Membrane performance evaluation with field adjustable parameters

Abstract

A system for assessing performance of a membrane filtration system, such as a reverse osmosis system, may predict operating conditions of the membrane filtration system by performing an assessment using values of key performance indicators (KPIs), relationships between the KPIs and predefined operating conditions of the membrane filtration system, and field adjustable parameters. KP I is a parameter indicative of the performance of the membrane filtration system. The system may also use at least one user command or feedback information to adjust the field adjustable parameter. The feedback information includes predicted operating conditions of the membrane filtration system resulting from performing the evaluation one or more times.

Description

Technical Field

[0001] This disclosure generally relates to membrane filtration systems, and more specifically to systems and methods for evaluating the performance of membrane filtration systems (e.g., reverse osmosis systems), wherein various parameters used in the evaluation are field-adjustable. Background Technology

[0002] Membrane filtration systems use microporous membranes to separate components from a liquid based on their characteristics, such as molecular weight, size, and / or charge. An example of such a system is a water treatment system that uses reverse osmosis to purify water by removing impurities—such as salts and organic matter—through a semi-permeable membrane. Monitoring the performance of the membrane filtration system is necessary to ensure the quality of the product (e.g., the purified water produced by the water treatment system). This monitoring can be performed by measuring and analyzing performance indicator parameters. When the performance of the membrane filtration system is unsatisfactory, the values ​​and changes in these performance indicator parameters can also be used to diagnose various problematic situations. Summary of the Invention

[0003] A system used to evaluate the performance of a membrane filtration system (such as a reverse osmosis system) can predict the operating conditions of the membrane filtration system by performing an evaluation using key performance indicators (KPIs), the relationship between the KPIs and predefined operating conditions of the membrane filtration system, and field-adjustable parameters. KPIs are parameters representing the performance of the membrane filtration system. The system can also adjust the field-adjustable parameters using at least one of user commands or feedback information. The feedback information includes the predicted operating conditions of the membrane filtration system obtained from performing the evaluation once or multiple times.

[0004] Examples of systems for evaluating the performance of membrane filtration systems are provided. A membrane filtration system may include: an inlet configured to receive feed water; a reverse osmosis unit including one or more semi-permeable membranes and configured to pass the received feed water through the one or more semi-permeable membranes to separate the received feed water into concentrate and permeate; a concentrate outlet for releasing the concentrate from the reverse osmosis unit; and a permeate outlet for releasing the permeate from the reverse osmosis unit. The system for evaluating the performance of the membrane filtration system may include evaluation circuitry and control circuitry. The evaluation circuitry may be configured to receive values ​​of key performance indicators (KPIs) and perform an evaluation using the received KPI values, the relationship between the KPIs and predefined operating conditions of the membrane filtration system, and field-adjustable parameters to generate predicted operating conditions for the membrane filtration system. The KPIs include parameters indicating the performance of the membrane filtration system. The control circuitry may be configured to receive user commands and adjust the field-adjustable parameters using the user commands.

[0005] Examples of methods for evaluating the performance of membrane filtration systems are also provided. The membrane filtration system can be configured to pass feedwater through one or more semi-permeable membranes to separate the feedwater into concentrate and permeate. The method may include: receiving values ​​of key performance indicators (KPIs), including parameters indicative of the performance of the membrane filtration system; predicting the operating conditions of the membrane filtration system by performing an evaluation using the received KPI values, the relationship between the KPIs and predefined operating conditions of the membrane filtration system, and field-adjustable parameters; receiving user commands; and adjusting the field-adjustable parameters using the received user commands.

[0006] Examples of non-transitory computer-readable storage media including instructions are also provided. When executed by the system, the instructions cause the system to perform a method for evaluating the performance of a membrane filtration system configured to pass feedwater through one or more semi-permeable membranes to separate feedwater into concentrate and permeate. The method may include: receiving values ​​of key performance indicators (KPIs), including parameters indicative of the performance of the membrane filtration system; performing an evaluation using the received KPI values, the relationship between the KPIs and predefined operating conditions of the membrane filtration system, and field-adjustable parameters to predict the operating conditions of the membrane filtration system; receiving user commands; and adjusting the field-adjustable parameters using the received user commands.

[0007] This invention is a summary of some of the teachings of this application and is not intended to be exclusive or exhaustive. Further details regarding the subject matter can be found in the detailed description and the appended claims. The scope of this invention is defined by the appended claims and their legal equivalents. Attached Figure Description

[0008] The accompanying drawings illustrate, by way of example, the various embodiments discussed in this document. The drawings are for illustrative purposes only and may not be drawn to scale.

[0009] Figure 1 A block diagram illustrating an embodiment of a membrane filtration system includes a reverse osmosis unit and a performance evaluation system for evaluating the performance of the membrane filtration system.

[0010] Figure 2 This illustrates a performance evaluation system (e.g., for evaluating the performance of membrane filtration systems). Figure 1 A block diagram of an embodiment of a performance evaluation system.

[0011] Figure 3 This illustrates a performance evaluation system (e.g., for evaluating the performance of membrane filtration systems). Figure 2 A block diagram of another embodiment of the performance evaluation system.

[0012] Figure 4 The illustration shows the connection with the membrane filtration system (e.g.) Figure 1 A block diagram of an embodiment of sensors and measurement circuits used in a membrane filtration system.

[0013] Figure 5 The illustration shows the instructions for evaluating membrane filtration systems (e.g.) Figure 1 A flowchart of an embodiment of a method for improving the performance of a membrane filtration system.

[0014] Figure 6 The instructions are shown for execution Figure 5 The flowchart is an example of an embodiment of the method for adjusting parameters.

[0015] Figure 7 A lookup table is shown illustrating an embodiment of a method for predicting the operating conditions of a membrane filtration system based on changes in performance parameter values.

[0016] Figure 8 A block diagram illustrating an embodiment of a single-module reverse osmosis unit is shown.

[0017] Figure 9 A block diagram illustrating an embodiment of a multi-stage reverse osmosis unit is shown.

[0018] Figure 10 The illustration shows the relationship between multi-stage reverse osmosis units (e.g.) Figure 9 A table of examples of selected performance parameters related to a multi-stage reverse osmosis unit.

[0019] Figure 11 The illustration shows a reverse osmosis unit (e.g.) Figure 1 , 8 A block diagram of an embodiment of a membrane filtration system (or a reverse osmosis unit of 9). Detailed Implementation

[0020] The following detailed description of the subject matter is taken with reference to the accompanying drawings, which illustrate, by way of description, specific aspects and embodiments in which the subject matter may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the subject matter. References to “a,” “an,” or “various” embodiments in this disclosure do not necessarily refer to the same embodiment, and such references contemplate more than one embodiment. The scope of the invention is defined by the appended claims and the full scope of the legal equivalents enjoyed by such claims.

[0021] This subject matter relates to methods and apparatus for evaluating the performance of membrane filtration systems (e.g., reverse osmosis systems) using field-adjustable parameters. The field-adjustable parameters can be adjusted manually and / or automatically based on field-specific conditions and / or evaluation results. Evaluations can be performed periodically (e.g., for monitoring purposes) and / or as needed (e.g., for troubleshooting purposes).

[0022] Membrane filtration systems (also known as “membrane systems”) can separate particles based on particle characteristics (such as molecular weight, size, and / or charge) by passing a liquid containing particles through a semi-permeable membrane. In this document, “user” includes anyone who operates the membrane filtration system and / or works to ensure its proper functioning.

[0023] Figure 1 A block diagram illustrating an embodiment of a membrane filtration system 100 and a performance evaluation system 140 for evaluating the performance of the membrane filtration system 100 is shown, including measurement circuitry 130 and a user interface 132. Measurement circuitry 130 can process signals sensed from the membrane filtration system 100 to generate input parameters for the performance evaluation system 140. User interface 132 can present the results of the performance evaluation of the membrane filtration system 100 and allow the user to control the evaluation. In the illustrated embodiment, the membrane filtration system 100 is a reverse osmosis system that can be used, for example, for water treatment. Although water treatment is discussed as an example, this subject matter can also be applied when the membrane filtration system 100 is used for other purposes.

[0024] The membrane filtration system 100 includes a reverse osmosis unit 102. The reverse osmosis unit 102 includes an inlet 104, a concentrate outlet 106, a permeate outlet 108, and a semi-permeable membrane 110. Figure 1 In this diagram, reverse osmosis unit 102 is shown as a single-module unit with a single membrane element, as an example for illustrative and non-limiting purposes, while membrane filtration system 100 may include one or more reverse osmosis units, each including one or more membrane elements connected in series and / or in parallel, including but not limited to those referenced below. Figure 8 and 9 Examples of discussion.

[0025] The reverse osmosis unit 102 can receive feed water containing water and impurities (e.g., salt and organic matter) and force the received feed water through a semi-permeable membrane 110 to separate the received feed water into concentrate and permeate. The concentrate is a liquid with a higher impurity concentration than the feed water. When the membrane filtration system 100 is used for water treatment, the concentrate is also referred to as concentrated water, wastewater, brine, etc. The concentrate includes substances removed from the feed water to produce permeate as a product of the membrane filtration system (i.e., concentrate = feed water - permeate). The permeate is a liquid with a lower impurity concentration than the feed water (ideally zero impurities in the case of water purification). When the membrane filtration system 100 is used for water treatment, the permeate is a product of the membrane filtration system 100, produced by removing the concentrate from the feed water (i.e., permeate = feed water - concentrate), and is also referred to as pure water, filtered water, etc. The salt concentration of the liquid (feed water, concentrate, or permeate) can be used as a measure of the impurity concentration of the liquid and can be indicated by the conductivity of the liquid. The salt concentration of a liquid can also be used as a measure of the concentration of certain ions (e.g., chloride) in the liquid because it is more convenient to directly sense the conductivity of the liquid in the membrane filtration system 100 compared to ion concentration. Therefore, in various embodiments, the conductivity of the liquid is measured as an alternative to the salt concentration of the liquid, and the conductivity of the liquid can be used to indicate changes in salt concentration. A feed water containing salt (sodium chloride) and water can be used to test the membrane filtration system 100.

[0026] Inlet 104 receives feed water. Concentrate outlet 106 releases concentrate. Permeate outlet 108 releases permeate. The membrane filtration system 100 may include an inlet pipe 114 connected to inlet 104, a concentrate outlet pipe 116 connected to concentrate outlet 106, and a permeate outlet pipe 118 connected to permeate outlet 108. Feed water flows through inlet pipe 114 and enters reverse osmosis unit 102 at inlet 104. Concentrate is released from reverse osmosis unit 102 into concentrate outlet pipe 116 at concentrate outlet 106. Permeate is released from reverse osmosis unit 102 into permeate outlet pipe 118 at permeate outlet 108. As will be understood by those skilled in the art, membrane filtration system 100 may include other components, such as a pump connected to inlet pipe 114, to pump feed water to reverse osmosis unit 102 at a desired pressure.

[0027] Performance evaluation system 140 can evaluate the performance of membrane filtration system 100. The performance of a membrane filtration system (such as a reverse osmosis system) can be evaluated by monitoring parameters called Key Performance Indicators (KPIs). For example, membrane filtration system manufacturers have developed technologies and procedures to evaluate membrane performance by comparing the operating KPIs of the membrane filtration system with pre-established standard condition KPIs. DuPont Water Solutions, in its FilmTec... TMAn example is discussed in the 16th edition of *Reverse Osmosis Membrane Technology Handbook*, February 2023, pages 152-153. Such techniques and procedures can provide general information about membrane performance but do not take into account site-specific issues. Therefore, for each specific site where a membrane filtration system operates, general information about membrane performance may be inaccurate and / or insufficient. For example, a manufacturer's troubleshooting guide may link changes in various KPIs to certain operational problems without providing quantitative information that links increased or decreased KPI values ​​to the level of operational problems. Furthermore, some changes in certain KPIs may be caused by different operational problems specific to the site, thus requiring investigations beyond the general guidance that membrane manufacturers can provide. Therefore, a membrane performance assessment that takes into account the specific needs of the site in which the membrane filtration system operates is needed.

[0028] This topic provides membrane performance evaluation based on various KPIs using an evaluation system with adjustable parameters and / or machine learning capabilities. Field-adjustable parameters are used to control the operation of the performance evaluation system 140, allowing for customized membrane performance evaluations based on specific field operating conditions. This machine learning capability allows the evaluation system 140 to automatically control the field-adjustable parameters based on feedback information including evaluation results. Therefore, the field-adjustable parameters and / or machine learning capabilities enable the performance evaluation system 140 to consider field-specific and / or application-specific operating conditions, increasing the quantity and accuracy of membrane performance information, thereby facilitating performance monitoring and / or troubleshooting of the membrane filtration system 100.

[0029] Sensors can be used to sense signals, thereby determining various KPIs. For example, such as Figure 1As shown, one or more sensors 134 are located at or near inlet 104 (e.g., in inlet pipe 114 as shown), one or more sensors 136 are located at or near concentrate outlet 106 (e.g., in concentrate outlet pipe 116 as shown), and one or more sensors 138 are located at or near permeate outlet 108 (e.g., in permeate outlet pipe 118 as shown). Sensors(one or more) 134, 136, and 138 may each include a pressure sensor, conductivity sensor, flow sensor, temperature sensor, pH sensor, and / or oxidation-reduction potential (ORP) sensor, depending on the KPI used for membrane performance evaluation. Measurement circuitry 130 can process the signals generated by sensors(one or more) 134, 136, and 138 to determine the KPI values ​​used by performance evaluation system 140. In various embodiments, sensors(one or more) 134, 136, and 138 may be integrated into membrane filtration system 100. Measurement circuit 130 may be built into or otherwise connected to membrane filtration system 100 for communicative connection to sensors 134(one or more), 136(one or more), and 138(one or more). Performance evaluation system 140 may be built into or otherwise connected to measurement circuit 130 to receive KPI values ​​determined by measurement circuit 130. User interface 132 may be built into or otherwise connected to performance evaluation system 140 to receive input from the user for controlling membrane performance evaluation and presenting evaluation results. User interface 132 may also be directly connected to measurement circuit 130 to receive and present KPI values ​​determined by measurement circuit 130.

[0030] Figure 2A block diagram illustrating an embodiment of a performance evaluation system 240 for evaluating the performance of a membrane filtration system 100 is shown. The performance evaluation system 240 may represent an example of a performance evaluation system 140 and includes a data input 242, an evaluation circuit 244, and an adjustment circuit 246. The data input 242 represents any suitable interface between the measurement circuit 130 and the evaluation circuit 244. The data input 242 may, for example, receive KPI values ​​indicative of the performance of the membrane filtration system 100 from the measurement circuit 130 for use as input to the evaluation circuit 244. The evaluation circuit 244 can receive the KPI values ​​via the data input 242 and perform an evaluation using the received KPI values, the relationship between the KPIs and predefined operating conditions of the membrane filtration system 100, and field-adjustable parameters to generate predicted operating conditions for the membrane filtration system 100. The evaluation results, including the predicted operating conditions of the membrane filtration system 100, may be sent to a user interface 132 for presentation. The adjustment circuit 246 may receive user commands and / or feedback information and use the received user commands and / or feedback information to adjust the field-adjustable parameters. Feedback information may include predicted operating conditions of the membrane filtration system 100 obtained from one or more performance evaluations.

[0031] The performance of a membrane filtration system can be evaluated for monitoring and troubleshooting purposes. While existing systems use general criteria to evaluate the performance of membrane filtration systems, this subject matter improves the techniques used to evaluate the performance of membrane filtration systems by taking into account site-specific conditions. Site-specific conditions may include information specific to the site in which the membrane filtration system operates, such as the characteristics of the local water source, local requirements and operating limitations, and various quantitative information that cannot be generalized (e.g., by the manufacturer of the membrane filtration system). In one embodiment, regulation circuitry 246 receives user commands and uses the received user commands to regulate site-adjustable parameters. This incorporates the user's expertise (including site-specific conditions known and understood by the user) into the performance evaluation system 240. In another embodiment, regulation circuitry 246 receives feedback information and uses the received feedback information to regulate site-adjustable parameters. This allows performance evaluation system 240 to improve the accuracy of predicted operating conditions for the membrane filtration system by reducing the difference between predicted and actual operating conditions under site-specific conditions. In yet another embodiment, regulation circuitry 246 receives user commands and feedback information and uses the received user commands and received feedback information to regulate site-adjustable parameters. This improves the accuracy of assessment results performed by the performance evaluation system 240 using the capabilities of both human users and machines. In this way, this subject matter improves the technology for evaluating the performance of membrane filtration systems (e.g., reverse osmosis systems) by adapting or optimizing the evaluation process according to the specific information and conditions of the site in which the membrane filtration system operates.

[0032] Figure 3A block diagram illustrating an embodiment of a performance evaluation system 340 for evaluating the performance of a membrane filtration system 100 is shown. Figure 3 The diagram also shows a user interface 332 that is communicatively connected to the performance evaluation system 340. The performance evaluation system 340 may represent another example of the performance evaluation system 140 or a more specific embodiment of the performance evaluation system 240. The performance evaluation system 340 may include a data input 342, evaluation circuitry 344, adjustment circuitry 346, and a storage device 352.

[0033] Data input 342 represents any suitable interface between measurement circuit 130 and evaluation circuit 344. Data input 342 can receive KPI values ​​that indicate the performance of membrane filtration system 100. Examples of KPIs include:

[0034] • Water supply pressure: The pressure of the water supply, also known as the inlet pressure;

[0035] • Concentrate pressure: The pressure of the concentrate;

[0036] • Permeate pressure: The pressure of the permeate;

[0037] • Water supply conductivity: The electrical conductivity of the water supply, replacing the salt concentration of the water supply;

[0038] • Concentrate conductivity: The conductivity of the concentrate, replacing the salt concentration of the concentrate;

[0039] • Permeate conductivity: The conductivity of the concentrate, replacing the salt concentration of the permeate;

[0040] • Water supply flow rate: The flow rate of water supplied at inlet 104;

[0041] • Concentrate flow rate: Concentrate flow rate at outlet 106;

[0042] • Permeate flow rate: Permeate flow rate at the outlet 108;

[0043] • Parameters derived from the above parameters;

[0044] • Water supply temperature: The temperature of the water supply;

[0045] • Feedwater pH: The pH value of the feedwater;

[0046] • Concentrate pH: The pH value of the concentrate; and

[0047] • Feedwater oxidation-reduction potential (ORP) or chlorine concentration: A measure of the chlorine content in the feedwater (ORP is directly proportional to chlorine concentration).

[0048] Examples of parameters derived from the above parameters include:

[0049] • Salt passage: The ratio of permeate conductivity to feedwater conductivity (instead of the ratio of permeate salt concentration to feedwater salt concentration);

[0050] • Pressure differential: The difference between the concentrate pressure and the feedwater pressure (pressure differential = concentrate pressure - feedwater pressure); and

[0051] • The ratio of infiltration flow rate to feed water flow rate.

[0052] In various embodiments, the KPI values ​​are normalized using the raw values ​​of the KPIs and one or more other KPIs or other operating parameters (e.g., conductivity, temperature) that affect the raw values. This normalization is discussed, for example, in ASTM standard D4516-19a, “Standard Practice for Standardizing Reverse Osmosis Performance Data” (ASTM International, West Conshohocken, PA, 2019, DOI: 10.1520 / D4516-19A, www.astm.org). In various embodiments, multiple (e.g., 3, 4, or 5) KPIs selected from the examples above may be used to evaluate the performance of membrane filtration system 100.

[0053] In various embodiments, sensors (e.g., sensors 134, 136, and 138) are used to sense signals indicating pressure, conductivity, and / or flow rate to determine the value of the KPI. Feed water pressure, concentrate conductivity, and concentrate flow rate can each be measured using a pressure sensor—for example, in inlet pipe 114. Concentrate pressure, concentrate conductivity, and concentrate flow rate can each be measured using a conductivity sensor—for example, in concentrate outlet pipe 116. Permeate pressure, permeate conductivity, and permeate flow rate can each be measured using a flow sensor—for example, in permeate outlet pipe 118.

[0054] Evaluation circuit 344 can receive KPI values ​​via data input 342. In various embodiments, depending on how the performance evaluation system interfaces to measurement circuit 130, data input 342 may present a direct electrical connection (e.g., wires directly connecting evaluation circuit 344 to measurement circuit 130), or another form of interface between evaluation circuit 344 and measurement circuit 130 (e.g., including cables, connectors, and / or circuitry for converting data output from measurement circuit 130 into data readable by evaluation circuit 344).

[0055] Evaluation circuit 344 can generate predicted operating conditions for membrane filtration system 100 by performing an evaluation using KPI values ​​received via data input 342, the relationship between the KPIs and predefined operating conditions of membrane filtration system 100, and field-adjustable parameters. The "field-adjustable" parameters are adjustable, for example, when the membrane filtration system 100 is deployed and operated by a user working in the operating field. The evaluation can be performed by executing an evaluation algorithm that includes the field-adjustable parameters. Field-adjustable parameters may include, for example, parameters used in the relationship between KPIs and predefined operating conditions and / or parameters used to determine the inputs for that relationship. Predefined operating conditions may include operational problems to be addressed in order to maintain the normal operation of membrane filtration system 100. Examples of such problems (and how to address them) include: damage to the semipermeable membrane 110 (replace the membrane), scaling on the surface of the semipermeable membrane 110 (clean the membrane filtration system 100, improve the pretreatment of the feed water, and / or apply a more effective chemical antiscalant), organic scaling of the semipermeable membrane 110 (clean the membrane filtration system 100 and improve the pretreatment of the feed water), leaks in the inlet pipe 114, concentrate outlet pipe 116, or permeate outlet pipe 118 (replace the leaking pipe), unacceptable feed water quality (the pretreatment of the feed water needs improvement), and so on. The evaluation circuit 344 can detect changes in KPI values ​​by comparing the received KPI value with the corresponding baseline value of the KPI (e.g., a standard condition value). The relationship between the KPI and predefined operating conditions can map the range of KPI values ​​to predefined operating conditions. Predefined operating conditions may include quantitatively defined operating conditions (e.g., each including the degree of problem severity or other measures). Field-adjustable parameters may include baseline values ​​for KPIs (e.g., standard condition values) and / or the amount of variation in KPI values ​​within the relationship. This relationship may be in the form of a lookup table, or otherwise represented in lookup table form, as referenced below. Figure 7 Further discussion is needed.

[0056] The adjustment circuit 346 may receive user commands and / or feedback information, and use the received user commands and / or feedback information to adjust field-adjustable parameters. In one embodiment, the adjustment circuit 346 receives user commands and uses the received user commands to adjust field-adjustable parameters. In another embodiment, the adjustment circuit 346 receives feedback information and uses the received feedback information to adjust field-adjustable parameters. In yet another embodiment, the adjustment circuit 346 receives both user commands and feedback information, and uses both the received user commands and the received feedback information to adjust field-adjustable parameters. User commands may each specify one or more of the field-adjustable parameters to be adjusted. Feedback information may include predicted operating conditions of the membrane filtration system 100 obtained from performing one or more evaluations.

[0057] In various embodiments, the adjustment circuit 346 may adjust field-adjustable parameters by executing a machine learning algorithm 350. The adjustment circuit 346 may receive actual operating conditions of the membrane filtration system 100 and execute the machine learning algorithm 350 to compare predicted operating conditions with corresponding actual operating conditions to determine a measure of difference between the predicted and actual operating conditions. The actual operating conditions may be assessed manually, automatically (e.g., using sensors), and / or semi-automatically (e.g., using sensors with user input). The adjustment circuit 346 may then execute the machine learning algorithm 350 to reduce the measure of difference by adjusting one or more parameters of the field-adjustable parameters and repeatedly evaluating and determining the measure of difference between the predicted and actual operating conditions until the measure of difference is below an acceptable level. In various embodiments, this acceptable level may be a specified level (e.g., specified by the user), a level reached after a specified number of repeated evaluations, a level where the reduction in the measure of difference resulting from further repeated evaluations and determination of the measure of difference is considered negligible, or a level reached upon receiving a stop command (e.g., from the user).

[0058] The storage device 352 can store the evaluation results generated by the evaluation circuit 344, as well as machine-executable instructions for operating the performance evaluation system 340.

[0059] User interface 332 may represent an example of user interface 132 and may present predicted operating conditions to the user and receive user commands from the user. User commands include, but are not limited to, commands for adjusting field-adjustable parameters. In various embodiments, user commands are used to adjust field-adjustable parameters (e.g., by specifying a new value for each field-adjustable parameter to be adjusted). In various embodiments where feedback information is used to adjust field-adjustable parameters, user commands may include commands that serve as input to machine learning algorithm 350 for adjusting field-adjustable parameters.

[0060] In various embodiments, performance evaluation systems 140, 240, and 340 may each be implemented using a combination of hardware and software. For example, the circuitry of performance evaluation systems 140, 240, and 340 may each be implemented using dedicated circuitry configured to perform one or more functions discussed herein and / or general-purpose circuitry programmed to perform such functions. Such general-purpose circuitry includes, but is not limited to, a microprocessor or a portion thereof, a microcontroller or a portion thereof, and programmable logic circuitry or a portion thereof. In various embodiments, evaluation circuitry 244, conditioning circuitry 246, evaluation circuitry 344, and conditioning circuitry 346 may each include a processor or a portion thereof. In various embodiments, the processor may include one or more of a microprocessor, microcontroller, digital signal processor (DSP), field-programmable gate array (FPGA), other digital logic, analog circuitry, and / or other circuitry components.

[0061] Figure 4 A block diagram is shown illustrating an embodiment of a sensor and measurement circuitry 430 used with a membrane filtration system (e.g., membrane filtration system 100). In the illustrated embodiment, the sensors include: an inlet sensor 434, which may represent an example of sensor 134(one or more), a concentrate output sensor 436, which may represent an example of sensor(one or more) 136, and a permeate output sensor 438, which may represent an example of sensor 138(one or more). The inlet sensor 434 may include a pressure sensor 434P positioned to sense a signal indicating feedwater pressure, a conductivity sensor 434C sensing a signal indicating feedwater conductivity, a flow sensor 434F sensing a signal indicating feedwater flow rate, and / or one or more other sensors 434S sensing one or more other signals required to generate one or more KPIs related to the feedwater. Concentrate output sensor 436 may include a pressure sensor 436P positioned to sense a signal indicating concentrate pressure, a conductivity sensor 436C sensing a signal indicating concentrate conductivity, a flow sensor 436F sensing a signal indicating concentrate flow rate, and / or one or more other sensors 436S sensing one or more other signals required to generate one or more KPIs related to the concentrate. Permeate output sensor 438 may include a pressure sensor 438P positioned to sense a signal indicating permeate pressure, a conductivity sensor 438C sensing a signal indicating permeate conductivity, a flow sensor 438F sensing a signal indicating permeate flow rate, and / or one or more other sensors 438S sensing one or more other signals required to generate one or more KPIs related to the permeate. Examples of other sensors(s) 434S, 436S, and 438S include temperature sensors, pH sensors, and ORP sensors. In various embodiments, sensors may include all of these sensors or any subset of these sensors to meet the need to generate KPIs required to evaluate the performance of the membrane filtration system. In various embodiments, sensors may each be built into or detachably positioned within the membrane filtration system. Measurement circuit 430 can represent an example where measurement circuit 130 can use signals sensed by a sensor to determine the value of a KPI.

[0062] Figure 5A flowchart illustrating an embodiment of a method 560 for evaluating the performance of a membrane filtration system (e.g., membrane filtration system 100) is shown. The membrane filtration system passes feed water through a semi-permeable membrane to separate the feed water into concentrate and permeate. Performance evaluation systems 140, 240, or 340 may be used to perform method 560. For example, performance evaluation system 340 may be configured to perform method 560. Storage device 352 may include a non-transitory computer-readable storage medium containing instructions that, when executed by performance evaluation system 340, cause performance evaluation system 340 to perform method 560.

[0063] At point 561, KPI values ​​indicating the performance of the membrane filtration system are received. In various embodiments, signals sensed from the membrane filtration system are used to determine the KPIs. These signals may each indicate the pressure, conductivity, or flow rate of the feed water, concentrate, or permeate. (Refer to above) Figure 3 Examples of KPIs were discussed.

[0064] At point 562, an evaluation is performed using field-adjustable parameters to predict the operating conditions of the membrane filtration system. The evaluation can be performed using received KPI values, the relationship between the KPIs and predefined operating conditions of the membrane filtration system, and the field-adjustable parameters. The predicted operating conditions of the membrane filtration system can include problems that need to be addressed to maintain the normal operation of the membrane filtration system. Identifying such problems can include detecting changes in KPI values ​​by comparing the received KPI values ​​to corresponding baseline values ​​of the KPIs (e.g., standard condition values). The relationship between the KPIs and the predefined operating conditions of the membrane filtration system maps a range of KPI values ​​to the operating conditions to be predicted (e.g., the problems to be addressed). In one embodiment, some of the predefined problems to be addressed may each include a measure of severity.

[0065] At point 563, the field-adjustable parameters are adjusted using user commands and / or feedback information including evaluation results. The feedback information may include predicted operating conditions of the membrane filtration system obtained from performing one or more evaluations. The adjusted field-adjustable parameters will be applied to repeat evaluations and / or perform future evaluations. In various embodiments, the field-adjustable parameters may be adjusted using only user commands, only feedback information, or both user commands and feedback information.

[0066] Figure 6A flowchart illustrating an embodiment of a method 670 for adjusting parameters during step 563 of method 560 is shown. Performance evaluation systems 140, 240, or 340 may be used to execute method 670. For example, performance evaluation system 340 may be configured to execute method 670. Storage device 352 may include a non-transitory computer-readable storage medium containing instructions that, when executed by performance evaluation system 340, cause performance evaluation system 340 to execute method 670. In various embodiments, at least a portion of method 670 is executed by performing a machine learning algorithm.

[0067] At 671, the evaluation of step 562 in method 560 is performed to predict the operating conditions of the membrane filtration system.

[0068] At point 672, a measure of the difference between the actual operating conditions and the predicted operating conditions of the membrane filtration system is determined by receiving the actual operating conditions and comparing them with the corresponding predicted operating conditions.

[0069] At 673, it is determined whether the difference is acceptable. If the difference is acceptable, the evaluation at 671 can be performed again without adjusting the field-adjustable parameters. If the difference is unacceptable, one or more parameters among the field-adjustable parameters are adjusted at 674 to reduce the difference measure, and the evaluation and determination of the difference measure are repeated until the difference is determined to be acceptable at 673. In various embodiments, the difference is accepted when the difference measure is below a specified level, when the evaluation and determination of the difference measure are repeated a specified number of times, when further reduction of the difference measure achieved by repeatedly evaluating and determining the difference measure is considered negligible, or when a stop command is received (e.g., from a user).

[0070] Figure 7 A lookup table 780 is shown, illustrating an embodiment of a method for predicting operating conditions of a membrane filtration system based on changes in performance parameter values. The lookup table can represent examples of relationships between KPIs and predefined operating conditions of the membrane filtration system, such as those used by evaluation circuits 244 or 344 to generate predicted operating conditions for the membrane filtration system, or relationships used in step 562 of method 560 to predict operating conditions of the membrane filtration system. In the illustrated embodiment, lookup table 780 maps KPI value ranges to predefined operating conditions to be predicted. Note that lookup table 780 illustrates the relationship between KPI value ranges and predefined operating conditions to be predicted by way of example, not limitation. In various embodiments, the relationships shown in lookup table 780 may include all predefined operating conditions and all KPIs, as well as the range of their values ​​required to predict those predefined operating conditions.

[0071] In lookup table 780, a combination of value ranges for three KPIs—permeate flow rate, salt throughput, and differential pressure—is used to predict three predefined operating conditions for the membrane filtration system: normal conditions, presence of oxidants in the feed water, and membrane fouling. Changes in the value of each KPI are percentage changes relative to its baseline value (e.g., standard condition value). The standard condition value is a value established to indicate normal or optimal operation of the membrane filtration system. As an example, a 7.5% threshold is used in lookup table 780 as a quantification standard for predicting operating conditions. That is, changes in KPI values ​​within 7.5% of their respective baselines are considered normal. In lookup table 780, changes in permeate flow rate within 7.5%, changes in salt throughput within 7.5%, and changes in differential pressure within 7.5% collectively predict normal conditions. Increases in permeate flow rate values ​​greater than 7.5%, increases in salt throughput values ​​greater than 7.5%, and changes in differential pressure values ​​within 7.5% collectively predict the presence of oxidants in the feed water. A decrease in permeate flow rate greater than 7.5%, a change in salt permeability within 7.5%, and an increase in differential pressure within 7.5% all contribute to the prediction of semipermeable membrane fouling. The threshold of 7.5 is used as an example and can be set to other values, such as 10% or 20%.

[0072] In various embodiments, the lookup table may include different thresholds for different KPIs and / or different operating conditions. Different thresholds can be established empirically for each KPI and each predefined operating condition. In various embodiments, the field-adjustable parameters discussed above may include thresholds and / or baseline values ​​for KPIs, such that the individual thresholds and / or baseline values ​​can be adjusted by the user and / or automatically (e.g., through machine learning algorithms).

[0073] Figure 8 A block diagram illustrating an embodiment of a single-module reverse osmosis unit 802 is shown. The reverse osmosis unit 802 may represent an example of the reverse osmosis unit 102. Although in Figure 8 The reverse osmosis unit 802 is shown as a single-module reverse osmosis unit comprising multiple membrane elements 802A, 802B, ..., and 802N. The reverse osmosis unit 802 may include one or more membrane elements (where N is 1 or any other suitable number determined by those skilled in the art). When implemented in a membrane filtration system (e.g., membrane filtration system 100), the reverse osmosis unit 802 may include a pressure vessel containing membrane elements 802A, 802B, ..., and 802N. Figure 1 The reverse osmosis unit 102 shown may represent an example of each of the membrane elements 802A, 802B, ... and 802N.

[0074] In the illustrated embodiment, membrane element 802A includes an inlet pipe 814A, a concentrate outlet pipe 816A, and a permeate outlet pipe 818A; membrane element 802B includes an inlet pipe 814B, a concentrate outlet pipe 816B, and a permeate outlet pipe 818B, ..., while membrane element 802N includes an inlet pipe 814N, a concentrate outlet pipe 816N, and a permeate outlet pipe 818N. Inlet pipe 814A (the inlet pipe of the first membrane element) is connected to the main inlet pipe 814 of the reverse osmosis unit 802 to receive feed water fed to the reverse osmosis unit 802. Concentrate outlet pipe 816A is connected to inlet pipe 814B, ..., while concentrate outlet pipes 816(N-1) are connected to inlet pipe 814N, such that the concentrate from each membrane element becomes feed water for the next membrane element. The concentrate outlet pipe 816N (the concentrate outlet pipe of the last membrane element) is connected to the main concentrate outlet 816 of the reverse osmosis unit 802, such that the concentrate flowing out of membrane element 802N (the last membrane element) is the total concentrate from the reverse osmosis unit 802. Permeate outlet pipes 818A, 818B, ..., 818N are each connected to the main permeate outlet pipe 818 of the reverse osmosis unit 802, such that the total permeate of the reverse osmosis unit 802 includes the permeate from all membrane elements 802A, 802B, ..., and 802N. In other words, the product of the reverse osmosis unit 802 is the sum of the products from all membrane elements of the reverse osmosis unit 802.

[0075] like Figure 8 As shown, one or more sensors 834 are located in inlet pipe 814, one or more sensors 836 are located in concentrate outlet pipe 816, and one or more sensors 838 are located in permeate outlet pipe 818. Sensors 834, 836, and 838 may be placed outside the pressure vessel containing membrane elements 802A, 802B, ..., and 802N. These sensors allow signals to be sensed to generate KPIs related to feed water, concentrate, and permeate of the reverse osmosis unit 802. Examples of sensors(one or more) 834, sensors(one or more) 836, and sensors(one or more) 838 may each include pressure sensors, conductivity sensors, flow sensors, temperature sensors, pH sensors, and / or ORP sensors, depending on the KPIs used to evaluate the performance of the reverse osmosis unit 802 (or the membrane filtration system including the reverse osmosis unit 802).

[0076] Figure 9 A block diagram illustrating an embodiment of a multi-stage reverse osmosis unit 902 is shown. The reverse osmosis unit 902 may represent another example of a reverse osmosis unit 102. Although in Figure 9The diagram shows a two-stage 2:1 reverse osmosis unit – it includes a first (upstream) stage with two membrane element sequences 902A and 902B and a second (downstream) stage with a membrane element sequence 902C, but the reverse osmosis unit 902 may include any number of stages, each stage including any number of membrane elements. Figure 8 The reverse osmosis unit 802 shown may represent an example of each of the membrane element sequences 902A, 902B, and 902C. That is, as implemented in a membrane filtration system (e.g., membrane filtration system 100), the reverse osmosis unit 902 may include multiple membrane element sequences (e.g., 902A, 902B, and 902C), each membrane element sequence including a pressure vessel containing one or more membrane elements.

[0077] In the illustrated embodiment, membrane element sequence 902A includes inlet pipe 914A, concentrate outlet pipe 916A, and permeate outlet pipe 918A; membrane element sequence 902B includes inlet pipe 914B, concentrate outlet pipe 916B, and permeate outlet pipe 918B; and membrane element sequence 902C includes inlet pipe 914C, concentrate outlet pipe 916C, and permeate outlet pipe 918C. Inlet pipes 914A and 914B (the inlet pipes of the first-stage membrane element sequence) are connected to the main inlet pipe 914 of the reverse osmosis unit 902 to receive feed water fed to the reverse osmosis unit 902. Concentrate outlet pipes 916A and 916B are connected to inlet pipe 914C, such that the concentrate from each membrane element sequence of the first stage becomes feed water for the next-stage membrane element sequence. The concentrate outlet pipe 916C (the concentrate outlet pipe of the second-stage membrane element sequence) is connected to the main concentrate outlet 916 of the reverse osmosis unit 902, such that the concentrate flowing out of membrane element 902C (the second-stage membrane element sequence) is the total concentrate from the reverse osmosis unit 902. Permeate outlet pipes 918A, 918B, and 918C are each connected to the main permeate outlet pipe 918 of the reverse osmosis unit 902, such that the total permeate of the reverse osmosis unit 902 includes the permeate from all membrane element sequences 902A, 902B, and 902C. In other words, the product of the reverse osmosis unit 902 is the sum of the products from all membrane element sequences of the reverse osmosis unit 902.

[0078] like Figure 9As shown, one or more sensors 934 may be located in inlet pipe 914, one or more sensors 935 may be located in inlet pipe 914C, one or more sensors 936 may be located in concentrate outlet pipe 916, and one or more sensors 938 may be located in permeate outlet pipe 918. Sensors 934, 935, 936, and 938 may be placed outside the pressure vessel containing the membrane element sequences 902A, 902B, and 902C. These sensors allow sensing of signals used to generate KPIs related to the feed water, concentrate, and permeate of the reverse osmosis unit 902, as well as the interstage feed water (feed water of the second stage). Examples of sensors(one or more) 934, 935, 936, and 938 may each include pressure sensors, conductivity sensors, flow sensors, temperature sensors, pH sensors, and / or ORP sensors, depending on the KPIs used to evaluate the performance of the reverse osmosis unit 902 (or the membrane filtration system including the reverse osmosis unit 902).

[0079] Figure 10 A table illustrating examples of selected KPIs associated with a multi-stage reverse osmosis unit (e.g., reverse osmosis unit 902) is shown. Examples are presented for feedwater parameters, interstage parameters, permeate parameters, and concentrate parameters, each determined, for example, using signals sensed by sensors 934, 935, 936, and 938, respectively. In addition to overall unit performance monitoring and / or troubleshooting, interstage parameters also allow for performance monitoring and / or troubleshooting of individual stages of reverse osmosis unit 902. Figure 10 The selection of parameters to be measured as KPIs shown is an example for illustrative purposes and not for limiting purposes. Those skilled in the art will understand that other options can be selected. Figure 10 Any combination of the parameters shown and other parameters suitable for indicating the performance of the reverse osmosis unit 902 may be used for monitoring and / or troubleshooting purposes.

[0080] Figure 11 A block diagram illustrating an embodiment of a membrane filtration system 1100, including a reverse osmosis unit 1102, is shown as an example to which this subject matter can be applied. The membrane filtration system 1100 may represent an example of the membrane filtration system 100, wherein examples of the reverse osmosis unit 1102 include reverse osmosis units 102, 802, and 902. Reverse osmosis units 102, 802, and 902 are illustrated and discussed herein by way of example and not limitation. In various embodiments, this subject matter can be applied to membrane filtration systems including any number and combination of membrane elements.

[0081] Reverse osmosis unit 1102 includes an inlet pipe 1114, a concentrate outlet pipe 1116, and a permeate outlet pipe 1118. Membrane filtration system 1100 includes: a pump 1184 that pumps feed water through inlet pipe 1114 to reverse osmosis unit 1102; an inlet valve 1186 connected between pump 1184 and inlet valve 1186 to control the flow rate and pressure of the feed water received by reverse osmosis unit 1102; and a concentrate valve 1188 connected to concentrate outlet pipe 1118 to control the flow rate and pressure of concentrate leaving reverse osmosis unit 1102. Pump 1184 and / or valve 1186 can be used to provide the pressure required for reverse osmosis. Inlet pump 1186 can be a throttle valve or a variable frequency drive (VFD) control valve. Inlet pump 1188 can be another throttle valve. Permeate (e.g., the product of membrane filtration system 1100) is released from reverse osmosis unit 1102 through permeate outlet pipe 1118.

[0082] Some non-restrictive examples (Examples 1-25) are provided below:

[0083] In Example 1, a system for evaluating the performance of a membrane filtration system is provided. The membrane filtration system may include: an inlet configured to receive feed water; a reverse osmosis unit including one or more semi-permeable membranes and configured to pass the received feed water through the one or more semi-permeable membranes to separate the received feed water into concentrate and permeate; a concentrate outlet for releasing the concentrate from the reverse osmosis unit; and a permeate outlet for releasing the permeate from the reverse osmosis unit. The system for evaluating the performance of the membrane filtration system may include evaluation circuitry and control circuitry. The evaluation circuitry may be configured to receive values ​​of key performance indicators (KPIs) and perform an evaluation using the received KPI values, the relationship between the KPIs and predefined operating conditions of the membrane filtration system, and field-adjustable parameters to generate predicted operating conditions for the membrane filtration system. The KPIs include parameters indicating the performance of the membrane filtration system. The control circuitry may be configured to receive user commands and adjust the field-adjustable parameters using the user commands.

[0084] In Example 2, the subject of Example 1 can optionally be configured such that: the evaluation circuit is configured to determine the change in KPI value by comparing the received KPI value with the corresponding baseline value of the KPI, and the relationship maps the range of KPI values ​​to predefined operating conditions.

[0085] In Example 3, the subject of Example 2 can optionally be configured such that: the field-adjustable parameters include a range of KPI values, and the adjustment circuit is configured to adjust the range of KPI values.

[0086] In Example 4, any one or any combination of the topics in Examples 2 and 3 can optionally be configured such that: the field-adjustable parameters include the baseline value of the KPI, and the adjustment circuit is configured to adjust the baseline value of the KPI.

[0087] In Example 5, the subject matter of any one or any combination of Examples 1 to 4 may optionally be configured such that: the adjustment circuit is further configured to receive feedback information from performing one or more evaluations and to adjust field adjustable parameters using the feedback information, said feedback information including predicted operating conditions of the membrane filtration system obtained from one or more evaluations.

[0088] In Example 6, the subject of Example 5 can optionally be configured such that the adjustment circuit is configured to adjust the field-adjustable parameters by using the received feedback information to execute a machine learning algorithm.

[0089] In Example 7, the subject of Example 6 can optionally be configured such that: the adjustment circuit is configured to determine a measure of the difference between the actual operating conditions of the membrane filtration system and the predicted operating conditions of the membrane filtration system, and to reduce the measure of difference by adjusting one or more of the field-adjustable parameters and repeatedly performing the evaluation and determination of the measure of difference until the measure of difference is below an acceptable level.

[0090] In Example 8, the subject matter of any one or any combination of Examples 1 to 7 may optionally be configured to further include a sensor configured to sense a signal from the membrane filtration system, and a measurement circuit configured to receive the sensed signal from the sensor and generate a KPI using the sensed signal.

[0091] In Example 9, the subject matter of Example 8 may optionally be configured such that: the sensor includes one or more flow sensors, one or more conductivity sensors, and one or more pressure sensors. Each of the one or more flow sensors is configured and positioned to sense a flow signal indicating the flow rate of feed water at the inlet, the flow rate of concentrate at the concentrate outlet, or the flow rate of permeate at the permeate outlet. Each of the one or more conductivity sensors is configured and positioned to sense a conductivity signal indicating the conductivity of feed water, the conductivity of concentrate, or the conductivity of permeate. Each of the one or more pressure sensors is configured and positioned to sense a pressure signal indicating the pressure of feed water at the inlet, the pressure of concentrate at the concentrate outlet, or the pressure of permeate at the permeate outlet.

[0092] In Example 10, the subject matter of Example 9 may optionally be configured such that the sensor further includes at least one of the following: a temperature sensor configured and positioned to sense a temperature signal indicating the temperature of the feed water; a first pH sensor configured and positioned to sense a first pH signal indicating the pH value of the feed water; a second pH sensor configured and positioned to sense a second pH signal indicating the pH value of the concentrate; or an oxidation-reduction potential (ORP) sensor configured and positioned to sense an ORP signal indicating the chlorine concentration of the feed water.

[0093] In Example 11, a method for evaluating the performance of a membrane filtration system is also provided. The membrane filtration system can be configured to pass feedwater through one or more semi-permeable membranes to separate the feedwater into concentrate and permeate. The method may include: receiving values ​​of key performance indicators (KPIs), including parameters indicative of the performance of the membrane filtration system; performing an evaluation using the received KPI values, the relationship between the KPIs and predefined operating conditions of the membrane filtration system, and field-adjustable parameters to predict the operating conditions of the membrane filtration system; receiving user commands; and adjusting the field-adjustable parameters using the received user commands.

[0094] In Example 12, the subject matter of Example 11 may optionally additionally include: sensing signals that indicate the pressure, conductivity, or flow rate of feed water, concentrate, or permeate in the respective membrane filtration system; and using the sensed signals to determine KPIs.

[0095] In Example 13, the one or more semi-permeable membranes present as in Example 12 may include a plurality of semi-permeable membranes, and the membrane filtration system may include a first stage and a second stage. The first stage is configured to pass feed water through a first group of one or more membranes of the plurality of semi-permeable membranes to separate the feed water into interstage concentrate and a first portion of permeate. The second stage is configured to pass the interstage concentrate through a second group of one or more membranes of the plurality of semi-permeable membranes to separate the interstage concentrate into a concentrate and a second portion of permeate. The subject matter of Example 12 may optionally also include: sensing interstage signals, each indicating pressure, conductivity, or flow rate of the interstage concentrate in the membrane filtration system; and using the sensed interstage signals to determine KPIs.

[0096] In Example 14, the subject matter of any one or any combination of Examples 12 and 13 may optionally further include sensing one or more additional signals, said additional signals including at least one of the following: a temperature signal indicating the temperature of the feed water, a first pH signal indicating the pH value of the feed water, a second pH signal indicating the pH value of the concentrate, or an oxidation-reduction potential (ORP) signal indicating the chlorine concentration of the feed water.

[0097] In Example 15, the subject matter of predicting the operating conditions of a membrane filtration system, as present in any one or any combination of Examples 11 to 14, may optionally include: identifying problems to be addressed in order to maintain the proper operation of the membrane filtration system.

[0098] In Example 16, the subject of the identification problem present in Example 15 may optionally include: detecting changes in KPI values ​​by comparing the received KPI values ​​with the corresponding baseline values ​​of the KPIs, and the relationship mapping the range of KPI values ​​to a predefined problem to be addressed.

[0099] In Example 17, the subject matter of any one or any combination of Examples 11 to 16 may optionally include: receiving feedback information, which includes predicted operating conditions of the membrane filtration system obtained by performing one or more evaluations; and adjusting field-adjustable parameters using the received feedback information.

[0100] In Example 18, the subject of adjusting field-adjustable parameters, as present in Example 17, may optionally include: adjusting field-adjustable parameters by performing a machine learning algorithm using the received feedback information.

[0101] In Example 19, the subject of adjusting field-adjustable parameters as present in Example 18 may optionally include: determining a measure of the difference between the actual operating conditions of the membrane filtration system and the predicted operating conditions of the membrane filtration system; and reducing the measure of difference by adjusting one or more parameters of the field-adjustable parameters and repeatedly performing the evaluation and determination of the measure of difference.

[0102] In Example 20, the subject of Example 19 may optionally further include: repeatedly performing the evaluation and determining the measure of difference until the measure of difference is below a specified level.

[0103] In Example 21, the subject of Example 19 may optionally further include: repeating the evaluation and determining the number of times the difference measure is specified.

[0104] In Example 22, the subject of Example 19 may optionally further include: repeating the evaluation and determining the measure of difference until any further reduction in the measure of difference is considered negligible.

[0105] In Example 23, the subject of Example 19 may optionally further include: stopping repeated evaluations and determining a measure of difference in response to a stop command.

[0106] In Example 24, a non-transitory computer-readable storage medium containing instructions is also provided. When executed by the system, the instructions cause the system to perform a method for evaluating the performance of a membrane filtration system configured to pass feedwater through one or more semi-permeable membranes to separate feedwater into concentrate and permeate. The method may include: receiving values ​​of key performance indicators (KPIs), including parameters indicative of the performance of the membrane filtration system; predicting operating conditions of the membrane filtration system by performing an evaluation using the received KPI values, the relationship between the KPIs and predefined operating conditions of the membrane filtration system, and field-adjustable parameters; receiving user commands; and adjusting the field-adjustable parameters using the received user commands.

[0107] In Example 25, the method present in Example 24 may optionally further include: receiving feedback information, which includes predicted operating conditions of the membrane filtration system obtained by performing evaluations once or multiple times; and adjusting field-adjustable parameters using the received feedback information.

[0108] The foregoing examples are not restrictive or exclusive, and the scope of this subject matter is defined by the entire specification (including the claims and drawings).

[0109] The above description includes references to the accompanying drawings, which form part of the detailed description. The drawings illustrate, by way of illustration, various embodiments in which the invention may be practiced. This application also refers to “examples”. These examples may include elements other than those shown or described. The above examples are not intended to be an exhaustive or exclusive list of examples and variations of the subject matter.

[0110] This application is intended to cover variations or modifications of the subject matter. It should be understood that the above description is illustrative and not limiting. The scope of the invention should be determined by reference to the appended claims and the full scope of their legal equivalents.

Claims

1. A system for evaluating the performance of a membrane filtration system, the membrane filtration system comprising: It is configured as an inlet for receiving water supply; A reverse osmosis unit includes one or more semi-permeable membranes and is configured to pass received feed water through the one or more semi-permeable membranes to separate the received feed water into concentrate and permeate. Concentrate outlet for releasing the concentrate from the reverse osmosis unit; The system includes a permeate outlet for releasing the permeate from the reverse osmosis unit, the system comprising: An evaluation circuit is configured to receive values ​​of key performance indicators (KPIs) and perform an evaluation using the received KPI values, the relationship between the KPIs and predefined operating conditions of the membrane filtration system, and field-adjustable parameters to generate predicted operating conditions for the membrane filtration system, wherein the KPIs include parameters indicating the performance of the membrane filtration system. as well as An adjustment circuit is configured to receive user commands and adjust the field-adjustable parameters using the user commands.

2. The system according to claim 1, wherein, The evaluation circuit is configured to determine changes in KPI values ​​by comparing the received KPI values ​​with the corresponding baseline values ​​of the KPIs, and the relationship maps the range of KPI values ​​to the predefined operating conditions.

3. The system according to claim 2, wherein, The field-adjustable parameters include the KPI value range, and the adjustment circuit is configured to adjust the KPI value range.

4. The system according to any one of claims 2 and 3, wherein, The field-adjustable parameters include the baseline value of the KPI, and the adjustment circuit is configured to adjust the baseline value of the KPI.

5. The system according to any one of claims 1 to 4, wherein, The adjustment circuit is also configured to receive feedback information and use the feedback information to adjust the field-adjustable parameters, the feedback information including predicted operating conditions of the membrane filtration system obtained by performing the evaluation once or multiple times.

6. The system according to claim 5, wherein, The adjustment circuit is configured to adjust the field-adjustable parameters by executing a machine learning algorithm using the received feedback information.

7. The system according to claim 6, wherein, The adjustment circuit is configured to determine a measure of the difference between the actual operating conditions of the membrane filtration system and the predicted operating conditions of the membrane filtration system, and to reduce the measure of difference by adjusting one or more of the field-adjustable parameters and repeating the assessment and determination of the measure of difference until the measure of difference is below an acceptable level.

8. The system according to any one of claims 1 to 7, further comprising: A sensor configured to sense signals from the membrane filtration system; and A measurement circuit is configured to receive sensed signals from the sensor and use the sensed signals to generate KPIs.

9. The system according to claim 8, wherein, The sensor includes: One or more flow sensors are each configured and positioned to sense a flow signal indicating the flow rate of the feed water at the inlet, the flow rate of the concentrate at the concentrate outlet, or the flow rate of the permeate at the permeate outlet. One or more conductivity sensors, each configured and positioned to sense a conductivity signal indicating the conductivity of the feed water, the conductivity of the concentrate, or the conductivity of the permeate; and One or more pressure sensors are each configured and positioned to sense pressure signals indicating the pressure of the water supply at the inlet, the pressure of the concentrate at the concentrate outlet, or the pressure of the permeate at the permeate outlet.

10. The system according to claim 9, wherein, The sensor also includes at least one of the following: A temperature sensor, configured and positioned to sense a temperature signal indicating the temperature of the water supply; A first pH sensor is configured and positioned to sense a first pH signal indicating the pH value of the feed water; A second pH sensor is configured and positioned to sense a second pH signal indicating the pH value of the concentrate; or An oxidation-reduction potential (ORP) sensor is configured and positioned to sense an ORP signal indicating the chlorine concentration of the feed water.

11. A method for evaluating the performance of a membrane filtration system, the membrane filtration system being configured to pass feed water through one or more semi-permeable membranes to separate the feed water into concentrate and permeate, the method comprising: Receive values ​​for key performance indicators (KPIs), which include parameters indicating the performance of the membrane filtration system; The operating conditions of the membrane filtration system are predicted by performing an evaluation using the received KPI values, the relationship between the KPIs and the predefined operating conditions of the membrane filtration system, and field-adjustable parameters. Receive user commands; and Adjust the field-adjustable parameters using the received user commands.

12. The method of claim 11, further comprising: Sensing signals that indicate the pressure, conductivity, or flow rate of the feed water, concentrate, or permeate in the membrane filtration system; and The KPI is determined using the sensed signal.

13. The method according to claim 12, wherein, The one or more semi-permeable membranes include a plurality of semi-permeable membranes, and the membrane filtration system includes a first stage and a second stage. The first stage is configured to pass the feed water through a first group of one or more membranes of the plurality of semi-permeable membranes to separate the feed water into interstage concentrate and a first portion of permeate. The second stage is configured to pass the interstage concentrate through a second group of one or more membranes of the plurality of semi-permeable membranes to separate the interstage concentrate into concentrate and a second portion of permeate. The method further includes: Sensing interstage signals, each representing the pressure, conductivity, or flow rate of the interstage concentrate in the membrane filtration system; and The KPI is determined using the sensed inter-level signals.

14. The method of any one of claims 12 and 13, further comprising sensing one or more additional signals, said additional signals comprising at least one of the following: A temperature signal indicating the temperature of the water supply; A first pH signal indicating the pH value of the supplied water; A second pH signal indicating the pH value of the concentrate; or The oxidation-reduction potential (ORP) signal indicates the chlorine concentration of the water supply.

15. The method according to any one of claims 11 to 14, wherein, Predicting the operating conditions of the membrane filtration system includes identifying the problems that need to be addressed to maintain the normal operation of the membrane filtration system.

16. The method according to claim 15, wherein, Identifying the problem includes detecting changes in KPI values ​​by comparing the received KPI values ​​with the corresponding baseline values ​​of the KPIs, and the relationship maps the range of KPI values ​​to a predefined problem to be addressed.

17. The method according to any one of claims 11 to 16, further comprising: Receive feedback information, the feedback information including predicted operating conditions of the membrane filtration system obtained by performing the evaluation once or multiple times; as well as The received feedback information is used to adjust the field-adjustable parameters.

18. The method according to claim 17, wherein, Adjusting the field-adjustable parameters includes using machine learning algorithms to adjust the field-adjustable parameters by using the received feedback information.

19. The method according to claim 18, wherein, Adjusting the field-adjustable parameters includes: Determine a measure of the difference between the actual operating conditions of the membrane filtration system and the predicted operating conditions of the membrane filtration system; and The difference measure can be reduced by adjusting one or more of the field-adjustable parameters and repeating the assessment to determine the difference measure.

20. The method of claim 19, further comprising: Repeat the assessment and determination of the difference measure until the difference measure is below a specified level.

21. The method of claim 19, further comprising: Repeat the evaluation and determine the specified number of times the difference measure is specified.

22. The method of claim 19, further comprising: The evaluation and determination of the difference measure are repeated until any further reduction in the difference measure is considered negligible.

23. The method of claim 19, further comprising: The repeated execution of the evaluation and determination of the difference measure is stopped in response to the stop command.

24. A non-transitory computer-readable storage medium including instructions, which, when executed by a system, cause the system to perform a method for evaluating the performance of a membrane filtration system configured to pass feed water through one or more semi-permeable membranes to separate the feed water into concentrate and permeate, the method comprising: Receive values ​​for key performance indicators (KPIs), which include parameters indicating the performance of the membrane filtration system; The operating conditions of the membrane filtration system are predicted by performing an evaluation using received KPI values, the relationship between the KPIs and predefined operating conditions of the membrane filtration system, and field-adjustable parameters. Receive user commands; as well as Adjust the field-adjustable parameters using the received user commands.

25. The non-transitory computer-readable storage medium according to claim 24, wherein, The method further includes: Receive feedback information, including predicted operating conditions of the membrane filtration system obtained from performing the evaluation once or multiple times; and The received feedback information is used to adjust the field-adjustable parameters.

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