of performance degradation of the RO membrane, and the desalination rate sequence, as well as with the transmembrane pressure sequence and the permeate flow rate sequence.

[0036] It should be noted that negative correlation means that when one variable increases, the other variable decreases accordingly, and the two variables change in opposite directions. When one variable changes from large to small or from small to large, the other variable also changes from small to large or from large to small. The specific relationship is determined by practical application, and this application does not impose any special restrictions.

[0037] Preferably, in this embodiment, the expression for the RO membrane quality coefficient is: , This represents the rate of change of the transmembrane pressure difference in the i-th period. This indicates the degree of performance degradation of the RO membrane in the i-th cycle. This indicates the second correlation in the i-th period. This represents the first correlation in the i-th period. This represents an exponential function with the natural constant as its base. This represents the RO membrane quality coefficient for the i-th cycle.

[0038] Since the transmembrane pressure difference and water purification efficiency are negatively correlated, the transmembrane pressure difference and the permeate flow rate sequence are also negatively correlated. Therefore, when calculating the correlation between the desalination rate sequence and the transmembrane pressure difference sequence and the permeate flow rate sequence, the first correlation and the second correlation obtained must have one negative number and one positive number. Therefore, after exponential function processing, the greater the difference between the first correlation and the second correlation, the greater the RO membrane quality coefficient.

[0039] At this point, the RO membrane quality coefficient for each cycle was obtained.

[0040] Step S003: After fitting the permeate flow rate curve, determine the inlet characteristic coefficient of the RO membrane based on the difference in permeate flow rate change rate, the mean of influent turbidity, the mean of element change rate, and the RO membrane quality coefficient.

[0041] The aforementioned RO membrane quality coefficient measures the RO membrane's condition by analyzing its own fouling changes. However, due to the high lag in RO membrane condition changes—meaning that significant changes only occur after a considerable period of wastewater treatment—this method, while ensuring accuracy in measuring RO membrane fouling, has relatively poor sensitivity and cannot guarantee the stability of purified water quality. This is especially true as the RO membrane's lifespan decreases, leading to greater fluctuations in purified water quality. Therefore, to improve the sensitivity of RO fouling control and ensure the stability of the wastewater reuse system, it is necessary to consider the impact of factors such as the water quality before RO membrane treatment on the RO membrane process and predict its condition.

[0042] In the chemical dosing process of wastewater treatment systems, flocculants and scale inhibitors are typically added to adsorb impurities such as colloids, particulate matter, and inorganic salts in the water. This removes impurities during pretreatment, ensuring the quality of the influent water for RO membrane treatment and reducing fouling of the RO membrane. When the dosage of these agents is low, the ability to remove impurities is relatively weak, resulting in relatively high turbidity in the influent water. Simultaneously, these impurities can affect water filtration, causing RO membrane clogging. Under these circumstances, the rate of RO membrane fouling increases, and the overall permeate flow rate shows a significant decreasing trend, resulting in a lower overall average permeate flow rate and a more consistent rate of decline. When too much of these chemicals are added, the quality of the incoming water can be guaranteed. However, due to the excessive dosage, the chemicals will cause too much floc in the water and an excessive concentration of ions. As a result, the overall water production rate will show a decreasing trend. In the early stage when the water quality is good, the RO membrane fouling is relatively mild and the decreasing trend of the water production rate is relatively small. In the later stage, the decreasing trend of the water production rate will increase significantly, that is, the overall water production rate will show a decreasing trend, resulting in a relatively small water production rate and obvious differences in the water production rate.

[0043] For any given period, the permeate flow rate sequence for that period is used as input. A polynomial fit is performed with the elements in the sequence as the ordinate and the element indices as the abscissa, outputting the fitted curve function. Polynomial fitting is a well-known technique and will not be elaborated further. Next, the derivatives of each index value on the fitted curve are calculated. All derivatives are used as input, and the Otsu thresholding algorithm is applied to output the segmentation threshold. The absolute value of the difference between the average derivative of all points in the permeate flow rate sequence greater than the segmentation threshold and the average derivative of all points less than the segmentation threshold is taken as the permeate flow rate change difference. Otsu thresholding is a well-known technique and will not be elaborated further.

[0044] Based on the above analysis, the RO membrane in-phase characteristic coefficients for each cycle are obtained according to the average influent turbidity, the mean of the element change rate in the permeate flow rate sequence, the RO membrane quality coefficient, and the difference in permeate flow rate change rate. The element change rate is the derivative of the corresponding element.

[0045] The characteristic coefficients of the RO membrane are positively correlated with the difference in average influent turbidity and permeate flow rate, and negatively correlated with the mean of element change rate in the permeate flow rate sequence and the RO membrane quality coefficient.

[0046] It should be noted that positive correlation means that when one variable increases, the other variable also increases, and the two variables change in the same direction. When one variable changes from large to small or from small to large, the other variable also changes from large to small or from small to large. The specific relationship is determined by the actual application, and this application does not impose any special restrictions.

[0047] Preferably, in this embodiment, the expression for the characteristic coefficients before the RO membrane is: : It is the first Characteristic coefficients of the RO membrane in each cycle, It is the first Average influent turbidity over one cycle It is the first The mean of the rate of change of elements in the permeable flow rate series over several periods. It is the difference in the rate of change of the permeate flow rate in the i-th cycle. It is the RO membrane quality coefficient for the i-th cycle.

[0048] In a greywater reuse system, the smaller the impact of the processes preceding the RO membrane treatment on the RO membrane treatment, and the more appropriate the dosage in the preceding treatment processes, the better the influent quality for the RO membrane treatment, and the less impact on RO membrane fouling. In this case, the turbidity of the RO membrane influent is relatively low, the average permeate flow rate during the RO membrane treatment process is relatively high, and the rate of change in permeate flow rate is relatively slow, without significant differences in the rate of change. At the same time, the better the RO membrane performance, the less impact the water quality fluctuations caused by the dosage on the stability of the RO membrane filtered water in the greywater reuse treatment, and therefore the smaller the corresponding characteristic coefficient before the RO membrane. Conversely, the more inappropriate the dosage, the larger the characteristic coefficient before the RO membrane.

[0049] Thus, the characteristic coefficients of the RO membrane in each cycle have been obtained.

[0050] Step S004: Based on the RO membrane quality coefficient and the RO membrane pre-characteristic coefficient, adjust the membrane cleaning interval after the previous cycle to obtain the membrane cleaning interval after each cycle.

[0051] For RO membrane treatment, the membrane cleaning process helps reduce RO membrane fouling, thereby ensuring the stability of the filtered water quality. However, relying solely on changes in RO membrane treatment can lead to a lag in judging the RO membrane's condition, especially when the process between RO membrane treatments affects the quality of the RO membrane feed water and the RO membrane's lifespan is relatively short. Therefore, the RO membrane quality coefficient and RO membrane pre-characteristic coefficient constructed above measure the changes in RO membrane condition from two perspectives: parameter changes during RO membrane treatment and changes in the quality of the RO membrane feed water. These two perspectives can then be combined to adjust the membrane cleaning interval.

[0052] Therefore, the membrane cleaning interval adjusted in the previous cycle is obtained by adjusting the membrane cleaning interval based on the RO membrane pre-characteristic coefficient and the RO membrane quality coefficient of each cycle and the previous cycle.

[0053] Preferably, in this embodiment, the adjusted membrane cleaning interval is: , This represents the RO membrane quality coefficient for the i-th cycle. This represents the average RO membrane quality coefficient across all cycles prior to the i-th cycle. Indicates the first Characteristic coefficients of the RO membrane in each cycle, Represents the normalization function. This represents an exponential function with the natural constant as its base. This represents the membrane cleaning interval after adjustment in the (i-1)th cycle. This represents the membrane cleaning interval adjusted for the i-th cycle. In this embodiment, the membrane cleaning interval for the first cycle is the default cleaning interval for the RO membrane.

[0054] When the performance of the RO membrane is poorer, and the preceding processes have a greater impact on the RO membrane treatment, the corresponding influent water quality and the rate at which the RO membrane becomes fouled will be faster. Consequently, the stability of the water quality produced by RO membrane filtration will be worse. In this case, membrane cleaning needs to be performed at shorter intervals to ensure that the water quality produced by the RO membrane treatment is good and stable. Therefore, this dynamic adjustment can be achieved by following the adjustment method described above.

[0055] At this point, the adjusted membrane cleaning interval for each cycle has been obtained.

[0056] Step S005: Clean the RO membrane based on the membrane cleaning interval adjusted for each cycle.

[0057] After obtaining the adjusted membrane cleaning interval for each cycle through the above steps, this interval is used as the interval for the next cleaning of the RO membrane. Thus, the membrane cleaning interval for each cycle is adjusted sequentially until the maximum service life of the RO membrane is reached.

[0058] It should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

[0059] 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 reclaimed water reuse control method for a wastewater treatment process, characterized by, The method includes the following steps: Collect various parameters of the RO membrane inlet pipe, concentrate outlet pipe and product water outlet pipe in each cycle, and preprocess the data of each parameter. For any given cycle, the transmembrane pressure difference is calculated based on the pressure parameter in the parameters, and a fitted straight line is obtained by fitting the line. The slope of the fitted straight line is used as the rate of change of the transmembrane pressure difference. The desalination rate is calculated based on the influent conductivity and product conductivity in the parameters. The transmembrane pressure difference, desalination rate, and product flow rate in the parameters are used to construct three correlation sequences according to time series. The three sequences are decomposed into a trend sequence. The first and second correlations are determined based on the magnitude of the correlation between the desalination rate and the transmembrane pressure difference and product flow rate within the cycle. The RO membrane quality coefficient is determined based on the rate of change of the transmembrane pressure difference, the variance of the trend sequence, and the difference between the first and second correlations for each cycle. Curve fitting is performed on all permeate flow rates within the cycle, and the difference in permeate flow rate change rate is determined based on the derivatives of all points in the fitted curve. The RO membrane in-feed characteristic coefficients for each cycle are obtained based on the mean of all influent turbidity, the mean of element change rate in all permeate flow rates, the RO membrane quality coefficient, and the difference in permeate flow rate change rate. The membrane cleaning interval for each cycle is adjusted based on the RO membrane pre-characteristic coefficient and the RO membrane quality coefficient of each cycle and the previous cycle. The RO membrane is cleaned based on the membrane cleaning interval adjusted for each cycle.

2. The wastewater treatment process-oriented reclaimed water reuse control method according to claim 1, characterized by, The parameters include inlet membrane pressure, concentrate pressure, permeate pressure, permeate flow rate, inlet turbidity, inlet conductivity, and permeate conductivity.

3. The method for controlling the reuse of reclaimed water in a wastewater treatment process as described in claim 1, characterized in that, The cycle is the time between two consecutive membrane cleanings.

4. The method for controlling the reuse of reclaimed water in a wastewater treatment process as described in claim 1, characterized in that, The vertical axis of the fitted straight line is the transmembrane pressure difference at each moment, and the horizontal axis is the acquisition order of each data point within the period.

5. The method for controlling the reuse of reclaimed water in a wastewater treatment process as described in claim 1, characterized in that, The method for determining the first and second correlations based on the magnitude of the correlation between the desalination rate within the cycle and the transmembrane pressure difference and permeate flow rate is as follows: Calculate the correlation between all desalination rates and all transmembrane pressure differences and all permeate flow rates in each cycle. The maximum value of the correlation is recorded as the first correlation, and the minimum value of the correlation is recorded as the second correlation.

6. The method for controlling the reuse of reclaimed water in a wastewater treatment process as described in claim 1, characterized in that, The RO membrane quality coefficient is negatively correlated with the rate of change of transmembrane pressure difference, the variance of the trend sequence, and the difference between the first and second correlations.

7. The method for controlling the reuse of reclaimed water in a wastewater treatment process as described in claim 1, characterized in that, The method for determining the difference in the rate of change of permeate flow rate based on the derivative of all points in the fitted curve is as follows: The derivatives of all points are used as input to the Otsu thresholding algorithm to obtain the segmentation threshold; the absolute value of the difference between the average derivative of all points in the water production flow sequence that is greater than the segmentation threshold and the average derivative of all points that is less than the segmentation threshold is taken as the difference in the rate of change of water production flow.

8. The method for controlling the reuse of reclaimed water in a wastewater treatment process as described in claim 1, characterized in that, The characteristic coefficients of the RO membrane are positively correlated with the mean of the influent turbidity and the difference in the rate of change of the permeate flow rate, and negatively correlated with the mean of the rate of change of elements in all permeate flows and the RO membrane quality coefficient.

9. The method for controlling the reuse of reclaimed water in a wastewater treatment process as described in claim 1, characterized in that, The method for adjusting the membrane cleaning interval for the current cycle based on the RO membrane pre-characteristic coefficient and the RO membrane quality coefficient of each cycle and previous cycles is as follows: , This represents the RO membrane quality coefficient for the i-th cycle. This represents the average RO membrane quality coefficient across all cycles prior to the i-th cycle. Indicates the first Characteristic coefficients of the RO membrane in each cycle, Represents the normalization function. This represents an exponential function with the natural constant as its base. This represents the membrane cleaning interval after adjustment in the (i-1)th cycle. This represents the membrane cleaning interval after adjustment in the i-th cycle.

10. The method for controlling the reuse of reclaimed water in a wastewater treatment process as described in claim 1, characterized in that, The method for cleaning the RO membrane based on the membrane cleaning interval adjusted for each cycle is as follows: The adjusted membrane cleaning interval for each cycle is used as the interval for RO membrane cleaning between that cycle and the next cycle.

Images