IoT-enabled deionization tank configured with artificial intelligence algorithms

Abstract

A method of treating water in a water treatment system, comprising: introducing water to be treated into an ion exchange bed of the water treatment system to produce treated water; receiving an output water quality indication from a controller associated with the ion exchange bed; in response to the output water quality indication, determining by an algorithm whether to replace the ion exchange bed based on a remaining capacity of the ion exchange bed, current operating parameters of the water treatment system, and historical data regarding operation of the water treatment system; and in response to the water quality indication, providing by the algorithm a recommendation to a service provider of the water treatment system that one of no action is needed, the ion exchange bed should be monitored, or a service order to replace the ion exchange bed should be generated.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to U.S. Patent Application No. 63 / 135,778, filed January 11, 2021, entitled “IoT Enabled Deionization Tank Configuration AI Algorithm,” which is incorporated herein by reference in its entirety for all purposes.

[0003] background

[0004] open field

[0005] The aspects and embodiments disclosed herein generally relate to methods and apparatus for monitoring, controlling and maintaining water treatment systems, and particularly to systems and methods for monitoring the condition of ion exchange-based water treatment systems.

[0006] Discussion of related technologies

[0007] Deionized (DI) water is a component in hundreds of applications, including medical, laboratory processes, pharmaceuticals, cosmetics, electronics manufacturing, food processing, electroplating, countless industrial processes, and even spotless rinsing water at local car washes. It is commonly used as an ultrapure ingredient, a cleaning solvent, or as the basis for process water recycling / reuse strategies. Deionized water meeting Water for Injection (WFI) purity standards is used as the base for saline and other solutions injected into the body during medical procedures. Its sterile and mineral-free purity helps ensure the quality and stability of the solutions when other components are added. DI laboratory water is commonly used to clean instruments and laboratory equipment, as well as to perform tissue and cell cultures, blood separation, and other laboratory procedures. In the pharmaceutical industry, deionized water is used to prepare culture media, formulate aqueous solutions, and clean containers and equipment. It is also used as a raw material, ingredient, and solvent in the processing, formulation, and manufacture of pharmaceuticals and nutritional products, active pharmaceutical ingredients (APIs) and intermediates, pharmacopoeia products, and analytical reagents. In semiconductor manufacturing, the mineral-absorbing, detergent-enhancing, and residue-free drying properties of deionized water make it suitable for rinsing and cleaning semiconductor wafers. It is also used in wet etching, bacterial testing, and many other processes throughout manufacturing facilities. Deionized water is commonly used for filling lead-acid batteries, cooling systems, and other applications. In many hair care, skin care, body care, baby care, sunscreen, and cosmetic products, deionized water is frequently used as an ingredient to increase purity, stability, and performance, where it is sometimes referred to as "aqua" on the product ingredient label. Due to its high relative permittivity, deionized water is used as a high-voltage dielectric in many pulsed power applications used in energy research. Deionized water is used both as an ingredient and a process element in food and beverage processing. As an ingredient, it provides stability, purity, and hygiene. As a process element, it contributes to effective hygiene. In plants, DI water facilitates the recycling of water and wastewater; increases the efficiency and lifespan of boilers and steam processes. Deionized water is used to pretreat boiler feedwater to reduce scaling and energy consumption, and to control deposits, residues, and corrosion in boiler systems. Therefore, DI water is an essential element in boiler water recycling. Deionized water can pre-treat cooling tower makeup water to help reduce scaling and energy consumption in power plants, refineries, petrochemical plants, natural gas processing plants, food processing plants, semiconductor plants, and other industrial facilities. When used as a rinse after washing cars, windows, and similar applications, deionized spot-free rinse water dries without leaving stains caused by dissolved solutes, eliminating the need for post-wash wiping.

[0008] For example, flow meters, conductivity and resistivity meters, temperature sensors, pH sensors, hydrogen sulfide sensors, and other scientific instruments are widely used in many remote locations for a variety of purposes, including monitoring the condition of water purification systems. Personnel often need to physically visit remote sites to monitor flow meters or other instruments (such as samplers) to collect data. Making multiple site visits across multiple locations is a challenging, labor-intensive, and costly task. Ensuring each site is functioning properly and regularly scheduling maintenance or service helps obtain accurate and reliable data.

[0009] Overview

[0010] According to one aspect, a method for treating water in a water treatment system is provided. The method includes: introducing water to be treated into an ion exchange bed of the water treatment system to produce treated water; receiving an output water quality indication from a controller associated with the ion exchange bed; in response to the output water quality indication, determining, by an algorithm, whether to replace the ion exchange bed based on the remaining capacity of the ion exchange bed, current operating parameters of the water treatment system, and historical data regarding the operation of the water treatment system; and in response to the water quality indication, providing, by the algorithm, a recommendation to the service provider of the water treatment system for one of the following: no action is required, the ion exchange bed should be monitored, or a replacement order for the ion exchange bed should be generated.

[0011] In some embodiments, the algorithm further provides an indication of the confidence level of the provided recommendations.

[0012] In some embodiments, the method further includes replacing the ion exchange bed in response to the algorithm indicating that it is necessary to replace the ion exchange bed.

[0013] In some embodiments, the algorithm determines the confidence level based on historical data regarding an instance of replacing the ion exchange bed or an instance of replacing the ion exchange bed in another water treatment system, the ion exchange bed alarm status, and the remaining capacity of the ion exchange bed.

[0014] In some embodiments, in response to providing a recommendation that the ion exchange bed should be monitored, the algorithm performs additional monitoring of one or more of the following: the status of the output water quality indicator, the flow rate of water through the ion exchange bed, water quality measurement, and the remaining capacity of the ion exchange bed.

[0015] In some embodiments, the algorithm modifies the recommendation that the ion exchange bed should be monitored into one of the recommendations that the ion exchange bed should be replaced or that no action needs to be taken.

[0016] In some embodiments, the additional monitoring includes receiving data multiple times per day on one or more of the following: the status of the output water quality indication, the flow rate of water through the ion exchange bed, or the water quality measurement.

[0017] In some embodiments, the current operating parameters of the water treatment system include the flow rate and conductivity of the treated water.

[0018] In some embodiments, the current operating parameters of the water treatment system also include the environmental conditions at the water treatment system.

[0019] In some embodiments, the current operating parameters of the water treatment system also include the time of year.

[0020] In some embodiments, historical data on the operation of the water treatment system includes environmental conditions at the water treatment system between previous replacement events of the ion exchange bed.

[0021] In some embodiments, historical data on the operation of the water treatment system includes the time of year for previous replacement events of the ion exchange bed.

[0022] According to another aspect, a method for treating water in a water treatment system is provided. The method includes: introducing water to be treated into an ion exchange bed of the water treatment system to produce treated water; receiving an output water quality indication from a controller associated with the ion exchange bed; in response to the output water quality indication, determining, by an algorithm, whether to replace the ion exchange bed based on the remaining capacity of the ion exchange bed, current operating parameters of the water treatment system, and historical data regarding the operation of the water treatment system; in response to the water quality indication, providing, by an algorithm, a recommendation to the service provider of the water treatment system that one of the following is necessary: ​​no action is required, the ion exchange bed should be monitored, or a replacement order for the ion exchange bed should be generated; and determining and providing a recommended ideal ion exchange capacity for the water treatment system based on the current configuration of the water treatment system, the average volume of water treated over each time period, and the previous ion exchange bed maintenance history for the water treatment system or another water treatment system.

[0023] In some embodiments, providing the recommended ideal ion exchange capacity includes providing a recommended number and size of ion exchange beds for the water treatment system, which will result in maintenance of the water treatment system's ion exchange beds after a predetermined amount of time has elapsed.

[0024] In some embodiments, the algorithm also determines and provides an estimate of the cost savings resulting from recommendations for ideal implantation ion exchange capacity.

[0025] In some embodiments, the algorithm also determines and provides a recommended current ion exchange capacity for the water treatment system based on data regarding historical ion exchange bed replacements, historical conductivity readings of water introduced into the water treatment system, and historical averages of the amount of water treated during the replacement cycle prior to a water quality alarm occurring in the water treatment system or one of the other water treatment systems.

[0026] According to another aspect, a non-transitory computer-readable medium is provided, comprising code executable on a computer, for implementing a method for monitoring water treatment in a water treatment system having an ion exchange bed. The method includes: in response to an output water quality indication from the water treatment system, determining whether to replace the ion exchange bed based on the remaining capacity of the ion exchange bed, current operating parameters of the water treatment system, and historical data regarding the operation of the water treatment system; in response to the water quality indication, providing a recommendation to the service provider of the water treatment system that one of the following is necessary: ​​no action is required, the ion exchange bed should be monitored, or a replacement order for the ion exchange bed should be generated; and determining and providing a recommended ideal ion exchange capacity for the water treatment system based on the current configuration of the water treatment system, the average volume of water treated over each time period, and previous ion exchange bed maintenance history for the water treatment system or another water treatment system.

[0027] In some embodiments, the code also enables a computer to determine and provide a recommended current ion exchange capacity for the water treatment system based on data regarding historical ion exchange bed replacements, historical conductivity readings of water introduced into the water treatment system, and historical averages of the amount of water treated during the replacement cycle prior to a water quality alarm occurring in the water treatment system or one of the other water treatment systems.

[0028] According to another aspect, a non-transitory computer-readable medium is provided, including code executable on a computer, for implementing a method for monitoring water treatment in a water treatment system having an ion exchange bed and a controller. The method includes: receiving an output water quality indication from a controller associated with the ion exchange bed; determining, in response to the output water quality indication, whether to replace the ion exchange bed based on the remaining capacity of the ion exchange bed, current operating parameters of the water treatment system, and historical data regarding the operation of the water treatment system; and, in response to the water quality indication, providing a recommendation via an algorithm to the service provider of the water treatment system that no action is required, the ion exchange bed should be monitored, or a replacement order for the ion exchange bed should be generated.

[0029] In some embodiments, the code also enables the computer to provide an indication of the confidence level of the provided recommendation. Brief description of the attached diagram

[0031] The accompanying drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component shown in different figures is represented by similar numbers. For clarity, not every component can be labeled in every drawing. In the drawings:

[0032] Figure 1 This is a flowchart of the method disclosed in this paper;

[0033] Figure 2A This is a schematic diagram of a water treatment system and its associated monitoring system;

[0034] Figure 2B This is a schematic diagram of a water treatment system;

[0035] Figure 3 This is a schematic diagram of a water treatment system and its associated monitoring system;

[0036] Figure 4 This is a schematic diagram of a data platform / monitoring system used in water treatment systems;

[0037] Figure 5 This is a schematic diagram of the overhaul of the deionized water treatment system;

[0038] Figure 6 This is a schematic diagram for water treatment system maintenance;

[0039] Figure 7 This is a table showing the reduction in revision orders achieved by implementing the artificial intelligence algorithms disclosed herein;

[0040] Figure 8 This is a table illustrating the reduction in human costs achieved by implementing the artificial intelligence algorithms disclosed herein;

[0041] Figure 9 This is a graph showing the reduction of remaining ion exchange capacity during ion exchange medium bed replacement achieved by implementing the artificial intelligence algorithm disclosed herein;

[0042] Figure 10 This is a diagram illustrating the patterns of false water quality alarms in an embodiment of a water treatment system;

[0043] Figure 11 It is a graph indicating the remaining ion exchange capacity during ion exchange medium bed replacement, implemented using the artificial intelligence algorithm disclosed herein; and

[0044] Figure 12 This is a graph illustrating patterns of false water quality alarms and cumulative water treatment volume in an embodiment of a water treatment system.

[0045] Detailed description

[0046] The aspects and embodiments disclosed herein are not limited to the details of the structure and arrangement of the components set forth in the following description or shown in the accompanying drawings. The aspects and embodiments disclosed herein can have other embodiments and can be practiced or performed in various ways.

[0047] The aspects and embodiments disclosed herein include a wireless monitoring system that enables the collection of data and monitoring of the status of various instruments, sensors, and scientific instruments at one or more locations. Data can be collected wirelessly, for example, via a GSM cellular phone network connected to a computer or handheld device using a modem, via Wi-Fi, or other wireless data collection methods known in the art (e.g., based on LTE Cat 1, LTE Cat M1, or Cat NB1 standards). In other embodiments, data can be collected from the monitoring system via a wired connection to a centralized monitoring system.

[0048] Aspects and embodiments of the wireless monitoring system can be used in the environment of a water treatment system. The water treatment system may include one or more unit operations. These one or more unit operations may include one or more pressure-driven water treatment devices (e.g., membrane filtration devices, such as nanofiltration (NF) devices, reverse osmosis (RO) devices, hollow fiber membrane filtration devices, etc.), one or more ion exchange water treatment devices, one or more electrically driven water treatment devices (e.g., electrodialysis (ED) or electrodeionization (EDI) devices), one or more chemical-based water treatment devices (e.g., chlorination or other chemical dosing devices), one or more carbon filters, one or more biologically based treatment devices (e.g., aerobic biological treatment vessels, anaerobic digesters, or biofilters), and one or more radiation-based water treatment devices (e.g., ultraviolet irradiation systems).

[0049] The wireless monitoring system uses an artificial intelligence algorithm to determine when to schedule maintenance for the ion exchange beds in the water treatment system and to provide recommendations on the capacity of the ion exchange beds.

[0050] Water treatment systems can be used to treat water for industrial purposes (e.g., for semiconductor manufacturing plants, food processing or preparation sites, for chemical processing plants), produce purified water for use as laboratory water, for medical device manufacturing or pharmaceutical production, or provide water suitable for irrigation or drinking water purposes to a site. In other embodiments, water treatment systems can be used to treat wastewater from industrial or municipal sources.

[0051] A water treatment system may include one or more sensors, probes, or instruments for monitoring one or more parameters of water entering or leaving any one or more unit operations. These sensors, probes, or instruments may include, for example, flow meters, level sensors, conductivity meters, resistivity meters, chemical concentration meters, turbidity monitors, chemical species-specific concentration sensors, temperature sensors, pH sensors, oxidation-reduction potential (ORP) sensors, pressure sensors, or any other sensors, probes, or scientific instruments that can be used to provide indications of desired characteristics or parameters of water entering or leaving any one or more unit operations.

[0052] Monitoring systems can be used to collect data from sensors, probes, or scientific instruments included in a water treatment system, and can provide the collected data to local operators of the water treatment system or to personnel far from the water treatment and monitoring system, such as water treatment system service providers.

[0053] Monitoring systems may include computer systems on which artificial intelligence (AI) algorithms are run, designed to provide output in the form of operational recommendations based on AI predictions regarding the likelihood that replacement of a resin-based deionization (DI) tank (the terms DI tank and ion exchange bed are used synonymously herein) is necessary in the water treatment system at the customer's site. Tank replacement is typically recommended once a quality sensor indicates an alarm condition, such as insufficient water quality (which is usually determined based on the conductivity of water treated in a DI bed or tank during customer use). In some cases, water quality sensors in a water treatment system may issue false positive water quality alarms due to, for example, excessive or low flow conditions, temporary spikes in incoming water contamination, or sensor malfunction. Aspects and implementations of the AI ​​algorithm can determine that the water quality alarm is "true" rather than a false alarm.

[0054] This algorithm recommends ideal DI capacity for a client's site to provide superior service (defined as annual tank replacement). DI capacity is defined as the volume of water (in gallons) that a DI system can handle before failing to meet quality specifications. For first order, the DI capacity recommendation can be based on calculations involving the number, size, and water quality of tanks. The AI ​​algorithm utilizes a hybrid calculation that considers the aforementioned DI calculations along with simulated data relating to site conditions at all previous replacements and during client use, such as environmental conditions (e.g., dry, wet, hot, or cold weather), time of year, geographical location, etc. The algorithm can revise the first-order DI capacity recommendation based on historical data (e.g., DI capacity at a site over a predetermined previous time period, e.g., over the past two years) combined with current status updates regarding water flow rate and conductivity, as well as remaining DI tank capacity. Current status updates can be processed 12 times per day—once every two hours. Historical DI capacity measurements from other sites can also be used to revise the first-order DI capacity recommendation, which can be useful for new commissioned sites with little or no historical data. By using this algorithm, quality deviation events are reduced, and the cost of maintaining high-quality DI production can be lowered while minimizing unnecessary maintenance.

[0055] In response to a water quality alarm received at a DI tank at a maintenance site, embodiments of the disclosed AI algorithm can provide the operator responsible for triaging the alarm with suggested actions such as "Create maintenance order," "No action required," and "Monitor." These suggestions can be based on factors such as historical data about site conditions at the time of a previous DI tank replacement / alternation (time of year, environmental conditions, cumulative feedwater flow through the DI tank during replacement, etc.), current site water quality (e.g., conductivity) and flow conditions, and / or site environmental conditions, and / or time of year. Embodiments of the disclosed AI algorithm can also output a "prediction accuracy %"—that is, the confidence level at which the algorithm's prediction / suggestion is true. The algorithm makes this determination based on a combination of historical data (such as the historical data described above) and data about the current state of the system, including, for example, the alarm status (activated / recovered) of the purifier / worker tank and a measured decrease in remaining capacity.

[0056] Implementations of the disclosed AI algorithm can assess all sites actively under alert multiple times a day (e.g., 12 times). The AI ​​algorithm interprets each batch of new data collected from each assessment during the day and determines whether the overall recommended "action" should be changed or should remain unchanged.

[0057] AI-based implementations analyze historical and current data from the site, suggesting actions for "no action required" or "monitoring" that could be "restored" (i.e., restored to normal – "no action required") or converted to "creating a Checkpoint Change Order (SVO)." A pre-defined but user-modifiable timeframe (e.g., four days) ensures that alerts related to low or marginal flow conditions are processed quickly, especially common in DI tank channeling. For example, four days is typically sufficient for a tank to "purify" and produce high-quality water. However, if channeling has already begun, the tank is unlikely to resume producing high-quality water.

[0058] Implementations of the disclosed AI algorithm can provide ideal DI capacity recommendations specific to the customer's site (i.e., functional location) based on the current configuration (i.e., the size and number of DI tanks), customer water treatment / usage, and previous replacement / maintenance history. Ideal DI capacity is defined as a configuration that allows for “superior” service with an annual DI tank replacement / maintenance visit.

[0059] To achieve this, the algorithm utilizes the following data properties:

[0060] Total Traffic - Customer Usage in the Past 12 Months

[0061] o DI can size - in cubic feet of resin (e.g., 1.2ft) 3 -3.6ft 3 )

[0062] Average water supply conductivity - in micro Siemens (μS / cm) 2 The unit of measurement is conductivity, which is used to infer the total dissolved solids (TDS) concentration. This measurement can be retrieved from field maintenance visits and stored as a measurement point in an AI-alterable database. Alternatively, water supply conductivity can be continuously or periodically monitored automatically at the customer's site using conductivity sensors and reporting instruments, and periodically reported to a central monitoring station and stored in an AI-alterable database, as described in further detail below.

[0063] The calculated capacity is a formulaic calculation based on the expected capacity, which is based on the DI tank size and the feedwater TDS level determined from the average feedwater conductivity measurement.

[0064] Based on the above factors, the AI ​​algorithm suggests an ideal number and size of DI tanks, which can be defined by the maximum configuration size of five cascaded DI tanks in each DI tank train (four in the worker position and one in the purifier position).

[0065] In some embodiments, Digital Control Center (DCC) operations specialists primarily monitor the maintenance deionization site and its efficiency manually, aided by data reporting tools. When a quality event is triggered, a DCC specialist conducts a 100% review and makes a decision, either creating a replacement SVO or monitoring the system that triggered the alarm for a period of time. This process, for the DCC specialist, may take several minutes to 10 minutes to conduct a comprehensive review and make a decision.

[0066] As the number of monitored sites continues to grow, the number of full-time staff (FTEs) required to review each site is also increasing. (FTE assumption: 40 hours per week * 4 weeks = 160 potential hours * 0.7 (30% discount for non-productive time) = 1 FTE / 112 hours of bandwidth per month)

[0067] Each decision to create or not create an SVO involves a mathematical calculation based on tank configuration, but human nature may lead to shortcuts when making the final decision. Human judgment may produce different decisions when given similar facts in two different scenarios. This variability results in inconsistent site configurations, overanalysis, inconsistent swapping results, and ultimately, lower confidence in the site configuration.

[0068] Developing a predictive model to move this decision-making process to a repeatable, unbiased algorithm will ultimately result in a more efficient field, where the algorithm continuously learns from previous decisions (feedback) to make consistent decisions. Tank swaps will be performed confidently at the optimal time, allowing DCC experts time to review predefined gray areas to better train the model and further reduce anomalies. Being able to spend 10 minutes studying anomalies is a better utilization of expert time in deciding how to react to each individual alarm condition.

[0069] The prediction / recommendation model generated by the disclosed AI algorithm may include both swapping predictions / recommendations and optimal site capacity predictions / recommendations.

[0070] Generating swapping predictions / recommendations using AI algorithms can include developing predictive models to determine the probability that tank swapping is necessary at a given site, based on quality events (e.g., ion exchange bed water quality alarms), feedwater flow rate and quality (e.g., conductivity), and remaining capacity. The swapping prediction / recommendation model will have three possible outcomes:

[0071] a. Creating an SVO - No site sorting required. The condition is that the AI ​​algorithm has verified that water quality is declining and recommends replacing the DI tank.

[0072] b. Site monitoring – Site classification may be required. This is contingent on the AI ​​algorithm needing more data to predict whether relocation is necessary.

[0073] c. No action / ignored - No site classification required. Conditions remain favorable for the site to continue supplying the customer with sufficient quantity and quality of water.

[0074] The operation flowchart of AI algorithm is in Figure 1 As shown in the diagram. In action 100, the wastewater treatment system operates normally and continuously or periodically monitors flow and quality, such as the conductivity of the feedwater entering the system or the DI tank. At the local controller or remote server, the remaining capacity of the DI tank is calculated based on the initial capacity, the cumulative water flow through the DI tank, and the quality of the water entering the DI tank. In action 105, the controller of the wastewater treatment system (local or remote) determines whether the DI tank is in an alarm state due to poor water quality leaving the DI tank (e.g., conductivity higher than a set point). If there is no water quality alarm, the method returns to monitoring the feedwater flow and quality in action 100. If a water quality alarm exists, the AI ​​algorithm determines how to respond (action 110). As discussed above, the AI ​​algorithm may consider historical data regarding previous DI tank swaps, the estimated remaining capacity of the DI tank, and the current status of the feedwater quality and flow to determine how to respond to the water quality alarm. If the AI ​​algorithm determines that the alarm is a false alarm, it may provide a suggestion to do nothing (action 110), and the method returns to monitoring the feedwater flow and quality in action 100. If the AI ​​algorithm determines that the alarm indicates a true depletion (or impending depletion) of DI tank capacity, it can recommend replacing or swapping the DI tank (Action 115). Maintenance personnel should follow this recommendation (Action 120) before returning the wastewater treatment system to service and resuming monitoring of feedwater flow and quality as per Action 100. If the AI ​​algorithm determines that the alarm conditions may be false alarms—for example, if the remaining capacity is calculated to be excessive or if false alarms have occurred previously under similar conditions—the AI ​​algorithm can recommend entering a monitoring state (Action 105). In this monitoring state, feedwater parameters and other operating parameters of the wastewater system are monitored even when the alarm is active. The monitoring state continues until the alarm is closed, until the AI ​​algorithm determines the alarm is false and recommends inaction (Action 110), or until the AI ​​algorithm determines that the alarm actually indicates the need to replace or swap the DI tank (Action 115).

[0075] Generating optimal site capacity predictions / recommendations using AI algorithms can involve analyzing the site's water deionization system configuration and treated water flow history, providing outputs for current site capacity and ideal site configuration. The current site capacity output considers data on historical exchange cycles, historical water supply conductivity readings, and the amount of water used during exchange cycles prior to quality events. Based on this data, the AI ​​algorithm suggests what system capacity should be considered. This is important because customers may not be able to achieve an ideal site configuration due to local constraints.

[0076] The ideal site configuration output is the theoretical maximum site capacity given the following criteria: a) at least one rotation per year or a minimum number of rotations per year; and b) no more than four worker tanks and one purifier tank are allowed in the processing system queue. This is important because it provides information that allows customers or service providers to look for opportunities to become more efficient. Based on the ideal site configuration output, service providers can provide advice on adding more tanks to a customer's site. This will reduce the number of maintenance visits to the site, bringing it closer to the desired target of one maintenance visit per site per year. Based on the ideal site configuration output, if the site is determined to have more DI capacity than suggested, the service provider can recommend removing one or more DI tanks to reduce inventory requirements or help achieve low-flow performance at the site. The algorithm includes the target tank size for each site (e.g., 1.2 or 3.6 cubic feet).

[0077] Optimal site capacity forecasting / recommendations will allow service providers to calculate savings based on the number of trips eliminated. For sites where the recommended configuration can be implemented, hard savings can be recorded by comparing previous swapping history with anticipated swapping. This data remains valuable for sites where changes cannot be implemented immediately. At sites where the recommendations are likely to be implemented, circumstances may change.

[0078] An embodiment of a water treatment system (also referred to herein as a water treatment unit) and an associated monitoring system is described in Figure 2A The overall diagram is schematically shown as 100. A water treatment system may include one or more water treatment units or devices 105A, 105B, 105C. One or more water treatment devices may be fluidly arranged in series and / or parallel, such as... Figure 2B As shown. Although only three water treatment devices 105A, 105B, and 105C are shown, it should be understood that a water treatment system may include any number of water treatment units or devices.

[0079] The water treatment system 100 may also include one or more auxiliary systems 150A, 150B, 150C, such as pumps, pre- or post-filters, purification beds, heating or cooling units, sampling units, power supplies, or other auxiliary devices that are fluidly aligned with or otherwise coupled to or connected to one or more water treatment units 105A, 105B, 105C. The auxiliary systems are not limited to only three, but can be any number and type of auxiliary systems desired in a particular implementation. One or more water treatment units 105A, 105B, 105C and auxiliary systems 150A, 150B, 150C can communicate with a controller 110 (e.g., a computerized controller). The controller 110 can receive and / or send signals to the one or more water treatment units 105A, 105B, 105C and auxiliary systems 150A, 150B, 150C to monitor and control the one or more water treatment units 105A, 105B, 105C and auxiliary systems 150A, 150B, 150C. The one or more water treatment units 105A, 105B, 105C and auxiliary systems 150A, 150B, 150C can send or receive data related to one or more operating parameters from the controller 110 in analog or digital signals. The controller 110 can be located locally or remotely to the water treatment system 100 and can communicate with components of the water treatment system 100 via wired and / or wireless links (e.g., via a local area network or data bus). A water source 200 can supply water to be treated to the water treatment system 100. The water can be treated, or processed, through any of water treatment devices 105A, 105B, 105C and optionally one or more auxiliary systems 150A, 150B, 150C, and can be output to downstream devices or point of use 220. In some embodiments, the AI ​​algorithms disclosed herein run on the controller 110.

[0080] Back Figure 2AOne or more sensors, probes, or scientific instruments associated with each of the water treatment devices 105A, 105B, and 105C can communicate with the controller 110 via wired or wireless connections. The controller 110 may include, for example, local monitoring and data collection devices or systems. One or more sensors, probes, or scientific instruments associated with each of the water treatment devices 105A, 105B, and 105C can provide monitoring data to the controller 110 in the form of analog or digital signals. The controller 110 can provide data from the sensors or scientific instruments associated with each of the water treatment devices 105A, 105B, and 105C to different locations. One of these locations may optionally include a display 115 local to one of the water treatment devices 105A, 105B, and 105C or local at the site where the water treatment devices 105A, 105B, and 105C are located. Another of these locations may be a web portal 120, which may be hosted on a local or remote server or in the cloud 125. Another possible location among these locations is a distributed control system (DCS) 130, which may be located at the site or facility where the water treatment devices 105A, 105B, and 105C are situated. In some embodiments, alternatively or in addition to running on the controller 110, the AI ​​algorithms disclosed herein run on the DCS 130.

[0081] Data processing from one or more sensors, probes, or scientific instruments associated with each of the water treatment devices 105A, 105B, and 105C can be performed at controller 110, and aggregated data can be provided to one or more locations 115, 120, and 130. Alternatively, controller 110 can transmit raw data from one or more sensors, scientific instruments, or probes to one or more locations 115, 120, and 130. This data can be provided by one or more locations 115, 120, and 130 to the operator of the water treatment system or any individual water treatment device, the user of the treated water provided by the water treatment system, the supplier or service provider who can be responsible for maintaining one or more of the water treatment devices 105A, 105B, and 105C or the entire system 100, or any other interested party. For example, users of the water treatment system 100 can access data related to the quality and / or quantity of the treated water produced in the water treatment system 100 via web portal 120 or via site DCS system 130. Users can use this data for auditing purposes or to demonstrate compliance with regulations associated with the production of the treated water. Further optional configurations envision storing raw or processed data, or both, at one or more data storage devices at any of locations 110, 120, and 130.

[0082] Features associated with water treatment equipment 105A, 105B, and 105C are Figure 3 The example of a water treatment device (which may be any one or more of water treatment devices 105A, 105B, 105C) is indicated at 105. A water source 200 to be treated in water treatment device 105 (alternatively referred to herein as feed water) may be fluidly connected upstream of water treatment device 105. Water source 200 may be a source of untreated water, water output from a plant or from a point of use at the site of water treatment device 105, or upstream water treatment equipment. The water to be treated may be monitored by one or more sensors 205 upstream of the inlet of water treatment device 105, or otherwise by such sensors 205. The one or more sensors 205 may include, for example, flow meters, conductivity sensors, pH sensors, turbidity sensors, temperature sensors, pressure sensors, ORP sensors, or any one or more of the above-mentioned types of sensors. One or more sensors 205 may provide data on one or more measured parameters of the water to be treated in the water treatment device 105 to a local monitor 225 associated with the water treatment device 105, which may then transmit the data to the controller 110. The sensors 205 may provide data in the form of analog or digital signals. The local monitor 225 may be included in the controller 110 as hardware or software, or it may be a separate device. The sensors 205 may additionally or alternatively provide data on one or more measured parameters of the water to be treated in the water treatment device 105 directly to the controller 110.

[0083] Water to be treated may enter water treatment equipment 105 through inlet 104 and undergo treatment within water treatment equipment 105. One or more sensors 210 may be disposed inside water treatment equipment 105 to collect data relating to the operation of water treatment equipment 105 and / or one or more parameters of the water undergoing treatment in water treatment equipment 105. One or more sensors 210 may include, for example, pressure sensors, level sensors, conductivity sensors, pH sensors, OPR sensors, current or voltage sensors, or any one or more other types of sensors described above. One or more sensors 210 may provide data relating to the operation of water treatment equipment 105 and / or one or more parameters of the water undergoing treatment in water treatment equipment 105 to a local monitor 225, which may transmit this data to a controller 110. One or more sensors 210 may additionally or alternatively provide data relating to the operation of water treatment equipment 105 and / or one or more parameters of the water undergoing treatment in water treatment equipment 105 directly to the controller 110. Communication between one or more sensors 210 and local monitors 225 and / or controllers 110 can be via wired or wireless communication links.

[0084] After treatment in water treatment device 105, the treated water can exit through outlet 106 of water treatment device 105. One or more parameters of the treated water can be tested or monitored by one or more downstream sensors 215. One or more sensors 215 may include, for example, flow meters, conductivity sensors, pH sensors, turbidity sensors, temperature sensors, pressure sensors, ORP sensors, or any one or more other types of sensors described above. One or more sensors 215 may provide data on one or more measured parameters of the treated water to local monitor 225, which may then transmit this data to controller 110. One or more sensors 215 may additionally or alternatively provide data on one or more measured parameters of the treated water directly to controller 110. Communication between one or more sensors 215 and local monitor 225 and / or controller 110 may be via wired or wireless communication links. In some embodiments, alternatively or in addition to operating on controller 110, the AI ​​algorithms disclosed herein run on local monitor 225.

[0085] Local monitor 225 and / or controller 110 may include functions for controlling the operation of water treatment equipment 105. Based on measurement parameters of the water to be treated or treated water from sensors 205 and / or 215, measurement parameters from one or more internal sensors 210, or based on commands received from the operator, local monitor 225 and / or controller 110 may control inlet valve V or outlet valve V (or Figure 2B One or more auxiliary systems 150A, 150B, 150C (shown) are used to regulate the flow rate or residence time of water within the water treatment apparatus 105. Local monitor 225 and / or controller 110 may also control one or more internal controls 230 of the water treatment apparatus 105 to regulate one or more operating parameters of the water treatment apparatus 105, such as internal temperature, pressure, pH, current or voltage (for electrically based treatment apparatus), aeration, mixing rate or intensity, or any other desired operating parameters of the water treatment apparatus 105.

[0086] Local monitor 225 and / or controller 110 can monitor signals from one or more of the input sensor 205, internal sensor 210, and output sensor 215 to determine if an error condition or unexpected event has occurred, and can be configured to generate an error message or signal in response to the detection of an error condition or unexpected event. For example, if the input sensor 205 and output sensor 215 include inlet and outlet pressure sensors, local monitor 225 and / or controller 110 can be configured to receive inlet pressure data from the inlet pressure sensor and outlet pressure data from the outlet pressure sensor, and generate an alarm when the pressure difference between the feed water and the treated water exceeds a differential pressure setpoint. If one or more of the input sensor 205, internal sensor 210, and output sensor 215 include a leak detection module, the leak detection module is configured to shut down when moisture is detected in the housing of the water treatment unit 105, and local monitor 225 and / or controller 110 can be configured to generate an indication when the leak detection module detects moisture in the housing. In some embodiments, the leak detection module includes a sensor disposed on the floor where the water treatment unit is located, outside or on the outside of the unit’s housing, but close to the housing.

[0087] In one embodiment, it is represented by controller 110 and... Figure 4The monitoring system, further detailed in the diagram, may include one or more wired and / or wireless communication modules, such as modem 305, which may, for example, utilize cellular telephone networks, such as those based on LTE Cat 1, LTE Cat M1, or Cat NB1 standards, transmit data regarding the operation of water treatment equipment 105 and / or water to be treated and / or water after treatment in water treatment equipment 105 to: a remote server or one of locations 115, 120, 130; a processing unit (CPU) 310 (e.g., modem 305) operatively connected to the communication module; a memory 315 operatively connected to the CPU 310 (which may be used to store data received from sensors associated with the water treatment equipment and / or code for controlling the operation of one or more water treatment devices); and one or more additional interfaces 320 (which may include wired or wireless (e.g., Wi-Fi, Bluetooth)). The system includes modules (such as cellular modules) for connecting one or more scientific instruments or any of sensors 205, 210, 215 or other sensors associated with the water treatment device 105 or system to the central processing unit; a power supply 325 for providing power to the modem 305 and the central processing unit; and a housing 330 for accommodating components in location. In some embodiments, one or more modules 305 may include Bluetooth. An interface 320 is operatively configured to wirelessly transmit data via a personal area network (e.g., a short-range network compliant with the IEEE 802.15.1 standard) or using a wireless LAN protocol (e.g., Wi-Fi based on the IEEE 802.11 standard). In some embodiments, one or more interfaces 320 may include Bluetooth. An interface operatively configured to wirelessly transmit data via a personal area network (e.g., a short-range network compliant with the IEEE 802.15.1 standard) or using a wireless LAN protocol (e.g., Wi-Fi based on the IEEE 802.11 standard). Any or all components of controller 110 may be communicatively coupled to one or more internal buses 335. In some embodiments, memory 315 may include a non-transitory computer-readable medium comprising instructions that, when executed by CPU 310, cause CPU 310 to perform any of the methods disclosed herein.

[0088] Various monitoring devices, such as flow meters or other scientific instruments, are typically operatively connected to the CPU 310, so that data from the monitoring devices or scientific instruments is transmitted to the modem 305, in which the data can be accessed from a remote location via, for example, a cellular telephone network.

[0089] In one aspect of this disclosure, such as Figure 2AAs shown, a remote monitoring and control system architecture is used. This includes the modem 305 ( Figure 4 The controller 110, connected via cellular connectivity, is connected to various devices, such as one or more sensors (e.g., any one or more sensors 205, 210, 215) associated with water treatment equipment 105A, 105B, and 105C. These sensors may include a deionization tank resistivity monitor, a series of sensors and monitors (such as flow meters, conductivity meters, temperature and pH sensors for water purification systems such as reverse osmosis systems), or the sensors may comprise a series of unit operations combined into a complete system. Information from the various sensors is uploaded to an internal portal from the operating enterprise and may also be uploaded to a customer portal and customer DCS system 130. The entire network may be cloud-based.

[0090] An example of a local water treatment system or unit 100 that can be included in the aspects and embodiments disclosed herein is a deionization system. An example of a local water treatment system or unit 100 including a deionization system is... Figure 5 The diagram is generally shown at 400. Water to be treated is supplied from a water source 405 to an inlet pressure relief valve 410. The inlet pressure relief valve 410 regulates the inlet water pressure to prevent overpressure and potential system damage. The inlet water then passes through a solenoid valve 415 and a pre-filter 420. The pre-filter 420 removes particulate matter that may be present in the inlet water from the source 405. A first flow meter 425 monitors the flow rate of the inlet water from the pre-filter 420. An inlet water quality probe S1 is in fluid communication with the inlet water exiting the pre-filter 420. The inlet water quality probe S1 includes a conductivity sensor and a temperature sensor. The conductivity of the inlet water can depend on the concentration of ion species in the inlet water and the temperature of the inlet water. The temperature sensor provides data that is used to apply an offset or calibration to the data output from the conductivity sensor to reduce or eliminate the influence of temperature on the conductivity sensor reading. In some embodiments, the raw conductivity reading from the influent conductivity sensor can be linearly adjusted for a temperature different from a reference temperature of 25°C by a temperature coefficient (e.g., 2.0% per degree Celsius).

[0091] Inlet water flows from a first flow meter 425 to a first treatment column 430, which may be, for example, a carbon filter column. The water is treated in the first treatment column 430, leaves the first treatment column 430, and enters a second treatment column 435, which may be, for example, a cation exchange resin column.

[0092] After treatment in the second treatment column 435, the water exits the second treatment column 435 and enters a third treatment column or worker bed 440. The worker bed 440 may include, for example, an anion exchange resin column. A worker probe S2 is configured to measure at least one worker water parameter of the water from the worker bed 440. The worker probe S2 may include a conductivity sensor and a temperature sensor for providing temperature calibration of the data output from the conductivity sensor of the worker probe S2, as described above for the reference influent water quality probe S1. In some embodiments, the raw conductivity reading from the worker bed water conductivity sensor may be linearly adjusted for a temperature different from a reference temperature of 25°C by a temperature coefficient (e.g., 4.3% per degree Celsius). The temperature coefficient may be adjusted locally at the unit or remotely from a central server. The worker probe S2 may be located at the output of the worker bed 440 to measure the mass of the water exiting the worker bed 440. The actuator probe S2 may include an indicator light or display (not shown) that provides an indication of whether the conductivity of the water leaving the actuator bed 440 is within acceptable limits. In other cases, nonlinear temperature compensation may be used to adjust the conductivity value.

[0093] Water is treated in a working bed 440 and exits the working bed 440 into a purifier bed 445, which may be, for example, a mixed-bed resin ion exchange column. A purifier probe S3 is configured to measure at least one purifier water parameter of the water from the purifier bed 445. The purifier probe S3 may include a conductivity sensor and a temperature sensor for providing temperature calibration of the data output from the conductivity sensor of the purifier probe S3, as described above for the reference feed water quality probe S1. In some embodiments, the raw conductivity reading from the purifier bed water conductivity sensor may be linearly adjusted for a temperature different from a reference temperature of 25°C by a temperature coefficient (e.g., 5.2% per degree Celsius). The temperature coefficient may be adjusted locally at the unit or remotely from a central server. The purifier probe S3 may be positioned at the output of the purifier column 445 to measure the mass of the water exiting the purifier column 445. The purifier probe S3 may include an indicator light or display (not shown) that provides an indication of whether the conductivity of the water exiting the purifier column 445 is within acceptable limits. Water is treated in purifier column 445 and exits purifier column 445. The water exiting purifier column 445 can pass through a post-filter 450, which can be, for example, a column filter that filters out any resin particles from the treated water. A second flow meter 425 can be located downstream of purifier bed 445. The second flow meter 425 can be provided as a supplement to or replacement of the first flow meter 425.

[0094] The monitor / controller 455 may include Figure 3The features of one or both of the local monitors 225 and / or controllers 110 shown can be used to monitor and control various aspects of the system or unit 400. Monitor / controller 455 can receive a signal, for example, from leak detector module 460, which can provide an indication of the presence of a leak in the system or unit 400. For example, leak detector module 460 can be configured to shut down if moisture is detected in the housing 465 of the deionization system 400 or on the floor or other surface on which the housing 465 or system 400 is located. Monitor / controller 455 can be configured to generate an indication, alarm, or warning if leak detection module 460 detects moisture in housing 465. If a leak is detected, monitor / controller 455 can send a control signal to solenoid valve 415 to cut off the flow of water through the system. Monitor / controller 455 can also provide a signal to a service provider via a wired or wireless connection to indicate that system 400 may require maintenance. The monitor / controller 455 can be configured to receive and monitor flow data via signals received from one or both of the first and second flow meters 425, and can be configured to receive and monitor at least one measured influent parameter from the influent water quality probe S1, at least one worker water parameter from the worker probe S2, and at least one purifier water parameter from the purifier probe S3. Probes S1, S2, and / or S3 can provide conductivity measurements to the monitor / controller 455 at a periodic rate (e.g., once every five seconds) or continuously. Data from probes S1, S2, and / or S3 can be recorded periodically by the monitor / controller 455, for example, once every five minutes. If the flow or water quality measurement is outside the acceptable range, the monitor / controller 455 can signal to a service provider via a wired or wireless connection to indicate that the system 400 may require maintenance; for example, the resin in one of the worker bed 440 or purifier bed 445 may be depleted and need replacement, or one of the filters 420, 450 may be clogged and require maintenance.

[0095] Water treatment unit 400 (e.g., monitor / controller 455 of water treatment system 400) can be connected to a server (e.g., located in such a server as...). Figure 6 The server 510 at the centralized monitoring location 500 shown communicates with the local water treatment unit. The server 510 can be configured to receive at least one of the following: flow data, at least one measured influent parameter, at least one worker water parameter, and at least one purifier water parameter. In some embodiments, the AI ​​algorithms disclosed herein run on the server 510, either alternatively or in addition to running on the controller 110 and / or the local monitor 225 and / or the monitor / controller 455.

[0096] At least one of the controller 455 and the server 510 may be further configured to determine at least one of the following: the total cumulative flow based on aggregated flow data from one or both of the first flow meter 425 and the second flow meter 425; the total billing cycle flow based on flow data through the local water treatment unit 400 during the billing cycle; the total current replacement flow based on flow data during the current maintenance cycle of the working bed; the weighted daily average flow as defined below; the contaminant load based on at least one influent parameter; and the remaining capacity of the local water treatment unit based at least on the contaminant load.

[0097] Additional sensors (e.g., differential pressure sensors associated with filters 420, 450, flow sensors or flow totalizers associated with inlet relief valve 410 or first flow meter 425 or second flow meter 425) may also be present and communicate with monitor / controller 455, local monitor 225 and / or controller 110.

[0098] Certain aspects of this disclosure relate to a system and method for providing a service that allows the delivery of aquatic products according to specific quality requirements. In some cases, product supplies (e.g., aquatic products) are delivered and / or consumed by users without the user operating any product processing system (e.g., without operating a water treatment system) and directly consuming aquatic products with predefined quality characteristics. In some cases, certain aspects of this disclosure allow for the acquisition of user consumption behavior of products, such as water consumption, and such data or information can then be used by the system owner or service product provider to adjust, repair, replace, or maintain any component, subsystem, or parameter of, for example, a water treatment system. For example, one or more local treatment units or systems may be set up or located at a user facility having multiple ion exchange columns with multiple sensors or probes that monitor one or more characteristics of the ion exchange columns and / or one or more parameters of: the original influent or feed water, the effluent service product water, and / or the water leaving any ion exchange column. Thus, data can be transferred from one or more treatment systems (e.g., at the user's point of use) to information or data storage or containment facilities that are typically located remotely from the user facility or the water treatment system. The data or information acquired, transmitted, and / or stored may include, for example, properties of the quality of the influent or produced water, such as conductivity, pH, temperature, pressure, concentration of dissolved solids, redox potential, or flow rate. The data acquired, transmitted, and / or stored may also include operating parameters of one or more processing systems. For example, one or more processing systems may deliver deionized water products, wherein the processing system includes an ion exchange subsystem, and the data may include any one or more of the following: pressure (inlet and outlet pressures), flow rate, runtime, duration of ion exchange bed operation or service, or alarm status. Other information may include subsystem characteristics such as remote transmitter signal strength, ion exchange bed pressure, and / or differential pressure.

[0099] Regarding exemplary treatment systems, the system may include ion exchange beds or columns of cation exchange resins, anion exchange resins, or mixtures of cation and anion exchange resins. The process may involve delivering water with a predetermined quality (e.g., predetermined conductivity) over a predetermined time period (e.g., hourly, daily, weekly, monthly, quarterly, semi-annually). For example, the process may provide users with deionized water of a purity suitable for semiconductor manufacturing operations. Even if the treatment system is not owned or operated by the user, the delivered water may be deionized at the user's facility by one or more treatment systems. The system owner may provide the treatment system at the user's facility, connect it to a water source, operate the system, monitor its operating parameters, and deliver the treated deionized water to the user. The system owner may receive and store information or data from the treatment system regarding its parameters and the properties of the deionized water. The owner may monitor the system and proactively servicing or replacing any subsystem or component of the treatment system without user interaction. Thus, the owner or operator of the treatment system provides water products to users without user interaction. For example, if data from the treatment system indicates that one or more ion exchange columns need to be replaced, or that one or more ion exchange columns are nearing the end of their service life, the owner or operator can replace any column of the treatment system without user interaction. In exchange, the user compensates the owner or operator based on water consumption. Alternatively, the user can compensate the owner or operator based on subscriptions to deionized water products based on usage and availability (e.g., daily, weekly, or monthly subscriptions).

[0100] While deionized product water treated via an ion exchange column has been described as an example, other systems can also be implemented. For instance, one or more treatment systems can utilize reverse osmosis (RO) units. The owner or operator can remotely monitor the RO unit to ensure the delivery and quality of the water product, and to replace the RO membrane or column, pump, and / or filter of the RO unit. In exchange, the user can compensate the owner / operator based on the amount of product water consumed or according to periodic orders.

[0101] exist Figure 6 In this context, the centralized monitoring location, generally shown at 500, can receive data from one or more local water treatment systems, for example, from a controller 110 (and / or monitor / controller 455, or local monitor 225) associated with local water treatment units or systems 400A, 400B, 400C at multiple different sites 505A, 505B, 505C. The local water treatment unit or system 400A located at one of these sites (e.g., site 505A) may be or may include... Figure 5The local water treatment unit or system 400 is shown. Another site in these sites may include a second local water treatment unit or system 400B. The second local water treatment unit or system 400B may include unit operations similar to or corresponding to those unit operations of the local water treatment unit or system 400A, for example, a second inlet water quality probe (corresponding to the inlet water quality probe S1 of the treatment unit 400) is configured to measure at least one inlet water parameter of the second feed water to be treated in the second local water treatment unit, the second inlet water quality probe including a second conductivity sensor and a second temperature sensor, a second working bed (corresponding to the working bed 440 of the treatment unit 400) containing an ion exchange medium and configured to receive the second feed water to be treated, the second working probe (corresponding to the treatment unit 400) The working probe S2 of the treatment unit 400 is configured to measure at least one water parameter from the water in the second working bed, the second working probe including a second working conductivity sensor and a second working temperature sensor. A second purifier bed (corresponding to purifier bed 445 of the treatment unit 400) contains an ion exchange medium and is fluidly connected downstream of the second working bed. A second purifier probe (corresponding to purifier probe S3 of the treatment unit 400) is configured to measure at least one purifier water parameter from the water in the second purifier bed, the second purifier probe including a second purifier conductivity sensor and a second purifier temperature sensor. A second flow meter (corresponding to either the first flow meter 425 or the second flow meter 425 of the treatment unit 400) is located upstream of the second working bed and downstream of the second purifier bed, and is configured to measure the flow rate data of water introduced into the second local water treatment unit. A second controller (corresponding to controller 455 of the treatment unit 400) communicates with the second flow meter, the second inlet water quality probe, the second working probe, and the second purifier probe. The second controller is configured to receive flow data from the second flow meter, at least one measured inlet water parameter from the second inlet water quality probe, at least one worker water parameter from the second worker probe, and at least one purifier water parameter from the second purifier probe.

[0102] The second water treatment system 400B (such as water treatment system 400) can communicate with a server 510 at a centralized monitoring location 500. The server 510 can also be configured to receive at least one of the following from the second local water treatment unit: flow data from a second flow meter, at least one measured influent parameter from a second influent water quality probe, at least one worker water parameter from a second worker probe, and at least one purifier water parameter from a second purifier probe.

[0103] At least one of the controller 455 and server 510 of the local water treatment system 400 may be further configured to determine at least one of the following: the total cumulative flow based on aggregated flow data from one or both of the first flow meter 425 and the second flow meter 425; the total billing cycle flow based on flow data passing through the local water treatment unit 400 during the billing cycle; the total current replacement flow based on flow data during the current maintenance cycle of the working bed; the weighted daily average flow through the local water treatment unit 400; the contaminant load based on at least one influent parameter; and the remaining capacity of the local water treatment unit based at least on the contaminant load.

[0104] The second controller at the second water treatment unit 400B may be substantially similar to and correspond to the controller 455 of the local water treatment system 400, and may be configured to determine at least one of the following: the cumulative total flow of the second water treatment unit based on aggregated flow data through the second water treatment unit, the total flow of the second billing cycle based on flow data through the second water treatment unit during the billing cycle, the total current replacement flow based on flow data during the current maintenance cycle of the second working bed, the second weighted daily average flow of water through the second water treatment unit, the second contaminant load based on at least one influent parameter of the second water supply, and the remaining capacity of the second local water treatment unit based at least on the second contaminant load.

[0105] Data from any of units 400A, 400B, and 400C can be collected and stored respectively in a memory device operatively connected to each of the respective controllers 110, and continuously transmitted to server 510 via wired or wireless communication protocols or combinations thereof. However, typically, data at each unit is stored and accumulated during predetermined collection periods, and then intermittently transmitted to server 510. For example, data regarding various operating parameters can be collected continuously or persistently and stored in the memory device, and the controller can periodically (e.g., every five minutes, hourly, once or twice a day) transmit this data via a modem to a receiving modem operatively connected to server 510 via an Internet connection, where the accumulated data can be stored and analyzed. In other configurations, certain data types (such as alarms and associated notifications) can be preferentially transmitted immediately.

[0106] The centralized monitoring location 500 can analyze data provided by different controllers 110 to determine when one or more water treatment devices 105 in the water treatment systems at different sites 505A, 505B, 505C should be serviced. The centralized monitoring location 500 can create a schedule for servicing one or more water treatment devices 105 in the water treatment systems at different sites 505A, 505B, 505C and transmit the servicing schedule to one or more service provider locations 515A, 515B.

[0107] In some embodiments, a system for providing treated water includes a first water treatment unit, such as Figure 6 the illustrated local water treatment unit 400A. The first water treatment unit includes a first ion exchange bed containing an ion exchange medium therein, such as Figure 5 any one of the illustrated ion exchange columns or beds 430, 435, 440, or 445. The first ion exchange bed is configured to receive a first water stream to be treated, such as water from Figure 5 the water source 405 to be treated in Figure 5 any one of the flow meters 425 in Figure 2A , Figure 2B or Figure 4 the controller 110 of Figure 5 or the monitor / controller 455 of

[0108] In some embodiments, the first controller is configured to determine the first weighted average flow rate by applying a greater weight to the first current average flow rate than to the first cumulative average flow rate. The first controller can be configured to determine the first weighted average flow rate by performing the following calculation: (1) First weighted average flow rate = A × (first cumulative average flow rate) + B × (first current average flow rate), where 0.5 < A < 0.9, 0.1 < B < 0.5, and A + B = 1.

[0109] The first controller may also be configured to schedule a second replacement of the ion exchange medium at a second time, which is determined based on the estimated number of days remaining until the ion exchange medium will be depleted.

[0110] The system for providing treated water may also include a second water treatment unit, e.g., Figure 6 the local water treatment unit 400B shown in, which is disposed remote from the first water treatment unit. The second water treatment unit includes a second ion exchange bed containing an ion exchange medium therein, e.g., Figure 5 any one of the ion exchange columns or beds 430, 435, 440 or 445 shown in. The second ion exchange bed is configured to receive a second water stream to be treated, e.g., water from Figure 5 the water source 405 to be treated in. A second flowmeter (e.g., Figure 5 any one of the flowmeters 425 in) is positioned along a second flow path including the second ion exchange bed and is configured to measure a second flow rate of the second water stream passing through the second flow path. A second controller (e.g., Figure 2A , Figure 2B or Figure 4 the controller 110 of or Figure 5 the monitor / controller 455 of) communicates with the second flowmeter. The second controller is configured to receive second flow rate data regarding the second flow rate, calculate a second current average flow rate of the second water stream passing through the second ion exchange bed based on the second flow rate data, calculate a second cumulative average flow rate through the second water treatment unit, determine a second weighted average flow rate based on a weighted average of the second current average flow rate and the second cumulative average flow rate, and determine a second estimated number of days remaining until depletion of the ion exchange medium in the second ion exchange bed based on the second weighted average flow rate and the capacity of the ion exchange medium in the second ion exchange bed.

[0111] In some embodiments, the second controller is configured to determine the second weighted average flow rate by performing the following calculation:

[0112] (2) Second weighted average flow rate = C × (second cumulative average flow rate) + D × (second current average flow rate), where 0.5 < C < 0.9, 0.1 < D < 0.5, and C + D = 1.

[0113] It has been determined empirically that when using the weighted average flow rate to determine the remaining useful life or estimated time until depletion of the ion exchange medium bed or ion exchange column in the water treatment systems disclosed herein, B values and D values of approximately 0.3 in equations (1) and (2) respectively provide good results.

[0114] A central controller (e.g., Figure 6The monitoring system or server 510 is located remotely from the first water treatment unit and is configured to receive the estimated number of days remaining until the ion exchange media in the first ion exchange bed is depleted. The central controller is also configured to receive a second estimated number of days remaining until the ion exchange media in the second ion exchange bed is depleted and to determine whether the ion exchange media in the first and second ion exchange beds should be replaced during the same maintenance trip. As used herein, the term "same maintenance trip" can include a technician traveling from the service provider's location with sufficient materials to multiple ion exchange systems (optionally at different treatment system locations) and performing maintenance, such as replacing ion exchange media (or ion exchange cartridges), in those systems before returning to the service provider's location.

[0115] The central controller is configured to determine whether to replace the ion exchange media in the first and second ion exchange beds within the same maintenance trip by weighing the costs associated with regenerating the ion exchange media in the first and second ion exchange beds against the costs associated with different maintenance trips to each of the first and second sites. For example, if one or both of the ion exchange media in the first and second ion exchange beds are not completely depleted, regenerating the ion exchange media from the first and second ion exchange beds may require an additional $X in chemical and labor costs compared to the cost of regenerating the media when they are completely depleted. The fuel and labor costs of separate maintenance trips to the locations of the first and second ion exchange beds may be $Y. The fuel and labor costs of traveling to and servicing the locations of the first and second ion exchange beds within the same maintenance trip may be $Z. If the cost savings associated with combining these maintenance trips outweigh the additional cost of regenerating the ion exchange media, for example, if $Y - $Z > $X, then servicing the first and second ion exchange beds within the same maintenance trip rather than in different maintenance trips may be economically beneficial. In some cases, if the depletion level of the second ion exchange medium (or cartridge) at the second location is within a threshold number of days, the central controller can even schedule the replacement of the second ion exchange medium as part of the same maintenance request to replace the first ion exchange bed (or cartridge) at the first location, even before the bed depletion is determined.

[0116] In some embodiments, the water treatment system includes a central server (e.g., Figure 6 The monitoring system or server 510) and multiple water treatment units, each of which is located far from the central server, for example Figure 6Local water treatment units 400A, 400B, and / or 400C. Each corresponding local water treatment unit includes an ion exchange medium, for example, set in... Figure 5 The ion exchange medium in any of the ion exchange columns or beds 430, 435, 440, or 445 shown. The ion exchange medium is configured to receive water to be treated, for example from... Figure 5 The system provides water from source 405 to be treated, and supplies treated water. At least one flow meter is installed, for example... Figure 5 The system includes any one of the flow meters 425 to monitor the water flow rate in the water treatment unit. The water treatment system also includes a controller, such as... Figure 2A , Figure 2B or Figure 4 Controller 110 or Figure 5 The monitor / controller 455 is configured to determine the unadjusted flow rate of water through the water treatment unit within a predetermined time period, determine the historical flow rate of water through the water treatment unit, determine at least one of the expected remaining maintenance capacity and the predicted number of days of depletion for the ion exchange medium based on the unadjusted flow rate, the historical flow rate and the total capacity of the ion exchange medium, and transmit at least one of the expected remaining maintenance capacity and the predicted number of days of depletion to a central server.

[0117] Each water treatment unit of the water treatment system may also include a conductivity sensor, for example, Figure 3 One of the input sensors 205 or Figure 5 One of sensors S1 or S2, the conductivity sensor is configured to measure the conductivity of water introduced into the ion exchange medium of each respective water treatment unit. The controller can also be configured to adjust at least one of the predicted depletion days and total capacity of the respective ion exchange medium based on the conductivity measured from the conductivity sensor.

[0118] The central server can be configured to generate a maintenance request to replace the ion exchange media in a specific water treatment unit if the predicted exhaustion days for that unit are less than the maintenance lag time. This maintenance request may include a request to replace the ion exchange media in an ion exchange column or to replace the entire ion exchange column (or cartridge). Responding to a maintenance request may involve generating a maintenance rework order, determining the expected time for performing the maintenance activity, and contacting the customer to schedule the maintenance.

[0119] The central server can be configured to generate a maintenance request to replace the corresponding ion exchange medium in a specific water treatment unit if the remaining capacity of that unit is less than the minimum capacity. The central server can be further configured to combine at least two maintenance requests from at least two different water treatment units into a single aggregated maintenance request to replace the corresponding ion exchange medium in those at least two water treatment units if the separation distance between them is less than the maximum separation distance.

[0120] The time between maintenance instances involving the replacement of ion exchange media in an ion exchange column can be calculated based on water quality parameters such as the concentration of ionic contaminants in the influent to be treated and the flow rate of water through the water treatment system. Conductivity sensors (e.g., Figure 3 One or more of the input sensors 205 shown in the figure Figure 5 One of the sensors S1 or S2 in the flow sensor can be used to measure the concentration of ionic contaminants in the influent water to be treated. (e.g., flow sensor) Figure 3 Another one of the input sensors 205 shown in the figure Figure 3 Another one of the output sensor 215 or internal sensor 210 shown in the figure Figure 5 One of the flow meters (425) can be used to measure the flow rate of water being treated in a water treatment system at the user's site. Based on measurements from conductivity and flow sensors in the water treatment system, the service provider can determine the frequency at which the ion exchange column should be serviced. The capacity of the ion exchange column is based on the type and amount of resin used. Capacity is expressed in grains. The total amount of water that can be treated is based on the capacity of the ion exchange column and the contaminant load in the feed water (expressed as feed water conductivity). The conversion equation is shown below:

[0121] (3) Conductivity (uS / CM) × Cond_TDS_Conv_Factor = Total dissolved solids (TDS) (unit: PPM)

[0122] (4) TDS / PPM_GPG_Conv_Factor = Pollutant Load (unit: grains / gallon)

[0123] The Cond_TDS_Conv_Factor and PPM_GPG_Conv_Factor factors in the above equation can be determined empirically.

[0124] In some configurations, capacity calculation can begin (or can be reset) when the ion exchange column is replaced. When water begins to flow through the ion exchange column, the feed water conductivity is converted into contaminant load according to equations (3) and (4) above. Each gallon of water flowing through reduces the ion exchange column capacity by the number of gallons flowing × contaminant load. At the beginning of each day, the system calculates the estimated remaining days until the ion exchange column is depleted (estimated remaining days) using the average conductivity of the previous few days, the 10-day average total flow rate, and the current remaining capacity according to the following equation: (5) (Current remaining capacity / (Average daily conductivity * Cond_TDS_Conv_Factor / PPM_GPG_Conv_Factor)) / 10-day average total flow rate = Estimated remaining days

[0125] The estimated remaining days are compared to the estimated remaining days alarm setpoint. If the estimated remaining days are less than the setpoint, an estimated remaining days alarm is generated.

[0126] If the percentage of remaining capacity is less than the remaining capacity alarm setpoint, a remaining capacity alarm will be generated.

[0127] Alternatively, capacity determination can be based on a historical weighted calculation of the average flow relative to the flow of the previous day. For example, the historical daily average flow and the previous day's average flow can be weighted, for example, using 1:1, 2:1, 3:1, 4:1, 5:1, 6:1, 7:1, 3:2, 4:3, 5:2, 5:3, 6:5, 7:2, 7:3, 7:4, 7:5, and 7:6.

[0128] In some embodiments, the estimated number of days remaining until the ion exchange bed in the ion exchange column of the water treatment system is depleted is based on the current replacement daily average flow rate and the cumulative average flow rate of water passing through the ion exchange bed. The current replacement daily average flow rate can be calculated as the average flow rate of water passing through the ion exchange bed each day. In other cases, if the ion exchange bed is replaced or swapped on day 1 (day 1), and the flow rates of water passing through the ion exchange bed on days 1 through 3 are 100 gallons, 110 gallons, and 105 gallons, respectively, then the current replacement daily average flow rate up to day 3 will be (100 + 110 + 105) / 3 = 105 gallons / day. The cumulative average flow rate can be calculated as the average flow rate of water passing through the ion exchange bed each day since the ion exchange column or system housing the ion exchange bed was initially put into operation. For example, if an ion exchange bed operates for 100 days, and the total amount of water flowing through the ion exchange bed during those 100 days is 10,000 gallons, then the cumulative average flow rate at the end of the 100 days would be 10,000 / 100 = 100 gallons / day. Alternatively, the cumulative average flow rate can be calculated as the average daily flow rate of water through the ion exchange bed for all available historical flow rates per day, or as the average daily flow rate of water through the ion exchange bed only over a predetermined number of time periods between past instances of ion exchange bed replacement or replacement. Calculating the cumulative daily average flow rate can include calculating the average daily flow rate of water over multiple time periods, which include multiple instances of ion exchange bed replacement. Calculating the cumulative daily average flow rate of water can also include calculating the average daily flow rate of water through the water treatment system over a previous time period, which includes a predetermined number of instances of ion exchange bed replacement immediately preceding the receipt of an instruction to replace the ion exchange bed. The average daily flow rate over a previous time period is the average daily flow rate of water through the ion exchange column between one or more instances of ion exchange media replacement prior to the most recent ion exchange media replacement. Calculating the average daily flow rate of water over previous time periods may include giving greater weight to the flow rate of water passing through the ion exchange bed that is closer in time to the current time period than to the flow rate of water passing through the water treatment system that is further away in time from the current time period. In some embodiments disclosed herein, the average daily flow rate over previous time periods may be used as the cumulative average daily flow rate.

[0129] The estimated number of days remaining until the ion exchange bed or column is depleted can be based on the current tank capacity of the ion exchange bed, the average conductivity of the water during the current time period since the ion exchange bed or column was last replaced or replaced, and the average daily flow rate of the water passing through the ion exchange bed since the ion exchange bed or column was last replaced or replaced. Therefore, determining the estimated number of days remaining until the ion exchange bed or column is depleted may include measuring the conductivity of the water to be treated during the current time period, determining the current average conductivity of the water to be treated during the current time period, and using the current average conductivity of the water to be treated in the equation used to determine the estimated number of days remaining until the ion exchange bed or column is depleted. The current average conductivity of the water to be treated may be used as, for example, the average daily conductivity in equation (5) above. Additionally or alternatively, determining the estimated number of days remaining until the ion exchange bed or column is depleted may include performing the following calculations:

[0130]

[0131] in,

[0132] w 累积 It is a weighting factor applied to the cumulative daily average flow.

[0133] w 当前 It is a weighting factor applied to the current average flow rate.

[0134] w 累积 +w 当前 =1, 0.5≤w 累积 ≤0.9, 0.1 <w 当前 <0.5,

[0135] F 累积 =Cumulative daily average flow,

[0136] F 当前 =Current average flow rate,

[0137] D 剩余 = The estimated number of days remaining until exhaustion.

[0138] TC 当前 =Current tank capacity,

[0139] ρ 当前 = Current daily average conductivity.

[0140] When determining the estimated number of days remaining until the ion exchange bed is exhausted, the weighted daily average flow rate can be determined by applying a greater weight to the cumulative daily average flow rate than to the current replacement daily average flow rate. The weighted daily average flow rate can be used in calculations used to determine the estimated number of days remaining until the ion exchange bed is exhausted, for example, as the average flow rate used to calculate the total 10-day average flow rate in equation (5) above. Determining the weighted daily average flow rate may include, for example, performing the following calculations:

[0141] (7)F 加权 =[(w 累积 )×9F 累积 )]+[(w 当前 )×9F 当前 )]

[0142] in,

[0143] F 加权 =Weighted daily average flow

[0144] F 当前 =Current daily average flow rate

[0145] F 累积 =Cumulative daily average flow,

[0146] 0.5≤w 累积 ≤0.9,

[0147] 0.1 <w 当前 <0.5,

[0148] w 累积 +w 当前 =1.

[0149] In various embodiments, 0.2 <w 当前 <0.4 and / or w 当前 Approximately 0.3. It has been empirically determined that when using a weighted average flow rate to determine the remaining useful life or estimated time until the ion exchange medium bed or ion exchange column in the water treatment system disclosed herein is depleted, w in equation (7) is approximately 0.3. 当前 The value provided a good result.

[0150] The calculations cited above can be used, for example, by utilizing Figure 2A or Figure 3 The controller 110 shown or using Figure 5 The monitor / controller 455 shown executes locally on the water treatment system, or it can execute at a monitoring system or server 510 at a centralized monitoring location 500, which is located at a distance from one or more water treatment systems being monitored, such as... Figure 6 As shown in the image.

[0151] A request to replace the ion exchange bed can be generated based on the estimated number of days remaining until the ion exchange bed is exhausted. This request can, for example, utilize... Figure 2A or Figure 3 The controller 110 shown or using Figure 5 The monitor / controller 455 shown is generated locally within the water treatment system and can be transmitted to the monitoring system or server 510 at the centralized monitoring location 500. Alternatively, a request to replace the ion exchange bed can be generated manually by the monitoring system or server 510 at the centralized monitoring location 500.

[0152] Service providers can schedule maintenance on ion exchange columns while they still have a certain amount of treatment capacity (e.g., 10% remaining capacity (a 10% remaining capacity alarm setpoint)) to provide a safety margin to prevent treated water from reaching unacceptable quality. Service providers can also schedule maintenance at predetermined timeframes (e.g., 5 to 10 days before the expected capacity of the ion exchange column becomes depleted). Service providers can set fees for producing a specified volume of treated water at the user's site based on the calculated frequency at which the ion exchange column should be maintained.

[0153] Service providers can also, or alternatively, schedule maintenance of water treatment systems based on alarms or runaway signals provided by the water treatment system. Alarms or runaway signals may be sent in response to one or more monitored parameters exceeding a set point or expected range (e.g., 5% or more higher than the five-day or ten-day average) at a single point in time or over a period of time (e.g., five days or more). For example, for parameters such as... Figure 5 As shown in the deionization system maintenance diagram, the worker probe S2 can provide an indication that the conductivity of the water leaving the ion exchange column 440 is increasing to a level indicating that the ion exchange bed in the ion exchange column 440 is nearing depletion. The maintenance provider can receive notification of this indication from the worker probe S2 via, for example, a monitor / controller 455, and can schedule maintenance of the ion exchange column 440. Based on the conductivity reading from the worker probe S2 and the measured flow rate through the system, the maintenance provider can calculate the remaining processing capacity of the ion exchange bed in the ion exchange column 445 and adjust the schedule for maintenance of the ion exchange column 445 accordingly. In some embodiments, the ion exchange column 440 should be maintained approximately two days after the indication provided by the sensor S1. Additionally, maintenance can be scheduled if the purifier probe S3 provides an indication that the conductivity of the water leaving the ion exchange column 445 is approaching or exceeding an unacceptable level, if the leak sensor 460 provides an indication of water leakage, or if one or more pressure sensors (e.g., Figure 3If one or more of the sensors 205, 210, or 215 provide an indication of unacceptable pressure or an unacceptably high tendency for pressure on one or more components of the treatment system, the service provider may schedule a maintenance call to inspect one or more components of the water treatment system.

[0154] Service providers may also, or alternatively, base their decisions on instructions from... Figure 2B Signals indicating one or more potential system problems of one of the auxiliary systems 150A, 150B, and 150C shown may be used to schedule maintenance. These potential system problems include, for example, pump failure, unexpected high power draw from one of the auxiliary systems, or unacceptable pressure drops on one of the auxiliary systems. Any warnings, alarms, or out-of-control signals provided to the service provider may also be provided, or alternatively, to users of the treated water produced by the water treatment system, operators of the water treatment system or its components, or owners of the system or its components (if the owner is not the service provider).

[0155] In some embodiments, a central server 510 located at a centralized monitoring location 500 can determine when and which components of the water treatment system at various user or customer sites 505A, 505B, 505C should be serviced. The central server at the centralized monitoring location 500 can transmit maintenance schedules to one or more service provider locations 515A, 515B. The central server 510 at the centralized monitoring location 500 can send maintenance requests or schedules to one or more service provider locations 515A, 515B, optimizing factors such as travel time between service provider locations 515A, 515B and the sites where the equipment may need maintenance. For example, the central server can send maintenance schedules to service provider locations that are closer to the sites with the equipment that should be maintained than another service provider location. The central server can adjust maintenance schedules so that, based on the remaining treatment capacity of one or more components, maintenance is performed earlier or later than the optimal time for one or more components of the water treatment system at one of the user's or customer's sites 505A, 505B, and 505C. Doing so will allow multiple components to be maintained in a single maintenance trip, thus reducing overall costs by decreasing the number of individual maintenance trips performed by the service provider. For example, if maintenance is scheduled to replace ion exchange columns (or multiple ion exchange columns) at a first site, and a second site adjacent to the first site has one or more ion exchange columns with less than about 10% more remaining capacity than their remaining capacity alarm setpoint and / or a week or less of expected remaining days, then the replacement of the ion exchange columns at the second site can be scheduled to be performed during the same maintenance trip as the replacement of the ion exchange columns at the first site.

[0156] When deciding when to replace nearly depleted ion exchange columns at different sites, the costs associated with regenerating the ion exchange column can also be considered. For some ion exchange columns, if the resin in the column still has remaining treatment capacity, the resin bed can be completely depleted before regeneration. To deplete the resin bed, additional chemicals can be passed through it. An ion exchange column with 20% remaining capacity may require more chemicals to deplete and then regenerate compared to a similar column with 10% remaining capacity. The chemicals used to deplete the resin bed in the ion exchange column have associated costs. Therefore, in the example above, if the costs associated with traveling to the second site (e.g., fuel costs and labor time) plus the costs associated with the chemicals used to regenerate the ion exchange column at the second site earlier than necessary exceed the costs that might be associated with replacing the ion exchange column at the second site on a different maintenance trip than the one used to replace the ion exchange column at the first site (e.g., fuel, labor, etc.), then different maintenance trips could be scheduled for the two different sites, rather than just one trip.

[0157] In some embodiments, for example, a first water treatment system may be located at a first site, while a second water treatment system may be located at a second site at a distance from the first site. A method for servicing water treatment systems at the first and second sites may include determining whether to replace the ion exchange bed of the water treatment system at the first site and the second ion exchange bed of the water treatment system at the second site during the same maintenance trip. Determining whether to replace the ion exchange bed of the first water treatment system at the first site and the second ion exchange bed of the second water treatment system at the second site during the same maintenance trip may include weighing the costs associated with regenerating the ion exchange bed from the first site and from the second site relative to the costs associated with different maintenance trips to the first and second sites. Furthermore, the first water treatment system may be located at a first site in a network of multiple different sites, each site including at least one water treatment system having an ion exchange bed, and the method for servicing the water treatment system may further include determining a subset of the ion exchange beds at the multiple sites to be replaced during the same maintenance trip.

[0158] Components of a water treatment system that can be serviced by a service provider are not limited to ion exchange columns, and one or more water quality parameters used to determine when to service components of the water treatment system are not limited to conductivity or ion concentration and flow rate. In other embodiments, the water treatment system may include a turbidity sensor upstream of one or more water treatment units. The one or more water treatment units may have a limited capacity to remove turbidity from water undergoing treatment in the one or more water treatment units. The one or more water treatment units may include, for example, filters (e.g., sand filters or other forms of solid-liquid separation filters) that have a limited capacity to remove solids from the water before becoming clogged or otherwise ineffective for further turbidity treatment. The flow rate of water passing through one or more water treatment units and the turbidity of the water to be treated can be monitored to determine the expected service life of the one or more water treatment units. Maintenance of the one or more water treatment units can then be scheduled to be performed before the end of the service life of the one or more water treatment units.

[0159] In another example, one or more water treatment devices may include pressure-driven separation devices, such as nanofiltration devices or reverse osmosis devices, and parameters used to determine when one or more water treatment devices should be serviced include pH and / or temperature measured by one or more pH sensors or temperature sensors upstream, downstream, or inside one or more water treatment devices.

[0160] The aspects and embodiments disclosed herein also include methods for modifying existing wastewater treatment systems to perform the methods disclosed herein. Modifying an existing wastewater treatment system may include programming local and / or remote controllers of the wastewater treatment system to perform embodiments of the AI ​​algorithms disclosed herein. Alternatively, a service provider may provide a customer with a non-transitory computer-readable medium including instructions that, when executed on a local and / or remote controller of the wastewater treatment system, cause the wastewater treatment system to perform embodiments of the AI ​​algorithms disclosed herein. The service provider may provide the customer with written, oral, or electronic instructions explaining how to operate the AI ​​algorithm and how to interpret and react to its output.

[0161] Example 1: Hybrid bed tanks - a recommendation to reduce site access

[0162] One customer site has had its DI tanks replaced three times in the past year.

[0163] The tank is configured as follows:

[0164] • Dimensions: 1.2 cubic feet

[0165] • One mixed-bed working vessel and one mixed-bed purifier vessel (2 vessels in total)

[0166] • Average water supply conductivity: 394 microSiemens (μS / cm) 2 )

[0167] Each water exchange was carried out thoroughly, processing approximately 700 to 1000 gallons of water. Total gallons consumed: approximately 2553.

[0168] The total cost of the three exchanges is approximately $1,000.

[0169] The capacity of the mixed-bed DI resin is 7600 grains per cubic foot.

[0170] 24.4 is the conversion factor from micro-Siemens to grains / gallon, for example (μS / cm). 2 (grit / gallon). The theoretical maximum of 2553 gallons would require __X__ tanks. If the site remains 1.2 ft. 3 The number of tanks: 2553 / {[24.4×(1.2×7600)] / 394}=4.5 (because the system uses 1 purifier tank, so it is rounded down to 4 working tanks).

[0171] If the venue is switched to use 3.6ft 3 The number of tanks: 2553 / {[24.4×(3.6×7600)] / 394}=1.5 (rounded up to two tanks - one purifier tank and one working tank).

[0172] Savings: From $1,000 for three trips to $333 for one trip → saving approximately $666.

[0173] Therefore, significant savings can be achieved by reducing the number of annual customer site visits. This also provides customers with a higher level of reliability, as the time between each deionization cylinder replacement would be much longer for deionization as a batch process. Furthermore, there are fewer quality alerts, which should indicate superior water quality.

[0174] Example 2: Revision order reduced

[0175] After implementing the tank replacement prediction model as disclosed herein, positive trends have been identified in site efficiency related to the number of Service Change Orders (SVOs) created for each site and the amount of resin consumed per replacement, while maintaining the desired level of quantity and quality of water delivered to customers.

[0176] Inspection and revision order analysis - One metric for site efficiency is SVO count / site.

[0177] • Before model implementation - From FY1 to FY2, the number of IoT devices (sites or functional locations) increased by 21.6%, and the number of SVOs increased by 73.0%. Compared to the previous fiscal year, the number of SVOs / devices increased by 42.3%, and the labor cost expenditure per site was $158 (totaling $245k).

[0178] • After model implementation – from FY 2 to FY 3, the number of IoT devices increased by 19.3%, and the number of SVOs increased by 28.7%. Compared to FY 1 to FY 2, the number of SVOs / devices increased by only 7.8%, and labor costs per site decreased by $132 (total cost savings of $243k).

[0179] Most of these savings occur during the last five months of the fiscal year in which the model is implemented.

[0180] Please note that savings can still be achieved even if the average labor rate increases from FY2 to FY3.

[0181] Data on the number of active equipment, quality alarms, SVO counts, and labor costs for fiscal years 1 through 3 are respectively available at [link to relevant documentation]. Figure 7 and Figure 8 The table indicates this.

[0182] Figure 7 The data shows that while the annual growth rate of equipment counts remained largely unchanged (around 20%), the number of SVOs (Specialized Vehicles) undergoing maintenance at these sites grew at a much slower rate. The “Linear Ratios” list indicates that the SVO count / site ratio should have been around 3.8 in FY3, but due to efficiency improvements associated with the project, the SVO count / site ratio was only 3.5.

[0183] Figure 7 and Figure 8 The data also shows that from FY1 to FY2, the number of IoT devices increased by 21.6%, and the number of SVOs increased by 73.0%. Compared to FY1, the number of SVOs / devices increased by 42.3% in FY2, and labor costs per site increased by $158 (totaling $245k). From FY2 to FY3, the number of IoT devices increased by 19.3%, and the number of SVOs increased by 28.7%. Compared to FY2, the number of SVOs / devices increased by only 7.8% in FY3, and labor costs per site decreased by $132 (totaling $243k in savings).

[0184] Resin consumption - Site efficiency is highest when replacement occurs at 0% remaining capacity and the effective worker quality alarm is activated.

[0185] The tank swapping prediction model was implemented in May of FY3.

[0186] • Before model implementation – from FY 1 to FY 2 – the average remaining capacity at the time of turnover for this fiscal year was 29%.

[0187] • After model implementation – from FY2 to FY3 – the average remaining capacity at the time of replacement in FY3 was 25%, indicating a 5% year-over-year increase in resin usage. When only the months from May to September, when the machine learning model was in place, were considered, the average remaining capacity at the time of replacement decreased to 22%, a 7% increase compared to FY2.

[0188] Since the model was implemented, the amount of resin used has effectively increased by 24%, ((29%-22%) / 29%) = 24.1%.

[0189] • Post-model data – starting from FY 3 – The first month of this fiscal year saw the average remaining capacity at the time of turnover continue to decline to 18%, continuing the trend of bringing the average remaining capacity close to 0%.

[0190] Figure 9 The chart shows the remaining capacity during the monthly rollover from October 2 of FY2 to October 3 of FY3.

[0191] Example 3: Avoid revision orders targeting false alarms

[0192] At one customer site, several worker tank alarms have been triggered since the most recent tank replacement in February 2021. Based on a previous review of manual alarm classification, this seemed quite reasonable: a tank replacement inspection order could have been in place at any point when a worker alarm occurred. Under the control of an AI algorithm, it considered not only worker quality alarms but also the fact that the site still had expected capacity (remaining capacity >30%) and recent usage, and advised the user to monitor the site. As of December 2021, the site was still producing in-spec water and depleting the tank as much as possible. Figure 10 A visual representation of the cumulative traffic and spurious worker alarm events through the DI system is shown, where vertical lines represent instances of spurious worker tank alarms.

[0193] Example 4: Delay creating revision orders to confirm their necessity

[0194] Sites with optimally configured conductivity and flow pattern variables exhibit the recommended progression from no action to monitoring to the creation of SVO.

[0195] Figure 11 A table is shown that displays the resin consumption and gallons between tank exchanges for this site. Figure 12 A graph showing the cumulative water and equipment tank alarm events processed between swaps is displayed.

[0196] When a replacement occurs while water that meets specifications is still being supplied, a 10 / 21 / 21 replacement is correctly calculated. For a 10 / 21 / 21 replacement, the AI ​​algorithm's recommendations range from no action to creating an SVO.

[0197] The 12 / 15 / 21 swap is an example of an AI algorithm that suggests waiting one day to perform the swap, even though the worker tank quality alarm occurred on 12 / 14. The algorithm appropriately suggested monitoring on 12 / 14 because not all available sensor data meets the criteria for creating an SVO. When checking additional sensor data, the extra day allows for an additional 3% resin consumption.

[0198] Three of the last four replacements occurred between days 23 and 26, utilizing most of the resin. Only the replacement that occurred after 20 days was correctly identified because this cycle had a higher daily consumption rate than normal, and therefore the resin was depleted more quickly.

[0199] Having described several aspects of at least one embodiment of the present disclosure, it should be appreciated that various changes, modifications, and improvements will readily occur to those skilled in the art. Such changes, modifications, and improvements are intended to be part of this disclosure and are intended to be within the spirit and scope of this disclosure. Therefore, the foregoing description and drawings are merely examples.

[0200] The wording and terminology used herein are for descriptive purposes and should not be considered restrictive. As used herein, the term "a plurality of" refers to two or more items or components. The terms "comprising," "including," "carrying," "having," "comprise," and "involving," whether in the written description or in the claims, are open-ended terms, meaning "including but not limited to." Therefore, the use of such terms is intended to cover the items listed thereafter and their equivalents, as well as additional items. For claims, only the transitional phrases "consisting of..." and "consisting substantially of..." are closed or semi-closed transitional phrases, respectively. The use of sequential terms such as "first," "second," and "third" to modify claim elements in claims does not, in itself, imply any priority, order, or chronological sequence of actions performed by one claim element relative to another claim element, but is merely used as labels to distinguish one claim element with a specific name from another element with the same name (but using an ordinal number), thus differentiating claim elements.

Claims

1. A method for treating water in a water treatment system, the method comprising: The water to be treated is introduced into the ion exchange bed of the water treatment system to produce treated water; Receive output water quality indication from the controller associated with the ion exchange bed; In response to the output water quality indication, an algorithm determines whether to replace the ion exchange bed based on the remaining capacity of the ion exchange bed, the current operating parameters of the water treatment system, and historical data on the operation of the water treatment system. In response to the water quality indication, the algorithm provides a recommendation to the service provider of the water treatment system for one of the following: no action is required, the ion exchange bed should be monitored, or a revision order for replacing the ion exchange bed should be generated; as well as Based on the current configuration of the water treatment system, the average volume of water treated in each time period, and the previous ion exchange bed maintenance history of the water treatment system or one of the other water treatment systems, the algorithm determines and provides a recommended ideal ion exchange capacity for the water treatment system. The proposed ideal ion exchange capacity includes the proposed number and size of ion exchange beds for the water treatment system, which will result in the replacement of the ion exchange beds of the water treatment system after a predetermined period of time.

2. The method according to claim 1, wherein, The algorithm also determines and provides an estimate of the cost savings resulting from the proposed ideal ion exchange capacity.

3. The method according to claim 1, wherein, The algorithm also determines and provides a recommended current ion exchange capacity for the water treatment system based on data regarding historical ion exchange bed replacements, historical conductivity readings of water introduced into the water treatment system, and historical averages of the amount of water treated during the replacement cycle prior to a water quality alarm occurring in the water treatment system or one of the other water treatment systems.

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