CN118320624A - Reverse osmosis membrane fouling and blocking abnormality early warning method and system - Google Patents
Reverse osmosis membrane fouling and blocking abnormality early warning method and system Download PDFInfo
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- 238000001223 reverse osmosis Methods 0.000 title claims abstract description 169
- 238000000034 method Methods 0.000 title claims abstract description 57
- 238000009285 membrane fouling Methods 0.000 title claims abstract description 40
- 230000005856 abnormality Effects 0.000 title claims description 12
- 230000000903 blocking effect Effects 0.000 title claims description 11
- 239000012528 membrane Substances 0.000 claims abstract description 198
- 230000004907 flux Effects 0.000 claims abstract description 77
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 70
- 239000013598 vector Substances 0.000 claims abstract description 36
- 238000009792 diffusion process Methods 0.000 claims abstract description 14
- 238000003062 neural network model Methods 0.000 claims abstract description 12
- 239000000126 substance Substances 0.000 claims description 29
- 239000003344 environmental pollutant Substances 0.000 claims description 14
- 231100000719 pollutant Toxicity 0.000 claims description 14
- 238000004364 calculation method Methods 0.000 claims description 13
- 230000008569 process Effects 0.000 claims description 12
- 150000002500 ions Chemical class 0.000 claims description 11
- 230000002829 reductive effect Effects 0.000 claims description 8
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- 230000003204 osmotic effect Effects 0.000 claims description 6
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- 238000004220 aggregation Methods 0.000 claims description 5
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- 239000012141 concentrate Substances 0.000 claims description 3
- 239000012527 feed solution Substances 0.000 claims description 3
- 239000012466 permeate Substances 0.000 claims description 3
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- 230000008859 change Effects 0.000 description 9
- OSGAYBCDTDRGGQ-UHFFFAOYSA-L calcium sulfate Chemical compound [Ca+2].[O-]S([O-])(=O)=O OSGAYBCDTDRGGQ-UHFFFAOYSA-L 0.000 description 8
- 238000012544 monitoring process Methods 0.000 description 8
- 239000010703 silicon Substances 0.000 description 7
- 229910052710 silicon Inorganic materials 0.000 description 7
- 238000011282 treatment Methods 0.000 description 7
- XUIMIQQOPSSXEZ-UHFFFAOYSA-N Silicon Chemical compound [Si] XUIMIQQOPSSXEZ-UHFFFAOYSA-N 0.000 description 6
- 238000004140 cleaning Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 5
- BHPQYMZQTOCNFJ-UHFFFAOYSA-N Calcium cation Chemical compound [Ca+2] BHPQYMZQTOCNFJ-UHFFFAOYSA-N 0.000 description 4
- 238000009825 accumulation Methods 0.000 description 4
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- 229910001424 calcium ion Inorganic materials 0.000 description 4
- 230000002035 prolonged effect Effects 0.000 description 3
- 238000012546 transfer Methods 0.000 description 3
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 description 2
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 2
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- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 description 1
- BVKZGUZCCUSVTD-UHFFFAOYSA-L Carbonate Chemical compound [O-]C([O-])=O BVKZGUZCCUSVTD-UHFFFAOYSA-L 0.000 description 1
- FYYHWMGAXLPEAU-UHFFFAOYSA-N Magnesium Chemical compound [Mg] FYYHWMGAXLPEAU-UHFFFAOYSA-N 0.000 description 1
- QAOWNCQODCNURD-UHFFFAOYSA-L Sulfate Chemical compound [O-]S([O-])(=O)=O QAOWNCQODCNURD-UHFFFAOYSA-L 0.000 description 1
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- 238000011033 desalting Methods 0.000 description 1
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Abstract
The invention discloses a reverse osmosis membrane fouling anomaly early warning method and system, comprising the following steps: basic performance data of the reverse osmosis membrane is obtained, and a coefficient B of the reverse osmosis membrane under different water qualities is calculated according to a dissolution-diffusion model; calculating a data set running under different water qualities according to the basic performance data of the reverse osmosis membrane; inputting the data set into a time sequence, and drawing a graph of time-reverse osmosis membrane flux; inputting the graph into a preset neural network model, decomposing each item of sub-data of the corresponding reverse osmosis membrane, and performing vector setting on each item of sub-data to obtain a sub-data vector; constructing an early warning model according to the sub-data vector to early warn the reverse osmosis membrane; by utilizing the neural network and the machine learning technology, the system can learn and identify complex modes and trends which possibly cause fouling, and early warning of the decline of the membrane performance is realized, so that maintenance or preventive measures are taken before the problem becomes a serious fault.
Description
Technical Field
The invention relates to the technical field of reverse osmosis membrane fouling anomaly early warning, in particular to a reverse osmosis membrane fouling anomaly early warning method and system.
Background
At present, in the production process of high-standard industrial pure water (such as boiler chemical water) and the wastewater treatment process, the reverse osmosis (Reverseosmosis, RO) device is widely applied as a desalting treatment process. Although the pretreatment process of reverse osmosis feed water is already mature, the occurrence of reverse osmosis membrane pollution is unavoidable. Along with the gradual aggravation of the fouling of the reverse osmosis membrane, the pressure difference between the sections of the reverse osmosis device is increased, and when the pressure difference between the sections is accumulated to a set threshold value, the membrane component of the reverse osmosis device is required to be subjected to shutdown chemical cleaning, so that the operation efficiency of the reverse osmosis device is directly influenced. Therefore, the early warning of the fouling of the reverse osmosis membrane is particularly important.
The Chinese patent document with publication number CN114692723A discloses a reverse osmosis membrane fouling and blocking early warning method and system, comprising the following steps: a machine learning method is adopted, and a reverse osmosis membrane fouling early warning model is constructed based on historical actual measurement data of a reverse osmosis device to be detected; obtaining measured data of a reverse osmosis device to be measured, and selecting characteristic variables to perform denoising and preprocessing; the method has the technical contents that the real-time data of the reverse osmosis device collected by the monitoring sensor is used for judging the fouling situation of the reverse osmosis membrane, but different judgment cannot be made according to different water qualities, the theoretical basis is in the same water quality, the applicability is poor, and the abnormal fouling of the reverse osmosis membrane cannot be early warned according to different water quality situations; therefore, a reverse osmosis membrane fouling abnormality early warning method and a reverse osmosis membrane fouling abnormality early warning system are provided.
Disclosure of Invention
In view of this, embodiments of the present invention wish to provide a method and a system for early warning of fouling and blocking of a reverse osmosis membrane, so as to solve or alleviate the technical problems existing in the prior art, and at least provide a beneficial choice;
the technical scheme of the embodiment of the invention is realized as follows: in a first aspect, a reverse osmosis membrane fouling anomaly early warning method includes the following steps:
S1: basic performance data of the reverse osmosis membrane is obtained, and a coefficient B of the reverse osmosis membrane under different water qualities is calculated according to a dissolution-diffusion model;
s2: calculating a data set running under different water qualities according to the basic performance data of the reverse osmosis membrane;
S3: inputting the data set into a time sequence, and drawing a graph of time-reverse osmosis membrane flux;
S4: inputting the graph into a preset neural network model, decomposing each item of sub-data of the corresponding reverse osmosis membrane, and performing vector setting on each item of sub-data to obtain a sub-data vector;
s5: and constructing an early warning model according to the sub-data vector to early warn the reverse osmosis membrane.
Further, in the method for early warning of fouling and blocking abnormality of a reverse osmosis membrane, the step of obtaining basic performance data of the reverse osmosis membrane and calculating coefficients B of the reverse osmosis membrane under different water qualities according to a dissolution-diffusion model includes:
In the dissolution-diffusion model:
Where J s is the molar flux of the solute, C f is the feed solution concentration, and C p is the permeate concentration.
Further, in the reverse osmosis membrane fouling anomaly early warning method, the step of calculating the data set running under different water qualities according to the basic performance data of the reverse osmosis membrane comprises the following steps:
the dataset included membrane flux data, calculation of membrane flux:
Wherein the membrane flux J is determined by the ratio of the self resistance R m and the pollution layer resistance R c to the membrane passing pressure difference p and the osmotic pressure difference pi m, and the osmotic pressure difference pi m is calculated as follows:
r is a universal gas constant, T is absolute temperature;
And (3) finishing to obtain:
Further, in the reverse osmosis membrane fouling anomaly early warning method, the data set also comprises the type, the amount and the fouling rate of the foulants; wherein the ionic strength in the foulant is calculated as a function of:
f(I)＝aI2+bI+c；
a. b and c are respectively function coefficients, and I is the ionic strength of the scaling substances;
The solubility product of the foulants is:
K＝αI；
Alpha is a temperature correction coefficient;
At a certain concentration of foulants, the foulants begin to scale:
S is the saturation of the foulant and CF is the concentration factor.
Further, in the reverse osmosis membrane fouling anomaly early warning method, the step of inputting the data set into a time sequence and drawing a graph of time-reverse osmosis membrane flux includes:
The membrane flux is reduced due to the adhesion of original pollutants of the water body and generated scaling substances on the reverse osmosis membrane, the amount of the original pollutants of the water body and the generated scaling substances are changed by time, and the calculation process is as follows:
r c＝rcg(t),rc is the resistance coefficient of the pollution layer, g (t) is the variation of the resistance of the pollution layer with time;
The method comprises the following steps:
The amount of the scaling substances is divided into two stages, namely an ion aggregation stage and a scaling stage:
Where I n is the fouling concentration, I n =s, v is the fouling rate.
Further, in the method for early warning of fouling and blocking abnormality of a reverse osmosis membrane, the concentration of ions of the foulant is determined according to the flux of the membrane, and the more the filtered water body is, the more ions remain on one side of the reverse osmosis membrane, and the following steps are:
Jt＝In；
substituting respectively to obtain:
In the time-reverse osmosis membrane flux graph, before t=i, the membrane flux of the reverse osmosis membrane is determined by the amount of the original contaminant of the water body, and after t=i, the membrane flux of the reverse osmosis membrane is determined by both the amount of the original contaminant of the water body and the amount of the generated scaling substance.
Further, in the reverse osmosis membrane fouling anomaly early warning method, the step of inputting the graph into a preset neural network model, decomposing each item of sub-data of the corresponding reverse osmosis membrane, and performing vector setting on each item of sub-data to obtain a sub-data vector comprises the steps of:
The information contained in the graph corresponds to the data collected by the reverse osmosis membrane fouling anomaly early warning system, and the collected data comprise: the conductance, pH, ORP, temperature, flow and pressure of the concentrate and produced water, the operating frequency feedback of the high pressure pump, and one or more of the current and differential pressure across the inlet and outlet of the reverse osmosis unit.
Further, the method for early warning the fouling and blocking abnormality of the reverse osmosis membrane, wherein the step of constructing an early warning model according to the sub-data vector includes:
And setting a data threshold range according to the sub-data vector, and if the measured data is not in the data threshold range, sending out an abnormal alarm.
In another aspect, a reverse osmosis membrane fouling anomaly early warning system is provided, which is applied to any one of the reverse osmosis membrane fouling anomaly early warning methods, and the reverse osmosis membrane fouling anomaly early warning system includes:
an acquisition unit: basic performance data of the reverse osmosis membrane is obtained, and a coefficient B of the reverse osmosis membrane under different water qualities is calculated according to a dissolution-diffusion model;
a calculation unit: calculating a data set running under different water qualities according to the basic performance data of the reverse osmosis membrane;
And a drawing unit: inputting the data set into a time sequence, and drawing a graph of time-reverse osmosis membrane flux;
an input unit: inputting the graph into a preset neural network model, decomposing each item of sub-data of the corresponding reverse osmosis membrane, and performing vector setting on each item of sub-data to obtain a sub-data vector;
an early warning unit: and constructing an early warning model according to the sub-data vector to early warn the reverse osmosis membrane.
Compared with the prior art, the invention has the beneficial effects that:
1. by utilizing a neural network and a machine learning technology, the system can learn and identify complex modes and trends which possibly cause fouling and blockage, and early warning of the decline of the membrane performance is realized, so that maintenance or preventive measures are taken before the problem becomes a serious fault;
2. The neural network model can dynamically adjust the prediction algorithm by continuously collecting and analyzing the data, so that the prediction accuracy is improved; the prior art may rely on static thresholds or empirical rules, which are not as flexible and accurate as data-based models;
3. By predictive maintenance rather than reactive maintenance, downtime and associated maintenance costs can be effectively reduced; the frequency of excessive cleaning or membrane replacement is reduced, the service life of the reverse osmosis membrane is prolonged, and meanwhile, the system can be combined with an automatic control module, so that the early warning is realized, the operation parameters can be automatically adjusted or the cleaning process can be started, and the system is more intelligent.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are necessary for the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a reverse osmosis membrane fouling anomaly early warning method of the invention;
Fig. 2 is a schematic connection diagram of the reverse osmosis membrane fouling anomaly early warning system of the invention.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The present invention may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit of the invention, whereby the invention is not limited to the specific embodiments disclosed below.
In the prior art, a plurality of sensors are arranged in the existing reverse osmosis device, a large amount of operation data are collected, the reverse osmosis membrane fouling early warning function is not achieved, on-site operators usually stop the machine for chemical cleaning of the membrane components of the reverse osmosis device according to experience and when the pressure difference between the segments reaches a threshold value, massive historical operation data are utilized, an artificial intelligence method is combined to early warn the fouling of the reverse osmosis membrane, early warning information is timely pushed to the operators, the operators are guided to adjust the operation working condition of the reverse osmosis device, the trend of the reverse osmosis membrane fouling is delayed, the operation period duration of the reverse osmosis device is prolonged, and the method has very important significance for controlling the reverse osmosis membrane fouling and improving the operation efficiency of the reverse osmosis device; for this reason, referring to fig. 1-2, the present invention provides a technical solution to solve the above technical problems: a reverse osmosis membrane fouling and blocking abnormality early warning method and a specific application method of a system.
In some embodiments of the present application, please refer to fig. 1 in combination:
A reverse osmosis membrane fouling and blocking abnormality early warning method comprises the following steps:
S1: basic performance data of the reverse osmosis membrane is obtained, and a coefficient B of the reverse osmosis membrane under different water qualities is calculated according to a dissolution-diffusion model; specifically, the basic performance data includes the water flux, rejection, pressure loss, and other physical and chemical parameters associated with the performance of the membrane; coefficient B can be seen to be an estimated mass transfer coefficient, which is important data for calculating concentration polarization factors, typically estimated by the mass transfer coefficient;
S2: calculating a data set running under different water qualities according to the basic performance data of the reverse osmosis membrane; specific: calculating the pollution to reverse osmosis membranes, the types of scaling substances (such as calcium, magnesium, silicon and the like) on different water qualities, the accumulation amount of the scaling substances on the surfaces of the membranes, the scaling rate and the like;
S3: inputting the data set into a time sequence, and drawing a graph of time-reverse osmosis membrane flux; specific: the data set is put into a time sequence, the time is taken as a horizontal axis, a change curve of membrane flux of the membrane along with the time is drawn, and the curve can reflect the attenuation trend of the performance of the reverse osmosis membrane in the continuous use process;
s4: inputting the graph into a preset neural network model, decomposing each item of sub-data of the corresponding reverse osmosis membrane, and performing vector setting on each item of sub-data to obtain a sub-data vector; specifically, the neural network is a machine learning tool capable of simulating complex nonlinear relations, and can extract and analyze corresponding secondary data, such as changes of membrane flux and rejection rate with time, from a curve, and vectorize the data to form sub-data vectors for further analysis, wherein each sub-data also comprises data detected on a reverse osmosis membrane device in the curve state;
S5: constructing an early warning model according to the sub-data vector to early warn the reverse osmosis membrane; specifically, the early warning model is constructed by the sub-data, can monitor the running state of the reverse osmosis membrane, and gives an early warning signal before the membrane performance is reduced to a certain threshold value so as to maintain or replace the membrane in time.
In one particular embodiment: a water treatment plant runs a set of reverse osmosis (R/O) system for preparing high-purity water; in order to ensure stable operation of the system, it is necessary to prevent fouling of the membrane and foresee maintenance time thereof, and the following are specific steps of the early warning method:
An operator obtains basic performance data of the membrane from a control console of the R/O system, for example, the water flux of the membrane is 2000 liters/hour, the rejection rate is 98%, the membrane inlet pressure is 15 bar, the membrane outlet pressure is 14 bar, and the mass transfer coefficient B of the membrane under different water qualities is calculated through a dissolution-diffusion model and is used for helping to evaluate the filtration efficiency of the membrane on different solutes;
Calculating pollution conditions under different water qualities by utilizing data (such as the concentration of Ca 2+,Mg2+,SiO2) from water quality detection and combining a large amount of historical data; assuming that a specific water quality is detected, the accumulation rate of silicon is abnormally increased;
Inputting silicon accumulation data over a year into a time series, plotting time versus membrane flux (e.g., rate of water through the membrane), from which it is observed that the membrane flux gradually decreases over time, especially faster during periods of high silicon concentration;
Inputting the flux and time change curve into a pre-trained neural network model; the model can learn the relationship between silicon accumulation and membrane flux decline from the curve trend and decompose out relevant sub-data (e.g., rate of flux decline, time interval, silicon concentration change, etc.) which are then converted into vector form for further mathematical processing and analysis;
And combining the sub-data vectors to construct an early warning model. The model is used for monitoring the possible fouling phenomenon in the operation of the R/O system, if the model predicts that the membrane flux will be reduced to an unacceptable level in a future period of time, the system will send out an early warning signal, and a maintenance engineer can clean or replace the membrane in advance based on the early warning, so that the membrane is prevented from being thoroughly failed in a short period of time due to excessive fouling; by the early warning method, the R/O system of the water treatment plant can be managed more dynamically and accurately, so that the running cost is saved, the service life of equipment is prolonged, and the stability of water quality is ensured.
In one embodiment, the step of obtaining the basic performance data of the reverse osmosis membrane and calculating the coefficient B of the reverse osmosis membrane under different water qualities according to the dissolution-diffusion model includes:
In the dissolution-diffusion model:
Where J s is the molar flux of the solute, C f is the feed solution concentration, and C p is the permeate concentration.
The dataset included membrane flux data, calculation of membrane flux:
Wherein the membrane flux J is determined by the ratio of the self resistance R m and the pollution layer resistance R c to the membrane passing pressure difference p and the osmotic pressure difference pi m, and the osmotic pressure difference pi m is calculated as follows:
r is a universal gas constant, T is absolute temperature;
And (3) finishing to obtain:
In this embodiment, under the action of pressure, the pollutant continuously generates adsorption precipitation on the surface of the reverse osmosis membrane, so that the membrane pollution resistance generated by the adsorption precipitation increases along with the aggregation of the pollutant, that is, the membrane pollution resistance is a dynamically changing amount along with time, in the same water quality, the average pollutant concentration is consistent by default, that is, the amount of the pollutant trapped in a certain time is consistent, and the relationship between the change of the membrane flux and the time can be obtained by calculating the change of the membrane flux through the amount of the pollutant.
In one embodiment, in the reverse osmosis (R/O) system of the water treatment plant, according to raw water data processed by the reverse osmosis membrane, for example, the concentration of pollutants in the raw water data is X mol/cubic meter, the data of the reverse osmosis membrane which runs safely for one year is processed, the variation of the membrane flux data is calculated, and the variation relationship between time and the membrane flux can be obtained according to the variation of the membrane flux, so as to obtain the membrane flux in each time period.
In one embodiment, the dataset further comprises a type, amount, and rate of fouling species; wherein the ionic strength in the foulant is calculated as a function of:
f(I)＝aI2+bI+c；
a. b and c are respectively function coefficients, and I is the ionic strength of the scaling substances;
The solubility product of the foulants is:
K＝αI；
Alpha is a temperature correction coefficient;
At a certain concentration of foulants, the foulants begin to scale:
S is the saturation of the foulant and CF is the concentration factor.
The membrane flux is reduced due to the adhesion of original pollutants of the water body and generated scaling substances on the reverse osmosis membrane, the amount of the original pollutants of the water body and the generated scaling substances are changed by time, and the calculation process is as follows:
r c＝rcg(t),rc is the resistance coefficient of the pollution layer, g (t) is the variation of the resistance of the pollution layer with time;
The method comprises the following steps:
The amount of the scaling substances is divided into two stages, namely an ion aggregation stage and a scaling stage:
Where I n is the fouling concentration, I n =s, v is the fouling rate.
The ion concentration of the scaling substances is determined according to the membrane flux, and the more the filtered water body is, the more ions remain on one side of the reverse osmosis membrane, and the more the ions remain on the other side of the reverse osmosis membrane are:
Jt＝In；
substituting respectively to obtain:
In the time-reverse osmosis membrane flux graph, before t=i, the membrane flux of the reverse osmosis membrane is determined by the amount of the original contaminant of the water body, and after t=i, the membrane flux of the reverse osmosis membrane is determined by both the amount of the original contaminant of the water body and the amount of the generated scaling substance.
In the embodiment, the rejection rate of the membrane to carbon dioxide is almost 0 in the reverse osmosis process, but the rejection rate of the membrane to calcium ions, silicon ions and the like is hundred percent, raw water is gradually concentrated along with the operation of the reverse osmosis membrane, so that cations such as calcium ions and the like in concentrated water are gradually increased, meanwhile, the PH value is also increased, bicarbonate ions in the water are converted into carbonate ions by the increase of the PH value, and scaling reaction occurs on the reverse osmosis membrane to generate some insoluble salts, so that the reverse osmosis membrane is polluted, the membrane flux is reduced, and the use of the reverse osmosis membrane is influenced; the relationship between foulants and membrane flux was calculated.
In a specific embodiment, the reverse osmosis (R/O) system of the water treatment plant is also used, a model for generating the scaling substances is built according to raw water data, and scaling substances are generated on the reverse osmosis membrane after a certain time so as to influence membrane flux of the membrane, so that the relationship between the membrane flux and the scaling substances in time is obtained.
The influence relationship of the aggregation of the pollutants on the membrane and the generation of the scaling substances on the membrane flux is synthesized, a time-membrane flux graph is drawn, the change condition of two important factors influencing the membrane flux under the condition of water quality is obtained, the change process of the membrane flux can be accurately understood, meanwhile, the time of generating the fouling of the reverse osmosis membrane can be predicted through the data, and when the membrane flux reaches a certain value, the reverse osmosis membrane can be judged to generate the fouling:
Wherein beta is a scaling factor, the t value is certainly greater than i, and under the t time, the membrane flux loss caused by scaling is subtracted from the membrane flux accumulated by pollutants, and if the value is smaller than the preset membrane flux value, the reverse osmosis membrane can be proved to be blocked;
meanwhile, the life of the reverse osmosis membrane can be predicted according to the formula, the data of the existing reverse osmosis membrane, such as working time, can be obtained, and the time of fouling and blocking of the reverse osmosis membrane under the existing working condition can be predicted.
In another embodiment, suppose you are the operator of a water purification plant, responsible for managing a water treatment system using reverse osmosis membrane technology. The system is mainly used for removing dissolved minerals from extracted groundwater to produce drinking water. You have noted that the membrane performance begins to decline and the membrane flux decreases, and a method needs to be devised to predict and prevent fouling.
Starting data:
By testing water samples in the system, it was found that the concentration of calcium ions (Ca 2+) and sulfate ions (SO 4 2-) were higher, which generally resulted in the formation of scaling species of calcium sulfate (CaSO 4), with the following data:
Initial membrane flux (J) 50L/m 2
Resistance coefficient of initial contamination layer (. Alpha.0): 0.1
The rate of change of the resistance of the contamination layer with time (dα/dt) was 0.02/hr
Temperature correction coefficient (θ) of calcium sulfate 1.021
The solubility product (Ksp 0) of calcium sulfate at 25 ℃ is 2.4x10-5
The current operating temperature (T) is 30 DEG C
Reference temperature (T0) 25 DEG C
The scaling rate (v) of calcium sulfate is blank and needs to be measured.
The calculation process comprises the following steps:
Membrane flux monitoring membrane flux data was examined over the last several hours and a gradual decline in membrane flux was seen.
Calculation of solubility product (Ksp) the solubility product at the reference temperature is adjusted to the current operating temperature using a temperature correction coefficient.
Ksp(T)＝Ksp0*θ(T-T0)
Ksp(30℃)＝2.4x10-5*1.021(30-25)≈2.6x10-5
Calculation of saturation (S) assuming that the concentrations of Ca 2+ and SO4 2- before the current membrane were 200mg/L and 250mg/L, respectively, were measured, converted to molar concentrations and substituted into the formula to calculate the saturation.
S＝[Ca2+]*[SO42-]/Ksp(T)
Variation of membrane flux:
J(t)＝J0/(1+α(t))
α(t)＝α0+dα/dt*t
by monitoring and recording changes in membrane flux, real-time alpha and J values can be obtained.
Monitoring of the amount of foulants the fouling rate v can be determined by comparing the decrease in membrane flux and the foulant deposition at different time points.
Real-time monitoring in the reverse osmosis process, namely, by continuously monitoring and recording water quality parameters, membrane flux and other important indexes, the model parameters can be dynamically adjusted, and the generation of scaling substances and the possibility of membrane fouling can be predicted.
With these calculations and monitoring you can perform preventive cleaning or chemical treatments, such as adding a scale inhibitor or adjusting pH to prevent scaling, or planned shut-down to clean the membrane surface before the amount of foulants reaches a critical level to ensure continued operation and efficient performance of the system.
In one embodiment, the step of inputting the graph into a preset neural network model, decomposing each item of sub-data of the corresponding reverse osmosis membrane, and performing vector setting on each item of sub-data to obtain a sub-data vector includes:
The information contained in the graph corresponds to the data collected by the reverse osmosis membrane fouling anomaly early warning system, and the collected data comprise: the conductance, pH, ORP, temperature, flow and pressure of the concentrate and produced water, the operating frequency feedback of the high pressure pump, and one or more of the current and differential pressure across the inlet and outlet of the reverse osmosis unit.
In one embodiment, a data threshold range is set according to the sub-data vector, and if the measured data is not within the data threshold range, an anomaly alarm is issued.
In another aspect, a reverse osmosis membrane fouling anomaly early warning system is provided, which is applied to any one of the reverse osmosis membrane fouling anomaly early warning methods, and the reverse osmosis membrane fouling anomaly early warning system includes:
An acquisition unit: basic performance data of the reverse osmosis membrane is obtained, and a coefficient B of the reverse osmosis membrane under different water qualities is calculated according to a dissolution-diffusion model; specifically, basic data about the performance of the reverse osmosis membrane, such as flux, rejection rate, working pressure, temperature, etc., are collected by a sensor provided on the reverse osmosis membrane;
A calculation unit: calculating a data set running under different water qualities according to the basic performance data of the reverse osmosis membrane; specifically, calculating data sets which can be generated by the reverse osmosis membrane under different conditions, such as membrane flux under various working pressures, temperatures and feed water quality changes;
And a drawing unit: inputting the data set into a time sequence, and drawing a graph of time-reverse osmosis membrane flux;
An input unit: inputting the graph into a preset neural network model, decomposing each item of sub-data of the corresponding reverse osmosis membrane, and performing vector setting on each item of sub-data to obtain a sub-data vector; specifically, the model decomposes the information in the graph into relevant sub-data, such as flux at specific time points, fouling indicators, factors that may affect membrane performance, etc., and builds vectors for these sub-data, resulting in a format that can be used for machine learning.
An early warning unit: and constructing an early warning model according to the sub-data vector to specifically early warn the reverse osmosis membrane, wherein the early warning model can predict future performance change and pollution and blockage risks based on the performance and historical data of the current membrane flux. When the model predicts that a potential anomaly is sensed, it may trigger an early warning informing the operator or the automated control system to take action, such as performing a membrane surface cleaning or replacing a membrane element, to avoid a decrease or damage in system performance.
The above examples merely illustrate embodiments of the invention that are specific and detailed for the relevant practical applications, but are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (9)
1. The reverse osmosis membrane fouling and blocking abnormality early warning method is characterized by comprising the following steps of:
S1: basic performance data of the reverse osmosis membrane is obtained, and a coefficient B of the reverse osmosis membrane under different water qualities is calculated according to a dissolution-diffusion model;
s2: calculating a data set running under different water qualities according to the basic performance data of the reverse osmosis membrane;
S3: inputting the data set into a time sequence, and drawing a graph of time-reverse osmosis membrane flux;
S4: inputting the graph into a preset neural network model, decomposing each item of sub-data of the corresponding reverse osmosis membrane, and performing vector setting on each item of sub-data to obtain a sub-data vector;
s5: and constructing an early warning model according to the sub-data vector to early warn the reverse osmosis membrane.
2. The reverse osmosis membrane fouling anomaly pre-warning method according to claim 1, wherein the method comprises the following steps of: the step of obtaining basic performance data of the reverse osmosis membrane and calculating the coefficient B of the reverse osmosis membrane under different water qualities according to a dissolution-diffusion model comprises the following steps:
In the dissolution-diffusion model:
Where J s is the molar flux of the solute, C f is the feed solution concentration, and C p is the permeate concentration.
3. The reverse osmosis membrane fouling anomaly pre-warning method according to claim 2, wherein the method comprises the following steps of: the step of calculating the data set running under different water qualities according to the basic performance data of the reverse osmosis membrane comprises the following steps:
the dataset included membrane flux data, calculation of membrane flux:
Wherein μ is the water viscosity, the membrane flux J is determined by the ratio of the self resistance R m and the pollution layer resistance R c to the transmembrane pressure difference p and the osmotic pressure difference pi m, and the osmotic pressure difference pi m is calculated as follows:
r is a universal gas constant, T is absolute temperature;
And (3) finishing to obtain:
4. The reverse osmosis membrane fouling anomaly pre-warning method according to claim 3, wherein the method comprises the following steps of: the dataset also includes the type, amount, and rate of fouling species; wherein the ionic strength in the foulant is calculated as a function of:
f(I)＝aI2+bI+c；
a. b and c are respectively function coefficients, and I is the ionic strength of the scaling substances;
The solubility product of the foulants is:
K＝αI；
Alpha is a temperature correction coefficient;
At a certain concentration of foulants, the foulants begin to scale:
S is the saturation of the foulant and CF is the concentration factor.
5. The method for early warning of fouling and plugging abnormality of a reverse osmosis membrane according to claim 4, characterized by comprising the steps of: the step of inputting the data set into a time sequence and drawing a graph of time-reverse osmosis membrane flux comprises the following steps:
The membrane flux is reduced due to the adhesion of original pollutants of the water body and generated scaling substances on the reverse osmosis membrane, the amount of the original pollutants of the water body and the generated scaling substances are changed by time, and the calculation process is as follows:
r c＝rcg(t),rc is the resistance coefficient of the pollution layer, g (t) is the variation of the resistance of the pollution layer with time;
The method comprises the following steps:
The amount of the scaling substances is divided into two stages, namely an ion aggregation stage and a scaling stage:
Where I n is the fouling concentration, I n =s, v is the fouling rate.
6. The method for early warning of fouling and plugging abnormality of a reverse osmosis membrane according to claim 5, wherein the method comprises the steps of: the ion concentration of the scaling substances is determined according to the membrane flux, and the more the filtered water body is, the more ions remain on one side of the reverse osmosis membrane, and the more the ions remain on the other side of the reverse osmosis membrane are:
Jt＝In；
substituting respectively to obtain:
In the time-reverse osmosis membrane flux graph, before t=i, the membrane flux of the reverse osmosis membrane is determined by the amount of the original contaminant of the water body, and after t=i, the membrane flux of the reverse osmosis membrane is determined by both the amount of the original contaminant of the water body and the amount of the generated scaling substance.
7. The reverse osmosis membrane fouling anomaly pre-warning method according to claim 6, wherein the method comprises the following steps: the step of inputting the graph into a preset neural network model, decomposing each item of sub-data of the corresponding reverse osmosis membrane, and performing vector setting on each item of sub-data to obtain a sub-data vector comprises the following steps:
The information contained in the graph corresponds to the data collected by the reverse osmosis membrane fouling anomaly early warning system, and the collected data comprise: the conductance, pH, ORP, temperature, flow and pressure of the concentrate and produced water, the operating frequency feedback of the high pressure pump, and one or more of the current and differential pressure across the inlet and outlet of the reverse osmosis unit.
8. The reverse osmosis membrane fouling anomaly pre-warning method according to claim 1, wherein the method comprises the following steps of: the step of constructing an early warning model according to the sub-data vector to early warn the reverse osmosis membrane comprises the following steps:
And setting a data threshold range according to the sub-data vector, and if the measured data is not in the data threshold range, sending out an abnormal alarm.
9. A reverse osmosis membrane fouling anomaly early warning system, characterized in that the reverse osmosis membrane fouling anomaly early warning system is applied to the reverse osmosis membrane fouling anomaly early warning method according to any one of claims 1 to 8, and comprises:
an acquisition unit: basic performance data of the reverse osmosis membrane is obtained, and a coefficient B of the reverse osmosis membrane under different water qualities is calculated according to a dissolution-diffusion model;
a calculation unit: calculating a data set running under different water qualities according to the basic performance data of the reverse osmosis membrane;
And a drawing unit: inputting the data set into a time sequence, and drawing a graph of time-reverse osmosis membrane flux;
an input unit: inputting the graph into a preset neural network model, decomposing each item of sub-data of the corresponding reverse osmosis membrane, and performing vector setting on each item of sub-data to obtain a sub-data vector;
an early warning unit: and constructing an early warning model according to the sub-data vector to early warn the reverse osmosis membrane.
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