HK1238194B - Intelligent fluid filtration management system - Google Patents

Description

Intelligent fluid filtration management system

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application Serial No. 62/036,344, filed on August 12, 2014, entitled “Smart Flux Management System for Cross-Flow Membrane Systems,” and U.S. Provisional Application Serial No. 62/145,793, filed on April 10, 2015, entitled “Smart Fluid Filtration Management System,” the contents of each of which are hereby incorporated by reference.

Background Art

In practice, filtration can be broadly divided into six separation categories: solid-gas, solid-liquid, solid-solid, liquid-liquid, gas-liquid, and gas-gas. Filtration technology is used to separate pollutants and value-added materials in a wide range of process applications, such as automotive and aerospace fuel and air filtration, household and industrial air filtration, food and beverage concentration and sterilization, pharmaceutical molecule isolation and purification, medical applications such as kidney dialysis and blood oxygenation, drinking water treatment, industrial process water purification, and waste treatment and environmental remediation. For example, filtration is the most important and widely used water purification method because it can completely and continuously filter impurities through size exclusion, preferential adsorption, and large-scale diffusion (Howe and G. Tchobanoglous, Water Treatment: Principles and Design, John Wiley & Sons, Inc., Hoboken, New Jersey, 2nd edition, 2005). Virtually all municipal and industrial water and wastewater treatment facilities, most groundwater treatment facilities, and large and small desalination facilities employ some form of filtration to remove problematic materials such as microorganisms, clays, sediments, oils, and other organic and inorganic solutes (Crittenden, J., et al. (2012) Water Treatment: Principles and Design, MWH, Hoboken, NJ, USA).

In general, fluid filtration consists of separating and removing targeted suspended and dissolved solids from water by the relative velocity of the solids passing through the separation media. Fluid filtration systems most commonly include the following treatment technologies: granular media filtration (e.g., sand, anthracite, garnet, nut shells, nonwoven fabrics, and other non-reactive waste biomass), ion exchange media filtration, adsorption media filtration (e.g., granular activated carbon or GAC, zeolites, polymers, and organoclays), reactive media filtration (e.g., greensand and oxidation filtration, biosand filtration, bioGAC filtration), low-pressure membrane filtration (e.g., microfiltration and ultrafiltration), and high-pressure membrane filtration (e.g., nanofiltration and reverse osmosis).

Most filtration processes are limited by the accumulation of removed material on or in the filter media. For example, when a membrane is used to filter impurities from a water sample, the flux will gradually decrease over time as the membrane becomes clogged or "fouled" with inorganic particles, organic matter, and/or biological microorganisms. Membrane fouling often results in a significant drop in flux or throughput, impacting process efficiency and the quality of the water produced. Indeed, filter clogging and its mitigation remain a major operational challenge in filtration technology due to its dramatic impact on filtrate quality, maintaining target filtration throughput, energy efficiency, and filter damage.

Filter clogging is the inevitable phenomenon that occurs during filtration, but can be alleviated by routine maintenance strategy before needing to change completely.Specifically, flux maintenance technology can be defined as by removing the reversible dirt and sediment on or in the filter and/or suppressing their future deposition to carry out the system process to recover filtrate flux.Common maintenance strategy includes various forms of mechanical and chemical cleaning, such as filtrate backwashing and original position chemical cleaning (for example, corrosive agent, oxidant/disinfectant, acid, chelating agent and surfactant) (Liu, C., etc. (2006) Membrane Chemical Cleaning:FromArt to Science, Pall Corporation, Port Washington, NY 11050, USA).However, each maintenance response can negatively affect the efficiency of process by increasing system downtime, consuming commercial filtrate product, consuming expensive cleaning chemicals and damaging filter by harsh cleaning method.Currently, these filter maintenance technologies use predetermined design standard--frequency, intensity and duration to implement--and can not adapt to the space and time variation in given filtration process in real time. Therefore, there is a need for adaptive process control techniques for operating filtration-based processes in order to optimize maintenance response and minimize the impact of filter fouling on operating energy requirements and life cycle performance.

Considerable effort is associated with removing and replacing expired filters, and this can result in significant system downtime and costs. The useful life of filter modules, filter media, ion exchange resins, or granular activated carbon is location-specific, based on the unique environmental conditions and water quality used for a given treatment purpose. Maximizing plant efficiency, therefore, requires predicting the useful life of the modules based on information directly related to their specific performance in a given application. These and other shortcomings are addressed in the present disclosure.

Summary of the Invention

It should be understood that, as required, both the following overview and the following detailed description are merely exemplary and illustrative and not restrictive. Methods and systems for intelligent fluid filtration management are provided. The methods and systems can monitor one or more parameters associated with one or more membranes of a filtration system. Conditions of one or more filters can be determined based on the one or more monitored parameters of the filtration system. The conditions can include one or more of an impending filter maturation or filtration readiness, a detected filter maturation or filtration readiness, an impending integrity breach, a detected integrity breach, an impending permeability loss, a detected permeability loss, combinations thereof, and the like. One or more maintenance procedures can be performed based on the determined conditions. The one or more maintenance procedures can include one or more of a filter cleaning procedure, a filter isolation procedure, a filter repair procedure, a filter replacement procedure, and a filter pinning procedure.

In one aspect, the filtration management system may monitor at least one of a change in fluid filtrate throughput during constant pressure operation and a change in pressure during constant filtrate throughput operation. A fouling mechanism may be determined based on at least one of a change in filtrate throughput and a change in pressure. The fouling mechanism may be determined by performing a mathematical analysis of the filtrate flow change or pressure change according to one or more predetermined fouling models. The one or more predetermined fouling models may include one or more of a Hermia model, a modified Hermia model, and a series resistance model. A cleaning regimen may be selected based on the determined fouling mechanism. The cleaning regimen may include selecting a cleaning method and one or more parameters associated with the cleaning method.

In one aspect, the filtration system can include a pressure pump configured to apply pressure to a fluid flowing between a first chamber and a second chamber. The filtration system can also include a flow sensor configured to measure at least one parameter associated with the fluid flowing through a membrane deposited between the first chamber and the second chamber. The filtration system can include a pressure sensor configured to measure a pressure reading of the fluid flowing from the first chamber to the second chamber. Additionally, the filtration system can include a filtration management system configured to cause the pressure pump to apply a constant pressure to the fluid flowing through the membrane from the first chamber to the second chamber for a first predetermined time based on the pressure reading. The filtration management system can be further configured to cause the pressure pump to reverse the flow of the fluid through the membrane for a second predetermined time based on the at least one parameter.

Additional advantages will be partially set forth in the following description or may be learned through practice. Said advantages will be realized and attained by means of the elements and combinations specifically pointed out in the appended claims. It should be understood that, as required, the foregoing summary and the following detailed description are merely exemplary and illustrative and not restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments and, together with the description, serve to explain the principles of the methods and systems:

FIG1 shows a representative schematic diagram of a membrane process with constant throughput and variable feed pressure operation;

FIG2 shows a representative schematic diagram of a constant pressure variable flux operation of a membrane process;

FIG3 shows a representative diagram of a filtration management system;

FIG4 shows a representative flux distribution during forward filtration followed by backwash;

FIG5 shows a flow chart illustrating an exemplary method for operating a filter management system in a training mode;

FIG6 shows a representative flow chart of a filtration management system;

FIG7 shows a flow chart illustrating an exemplary method for operating a filtration management system in a control mode;

FIG8 illustrates a representative flow chart of the control mode operation of the filtration management system;

FIG9 shows a flow chart illustrating an exemplary method for operating a filtration management system in response to a step change in flux;

FIG10 shows a flow chart illustrating an exemplary method for operating a filtration management system in response to pulsed changes in flux;

FIG11 shows a representative schematic diagram of the response of the filtration management system to a pulse event of duration t _p ;

FIG12 shows a flow chart illustrating an exemplary method for operating a filtration management system;

FIG13 shows solutions for different fouling mechanisms and flux versus time curves for which fitting tests were performed to determine the specific observed particle fouling mechanism;

FIG14 shows another flow chart illustrating an exemplary method for operating a filtration management system;

FIG15 shows the experimental results using a filtration management system;

FIG16 shows the experimental results using a filter management system; and

FIG. 17 illustrates an exemplary computing device on which the disclosed methods and systems may operate.

Additional advantages of the present disclosure will be set forth in part in the following description and in part will be apparent from the description or may be learned by practicing the present disclosure. The advantages of the present disclosure will be realized and achieved by means of the elements and combinations specifically pointed out in the appended claims. It should be understood that, as required, the above summary and the following detailed description are merely exemplary and illustrative and do not limit the methods and systems.

DETAILED DESCRIPTION

The present disclosure may be understood more readily by reference to the following detailed description of methods and systems and the Examples included therein.

Before disclosing and describing the compounds, compositions, articles, systems, devices and/or methods of the present invention, it should be understood that, unless otherwise specified, they are not limited to specific synthetic methods, or unless otherwise specified, they are not limited to specific reagents, and therefore they can certainly vary. It should also be understood that the terms used herein are only for the purpose of describing specific aspects and are not intended to be limiting. Although any methods and materials similar or equivalent to those described herein can be used in implementing or testing the present disclosure, exemplary methods and materials are now described.

In order to disclose and describe the method and/or material involved in being published when quoting, all publications mentioned herein are incorporated herein by reference.Providing the publication discussed herein is only for the disclosure before the filing date of the present application.Any content herein should not be construed as admitting that the present disclosure has no right to be earlier than described publication due to previous disclosure.In addition, the publication date provided herein may be different from the actual publication date, and they may need to be confirmed separately.

As used in the specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to "a membrane," "a valve," or "a sensor" includes a mixture of two or more such membranes, valves, or sensors, etc.

In this article, ranges can be expressed as from "about" one specific value and/or to "about" another specific value. When such a range is expressed, additional aspects include from a specific value and/or to another specific value. Similarly, when a value is expressed as an approximation by using the antecedent "about", it will be understood that the specific value forms an additional aspect. It should be further understood that each endpoint of the range is meaningful relative to the other endpoint and independently of the other endpoint. It should also be understood that multiple values are disclosed herein, and each value is also disclosed as "about" the specific value in addition to the value itself. For example, if the value "10" is disclosed, then "about 10" is also disclosed. It is also understood that every unit between two specific units is also disclosed. For example, if 10 and 15 are disclosed, then 11, 12, 13, and 14 are also disclosed.

As used herein, the terms "optional" or "optionally" mean that the subsequently described event or circumstance may or may not occur, and that the description includes instances where said event or circumstance occurs and instances where it does not.

As used herein, "fluid" refers to any substance that continuously deforms or flows under applied shear stress. Fluids include, but are not limited to, liquids, gases, and plasmas.

As used herein, "filter" refers to any semipermeable barrier or porous device used to remove impurities from a fluid. Fluid filters may include, but are not limited to, membranes or sieves, packed media beds, fluidized media beds, membrane bioreactors, and ion exchange systems. Filter separation mechanisms include, but are not limited to, size exclusion, adsorption, preferential dissolution/diffusion, electromagnetic attraction/repulsion, electrostatic attraction/repulsion, chemical reaction, or a combination thereof.

As used herein, "fouling" refers to the deposition of organic and inorganic matter on the filter surface or within the filter pores and interstitial spaces. Fouling includes, but is not limited to, the deposition or adsorption of inorganic particles (e.g., clays, minerals, metals, etc.), immiscible hydrocarbons (e.g., oils and greases), dissolved and precipitated organic molecules, and bacteria or algae on the filters of the fluid filtration systems described herein.

As used herein, "feed stream" refers to any aqueous or non-aqueous fluid containing filterable solutes and/or particulate matter.

As used herein, "permeate stream" refers to any portion of a feed stream that has been directed through a filter by a pressure driven or gravimetric filtration device.

As used herein, "flux" refers to the flow rate of fluid per unit area through a filter. Flux can be, but is not limited to, permeate flux or tensile flux.

Unless otherwise expressly stated, it is not intended that any method described herein be construed as requiring that its steps be performed in a specific order. Therefore, in the absence of a method claim actually reciting the order in which its steps are to be followed or otherwise specifically stating in the claims or description that the steps are to be limited to a specific order, no order is intended to be inferred in any respect. This applies to any possible non-express basis for interpretation, including: matters of logic with respect to arrangement of steps or operational flow; clear meaning derived from grammatical organization or punctuation; and the number or type of embodiments described in this specification.

It is understood that the compositions disclosed herein have certain functions. Certain structural requirements are disclosed herein for performing the disclosed functions, and it is understood that there are multiple structures that can perform the same function related to the disclosed structures, and these structures generally achieve the same results.

A. Semipermeable membrane

In one aspect, the methods and systems of the present disclosure relate to a pressure-driven filtration process using a membrane as a separation barrier to remove solutes and suspended particles from a solution or liquid suspension. In one aspect, the membrane can be a semipermeable membrane.

Semipermeable membranes can be used to separate dissolved or dispersed materials from a feed stream. The separation process can involve contacting the feed solution with one surface of the semipermeable membrane under pressure to affect permeation of the solvent phase through the semipermeable membrane while preventing permeation of the dissolved or dispersed material.

Semipermeable membranes can be made of polymers, ceramics or metals. These polymers, ceramics or metal membranes can be packaged into elements and modules with many possible combinations (form factors), such as flat-plate modules, plate-and-frame modules, spiral-wound modules, tubular modules, hollow fiber modules, combinations thereof, and the like. In addition, these semipermeable membranes can all be synthesized to exhibit a wide range of selectivities and permeabilities, ranging essentially from microfiltration (MF) and ultrafiltration (UF) to nanofiltration (NF) and reverse osmosis (RO).

Both RO membranes and NF membranes can include a thin film discriminating layer fixed to a porous support, collectively referred to as a "composite membrane". MF and UF membranes can also include a composite arrangement. The porous support can provide physical strength, but the porous support can provide little flow resistance due to its porosity. On the other hand, the thin film discriminating layer can be less porous and can provide the primary means of separation of dissolved or dispersed materials. Therefore, the thin film discriminating layer can be primarily responsible for the "rejection" of a given membrane - the percentage of a particular dissolved or dispersed material (e.g., solute) that is rejected, and the "flux" - the flow rate per unit area of the solvent through the membrane.

Semipermeable membranes vary with respect to their permeability to different ions and organic and inorganic compounds. For example, " diffusion membranes " (e.g., NF and RO) are relatively impermeable to virtually all ions (including sodium and chloride) and uncharged solutes with a molecular weight higher than about 200 daltons. Therefore, RO membranes are widely used in the desalination of salt water or seawater to provide highly purified water for industrial, commercial or domestic use, because the rejection of the sodium and chloride ions of RO membranes is generally greater than about 90%. On the contrary, " low-pressure membranes " (e.g., MF and UF) can be relatively porous, and are therefore used to remove colloids and particulate matter (e.g., about 0.1 μm to about 10 μm for MF, 0.01 μm to 0.1 μm for UF). MF and UF can be used in municipal and industrial treatment applications, NF/RO pretreatment, chemical synthesis purification, etc. for particle and pathogen removal.

MF and UF membranes can be composed of inorganic or polymeric materials in a range of geometric shapes. The membranes can be formulated into various modular configurations, such as tubular configurations, plate-and-frame configurations, spiral wound configurations, hollow fiber configurations, combinations thereof, and the like. Polymeric MF and UF membranes can be composed of various polymers (such as cellulose acetate, polyvinylidene fluoride, polyacrylonitrile, polypropylene, polysulfone, and polyethersulfone). Polymeric membranes can be manufactured relatively economically to have various form factors, but can be limited to narrow operating ranges relative to moderate pH, temperature, and chemical tolerances. Ceramic membranes made of materials such as alumina, zirconium oxide, and titanium dioxide can be used in applications where conditions require operation at high temperatures or where harsh cleaning chemicals may be required.

MF and UF membranes can be constructed with a symmetrical pore structure to allow for in-situ scaling control by backwashing or backflushing. As used herein, "backwashing" or "backflushing" refers to reversing the direction of permeate flow by applying a pressure differential greater than the forward filtration transmembrane pressure, which can be mechanically and/or osmotically driven. Backwashing includes, but is not limited to, using permeate liquid, purified water, or chemically enhanced permeate liquid by adding supplemental chemicals such as acids, caustics, and/or oxidants.

In another aspect, the intelligent fluid filtration management process can be employed with the use of MF membranes, UF membranes, NF membranes, RO membranes, forward osmosis membranes, and pressure-retarded osmosis membranes without film coatings. Furthermore, the intelligent fluid filtration management process can be employed with the use of ceramic membranes and polymeric membranes. In yet another aspect, the semipermeable membrane is employed in a tubular configuration, a plate-and-frame configuration, a spiral-wound configuration, a hollow fiber configuration, or a membrane bioreactor configuration.

In yet another aspect, the intelligent fluid filtration management process can be employed in conjunction with the use of non-membrane based filtration, ion exchange and activated carbon systems such as sand filtration mixed media filtration, ion exchange, granular activated carbon and critical cartridges and spiral wound filtration systems, as the mechanisms of filtration and filter maintenance mirror those of the membrane based aspects described herein.

B. Fluid Filtration Management System

FIG1 illustrates an exemplary filtration system 100 managed by a filtration management system 105. In one aspect, the present disclosure relates to filtration system 100, which includes a pressure pump 110 configured to apply pressure to a fluid flowing between a first chamber 115 and a second chamber 120. Filtration system 100 may also include a flow sensor 125 configured to measure at least one parameter associated with the fluid flowing through a membrane 130 deposited between first chamber 115 and second chamber 120. Filtration system 100 may also include a pressure sensor 135 coupled to pressure pump 110 and flow sensor 125 and configured to measure a pressure reading of the fluid flowing from first chamber 115 to second chamber 120. Furthermore, filtration system 100 may include filtration management system 105 in communication with pressure pump 110, flow sensor 125, and pressure sensor 135. Filtration management system 105 may be configured to cause the pressure pump to apply a constant pressure to the fluid flowing from first chamber 115 to second chamber 120 for a first predetermined time based on the pressure reading from pressure sensor 135. Filtration management system 105 may cause pressure pump 110 to reverse the flow of fluid through membrane 130 at a constant pressure for a second predetermined time based on the at least one parameter.

Pressure-driven membrane filtration processes, such as microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), and reverse osmosis (RO), use a semipermeable membrane as a separation barrier to remove solutes and suspended particles from a solution or liquid suspension. The application of a transmembrane pressure (TMP) differential causes the solvent to flow through the membrane while retaining the solutes or particles in the feed.

The cleaning solvent flow rate (or flux) through the membrane can be governed by Darcy's equation, which states that the flux (volume flow rate per unit cross-sectional area of the membrane) is linearly proportional to the applied pressure differential:

During the filtration process, solutes and particles retained by the membrane can accumulate at the membrane surface. The mechanism of solute concentration accumulation at the membrane surface can be called concentration polarization. The increase in solute concentration at the membrane surface can lead to additional resistance to solvent flow through the membrane. These resistances can reduce the filtrate or permeate flux. Table 1 summarizes several related mechanisms of increased resistance and subsequent flux reduction.

Table 1.

The diversity of flux decline mechanism can cause different types of spatiotemporal variation of the permeate flux of different types of membrane elements, modules and membrane filtration systems. Each mechanism described in Table 1 can cause different types of time dependence of flux decline behavior. Over time, more solutes can be accumulated on the membrane. The accumulation of solutes can increase the total resistance of the solvent flow through the membrane, and over time cause the reduction of filtrate flux. In the tangential flow membrane elements and modules (such as tubular modules, plate and frame modules, spiral wound modules, hollow fiber modules or membrane bioreactor modules) of the commercial scale of many types, the degree of solute deposition on the membrane can also vary spatially. Therefore, together with the time dependence of flux, there can also be local spatial variation in the flux of each type of membrane module or element. For example, the flux loss distribution of NF and RO membranes can illustrate the organic fouling of lead element. The feature of the organic fouling of lead element can be the gradual flux loss carried out by filter cake formation. Furthermore, the flux loss distributions of NF and RO membranes may account for inorganic fouling on the tailing element, which may be characterized by drastic and sudden flux losses through inorganic precipitation and complete pore blocking.

Different flux decline mechanisms, various types of modules and element geometries, and the complex feed chemistry and composition in the commercial application of membrane processes may make it challenging to develop a general mechanistic model of flux decline and membrane fouling that can be implemented as an operation and process control algorithm. The process control of a membrane filtration system may involve maintaining a constant volume throughput from the membrane filtration system. In other words, the average permeate flux from the membrane modules can be monitored during the process. If there is any decline in throughput, the control algorithm can increase the driving force (such as the TMP applied) to maintain flux at the desired set point. This operating principle completely ignores any flux decline mechanism and is used for all types of pressure-driven membrane separation processes ranging from microfiltration to reverse osmosis and membrane types (ceramic membranes or polymeric membranes).

FIG2 schematically illustrates the change in applied pressure on the feed side of a membrane element relative to time. The applied pressure is increased to maintain a constant permeate flux through the membrane element. The applied pressure is increased to maintain a constant permeate flux through the membrane element. The applied pressure is continuously increased because different fouling mechanisms reduce overall membrane permeability during filtration. When the applied pressure reaches a maximum threshold value P _max , during the filtration cycle, t _f , filtration is stopped and a membrane cleaning mechanism is started. In some applications, the filtration cycle is on a fixed timer, and t _f is constant between successive cycles. In such cases, the maximum applied pressure P _max can be changed between filtration cycles. The cleaning mechanism can be different depending on the type of membrane element and the membrane process, and can range from backwash (BW), chemically enhanced backwash (CEB), permeate relaxation (PR), pressure pulse (PP), air scour (AS), chemical pulse (CP), feed flow reversal (FFR), cleaning in place (CIP), combinations thereof, etc. After executing the cleaning mechanism, the permeability of the membrane element is partially restored. The restored permeability is partially due to reversible fouling of the membrane elements during the filtration cycle, while the unrecovered permeability is partially due to irreversible fouling. In constant throughput mode of operation, some of the irreversible permeability loss of the membrane elements can also be caused by compression of the membrane elements due to the high pressures during operation. Furthermore, the energy requirements of the filtration process are time-dependent, as increasing pressure requires an increase in the power draw of the filtration system.

In a constant pressure variable throughput mode of operation, the TMP can be initially set, and the permeate flux decreases over time during the filtration cycle due to various flux decay mechanisms. Two modes of operation may be possible: (i) a fixed filtration time mode, in which the forward filtration time _tf is constant; and (ii) a fixed flux decay mode, in which the flux is allowed to reach a minimum value _Jf before the cleaning mechanism is triggered. After cleaning, flux recovery is typically incomplete due to irreversible membrane fouling.

FIG3 shows a representative diagram of a constant pressure variable flux operation depicting a membrane process. The vertical axis describes flux. The shaded area represents irreversible permeability loss. The mode of operating the filtration system can be a constant throughput (CT) variable transmembrane pressure (TMP) mode, in which pressure is gradually increased to maintain a constant permeate throughput through the membrane. The implementation of this process control may require a flow rate measurement device to record throughput, and a pressure control mechanism for regulating feed pressure. This type of process control may have problems with a proportional-integral-differential (PID) control loop; that is, process control can be reactive (feedback control mechanism), can be based on constant parameters, is not based on the actual mechanism of membrane flux decline, and does not provide optimal or adaptive control.

From a membrane process perspective, the problem with the above PID control loop is determining the flux set point. If the flux set point is the initial flux, J ₀ , then the pressure may increase too sharply during the initial stages of filtration, since the flux may drop quite quickly during these stages. This increase in pressure can compress the membrane quite severely.

The second problem with the PID control loop in the CT operating mode is related to a sudden surge in the contaminant concentration in the feed. When this surge hits the membrane, the flux drops suddenly. In response to this sudden drop in flux, the PID control loop can try to increase the pressure to keep the flux constant. In a membrane process operated in a mass transfer controlled manner, the flux may not respond linearly with the applied pressure. Therefore, the pressure can be increased significantly to achieve a relatively small flux enhancement. In addition, this flux increase can be at the expense of reduced permeability through the membrane, because the higher permeation resistance forces more solutes toward the membrane surface or embedded in the membrane pores, thereby more actively fouling the membrane. In some cases, increased feed pressure can enhance the mass transfer of the membrane by increasing the axial pressure drop, which causes an increase in crossflow. Therefore, increasing pressure in response to a sudden increase in feed solute concentration is not a prudent approach to flux control in a membrane process.

Flux decline in a membrane element can be a sign of fouling occurring during a filtration process. Flux decline can be considered a direct and unambiguous indicator of membrane fouling and performance loss. If flux decline can be tracked during large-scale commercial filtration operations, this information can be used to develop a control and operating architecture for a membrane process that can intelligently: a) assess the primary mechanism of fouling during a filtration process; b) learn to automatically trigger a cleaning mechanism once critical levels of fouling and flux decline are observed; c) adapt to the most economical operating mode for a given feedwater quality and process configuration; d) dynamically respond to sudden fluctuations in feedwater quality, even shutting down the process during harmful and catastrophic surges in feedwater contaminant levels; e) reduce energy consumption; f) automatically cycle between various modes of system maintenance (such as backwashing and in-place cleaning); g) extend the useful life of the membrane and optimize the cost of water treatment; combinations thereof, etc.

Permeate flux is generated by the pressure (driving force) applied during membrane filtration. In other words, the pressure applied is the cause, and flux is the effect. In the constant pressure (CP) operating mode, the driving force remains constant, and the subsequent flux change is simply a manifestation of how different fouling resistances accumulate over time, resulting in a decrease in the flux under the influence of a fixed driving force. However, in the constant throughput (CT) operating mode, the driving force itself changes in response to changes in flux. The change in driving force can change the process dynamics to a way that additional mechanisms can be called under different operating pressures. A common example of this change in fouling mechanism is the transition between osmotic pressure control and gel layer control above the critical TMP difference during filtration of some types of proteins and polymers. Below the threshold pressure, the polymer solution does not gel, and the main mechanism of concentration polarization is osmotic pressure accumulation. However, if the critical pressure of the gel concentration is reached, the main mechanism of flux decline becomes the growth of the gel layer.

The constant pressure (CP) operating mode can be used in process control architectures that rely on flux drop as a key signal. Constant pressure mode prevents changes in the causal relationship between pressure and flux.

Herein, one aspect of a proposed intelligent filtration management system is disclosed, which triggers backwashing in a filtration system comprising a membrane in a constant pressure operating mode. This disclosure demonstrates how following a flux decline pattern can allow for the collection of relevant information about the behavior of the filtration system under given driving force (TMP) and feed conditions, how this information can be used to adjust the performance of the filtration system, trigger backwashing or CIP in response to sudden changes in throughput, and distinguish between CP and CT operating modes. However, other filtration systems are contemplated.

Assume that the membrane filtration system begins operation with an initial flux of J ₀ corresponding to an initial TMP of ΔP _0. During the forward filtration cycle, the flux changes over time, and the changes are recorded at fixed time intervals of Δt. After a forward filtration time of t _f , a backwash cycle is initiated. During the backwash, a portion of the permeate collected during the filtration cycle is forced through the membrane from the permeate back to the feed side. The backwash flux, J _{BW ,} can be greater than the forward filtration flux, but the backwash duration, t _{BW ,} can be much less than the forward filtration time. This condition removes deposited solids from the membrane surface and carries them to the recirculating feed. This type of backwash can be employed during filtration operations based on ceramic membranes.

Figure 4 schematically depicts the flux versus time distribution during two consecutive filtration cycles with intermediate backwashing. The flux decreases over time, and the accumulation of permeate (or filtrate) during the filtration cycle produces

where _Am is the membrane area, and the final expression is based on discrete measurements of the flux integrated at fixed time intervals (there are N intervals) using the trapezoidal rule.

The total volume of permeate consumed during the backwash process is

Q _BW ＝A _m J _BW t _BW (2)

The backwash ratio is then defined as

For filtration operations with backwash, it is desirable to have the smallest possible backwash ratio, with a typical target of r _BW < 0.2. The net product water throughput from the membrane unit is

The rate of flux decline during the filtration cycle is given by

If the flow measurement device records the flux at regular intervals, the rate of flux decline at each instant _ti (instantaneous decline) can be expressed as

Equations (1) and (6) represent the integral and derivative, respectively, of the same time-dependent permeate flux order.

Without further scrutiny, one could attempt to use these flux measurements to construct a proportional-integral-derivative (PID) type control algorithm. This approach would require the definition of a set point, which could be the desired flux, and based on the deviation of a given flux measurement from the set point, the controlled variable (typically the applied pressure) would be adjusted to minimize the flux deviation from the set point. This is an approach formerly known as the constant throughput (CT) variable pressure model.

As described above, the PID control mechanism suffers from two major problems. First, the flux set point is determined. If the set point is the initial flux J ₀ , the pressure will increase too sharply during the initial stage of filtration because the flux drops quite quickly during these stages. This increase in pressure can compress the membrane quite violently. The second concern is related to the sudden surge in dirt concentration in the feed, causing a sudden drop in flux. In a PID filter management system, the pressure needs to be significantly increased to achieve a relatively small flux enhancement. This in turn may lead to a decrease in permeability caused by an increase in scaling, which is caused by forcing more solutes toward the membrane surface due to a higher permeation resistance.

1. Constant pressure variable throughput operation mode

If the power consumption of the process during both types of operation is considered, the constant pressure variable throughput mode of operation is slightly different in scope from the constant throughput variable pressure mode of operation. The power consumption of a given process can be related to the water horsepower given by

For constant pressure operation, J = J(t), while for constant throughput operation, the TMP difference is a function of time. The key difference in the power output of filtration systems operating in the two modes is that, for constant pressure operation, power consumption does not increase (it remains constant or decreases) as flux decreases during a filtration cycle; whereas, for constant throughput operation, power consumption increases during a filtration cycle. The increase in power consumption during CT operation is related to the generation of additional driving force to push permeate through the lower permeability filtration barrier at a constant rate.

In most commercial membrane elements and modules, the degree of concentration polarization, scaling and particle deposition varies axially along the length of the module. Such changes cause different degrees of flux decline at different positions of the module or element. The throughput from the module represents the spatial average permeate flux along the module multiplied by the membrane area. The local permeate flux in the module varies according to the scaling mechanism. In most applications, scaling is more serious at the downstream position of the module, resulting in lower flux from these positions. In many of these modules, tangential flow is used to limit the degree of scaling. In some applications, feed flow reversal (FFR) can be used to prevent excessive asymmetric scaling at one end of the module.

Regardless of the mechanism of membrane fouling in commercial modules, most modules and elements scale asymmetrically, and typically require replacement when only a portion of the element becomes largely and irreversibly fouled. Asymmetrical fouling of membrane elements is a consequence of the mass transfer characteristics of tangential flow filtration systems. It was of interest to explore how operating commercial-scale membrane elements in constant-pressure, variable-throughput, and constant-throughput, variable-pressure modes can result in varying degrees of asymmetrical fouling of the elements.

Fouling initiates at the downstream end of the membrane, causing permeability loss in these regions of the membrane. Filtrate recovery from these regions is reduced. In constant-pressure operation, lower permeability increases the volumetric tangential flow during the steady-state process. As crossflow velocity increases, axial friction losses also increase. While increased crossflow is beneficial for mass transfer and leads to the removal of the fouling layer in some membrane processes, higher crossflow is typically associated with higher axial pressure drop across the module. This process is exacerbated during constant-throughput operation because increasing the total driving pressure when a portion of the membrane loses permeability more actively increases the axial flow component. Therefore, while filtrate or permeate production from the module remains constant in this type of operation, production is primarily due to increased permeation from unfouled regions of the membrane at the leading edge of the element. Higher permeability from these locations increases permeation resistance and, therefore, foulant accumulation in these portions of the membrane. Ultimately, when the membrane becomes blocked but higher driving pressure is applied to the feed, axial flow increases, thereby increasing the frictional pressure drop across the module. The overall result is higher operating costs for membrane processes operating in constant-throughput, variable-pressure mode.

In another aspect, causing the pressure pump to reverse the constant pressure based on at least one parameter comprises comparing the determined at least one parameter to at least one threshold value. In yet another aspect, the at least one parameter is one or more of a fluid flow rate, a rate of change of the fluid flow rate, and a volume of fluid permeate passing through the membrane for a predefined time period.

In another aspect, the filtration system may further include a timer configured to adjust at least one of a time for applying the constant pressure and a time for reversing the constant pressure.

In another aspect, one or more of the first predetermined time and the second predetermined time is a constant value. In yet another aspect, one or more of the first predetermined time and the second predetermined time is determined based on a predefined formula.

On the other hand, the reverse pressure is a constant pressure.

2. Pressure pump

In various aspects, the filtration system includes a pressure pump configured to apply pressure to the fluid flowing between the first chamber and the second chamber. The pressure pump provides the pressure required to push the fluid through the membrane even when the membrane is rejecting impurities from passing through the membrane. Microfiltration and ultrafiltration can operate in a range of about 3 psi to about 50 psi, which is significantly lower than nanofiltration and reverse osmosis membranes (about 200 psi to about 1,200 psi).

3. Flow sensor

In various aspects, a filtration system includes a flow sensor configured to measure at least one parameter associated with a fluid flowing through a membrane deposited between a first chamber and a second chamber. The fluid flow sensor can be designed to indicate both instantaneous and average fluxes recorded by the filtration system. The time flux measurements can then be used to calculate integral and derivative flux terms required for process control.

4. Pressure sensor

In various aspects, a filtration system includes a pressure sensor. The pressure sensor can be configured to measure the pressure of a fluid flowing through a membrane separating a first chamber from a second chamber. The pressure sensor can be configured in such a manner that the fluid to be measured is not retained. In another aspect, the pressure sensor includes a membrane and a pressure sensing portion that senses the pressure of the fluid flowing within the membrane. In yet another aspect, the pressure sensor is distinct from and/or separate from the membrane.

5. Filtration management system

In one aspect, a filtration system may include a filtration management system. In one aspect, the filtration management system is in communication with a pressure pump, a flow sensor, and a pressure sensor. The filtration management system may be configured to cause the pressure pump to apply a constant pressure to fluid flowing from a first chamber to a second chamber for a first predetermined time based on a pressure reading. Furthermore, the filtration management system may cause the pressure pump to reverse the constant pressure for a second predetermined time based on at least one parameter. The reversed pressure may cause the fluid flow to move in a reversed direction across a membrane between the first and second chambers. The reversed pressure may also be a constant pressure.

C. Method for Operating a Fluid Filtration Management System in Training Mode

FIG5 illustrates a method 500 for managing a filtration system. In step 501, a constant pressure may be applied to a fluid flowing from a first chamber to a second chamber. In one aspect, the constant pressure may be applied by a pressure pump. In one aspect, a membrane may be deposited between the first chamber and the second chamber to allow the fluid to permeate the membrane from the first chamber to the second chamber.

In step 502, at least one parameter related to the flow of fluid from the first chamber through the membrane to the second chamber can be determined. In one aspect, a flow sensor can measure the at least one parameter. In one aspect, the at least one parameter can be one or more of the fluid flow rate, the rate of change of the fluid flow rate, the volume of fluid permeate passing through the membrane for a predefined time period, and scale formation on the membrane. In one aspect, scale formation on the membrane can be determined by measuring the conductivity of the membrane. In another aspect, scale formation on the membrane can be determined by in-situ visual inspection of the membrane surface.

In step 503, the constant pressure may be reversed based on a comparison of the at least one measured parameter with at least one threshold value. In one aspect, when the threshold value is met, the filtration management system may cause the pressure pump to reverse the constant pressure. In one aspect, the reverse pressure may be applied at a constant level and may be determined based on a predefined formula. In one aspect, the reverse pressure may be applied for a predefined time period. The predefined time period may be based on at least one measured factor. The measured factor may be the amount of scale accumulated on the membrane during each cycle. After the reverse pressure has been applied to the fluid for the predefined time period, the constant pressure may be reapplied in the original direction of fluid flow from the first chamber to the second chamber.

Intelligent process control through a filtration management system can involve some initial learning of the filtration system's response to a given stimulus (driving force). In membrane processes, this can be readily accomplished during the first few filtration cycles by operating the filtration system at a fixed TMP and observing the subsequent flux decline behavior. When commissioning a filtration system, the first few interactions of the feedwater with the filtration system can provide excellent indicators of how the water will foul the membranes and how the filtration and backwash cycles need to be adapted to the specific feedwater. Applying a preset TMP will result in some flux decline behavior in the filtration system, which can be recorded over a preset filtration time before backwash is triggered.

Consider implementing a filtration management system with the following four preset conditions: an initial TMP, which gives an initial flux, J ₀ ; a forward filtration time, t _f , _ini ; a maximum permissible flux ratio, J _N /J _f ; and a backwash ratio, r _BW . If the flux drop during this initial preset forward filtration time results in a flux ratio greater than the preset flux ratio, and after a subsequent backwash step, the flux is fully restored again to reach the initial flux, J ₀ , then the preset conditions remain sufficient for the current operation. However, if the flux ratio reaches a value below the preset ratio, J _N /J _f , before t _{f, ini} , the forward filtration cycle is stopped early, and a new filtration time, t _f < t _{f, ini ,} is selected for the next filtration cycle.

When the filtration time is shortened, the filtrate produced during the filtration cycle will be lower. Therefore, the backwash ratio will become higher. If the backwash ratio is greater than a preset limit, the process will be less economical because it produces less water within a cycle. To adjust the backwash ratio, there are several options, which may include modifying the backwash time and backwash flux. The backwash flux can then be changed and a second cycle consisting of a forward filtration and backwash step can be run using the modified parameters from the first cycle.

Figure 6 depicts a flow chart of the learning process. The learning module is a data acquisition module and a comparator that measures flux in real time and compares it to preset values (or values acquired in a previous training step). The preset values stored during a given filtration cycle are the initial flux (J _0,ini , which depends on the TMP setpoint), the filtration time (tf,ini ), and the flux ratio of the previous filtration cycle (r _flux = J _N /J ₀ ). The reaming preset values are the backwash parameters, namely, backwash time, backwash flux, and backwash ratio. The learning module records the flux versus time data and performs the following calculations in real time within the module:

1. Integration of the flux according to equation (1)

2. The derivative of the flux according to equation (6)

3. The error estimate is evaluated as:

Where _Kp , _Ki , and _Kd are the proportional, integral, and differential gains, respectively. The error estimate is used to control the filtration time and TMP setting for the next filtration run. It should be noted that the terms in Equation (7) provide a more realistic representation of the flux decline mechanism in a membrane filtration process. The proportional term describes how the local flux compares to the average flux of the previous cycle, the integral term describes how the cumulative yield from the current filtration cycle until time _ti compares to the total yield from the previous cycle, and the differential term describes how the instantaneous flux decline rate in the current filtration cycle compares to the total flux decline rate in the previous filtration cycle.

Equation (7) is a general PID control algorithm for controlling the performance of a filtration system and can be modified to act as any combination of proportional, integral, and derivative modes of process control. For example, by setting the integral gain to zero, the process can be defined as a PD controller. It is also worth noting that the process control algorithm is defined in such a way that the set point is updated after each filtration cycle to reflect the learning characteristics of the fouling mechanism. In addition, the learning process may involve adjusting the applied TMP or adjusting the filtration time or a combination thereof to fine-tune the filtration cycle. In many cases, the initial TMP set point is selected during the design of the filtration system with the membrane so that fouling is not severe. In this regard, the learning process can be used to increase the TMP set point. When adjusting the TMP set point, it may be more useful to consider the derivative input because the applied pressure directly affects the flux drop rate. A higher TMP results in a faster flux drop rate. On the other hand, when adjusting the filtration time, weight should be attributed to all three errors, i.e., the instantaneous flux, the integral of the flux (giving the amount passed), and the derivative of the flux.

The overall goal of the learning process was to determine the flux reduction kinetics for a fixed feedwater composition, keeping all other operating conditions fixed. The only parameters that were varied to adjust the process control mechanism were the applied TMP and filtration time. In some cases, the backwash flux and backwash time were also adjusted.

D. Method for Operating a Fluid Filtration Management System in Control Mode

FIG7 illustrates a method 700 for a filtration management system according to various aspects. In step 701, a constant pressure may be applied to a fluid flowing from a first chamber to a second chamber for a first predetermined time. A membrane may be deposited between the first chamber and the second chamber to allow the fluid to permeate the membrane from the first chamber to the second chamber. In one aspect, the first predetermined time may be a constant value. In another aspect, the first predetermined time may be a value based on a predefined formula.

In step 702, the constant pressure may be reversed and continued for a second predetermined time. In one aspect, the second predefined time may be a constant value. In one aspect, the second predefined time may be based on a predefined formula. In step 703, the constant pressure may be reapplied and continued for the first predefined time.

Control mode operation involves responding to sudden or expected changes in the flux in the filtration system during operation, as well as performing backwashing or CIP at necessary intervals. Once the process TMP, forward filtration time, and backwash parameters are established, control mode is initiated after several training cycles. The control PID equation in this case can be written as

in,

J _Av,0 is the average flux from the previous time step, Q _f,0 is the cumulative filtered volume (integral of the flux) from the previous filtration step,

as well as

is the linearized flux decline rate between the start and end of the previous filtration step.

Figure 8 depicts the normal operating mode. During control mode operation, the filtration management system will record flux at regular intervals. It is also assumed that once the TMP is established during the training period, it will not change further during normal operation. During normal operation, the flux will decrease over time according to the same pattern learned by the filtration management system, and after a certain time interval, the filtration cycle will stop, backwash will be initiated, and the filtration cycle will then restart after the backwash. The process of controlling the filtration time and backwash sequence involves comparing the instantaneous flux, derivative, and integrated flux (yield) measured using a flow monitoring device with the average flux, linearized rate of flux decline, and cumulative yield recorded by the filtration management system during the previous filtration step, respectively. The controller measures the errors in the proportional, differential, and integral components and determines the necessary actions. For example, when the cumulative yield during the current filtration cycle becomes the same as the cumulative yield from the previous cycle and the average flux decline rate of the current cycle becomes the same as the previous cycle, the filtration management system triggers backwash. This is the normal operating mode when there are no other changes in the filtration system's inputs or disturbances.

E. Method for Operating a Fluid Filtration Management System in Response to a Step Change in Filtrate Flow

FIG9 illustrates a method 900 for operating a fluid filtration management system in response to a step change in filtrate flow. In step 901, a constant pressure may be applied to a fluid flowing from a first chamber to a second chamber. In one aspect, a membrane is deposited between the first chamber and the second chamber to allow the fluid to permeate the membrane from the first chamber to the second chamber.

In step 902, a threshold to be exceeded on the filtration system can be determined. In one aspect, the threshold can be a threshold based on parameters such as, but not limited to, one or more of a fluid flow rate, a rate of change of a fluid flow rate, a volume of fluid permeate passing through a membrane for a predefined time period, scale formed on the membrane, combinations thereof, and the like.

In step 903, a reflow process may be initiated in response to determining that the threshold value has been exceeded. In one aspect, the reflow process may be applied for a predefined time period. In one aspect, the reflow process may include reversing a constant pressure. In one aspect, the reversing pressure may be a constant value. In one aspect, the reversing pressure may be determined based on a predefined formula.

If, during operation, a foulant adheres to the membrane and suddenly reduces its flux, this results in a step drop in permeate flux. The measured instantaneous flux and the rate of flux drop will immediately change in response to this step change. The integrated response (cumulative volume) will not be immediately apparent but will become apparent over several subsequent measurements. The filtration management system will now have multiple options for responding to the step change, with the action at the end of the decision-making process being to stop the filtration process and trigger a backwash. In proportional mode, the filtration management system will continue to record flux and perform time averaging, and once the time average drops below the average flux of the previous cycle, a backwash will be triggered.

In derivative mode, the filtration management system will record the instantaneous derivative and the average linearized derivative, expressed as Equation (10). It can be seen that the instantaneous derivative response (the rate of flux drop) will indicate a large error (infinite for a step function), and a control response based solely on the instantaneous derivative error will be too abrupt. However, the linearized derivative as in Equation (10) will provide a more moderate derivative error. If this linearized derivative becomes steeper than the linear derivative from the previous filtration cycle, the process can trigger a backwash.

In integrating mode, the filtration management system will continue to calculate the cumulative throughput by integrating the instantaneous flux. However, the integrated response will not be used to trigger a backwash. This response is used to calculate the backwash volume ratio and determine whether the backwash is effective or if a CIP should be called.

The filtration management system will respond to ongoing adverse conditions (for example, if backwashing fails to increase reflux) by stopping the filtration process earlier, enabling more frequent backwashing, and triggering CIP.

It should be noted that the filtration management system does not involve the use of PID error estimation to modify the TMP or any other parameters in the filtration cycle. It only continues to accumulate the average flux, average derivative and cumulative flux, and compares these with the values obtained in the previous filtration step. Once the average flux becomes equal to the average flux in the previous cycle, or the flux decline rate becomes some predetermined multiple greater than the average flux decline rate in the previous step, the process then stops filtering. This passive mode of flux management allows the membrane to recover from any fouling event by changing the frequency of the backwash cycle. It does not increase fouling by increasing TMP to restore flux. The process control method responds to any disturbance in flux by stopping the filtration process at an earlier time, cleaning the membrane more frequently, and (if unfavorable conditions persist) stopping filtration and triggering CIP.

F. Method for Operating a Fluid Filtration Management System in Response to Pulsed Changes in Filtrate Flow

Figure 10 shows a method 1000 for operating a filtration management system in response to pulsed changes in filtration flow. In step 1001, a constant pressure may be applied to a fluid flowing from a first chamber to a second chamber, wherein a membrane is deposited between the first chamber and the second chamber to allow the fluid to permeate the membrane from the first chamber to the second chamber.

In step 1002, at least one parameter related to the flow of a fluid from a first chamber through a membrane to a second chamber may be determined.

In step 1003, a descaling process may be initiated based on a comparison of the at least one measured parameter with at least one threshold value. In one aspect, the descaling process comprises a backwash process. In one aspect, the descaling process comprises a chemical cleaning process. In one aspect, the descaling process comprises a pressure pulse process. In one aspect, the descaling process comprises an air flush process. In one aspect, the descaling process comprises a chemical pulse process. In one aspect, the descaling process comprises a feed flow reversal process. In one aspect, the descaling process comprises a cleaning in place process.

If the flux suddenly decreases and then recovers after a period of time (a pulse function), the response of the filtration system should be to increase the frequency of backwashing, thereby reducing the filtration time during the pulse, and then gradually return to a lower frequency backwash and longer filtration step after the original operating conditions are restored. Such conditions can be triggered by events such as an increase in feed solute concentration for a short duration, and conventional flux control mechanisms during these events increase membrane fouling by increasing the TMP in response to the flux drop and exacerbating the fouling process. In the present method, constant pressure operation does not change the driving force to exacerbate fouling.

FIG11 depicts a typical filtration cycle sequence during a pulse event that triggers a sharper flux drop. The figure plots flux against time on the vertical axis. To clearly illustrate the situation and explain the concept of intelligent management of flux drop behavior, it is assumed that the flux drop behavior is linear. In addition, the flux varies within each filtration cycle between an initial flux J ₀ and a final flux J _N , while backwashing restores the flux to the initial flux after each cycle. For different applications, these simplifying assumptions can be relaxed. For example, after backwashing, flux recovery may not be complete. In addition, the flux drop behavior may not be linear. The average flux during each filtration cycle is represented by a circle and has a fixed value J _Av .

Assume that a pulse event changes the rate of flux decline at some point during the third filtration cycle. The faster rate of flux decline caused by this pulse event results in a steeper slope in the flux versus time curve. Given this different rate of flux decline, the minimum flux, J _{N ,} is reached earlier during this cycle. Although the average value is reached earlier, the average flux for this cycle remains J _Av . This means that the filtration cycle time, t _{f,3 ,} < t _{f,2 , is t f,3} . The cumulative filtration yield is also lower during this cycle. Once J _N is reached, backwashing begins, and the initial flux, J _{0 ,} for the fourth cycle is reached after backwashing. However, in the fourth cycle, the rate of flux decline is greater, and therefore the average flux and J _N are reached earlier than in the previous cycle. In other words, t _f,4 is less than t _f,3 . Furthermore, the cumulative filtrate yield is also lower compared to filtration cycle 4. The accelerated fouling caused by the pulse event reduces the duration of the filtration cycle and increases the frequency of backwash cycles.

In cycle 5, the pulse event subsides, and the original rate of flux decline is restored. This immediately increases filtration time compared to cycle 4. In cycle 6, the original parameters of the first filtration cycle are restored. Thus, the pulse event that triggers the accelerated flux decline reduces the duration of the filtration cycle and increases the frequency of backwash cycles. While this reduces filtrate production in response to any type of perturbation that causes membrane fouling, the mechanism prevents the membrane from irreversibly or actively fouling.

FIG12 illustrates a method 1200 for a filtration management system. In step 1201, at least one of a change in fluid filtrate throughput during constant pressure operation and a change in pressure during constant filtrate throughput operation may be monitored. In one aspect, monitoring the change in fluid filtrate throughput may include measuring flux through a membrane over a predefined time period. For example, the change in flux may be measured using a flow sensor. In another aspect, monitoring the change in pressure may include measuring pressure over a predefined time period. For example, the change in pressure may be measured using a pressure sensor.

In step 1202, the scaling mechanism may be determined based on at least one of a change in filtrate throughput and a change in pressure. In one aspect, determining the scaling mechanism may include performing a mathematical analysis of the flux change and/or pressure change according to one or more predetermined scaling models. As an example, the one or more predetermined scaling models may include a Hermia model, a modified Hermia model, or a series resistance model. In one aspect, determining the scaling mechanism may include performing a mathematical analysis of the flux change and/or pressure change according to one or more predetermined scaling models. As an example, the one or more predetermined scaling models may include a Hermia model, a modified Hermia model, or a series resistance model. In one aspect, the scaling mechanism may include concentration polarization, organic adsorption of chemically active molecules, scaling due to precipitation of salts and hydroxides, filter cake and pore blockage due to deposition of large suspended particles or small colloidal particles, gel formation due to deposition of inert macromolecules, biofouling due to deposition and growth of biologically active organisms, and the like.

Equation 12 describes the effect of membrane fouling on flux degradation as a function of the resistance to flow for a given driving force:

where J is the permeate flux through the membrane, ΔP is the transmembrane pressure driving force, μ is the fluid viscosity, _Rtot is the total hydraulic resistance, _Rm is the intrinsic membrane resistance, _Rcp is the resistance due to concentration polarization, _Ra is the resistance due to solute adsorption, _Rp is the resistance due to pore blocking and cake formation, and _Rg is the resistance due to surface gel formation.

In one aspect, when scaling is not occurring, the flux-pressure curve can be uniformly linear, as only intrinsic membrane resistance is a factor. However, the onset of scaling can be reflected in varying degrees of slope change, depending on the specific mechanism of scaling and its impact on the overall resistance to increased permeate flow. For example, the specific mechanism of particle scaling can be determined by fitting the flux-time curve to a series of pre-existing scaling models, based on the solutions of the following equations 13 and 14:

where t is the filtration time, k and n are constants that characterize the filtration process, and U is the permeate volume V or transmembrane pressure ΔP that varies depending on constant pressure or constant flux operation, respectively. Thus, by plotting ^d²t / ^dU² against dt/dU² and determining the value of the blocking index n, different blocking mechanisms can be identified from a single plot. FIG13 shows a solution to different fouling mechanisms and a single flux versus time curve that were fitted to a specific observed particle fouling mechanism (Maiti, Sadrezadeh et al. 2012). Furthermore, the general equation for cross-flow filtration, derived by modifying the Hermia empirical model, can be used:

Where _Jp is the permeate flux (m/s), t is the filtration time (s), _KCF is a phenomenon coefficient that depends on the specific fouling mechanism, _Jpss is the steady-state permeate flux (m/s), and n is again the blocking index, where n = 2, 1.5, 1, and 0 for complete pore blocking, intermediate pore shrinkage, standard pore blocking, and cake filtration/gel formation, respectively. Examples of using these models to assess membrane fouling are also found in (Chang, Yang et al. "Assessing the fouling mechanisms of high-pressure nanofiltration membrane using the modified Hermia model and the resistance-in-series model" Separation and Purification Technology 79 (2011) 329-336). Therefore, an intelligent learning process can perform real-time data analysis on empirically generated flux/pressure versus time curves to determine what fouling mechanisms are active and what the most effective and economical cleaning process to adopt.

In step 1203, a cleaning regimen may be selected based on the determined fouling mechanism. In one aspect, determining the cleaning regimen may include selecting a cleaning method and one or more parameters associated with the cleaning method. As an example, the cleaning method may include a backwash method. As an example, the one or more parameters may include one or more of pressure, duration, flow rate, temperature, a specific chemical additive, and a dosage of the specific chemical additive. In one aspect, the specific chemical additive may include one or more of an acid, a base, an oxidizer, a chelating agent, and the like.

Different types of cleaning schemes can be used for fouling control; however, the efficacy of a given cleaning scheme can be highly dependent on the foulant to be removed. Table 2 pairs various types of membrane foulants with the most effective cleaning techniques:

Table 2

On the one hand, selected filtration systems (such as NF and RO) suffer from heterogeneous and spatially dependent fouling, the effects of which cannot be effectively captured by monitoring full-scale system data. In these applications, embodiments of the intelligent filtration management system will include communication with independent fouling monitors strategically deployed along the full-scale filtration system. Monitoring the performance data of the fouling monitors will provide the intelligent filtration management system with greater sensitivity to respond to early signs of fouling and flux degradation.

On the one hand, diligent monitoring and testing of filter performance and integrity can be crucial in developing a clear understanding of the remaining useful life of the filter and determining when replacement is necessary. For example, a means of membrane integrity testing for immediate failure response can include targeted visual monitoring during integrity testing. Video capture can be used to monitor one or more membrane elements in real time, thereby detecting any integrity breaches of suspected membrane elements.

In a specific aspect, the filtration management system can continuously monitor the permeate quality of one or more membrane elements. When the integrity of a specific membrane becomes problematic, the filtration management system can isolate one or more elements for in-situ foam integrity testing. Once an integrity breach is detected, the filtration management system can issue appropriate alerts and isolate the defective membrane or membranes for maintenance, thereby maximizing the speed of repair and minimizing the impact on overall filtration system performance. The disclosed systems and methods can also be expanded to incorporate other system metrics for real-time system diagnostics and maintenance response in the event of filtration system failure due to equipment failure, leaks, etc.

In one aspect, the methods and systems can perform real-time trend analysis of one or more measured performance metrics. The methods and systems can monitor operational diagnostics for a given device, such as the remaining life of a filter and the expected replacement date. For example, real-time trend analysis can be achieved by analyzing permeate water quality trends to determine if and/or when a system integrity breach has occurred. In one aspect, filtration and regression analysis can be used to extrapolate when a predetermined minimum permeability for a given filtration system has been reached. Once it is determined that a reasonable integrity breach has occurred, an instantaneous and automated response can be initiated to minimize system downtime and prevent total system failure. In one aspect, the disclosed systems and methods are applicable to any filtration system, regardless of whether it is operating at constant flux or constant pressure.

FIG14 illustrates a method 1400 for managing a filtration system. In step 1401, one or more parameters associated with one or more filters of a filtration system may be monitored. In one aspect, the one or more parameters may include transformer filter pressure, permeate flux, permeate turbidity, permeate salinity, permeate pH, permeate salinity, permeate color, permeate hardness, permeate total organic concentration, concentrations of one or more predefined permeate ions, and concentrations of one or more predefined organic molecules. Many of these parameters can be measured in situ, such as pH, salinity, color, and turbidity; however, some may require periodic sampling and ex situ measurement, such as the concentrations of target inorganic and organic molecules. For example, color and turbidity can be measured spectrophotometrically using integrated probes to track the amount of light passing through the permeate or feed solution. Target concentrations of inorganic and organic components can be measured using separate instruments, such as inductively coupled plasma optical emission spectrometers, gas chromatography-mass spectrometers, and the like.

In step 1402, the conditions of one or more filters can be determined based on the one or more parameters monitored. On the one hand, when determining the conditions of one or more filters based on the one or more parameters monitored, statistical analysis can be performed based on the one or more parameters monitored. On the one hand, statistical analysis can include filtering and smoothing analysis, regression and trend analysis. As an example, filtering and smoothing analysis can include Wiener analysis, Kalman analysis, Butterworth analysis, Chebyshev analysis, elliptic analysis, Bessel analysis, Gaussian analysis, moving average analysis and Savitsky-Golay analysis. As an example, regression and trend analysis can include linear regression analysis, multiple regression analysis, factorial regression analysis, polynomial regression analysis, response surface regression, mixed surface regression, one-way analysis of variance (ANOVA), main effect ANOVA analysis, factorial ANOVA analysis, covariance analysis, slope uniformity analysis, linear fit analysis, least squares fit analysis, Kendell test analysis, Sen's slope test analysis, Wilcoxon-Mann-Whitney step trend analysis, genetic and neural network analysis, its combination etc.

In one aspect, determining the condition of one or more filters can include estimating the lifespan of the one or more filters. In one aspect, if the estimated lifespan falls below a predefined threshold (e.g., two days), a notification can be sent. As an example, the notification can include an estimated replacement date for one or more filters. Filter condition can be monitored relative to specific filter characteristics, such as permeability (e.g., transformer filter pressure and filtrate flow rate), the degree of irreversible fouling, and integrity testing. Filter condition can also be monitored relative to operating parameters and performance metrics, such as applied pressure, flux maintenance (e.g., mechanical and chemical maintenance, in-situ cleaning maintenance), and filtrate quality (e.g., turbidity). The values of these metrics, recorded in real time, are compared to predetermined thresholds to calculate the remaining lifespan of the module. Permeability measurements can be recorded in real time and used in a statistical model to extrapolate predicted permeability trends over time. The amount of time predicted by the model for the current filter to achieve the threshold permeability is the remaining module lifespan. When the remaining lifespan value reaches the predetermined threshold, the filter can be replaced.

In one aspect, determining the condition of the one or more filters can include determining the type of the one or more filters. As examples, the one or more filter types include tubular polymeric membranes, hollow fiber membranes, spiral wound membranes, tubular ceramic membranes, combinations thereof, and the like.

In one aspect, the condition of the one or more filters may include an impending integrity breach, a detected integrity breach, an impending permeability loss, and a detected permeability loss.

In step 1403, one or more maintenance procedures may be performed based on the determined conditions. The one or more maintenance procedures may be used according to the conditions. In one aspect, the one or more maintenance procedures may include a filter isolation procedure, a filter repair procedure, a filter replacement procedure, a filter pinning procedure, a combination thereof, or the like.

On the one hand, the response to a specific filter condition, whether caused by integrity damage or permeability loss, can be varied depending on the type of filter used in the system. For example, when a fiber failure of a hollow fiber membrane occurs, the failed fiber membrane can be isolated by inserting a small needle or epoxy resin at the end of the broken fiber, or can be permanently removed from use. As another example, spirally wound nanofiltration and reverse osmosis membranes can be replaced after a failure. Considering the high frequency and cost associated with filter failure and replacement, early detection of failures and appropriate preparation can be very valuable in economically efficient replacement practices.

Figures 15 and 16 show experimental results using the filtration management system and method described herein. A programmable logic controller is programmed to operate the filtration system under constant applied pressure. The flux of the filtration system is allowed to decay naturally until it reaches a pre-calculated minimum value, which then triggers a maintenance program, such as filtrate backwashing as shown in Figure 15. Unlike conventional filtration processes that utilize predefined maintenance program frequencies, the filtration management system can adapt to environmental conditions by allowing the filtration system performance to require appropriate maintenance programs such as cleaning solutions. Figure 16 shows how the filtration management system can therefore adapt to the dramatic variability of environmental conditions (such as feed water quality and temperature). Specifically, dramatic swings in oil concentration and water temperature may result in significant flux loss during the initial startup phase. In response, the filtration management system can increase the frequency of backwashing and the number of chemical cleanings, each denoted as "cleaning in place" (CIP). The filtration management system ultimately stabilized the performance of the membrane after approximately 24 hours of operation, which resulted in minimized flux decline and CIP frequency.

In an exemplary aspect, the methods and systems may be implemented on a computer 1701 as shown in FIG. 17 and described below. Similarly, the disclosed methods and systems may utilize one or more computers to perform one or more functions in one or more locations. FIG. 17 is a block diagram illustrating an exemplary operating environment for performing the disclosed methods. This exemplary operating environment is merely an example of an operating environment and is not intended to limit the scope of use or functionality of the operating environment architecture. Neither should the operating environment be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment.

The method and system of the present invention can operate with many other general or special computing system environments or configurations. Examples of known computing systems, environments, and/or configurations suitable for use with the system and method include, but are not limited to, personal computers, server computers, laptops, and multiprocessor systems. Additional examples include set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like.

The processing of the disclosed methods and systems can be performed by software components. The disclosed systems and methods can be described in the general context of computer-executable instructions, such as program modules, executed by one or more computers or other devices. Generally, program modules include computer code, routines, programs, objects, components, data structures, etc., that are used to perform specific tasks or implement specific abstract data types. The disclosed methods can also be practiced in grid-based and distributed computing environments, in which tasks are performed by remote processing devices linked through a communication network. In a distributed computing environment, program modules can be located in local and/or remote computer storage media, including memory storage devices.

Furthermore, those skilled in the art will appreciate that the systems and methods disclosed herein may be implemented by a general purpose computing device in the form of a computer 1701. The computer 1701 may include one or more components, such as one or more processors 1703, a system memory 1712, and a bus 1713 that couples the various components of the computer 1701, including the one or more processors 1703, to the system memory 1712. In the case of multiple processors 1703, the system may utilize parallel computing.

The bus 1713 may include one or more of several possible types of bus structures, such as a memory bus, a memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. For example, the architecture may include an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA) local bus, an Accelerated Graphics Port (AGP) bus, as well as a Peripheral Component Interconnect (PCI), PCI-Express bus, a Personal Computer Memory Card Industry Association (PCMCIA), a Universal Serial Bus (USB), and the like. The bus 1713 and all buses specified in this specification can also be implemented through a wired or wireless network connection, and one or more of the components of the computer 1701, such as one or more processors 1703, mass storage device 1704, operating system 1705, data processing software 1706, flux data 1707, network adapter 1708, system memory 1712, input/output interface 1710, display adapter 1709, display device 1711 and human-computer interface 1702 can be included in one or more remote computing devices 1714a, b, c at physically separate locations, connected through this form of bus, thereby actually implementing a fully distributed system.

The computer 1701 typically includes a variety of computer-readable media. Exemplary computer-readable media can be any available media that can be accessed by the computer 1701, and include, for example, but not intended to be limiting, volatile and non-volatile media, removable and non-removable media. The system memory 1712 may include computer-readable media in the form of volatile memory, such as random access memory (RAM), and/or non-volatile memory, such as read-only memory (ROM). The system memory 1712 may typically include data, such as flux data 1707, and/or program modules, such as an operating system 1705 and data processing software 1706, that can be accessed and/or operated by one or more processors 1703.

In another aspect, the computer 1701 may also include other removable/non-removable, volatile/non-volatile computer storage media. The mass storage device 1704 may provide non-volatile storage for computer code, computer-readable instructions, data structures, program modules, and other data for the computer 1701. For example, the mass storage device 1704 may be a hard disk, a removable magnetic disk, a removable optical disk, a magnetic cassette or other magnetic storage device, a flash memory card, a CD-ROM, a digital versatile disk (DVD) or other optical storage, a random access memory (RAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), etc.

Optionally, any number of program modules can be stored on the mass storage device 1704 (including, for example, an operating system 1705 and data processing software 1706). One or more of the operating system 1705 and the data processing software 1706 (or some combination thereof) can include elements of the programming and data processing software 1706. Flux data 1707 can also be stored on the mass storage device 1704. Flux data 1707 can be stored in any of one or more databases known in the art. Examples of such databases include Access, SQL Server, mySQL, PostgreSQL, etc. The database can be centralized or distributed across multiple locations within the network 1715.

In one aspect, a user can enter commands and information into the computer 1701 through input devices (not shown). Examples of such input devices include, but are not limited to, keyboards, pointing devices (e.g., a computer mouse, a remote control), microphones, joysticks, scanners, tactile input devices such as gloves and other body coverings, motion sensors, etc. These and other input devices can be connected to the one or more processors 1703 through a human-machine interface 1702 coupled to the bus 1713, but can be connected through other interfaces and bus structures, such as a parallel port, a game port, an IEEE 1394 port (also known as a FireWire port), a serial port, a network adapter 1708, and/or a universal serial bus (USB).

In another aspect, the display device 1711 can also be connected to the bus 1713 via an interface such as a display adapter 1709. It is contemplated that the computer 1701 may have more than one display adapter 1709, and the computer 1701 may have more than one display device 1711. For example, the display device 1711 can be a monitor, an LCD (liquid crystal display), a light emitting diode (LED) display, a television, a smart lens, smart glass, and/or a projector. In addition to the display device 1711, other output peripherals may also include components such as speakers (not shown) and printers (not shown) that can be connected to the computer 1701 via an input/output interface 1710. Any steps and/or results of the method can be output to an output device in any form. The output can be any form of visual representation, including but not limited to textual representation, graphical representation, animated representation, audio representation, tactile representation, etc. The display 1711 and the computer 1701 can be part of one device or separate devices.

Computer 1701 can operate in a networked environment using logical connections to one or more remote computing devices 1714a, b, c. For example, remote computing devices 1714a, b, c can be personal computers, computing stations (e.g., workstations), portable computers (e.g., laptops, mobile phones, tablet devices), smart devices (e.g., smartphones, smart watches, activity trackers, smart clothing, smart accessories), security and/or monitoring equipment, servers, routers, network computers, peer devices, edge devices, or other public network nodes. The logical connection between computer 1701 and remote computing devices 1714a, b, c can be made via a network 1715, such as a local area network (LAN) and/or a general wide area network (WAN). The network connection can be made via a network adapter 1708. Network adapter 1708 can be implemented in both wired and wireless environments. Such networking environments are conventional and common in homes, offices, enterprise-wide computer networks, intranets, and the Internet.

For purposes of illustration, application programs and other executable program components, such as an operating system 1705, are described herein as discrete blocks, but it is recognized that the programs and components may reside at different times in different storage components of the computing device 1701 and be executed by one or more processors 1703 of the computer 1701. An implementation of the data processing software 1706 may be stored on or transmitted across some form of computer-readable media. Any of the disclosed methods may be performed by computer-readable instructions embodied on a computer-readable medium. A computer-readable medium may be any available medium that can be accessed by a computer. By way of example, and not limitation, computer-readable media may include "computer storage media" and "communication media." "Computer storage media" may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. Exemplary computer storage media may include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer.

The methods and systems can employ artificial intelligence (AI) techniques such as machine learning and iterative learning. Examples of such techniques include, but are not limited to, expert systems, case-based reasoning, Bayesian networks, behavior-based AI, neural networks, fuzzy systems, evolutionary computation (e.g., genetic algorithms), swarm intelligence (e.g., ant algorithms), and hybrid intelligent systems (e.g., expert inference rules generated by neural networks or production rules from statistical learning).

While the methods and systems have been described in conjunction with preferred embodiments and specific examples, it is not intended to be limited in scope to the particular embodiments set forth, as the embodiments herein are intended in all respects to be illustrative rather than restrictive.

Although aspects of the present disclosure may be described and claimed using specific statutory categories (such as the system statutory category), this is for convenience only, and those skilled in the art will understand that aspects of the present disclosure may be described and claimed using any statutory category. Unless expressly stated otherwise, it is in no way intended that any method or aspect set forth herein be construed as requiring that its steps be performed in a particular order. Therefore, in the claims or specification, when a method claim does not specifically state that the steps are limited to a particular order, it is in no way intended that an order be inferred. This applies to any possible non-express basis for interpretation, including matters of logic with respect to the arrangement of steps or operational flow, clear meaning derived from grammatical organization or punctuation, or the number or type of aspects described in the specification.

It will be apparent to those skilled in the art that various modifications and variations of the present disclosure may be made without departing from the scope or spirit of the present disclosure. Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the description and practice of the methods and/or systems disclosed herein. It is intended that the description and examples be regarded as exemplary only, with the true scope and spirit of the present disclosure being indicated by the appended claims.

Claims (20)

1. A method comprising: Monitor one or more parameters related to one or more membranes in the filtration system during the current filtration cycle; During the current filtration cycle, based on one or more monitored parameters, it is determined that the flux decline rate of the current filtration cycle is greater than the flux decline rate of the previous filtration cycle; and Based on the determination that the flux decline rate of the current filtration cycle is greater than the flux decline rate of the previous filtration cycle, one or more maintenance procedures are executed. 2. The method of claim 1, wherein monitoring is performed as an average value of the filtration system or by monitoring local performance parameters within the filtration system to detect the spatial variability of individual fouling of at least one of the one or more membranes. 3. The method of claim 1, wherein the one or more parameters include one or more of the following: transformer filter pressure drop, filtrate flow rate, filtrate turbidity, filtrate salinity, filtrate pH, filtrate color, filtrate hardness, total organic concentration of filtrate, microbial count of filtrate, microbial count of feed, concentration of one or more predefined ions in filtrate, feed or concentrate, or concentration of one or more predefined nonionic molecules in filtrate, feed or concentrate. 4. The method of claim 3, wherein at least one of the monitored parameters is monitored at a front-end position (inflow material), a rear-end position (concentrate or repellent), and a filtrate end position (or permeate material), and wherein the at least one of the monitored parameters is monitored at one or more of the front-end position, the rear-end position, or the filtrate end position. 5. The method of claim 1, wherein determining the conditions of the one or more membranes based on the monitored one or more parameters includes performing statistical analysis based on the monitored one or more parameters. 6. The method of claim 1, wherein the conditions of the one or more membranes include one or more of the following: upcoming filter curing or filter preparation state, detected filter curing or filter preparation state, upcoming integrity failure, detected integrity failure, upcoming permeability loss, or detected permeability loss. 7. The method of claim 1, wherein the one or more maintenance procedures include one or more of a filter cleaning procedure, a filter isolation procedure, a filter repair procedure, a filter replacement procedure, or a filter pinning procedure. 8. A method comprising: Monitor at least one of the changes in fluid filtrate throughput during constant pressure operation or the changes in pressure during constant filtrate throughput operation during the current filtration cycle. The structural mechanism is determined when it is determined that the change in the filtrate throughput of the current filtration cycle is greater than the change in the filtrate throughput of the previous filtration cycle, or when it is determined that the change in the pressure of the current filtration cycle is greater than the change in the pressure of the previous filtration cycle. Select a cleaning scheme based on the identified scaling mechanism; and Perform the cleaning procedure according to the selected cleaning plan. 9. The method of claim 8, further comprising: Capture video of one or more membrane elements. 10. The method of claim 8, wherein monitoring changes in filtrate flow rate comprises measuring filtrate flow rate over a predefined time period, and wherein monitoring changes in pressure comprises measuring pressure over a predefined time period. 11. The method of claim 8, wherein determining the scaling mechanism comprises performing mathematical analysis of changes in filtrate flow rate or pressure based on one or more predetermined scaling models. 12. The method of claim 8, wherein the scaling mechanism comprises one or more of the following: concentration polarization, organic adsorption of chemically active molecules, scaling due to precipitation of salts and hydroxides, filter cake and pore blockage due to deposition of large suspended particles or small colloidal particles, gel formation due to deposition of inert macromolecules, or bioscale due to deposition and growth of bioactive organisms. 13. The method of claim 11, wherein the one or more predetermined scaling models include one or more of the Hermia model, the modified Hermia model, and the series resistance model. 14. The method of claim 8, wherein selecting the cleaning scheme includes selecting a cleaning method and one or more parameters associated with the cleaning method. 15. A system comprising: A pressure pump is configured to apply pressure to the fluid flowing between the first and second chambers during the current filtration cycle; A flow sensor configured to measure at least one parameter relating to the fluid flowing through a membrane deposited between the first and second chambers during the current filtration cycle; A pressure sensor is configured to measure the pressure reading of the fluid flowing from the first chamber to the second chamber during the current filtration cycle; and The filter management system is configured to: Based on the pressure reading, the pressure pump applies a constant pressure to the fluid flowing through the membrane from the first chamber to the second chamber for a first predetermined time; the filtration management system is also configured to: During the first predetermined time period, it is determined, based at least one parameter, that the linearized flux decrease rate of the current filtration cycle is greater than the linearized flux decrease rate of the previous filtration cycle, and based on the determination that the linearized flux decrease rate of the current filtration cycle is greater than the linearized flux decrease rate of the previous filtration cycle, the pressure pump is caused to reverse the fluid flow through the membrane for a second predetermined time; or After the first predetermined time, the pressure pump is activated to reverse the flow of fluid through the membrane for a second predetermined time. 16. The system of claim 15, wherein determining that the linearized flux decline rate of the current filtering cycle is greater than the linearized flux decline rate of the previous filtering cycle based at least on the at least one parameter comprises comparing the at least one parameter with at least one threshold. 17. The system of claim 16, further comprising a timer configured to adjust at least one of the time for applying the constant pressure or adjusting the time for reversing the constant pressure. 18. The system of claim 16, wherein the at least one parameter is one or more of fluid flow rate, rate of change of fluid flow rate, or fluid volume permeate passing through the membrane for a predefined time period. 19. The system of claim 16, wherein one or more of the first predetermined time or the second predetermined time is a constant value. 20. The system of claim 16, wherein one or more of the first predetermined time or the second predetermined time are determined based on a predefined formula.