Water quality monitoring in fire suppression systems

Abstract

Disclosed herein is a system for monitoring water quality in a water distribution line. The system comprises one or more water quality monitoring device(s) installed at predefined locations in the water line. The individual devices comprises a pair of probe sensors, and an electrochemical impedance spectroscopy (EIS) measurement unit operatively connected to the probe sensors, wherein the EIS measurement unit is configured to determine the impedance of the water between the pair of probe sensors and correspondingly generate an EIS response for the water. The system further comprises a computing unit that is configured to receive, from the individual devices, the EIS response associated with the water present at the predefined locations, and analyze, using the machine learning models, the received EIS responses to determine one or more attributes associated with the water present at the predefined locations, wherein the attributes comprise pH, conductivity, chloride concentration, and / or sulfate concentration.

Description

WATER QUALITY MONITORING IN FIRE SUPPRESSION SYSTEMSCROSS-REFERENCE TO RELATED APPLICATIONSThis application claims the benefit of US provisional patent application number 63 / 652305 filed May 28, 2024, the entire contents of which are incorporated herein by reference.BACKGROUND

[0001] Embodiments described herein relate to water quality monitoring systems and more particularly, to a system, device, and method for monitoring water quality in fire suppression systems.SUMMARY

[0002] Disclosed herein is a system for monitoring water quality in a water distribution line associated with an area of interest (AOI). The system comprises one or more water quality monitoring devices configured to be removably installed at predefined locations in the water distribution line. The individual ones of the devices comprises a pair of probe sensors configured to be at least partially disposed in the water distribution line upon installation of the corresponding device in the water distribution line, such that the pair of probe sensors remains in contact with water present in the water distribution line at the corresponding locations, and an electrochemical impedance spectroscopy (EIS) measurement unit operatively connected to the pair of probe sensors, wherein the EIS measurement unit is configured to determine an impedance of the water between the pair of probe sensors and correspondingly generate an EIS response for the water at the corresponding location. The system further comprises a computing unit in communication operatively connected to the one or more devices, wherein the computing unit is configured with one or more machine learning models and comprises one or more processors coupled to a memory storing instructions which when executed by the processors causes the computing unit to receive, from individual ones of the devices, the EIS response associated the water present at the predefined locations, and analyze, using the one or more machine learning models, the received EIS responses to determine one or more attributes associated with the water present at the predefined locations, wherein the one or more attributes comprises one or more of pH, conductivity, chloride concentration, and sulfate concentration.

[0003] In one or more embodiments, the EIS measurement unit is configured to supply an AC voltage signal of a predefined amplitude across the pair of probe sensors; regulate or vary frequency of the supplied AC voltage signal in a predefined frequency range, monitor a current,flowing between the pair of probe sensors, associated with the one or more frequencies in the predefined frequency range and correspondingly determine the impedance of the water between the pair of probe sensors, and generate the EIS response of the water at the corresponding location based on the determined impedance.

[0004] In one or more embodiments, the computing unit is configured to match the received EIS responses with pre-stored EIS responses associated with a plurality of known water samples having one or more known attributes, and correspondingly determine the one or more attributes associated with the water present at the predefined locations upon a positive matching.

[0005] In one or more embodiments, the computing unit is configured to determine a quality index of the water present at the predefined locations based on the level of the determined attributes in the water.

[0006] In one or more embodiments, the computing unit is configured to generate an alarm signal upon detection of the level of the determined attributes to exceed a predefined range.

[0007] In one or more embodiments, the computing unit is configured to train the computing unit, using the one or more machine learning models, based on the EIS responses associated with the plurality of known water samples and the corresponding known attributes, wherein the machine learning models is selected from one or more of a decision tree model, a random forest model, and a neural network model.

[0008] In one or more embodiments, the water distribution line is associated with a fire suppression system of the AOI, wherein the AOI is one or more of a ship, a residential or commercial building, an open area, or an irrigation area.

[0009] In one or more embodiments, the pair of probe sensors, the EIS measurement unit, and the computing unit are enclosed in a single housing defining a shape of the device, such that a portion of the pair of probe sensors extends at least partially out of the housing to be disposed in the water to be tested.

[0010] In one or more embodiments, the pair of probe sensors, and the EIS measurement unit are enclosed in a single housing defining a shape of the device, such that a portion of the pair of probe sensors extends at least partially out of the housing to be disposed in the water to be tested, wherein the computing unit is a central server or a mobile computing device associated with one or more users, wherein the one or more devices comprise a communication unit to establish a communication channel between the devices and the computing unit.

[0011] In one or more embodiments, the computing unit is configured to actuate the one or more devices at a predefined interval to analyze the EIS response and monitor the attributes of the water at the predefined interval.

[0012] Also disclosed herein is a device for monitoring water quality. The device comprises a pair of probe sensors configured to be at least partially disposed in water to be tested, such that the pair of probe sensors remain in contact with the water, an electrochemical impedance spectroscopy (EIS) measurement unit operatively connected to the pair of probe sensors, wherein the EIS measurement unit is configured to determine an impedance of the water and correspondingly generate an EIS response for the water, and a computing unit operatively connected to the EIS measurement unit. The computing unit is configured with one or more machine learning models and comprises one or more processors coupled to a memory storing instructions which when executed by the processors causes the computing unit to receive the EIS response associated with the water, and analyze, using the one or more machine learning models, the received EIS response to determine one or more attributes associated with the water, wherein the one or more attributes comprises one or more of pH, conductivity, chloride concentration, and sulfate concentration

[0013] In one or more embodiments, the EIS measurement unit is configured to supply an AC voltage signal of a predefined amplitude across the pair of probe sensors, regulate or vary frequency of the supplied AC voltage signal in a predefined frequency range, monitor a current, flowing between the pair of probe sensors, associated with the one or more frequencies in the predefined frequency range and correspondingly determine the impedance of the water between the pair of probe sensors, and generate the EIS response of the water based on the determined impedance.

[0014] In one or more embodiments, the computing unit is configured to match the received EIS responses with pre-stored EIS responses associated with a plurality of known water samples having one or more known attributes, and correspondingly determine the one or more attributes associated with the water upon a positive matching.

[0015] In one or more embodiments, the computing unit is configured to be trained, using the one or more machine learning models, based on the EIS responses associated with the plurality of known water samples and the corresponding known attributes.

[0016] In one or more embodiments, the pair of probe sensors, the EIS measurement unit, and the computing unit are removably enclosed in a single housing defining a shape of the device, such that a portion of the pair of probe sensors extends at least partially out of the housing to be disposed in the water to be tested.

[0017] In one or more embodiments, the water to be tested is associated with a water distribution line of a fire suppression system in an area of interest (AOI), wherein the AOI is one or more of a ship, a residential or commercial building, an open area, or an irrigation area.

[0018] In one or more embodiments, the device is configured to determine a quality index of the water based on the level of the determined attributes in the water.

[0019] In one or more embodiments, the device is configured to generate an alarm signal upon detection of the level of the determined attributes to exceed a predefined range.

[0020] Further disclosed herein is a method for monitoring water quality in a water distribution line associated with an area of interest (AOI). The method comprises the steps of at least partially disposing a pair of probe sensors at a predefined location in the water distribution line, such that the pair of probe sensors remain in contact with the water at the corresponding location, determining, using an electrochemical impedance spectroscopy (EIS) measurement unit operatively connected to the pair of probe sensors, an impedance of the water and correspondingly generating an EIS response for the water, and analyzing, by a computing unit, the generated EIS response to determine one or more attributes associated with the water present at the predefined location, wherein the one or more attributes comprises one or more of pH, conductivity, chloride concentration, and sulfate concentration

[0021] In one or more embodiments, the method of generating the EIS response comprises the steps of supplying, by the EIS measurement unit, an AC voltage signal of a predefined amplitude across the pair of probe sensors; regulating or varying, by the EIS measurement unit, frequency of the supplied AC voltage signal in a predefined frequency range, monitoring, by the EIS measurement unit, a current, flowing between the pair of probe sensors, associated with the one or more frequencies in the predefined frequency range and correspondingly determining the impedance of the water between the pair of probe sensors, and generating, by the EIS measurement unit, the EIS response of the water based on the determined impedance.

[0022] In one or more embodiments, the method comprises the steps of matching the received EIS responses with pre-stored EIS responses associated with a plurality of known water samples having one or more known attributes, determining the one or more attributes associated with the water present at the predefined location upon a positive matching, and determining a quality index of the water present at the predefined location based on the level of the determined attributes in the water.

[0023] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, furtheraspects, embodiments, features, and techniques of the invention will become more apparent from the following description taken in conjunction with the drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings are included to provide a further understanding of the subject disclosure of this invention and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the subject disclosure and, together with the description, serve to explain the principles of the subject disclosure.

[0025] In the drawings, similar components and / or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.

[0026] FIGs. 1A and IB illustrate exemplary representation of a device for monitoring water quality of a water sample, in accordance with one or more embodiments of the invention.

[0027] FIG. 1C illustrates an exemplary block diagram depicting functional modules of the device of FIG. 1 A, in accordance with one or more embodiments of the invention.

[0028] FIGs. ID and IE illustrate exemplary representation depicting the device of FIG. 1A and IB being fitted in a water distribution line, in accordance with one or more embodiments of the invention.

[0029] FIG. 2A illustrates an exemplary schematic representation of a system for monitoring water quality in a water distribution line associated with an area of interest or a fire suppression system, in accordance with one or more embodiments of the invention.

[0030] FIG. 2B illustrates an exemplary schematic representation of a fire suppression system equipped with the system of FIG. 2A, in accordance with one or more embodiments of the invention

[0031] FIG. 3 illustrates an exemplary schematic representation of a method for monitoring the water quality of a water sample in a water distribution line, in accordance with one or more embodiments of the invention.

[0032] FIG. 4A illustrates an exemplary plot depicting modulus impedance data for a good water sample and a bad water sample.

[0033] FIG. 4B illustrates an exemplary plot depicting phase angle impedance data for a good water sample and a bad water sample.DETAILED DESCRIPTION

[0034] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the subject disclosure as defined by the appended claims.

[0035] Various terms are used herein. To the extent a term used in a claim is not defined below, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.

[0036] In the specification, reference may be made to the spatial relationships between various components and to the spatial orientation of various aspects of components as the devices are depicted in the attached drawings. However, as will be recognized by those skilled in the art after a complete reading of the subject disclosure, the components of this invention described herein may be positioned in any desired orientation. Thus, the use of terms such as “above,” “below,” “upper,” “lower,” “first,” “second” or other like terms to describe a spatial relationship between various components or to describe the spatial orientation of aspects of such components should be understood to describe a relative relationship between the components or a spatial orientation of aspects of such components.

[0037] Fire suppression systems are important safety components employed in ships and residential or commercial buildings, designed to extinguish fires quickly and efficiently. These systems typically operate by dispersing water, foam, or other extinguishing agents through a network of pipes and nozzles to the site of a fire. In many cases, especially for systems utilizing water as the primary extinguishing agent, the water remains stagnant in the pipes for extended periods between maintenance checks or actual use in fire suppression. Over time, the quality of this stagnant water may degrade, leading to several issues.

[0038] One of the concerns may be the internal corrosion of the water lines, which may result from factors such as incorrect pH levels, high conductivity, and elevated concentrations of chloride and sulfate ions. This corrosion not only compromises the structural integrity of the fire suppression system but may also lead to the accumulation of corrosion products (e.g., rust and other particulates) within the pipes. These products may obstruct the flow of water or extinguishing agents, thereby impeding the effectiveness and reliability of the fire suppression system during critical emergencies.

[0039] There is, therefore, a need to overcome the above-mentioned limitations and drawbacks, by providing a solution that may monitor the water quality in fire suppression systems in real-time or periodically in an accurate and efficient way to determine the level of pH, conductivity, and concentration of sulfate and chloride.

[0040] Referring to FIGs. 1A to IE, a water quality monitoring device “device” 100 for monitoring the quality of a water sample is disclosed. In one or more embodiments, the water sample to be tested may be associated with a water distribution line of a fire suppression system installed in an area of interest (AOI). The AOI may be one or more of a ship, a residential or commercial building, an open area, or an irrigation area, but not limited to the like. However, the water sample to be tested may also be any other water body such as but not limited to the water supply line of buildings, and industries, lakes, ponds, rivers, and tap water.

[0041] The device 100 may include a pair of probe sensors Pl, P2 (collectively designated as 102, hereinafter), and an electrochemical impedance spectroscopy (EIS) measurement unit 104 (also referred to as EIS measurement device 104, herein) being enclosed in a housing or enclosure 110 such that a portion of the pair of probe sensors Pl, P2 (102) extends at least partially out of the housing 110, however, the EIS measurement unit 104 remains away and protected from ingression of the water. The device 100 may be configured to be at least partially disposed or immersed in the water sample to be tested as shown in FIGs. ID to 2, such that only the pair of probe sensors Pl, P2 (102) remain in contact with the water. Further, the EIS measurement unit 104 may be operatively connected to the pair of probe sensors 102 and further configured to determine the impedance of the water and correspondingly generate an EIS response for the water.

[0042] In one or more embodiments, the EIS measurement unit 104 may be configured to supply an AC voltage signal of a predefined amplitude across the pair of probe sensors Pl, P2 (102) and further regulate or vary the frequency of the supplied AC voltage signal in a predefined frequency range. The EIS measurement unit 104 may then monitor a current, flowing between the pair of probe sensors Pl, P2, associated with the one or more frequencies in the predefined frequency range and correspondingly determine the impedance of the water between the pair of probe sensors Pl, P2 (102) and generate the EIS response of the water based on the determined impedance.

[0043] The device 100 may further include a computing unit 106 (also referred to as a computing device 106, herein) connected to the EIS measurement unit 104, which may be enclosed in or removably attached to the same housing 110 of the device 100 such that the EIS measurement unit 104 and the computing unit 106 remain away and protected from anyingression of water. However, in some embodiments, the computing unit 106 may not be an integral part of the device 100 or the housing 110 but may remain in communication with the EIS measurement unit 104 (as shown in FIGs. 2A anf 2B) to receive the EIS response data.

[0044] In one or more embodiments, the computing unit 106 may be configured with one or more machine learning models 106-4 and may further comprise one or more processors 106-1 coupled to a memory 106-2 storing instructions which when executed by the processors 106-1 causes the computing unit 106 to perform one or more designated operations. A data collection and storage module 106-2 may enable the computing unit 106 to receive the EIS response associated with the water being generated by the EIS measurement unit 104. Further, a prediction and decision presenting module 106-5 may enable the computing unit 106 to analyze the received EIS response to determine one or more attributes associated with the water present at the predefined locations, and further display the monitored attributes. In one or more embodiments, the determined attributes of the water may comprise one or more of pH, conductivity, chloride concentration, and sulfate concentration.

[0045] Accordingly, the device 100 or computing unit 106 may determine a quality index of the water in real-time or periodically based on the level of the determined attributes in the water. For instance, when the level of the attributes of the water is within safe limits, the device 100 may categorize the tested water as good quality. Similarly, when the level of the attributes of the water is outside of the safe limits, the device 100 may categorize the tested water as poor quality. However, when the level of the attributes of the water is in extreme danger limits, the device 100 may categorize the tested water as extremely poor quality. Further, in one or more embodiments, the device 100 may be configured to generate an alarm signal upon detection of the level of the determined attributes to exceed a predefined range (safe limits).

[0046] In one or more embodiments, the computing unit 106 may be configured to match the received EIS responses with pre-stored EIS responses associated with a plurality of known water samples having one or more known attributes stored in a database associated with the device 100. The computing unit 106 may accordingly determine the attributes associated with the water upon a positive matching of the received EIS responses with any of the pre-stored EIS responses. FIG. 4 A illustrates an exemplary plot depicting modulus impedance data for a good water sample and a bad water sample. Further, FIG. 4B illustrates an exemplary plot depicting phase angle impedance data for a good water sample and a bad water sample.

[0047] In addition, in one or more embodiments, the computing unit 106 may be configured to be trained, using the machine learning models 106-4, to determine the attributes of the water, based on the EIS responses associated with the plurality of known water samples and thecorresponding known attributes. Further, the computing unit 106 may also be trained in realtime, using the machine learning models 106-4, based on the new EIS responses associated with the new water samples being tested and their corresponding determined attributes. This may help expand the database of the device 100 and enhance the attributes and water quality determining capability of the device 100. In one or more embodiments, the machine learning models 106-4 may be selected from one or more of a decision tree model, a random forest model, and a neural network model, but not limited to the like.

[0048] In one or more embodiments, as shown in FIGs. ID and IE, the housing 110 of the device 100 may additionally include a mounting fixture 112 that may facilitate attachment of the device 100 to a connection port 202 at different locations in the water distribution line, such that the device 100 remains secured to the water line with the probe sensors 102 of the device 100 being submerged or in contact with the water present inside the water line. In such embodiments, the device 100 may be kept secured to the water line for monitoring the water quality for a longer period. However, in other embodiments, one end of the device 100 may be held disposed in the water line for a predefined time such that probe sensors 102 of the device 100 come in contact with the water within the water line for monitoring the water quality at that instant in real-time. The detailed architecture and operation for monitoring the water quality in a water distribution line 204 associated with a fire suppression system have been described later in conjunction with FIGs. 2A and 2B. Further, in one or more embodiments, the device 100 may be disposed in other water samples by simply immersing an end of the device 100 in the water sample for a predefined time such that probe sensors 102 of the device 100 remain submerged or in contact with the water sample, allowing the EIS measurement unit 104 to generate the EIS response of the water. Once the EIS response is generated, the device 100 may be removed from the water sample and the computing unit 106 may determine the attributes and water quality index of the water sample in real-time or periodically.

[0049] Referring to FIG. 2A, a system 200 for monitoring water quality in a water distribution line 204 associated with an area of interest (AOI) is disclosed. In one or more embodiments, the water distribution line 204 may be associated with a fire suppression system 200A installed on one or more of a ship, a residential or commercial building, an open area, an irrigation area, but not limited to the like. Referring to FIG. 2B, the fire suppression system 200A may include a water distribution line 204 that may be connected to a water tank 206-1 and / or a water body 206-2, such as but not limited to the sea, a pond, a river, or a dam. The water distribution line 204 may include a set of conduits 204-1 to 204-N that extend from the water tank 206-1 and water body 206-2, and further distribute and extend towards one or more locations in the AOI.In addition, one or more sprinklers 208 may be fluidically connected to one or more connection ports 202 provided in the conduits 204-1 to 204-N extending into the AOI. Further, the fire suppression system 200A may include one or more pumps 210 and one or more flow control valves 212 to enable and control the flow of water from the water tank 206-1 and / or water body 206-2 towards different locations of the AOI, where the sprinklers 208 may spray or discharge the water in the event of a fire at the corresponding locations. Referring back to FIGs. 2A and 2B, the system 200 may include one or more water quality monitoring devices 100-1 to 100-N of FIG. 1 A to IE, where the devices 100-1 to 100-N may be installed on connections ports 202 at predefined locations in the water distribution line 204.

[0050] The individual ones of these devices 100-1 to 100-N may include a pair of probe sensors Pl, P2 (102) configured to be at least partially disposed in the water distribution line upon installation of the corresponding device 100 in the water distribution line 204, such that the pair of probe sensors 102 remains in contact with water present in the water distribution line at the corresponding locations. These devices 100-1 to 100-N may further include an electrochemical impedance spectroscopy (EIS) measurement unit 104 operatively connected to the pair of probe sensors 102, where the EIS measurement unit 104 may be configured to determine the impedance of the water between the pair of probe sensors 102 and correspondingly generate an EIS response for the water at the corresponding location.

[0051] Further, the system 200 may include a single computing unit 106 that may be in communication with the ELS measurement unit 104 operatively connected to the devices 100- 1 to 100-N. The computing unit 106 may be configured with one or more machine learning models 106-4 and may comprise one or more processors 106-1 coupled to a memory 106-2 storing instructions which when executed by the processors 106-1 causes the computing unit 106 to perform one or more designated operations. Accordingly, the computing unit 106 may receive, from individual ones of the devices 100-1 to 100-N, the EIS response associated with the water present at the predefined locations in the water line 204, and further analyze the received EIS responses to determine one or more attributes such as pH, conductivity, chloride concentration, and a sulfate concentration associated with the water present at the predefined locations in the water line 204. The computing unit 106 may then determine a quality index of the water present at the predefined locations in the water line 204 in real-time or periodically based on level of the determined attributes in the water.

[0052] In one or more embodiments, the pair of probe sensors 102, and the EIS measurement unit 104 may be enclosed in a single housing 110 defining the shape of the device 100, such that a portion of the pair of probe sensors 102 extends at least partially out of the housing 110to be disposed in the water to be tested. Further, the computing unit 106 may be a central server or a mobile computing device (also designated as 106 in FIGs. 2A) associated with one or more users. In such embodiments, the devices 100-1 to 100-N may include a communication unit 108 that enables the devices 100-1 to 100-N to establish a communication channel with the computing unit / server / mobile computing device 106 via a network. However, in some embodiments, the pair of probe sensors 102, the EIS measurement unit 104, and an individual computing unit 106 may be enclosed in a single housing 110 defining the shape of the device 100. These devices 100-1 to 100-N may then be in communication with a central server or a mobile computing device 106 associated with one or more users via the network.

[0053] In one or more embodiments, when the level of the attributes of the water at the predefined locations in the water line 204 is detected to be within safe limits, the system 200 may categorize the water present in the water line 204 as good quality. Further, when the level of the attributes of the water at any of the predefined locations in the water line 204 is detected to be outside of the safe limits, the system 200 may categorize the water at such locations to be of poor quality. Furthermore, when the level of the attributes of the water at any of the predefined locations in the water line 204 is detected to be in extreme danger limits, the system 200 may categorize the water at such locations to be of extremely poor quality. Accordingly, in one or more embodiments, the computing unit 106 may be configured to generate an alert signal indicating the water quality index at different locations in the water line in real-time or periodically. This may allow users to identify good and bad water quality zones and further treat the water in the water line 204 at the bad water quality locations. Further, in one or more embodiments, the device 100 may be configured to generate an alarm signal upon detection of the level of the determined attributes to exceed a predefined range (safe limits).

[0054] It is to be understood that while various embodiments are described herein in relation to multiple devices 100-1 to 100-N in the system 200, other embodiments consistent with this invention may also be applicable and considered without any limitations, and all such embodiments are well within the scope of this invention. For example, the teachings of the subject disclosure may be applicable in configurations where only one such device is present. Additionally, the teachings of the subject disclosure may be applicable in configurations where multiple devices may be present, but the system may independently control which devices are to be kept operational or non-operational at any given time, thereby determining which devices may actively perform measuring or not measuring. Furthermore, the teachings of the subject disclosure may also be applicable in configurations with multiple devices where only one device may be employed in measurement at any particular moment.

[0055] Referring to FIG. 3, method 300 for monitoring water quality in a water distribution line associated with an area of interest (A 01) is disclosed. Method 300 may involve the device 100(s) of FIG. 1 and the components associated with the system 200 of FIG. 2. In one or more embodiments, method 300 may include step 302 of at least partially disposing a pair of probe sensors at a predefined location in the water distribution line, such that the pair of probe sensors remains in contact with the water at the corresponding location. The predefined location may be the location where the quality of the water is to be tested. Method 300 may further include step 304 of determining, using an electrochemical impedance spectroscopy (EIS) measurement unit operatively connected to the pair of probe sensors, an impedance of the water and correspondingly generating an EIS response for the water. Further, method 300 may include step 306 of analyzing, by a computing unit, the EIS response generated at step 304 to determine one or more attributes associated with the water present at the predefined location, where the attributes may comprise one or more of pH, conductivity, chloride concentration, and a sulfate concentration.

[0056] In one or more embodiments, at step 304, method 300 may involve the steps of supplying, by the EIS measurement unit, an AC voltage signal of a predefined amplitude across the pair of probe sensors followed by regulating or varying, by the EIS measurement unit, frequency of the supplied AC voltage signal in a predefined frequency range. Further, at step 304, method 300 may involve monitoring, by the EIS measurement unit, a current, flowing between the pair of probe sensors, associated with the one or more frequencies in the predefined frequency range and correspondingly determining the impedance of the water between the pair of probe sensors. Accordingly, at step 304, the EIS measurement unit may generate the EIS response of the water based on the determined impedance.

[0057] Further, in one or more embodiments, at step 306, method 300 may include steps of matching the EIS responses generated at step 304 with pre-stored EIS responses associated with a plurality of known water samples having one or more known attributes and correspondingly determining the one or more attributes associated with the water present at the predefined location upon a positive matching. Later, method 300 may include step 310 of determining a quality index of the water present at the predefined location based on the level of the determined attributes in the water.

[0058] Thus, this invention overcomes the limitations and drawbacks associated with existing water quality monitoring processes, by providing a solution in the form of a device 100 and system 200 that may monitor the water quality in fire suppression systems in real-time orperiodically in an accurate and efficient way to determine the level of pH, conductivity, and concentration of sulfate and chloride.

[0059] In one or more embodiments, the device(s) 100 of FIGs. 1 A to 2 may include a battery (not shown) removably enclosed within the housing 110. The battery may be configured to supply electrical power to the EIS measuring unit 104, the communication unit 108, and the computing unit 106. Further, the device 100 may be configured to generate a battery status signal indicative of the charging level of the battery.

[0060] In one or more embodiments, the computing unit 106 may be configured on a printed circuit board (PCB) that may be securely positioned in the housing 110. Further, the probe sensors 102, the EIS measurement unit 104, the communication unit 108, and the battery may also be connected to the PCB and enclosed within the housing 110. In one or more embodiments, the PCB may accommodate the computing unit 106 that may be a microcontroller, but is not limited to the like, where the computing unit 106 may be configured to execute predefined operations. In one or more embodiments, the computing unit 106 may be but is not limited to a Radio System on Chip with Ultra-Low Energy Technology which is a highly integrated ultra-low-power Bluetooth Low Energy (BLE) microcontroller (MCU), which may be available in miniature packages and suitable for space-constrained applications where size and weight are critical considerations. However, in other embodiments, the computing unit 106 may include, but is not limited to an Arduino Nano, an ESP32-Pico-D4, and an ATtiny85. Further, in one or more embodiments, the communication unit 108 may be a Bluetooth module but is not limited to the like. However, in other embodiments, the communication unit 108 may also be a low-power radio frequency transceiver.

[0061] In one or more embodiments, the computing unit 106 may comprise the one or more processor(s) 106-1 that may be implemented as one or more microprocessors, microcomputers, microcontrollers, edge or fog microcontrollers, digital signal processors, central processing units, logic circuitries, and / or any devices that process data based on operational instructions. Among other capabilities, the one or more processor(s) 106-1 may be configured to fetch and execute computer-readable instructions stored in a memory of the computing unit 106. The memory 106-2 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory 106-1 may comprise any non-transitory storage device including, for example, volatile memory such as Random Access Memory (RAM), or non-volatile memory such as Erasable Programmable Read-Only Memory (EPROM), flash memory, and the like.

[0062] The computing unit 106 may include an interface(s). The interface(s) may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as I / O devices, storage devices, and the like. The interface(s) may facilitate communication to / from the processing unit. The interface(s) may also provide a communication pathway for one or more components of the computing unit 106. Examples of such components include, but are not limited to, processing unit / engine(s) and a local database. Further, the processing unit / engine(s) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) may be processor-executable instructions stored on a non- transitory machine -readable storage medium and the hardware for the processing engine(s) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s). In such examples, the computing unit 106 may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system and the processing resource. In other examples, the processing engine(s) may be implemented by electronic circuitry.

[0063] In one or more embodiments, the local database may comprise data that may be either stored or generated as a result of functionalities implemented by any of the components of the processors or the processing engines. In an embodiment, the local database may be separate from the computing unit 106.

[0064] In one or more embodiments, one or more machine learning models 106-4 may be used to train the computing unit 106 described herein. The machine learning models 106-4 may use one or more of supervised learning, semi-supervised, unsupervised learning, reinforcement learning, and / or other machine learning techniques). In one or more embodiments, the machine learning models 106-4 may include decision trees, random forest learning, support vector machines, regression analysis, Bayesian networks, dimensionality reduction algorithms, boosting algorithms, artificial neural networks (e.g., fully connected neural networks, deep convolutional neural networks, or recurrent neural networks), deep learning, and / or other machine learning models 106-4.

[0065] For example, in one or more embodiments, the machine learning models 106-4 may use unsupervised learning algorithms to train the one or more models (e.g., the decision tree or random forest learning). For example, in one or more embodiments, unsupervised learning algorithms may be configured to use input data that is not labeled, classified, or categorized. The unsupervised learning algorithms may be configured to identify similarities in the input data and to group new data based on the presence or absence of the identified similarities. Using unsupervised learning algorithms may be beneficial because it may allow for discovering hidden trends and patterns or extracting data features from the input data that would have been difficult to obtain if other techniques were used.

[0066] It is to be understood that the machine learning models 106-4 are described here as examples for techniques for identifying causes of defrost failures and determining corrective actions. However, other techniques, are also contemplated by the subject disclosure. As such, any computer-implemented techniques, or other machine-learning techniques for analyzing EIS response and determining the attributes (pH, conductivity, chloride concentration, and sulfate concentration) associated with the water are contemplated by the subject disclosure.

[0067] While the invention has been described with reference to exemplary embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention as defined by the appended claims. Modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention includes all embodiments falling within the scope of the invention as defined by the appended claims.

[0068] In interpreting the specification, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C . . ..and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.

Claims

CLAIMS1. A system for monitoring water quality in a water distribution line associated with an area of interest (AOI), the system comprising: one or more water quality monitoring devices configured to be removably installed at predefined locations in the water distribution line; wherein individual ones of the devices comprises: a pair of probe sensors configured to be at least partially disposed in the water distribution line upon installation of the corresponding device in the water distribution line, such that the pair of probe sensors remain in contact with water present in the water distribution line at the corresponding locations; and an electrochemical impedance spectroscopy (EIS) measurement unit operatively connected to the pair of probe sensors, wherein the EIS measurement unit is configured to determine an impedance of the water between the pair of probe sensors and correspondingly generate an EIS response for the water at the corresponding location; and a computing unit in communication with the EIS measurement unit operatively connected with the one or more devices, wherein the computing unit is configured with one or more machine learning models and comprises one or more processors coupled to a memory storing instructions which when executed by the processors causes the computing unit to: receive, from individual ones of the devices, the EIS response associated with the water present at the predefined locations; and analyze, using the one or more machine learning models, the received EIS responses to determine one or more attributes associated with the water present at the predefined locations, wherein the one or more attributes comprise one or more of pH, conductivity, chloride concentration, and sulfate concentration.

2. The system of claim 1, wherein the EIS measurement unit is configured to: supply an AC voltage signal of a predefined amplitude across the pair of probe sensors; regulate or vary frequency of the supplied AC voltage signal in a predefined frequency range; monitor a current, flowing between the pair of probe sensors, associated with the one or more frequencies in the predefined frequency range and correspondingly determine the impedance of the water between the pair of probe sensors; andgenerate the EIS response of the water at the corresponding location based on the determined impedance.

3. The system of any one of claims 1 and 2, wherein the computing unit is configured to match the received EIS responses with pre-stored EIS responses associated with a plurality of known water samples having one or more known attributes, and correspondingly determine the one or more attributes associated with the water present at the predefined locations upon a positive matching.

4. The system of any one of claims 1 to 3, wherein the computing unit is configured to determine a quality index of the water present at the predefined locations based on the level of the determined attributes in the water.

5. The system of any one of claims 1 to 4, wherein the computing unit is configured to generate an alarm signal upon detection of the level of the determined attributes to exceed a predefined range.

6. The system of any one of claims 1 to 5, wherein the computing unit is configured to train the computing unit, using the one or more machine learning models, based on the EIS responses associated with the plurality of known water samples and the corresponding known attributes, wherein the machine learning models is selected from one or more of a decision tree model, a random forest model, and a neural network model.

7. The system of any one of claims 1 to 6, wherein the water distribution line is associated with a fire suppression system of the AOI, and wherein the AOI is one or more of a ship, a residential or commercial building, an open area, or an irrigation area.

8. The system of any one of claims 1 to 7, wherein the pair of probe sensors, the EIS measurement unit, and the computing unit are enclosed in a single housing defining a shape of the device, such that a portion of the pair of probe sensors extends at least partially out of the housing to be disposed in the water to be tested.

9. The system of any one of claims 1 to 7, wherein the pair of probe sensors, and the EIS measurement unit are enclosed in a single housing defining a shape of the device, such that a portion of the pair of probe sensors extends at least partially out of the housing to be disposed in the water to be tested, wherein the computing unit is a central server or a mobile computing device associated with one or more users, wherein the one or more devices comprise a communication unit to establish a communication channel between the devices and the computing unit.

10. The system of any one of claims 1 to 9, wherein the computing unit is configured to actuate the one or more devices at a predefined interval to analyze the EIS response and monitor the attributes of the water at the predefined interval.

11. A device for monitoring water quality, the device comprising: a pair of probe sensors configured to be at least partially disposed in water to be tested, such that the pair of probe sensors remain in contact with the water; an electrochemical impedance spectroscopy (EIS) measurement unit operatively connected to the pair of probe sensors, wherein the EIS measurement unit is configured to determine an impedance of the water and correspondingly generate an EIS response for the water; and a computing unit operatively connected to the EIS measurement unit, the computing unit configured with one or more machine learning models and comprises one or more processors coupled to a memory storing instructions which when executed by the processors causes the computing unit to: receive the EIS response associated with the water; and analyze, using the one or more machine learning models, the received EIS response to determine one or more attributes associated with the water, wherein the one or more attributes comprise one or more of pH, conductivity, chloride concentration, and sulfate concentration.

12. The device of claim 11, wherein the EIS measurement unit is configured to: supply an AC voltage signal of a predefined amplitude across the pair of probe sensors; regulate or vary the frequency of the supplied AC voltage signal in a predefined frequency range; monitor a current, flowing between the pair of probe sensors, associated with the one or more frequencies in the predefined frequency range and correspondingly determine the impedance of the water between the pair of probe sensors; and generate the EIS response of the water based on the determined impedance.

13. The device of any one of claims 11 and 12, wherein the computing unit is configured to match the received EIS responses with pre-stored EIS responses associated with a plurality of known water samples having one or more known attributes, and correspondingly determine the one or more attributes associated with the water upon a positive matching.

14. The device of any one of claims 11 to 13, wherein the computing unit is configured to be trained, using the one or more machine learning models, based on the EIS responses associated with the plurality of known water samples and the corresponding known attributes.

15. The device of any one of claims 11 to 14, wherein the pair of probe sensors, the EIS measurement unit, and the computing unit are removably enclosed in a single housing defining a shape of the device, such that a portion of the pair of probe sensors extends at least partially out of the housing to be disposed in the water to be tested.

16. The device of any one of claims 11 to 15, wherein the water to be tested is associated with a water distribution line of a fire suppression system in an area of interest (AOI), wherein the AOI is one or more of a ship, a residential or commercial building, an open area, or an irrigation area.

17. The device of any one of claims 11 to 16, wherein the device is configured to determine a quality index of the water based on the level of the determined attributes in the water.

18. The device of any one of claims 11 to 17, wherein the device is configured to generate an alarm signal upon detection of the level of the determined attributes to exceed a predefined range.

19. A method for monitoring water quality in a water distribution line associated with an area of interest (AOI), the method comprising: at least partially disposing a pair of probe sensors at a predefined location in the water distribution line, such that the pair of probe sensors remain in contact with the water at the corresponding location; determining, using an electrochemical impedance spectroscopy (EIS) measurement unit operatively connected to the pair of probe sensors, an impedance of the water and correspondingly generating an EIS response for the water; and analyzing, by a computing unit, the generated EIS response to determine one or more attributes associated with the water present at the predefined location, wherein the one or more attributes comprise one or more of pH, conductivity, chloride concentration, and sulfate concentration.

20. The method of claim 19, wherein the method of generating the EIS response comprises the steps of: supplying, by the EIS measurement unit, an AC voltage signal of a predefined amplitude across the pair of probe sensors; regulating or varying, by the EIS measurement unit, the frequency of the supplied AC voltage signal in a predefined frequency range; monitoring, by the EIS measurement unit, a current, flowing between the pair of probe sensors, associated with the one or more frequencies in the predefined frequency range andcorrespondingly determining the impedance of the water between the pair of probe sensors; and generating, by the EIS measurement unit, the EIS response of the water based on the determined impedance.

21. The method of claim 19, wherein the method comprises the steps of: matching the received EIS responses with pre-stored EIS responses associated with a plurality of known water samples having one or more known attributes; determining the one or more attributes associated with the water present at the predefined location upon a positive matching; and determining a quality index of the water present at the predefined location based on level of the determined attributes in the water.

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