Description:TECHNICAL FIELD
[0001] The present disclosure pertains to the field of water purification and quality control systems. More specifically, the present disclosure relates to an artificial intelligence (AI)-enabled Total Dissolved Solids (TDS) regulation system.

BACKGROUND
[0002] TDS fluctuations in water are common and may include concentrations of ions like arsenic, lead, and fluoride, while excessively low TDS water may lack essential minerals. This poses significant health risks if unregulated.
[0003] Conventional Total Dissolved Solids (TDS) control systems employed in residential and commercial water purification setups typically utilize manually operated valves and filters. These systems are commercially available in various sizes to accommodate differing water flow requirements. However, such traditional architectures are inherently limited in their ability to adapt to dynamic variations in feedwater quality and generally require frequent manual intervention by trained technicians for calibration and operation.
[0004] A significant drawback of existing TDS controllers lies in their lack of real-time analytical capability and automated system diagnostics. As a result, inconsistencies in output water quality and inefficient utilization of water and energy resources are common. The systems are generally incapable of identifying or responding to sudden fluctuations in the TDS levels of the incoming municipal water supply. Moreover, the calibration of manual or semi-automated valves is prone to human error, which often leads to deviations from the desired water quality.
[0005] Furthermore, existing solutions do not allow for personalization or intelligent blending of purified high TDS and low TDS water streams based on user-specific mineral content preferences. The inability to perform predictive adjustments or to factor in historical usage patterns and environmental trends contributes further to suboptimal system performance.
[0006] Municipal and well water sources are known to exhibit significant variability in drinking water quality over time, often containing elevated and unhealthy concentrations of total dissolved solids. In such contexts, the need for precise and adaptive TDS control becomes critically important to ensure safe and consistent drinking water.
[0007] The present invention addresses these shortcomings by providing an intelligent, self-regulating TDS control system that utilizes an artificial intelligence (AI) enabled system integrated with real-time sensor networks and a proprietary mobile interface. Users may define their desired TDS range via the mobile application, and the AI enabled system dynamically calculates optimal blending ratios using real-time sensor readings, historical data, and predefined heuristics. Valve modulation is executed through a precision-controlled stepper motor, enabling granular adjustments that reduce water wastage and minimize strain on purification membranes.
[0008] There is, therefore, a need to address the challenges by offering a sustainable, low-maintenance, and user-centric solution for modern water purification needs by embedding adaptive intelligence into everyday water usage environments.

OBJECTS OF THE PRESENT DISCLOSURE
[0009] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0010] An object of the present disclosure is to provide an AI-enabled Total Dissolved Solids (TDS) control system for water purification units, capable of dynamically regulating TDS levels in real time based on user-defined preferences and fluctuating feedwater characteristics.
[0011] Another object of the present disclosure is to offer a cloud-connected system that receives user inputs via a proprietary mobile application and transmits desired TDS settings to the control unit of the water purification system.
[0012] Another object of the present disclosure is to integrate different types of sensors within different water supply channels to continuously monitor and relay real-time water quality data to an embedded microcontroller.
[0013] Another object of the present disclosure is to deploy a lightweight artificial intelligence model within the control system that analyzes real-time sensor data, historical usage patterns, and predefined heuristics to predictively modulate a motorized blending valve.
[0014] Another object of the present disclosure is to maintain user-specific TDS ranges by enabling fine-grained, step-wise control over the valve actuation through a stepper motor, ensuring efficient blending of treated and raw water streams.
[0015] Another object of the present disclosure is to provide a low-maintenance, self-regulating system that continuously adapts to changes in feedwater quality and usage behaviour, thus optimizing membrane health and minimizing resource wastage.
[0016] Another object of the present disclosure is recognition of understanding that readings of sensors and actuators change over time even with exact same input parameters and hence need intelligent baseline correction through AI inputs.
[0017] Another object of the present disclosure is to enable a sustainable and intelligent water purification ecosystem that is suitable for both residential and commercial applications, ensuring safe, healthy, and personalized drinking water quality.

SUMMARY
[0018] Various aspects of the present disclosure pertain to the field of water purification and quality control systems, and more specifically, to an artificial intelligence (AI)-enabled Total Dissolved Solids (TDS) regulation system. The invention is particularly applicable to managing and maintaining optimal TDS levels in drinking water supplied through municipal pipelines, such as those commonly found in urban regions of India. The system ensures real-time regulation of mineral content in water through adaptive sensor feedback, Internet of Things (IoT)-based user interfacing, and cloud-connected control logic.
[0019] An aspect of the present disclosure pertaining to a system for regulating the TDS level of water in a household or industrial water purification setup is disclosed. The system includes a plurality of sensors that may be configured to provide real-time data related to TDS, pressure, current and flow to a controller. The plurality of sensors includes at least one TDS sensor is configured to measure the TDS level of water at one or more locations including feed, permeate, and concentrate lines; at least one pressure sensor is configured to detect hydraulic pressure; and at least one flow sensor is configured to monitor water flow rate.
[0020] Furthermore, a microcontroller is operably coupled to the plurality of sensors and is configured to receive sensor data in real time and execute control commands for TDS adjustment. A stepper motor is electrically coupled to the microcontroller via a motor driver circuit, where the motor driver circuit is configured to receive digital control signals from the microcontroller and convert them into actuation signals for the stepper motor. A precision-controlled valve is mechanically coupled to the stepper motor, and is configured to modulate a mixing ratio between a high-TDS water stream and a low-TDS water stream to control the output TDS level. The input data is dynamically analysed and base-line corrected through AI enabled system. The system further includes a user interface communicatively coupled to the microcontroller via a cloud-based server, wherein the user interface is configured to receive user-defined target TDS values and transmit control preferences to the microcontroller.
[0021] In an aspect, the system further includes an artificial intelligence (AI) model trained and continuously updated through real time sensor data, including conductivity variations, pressure, and flow trends. The AI model is configured to compute a control profile comprising a delta between a measured TDS level and the user-specified target TDS level; an inference of required valve adjustment steps based on learned regression parameters that consider valve hysteresis and flow dynamics; and an adaptive actuation instruction for the stepper motor. The stepper motor executes incremental valve adjustments in response to the AI-generated control profile such that the water TDS level is dynamically modulated to converge toward the user-specified target.
[0022] In an aspect, the cloud-based server is further configured to receive real-time sensor data from the microcontroller, including total dissolved solids (TDS), pressure, and flow measurements; process the data to generate predictive control parameters using a trained machine learning model; transmit inference results to the microcontroller to drive the stepper motor actuation for controlling the precision-controlled valve; and receive and store user preferences via the user interface to generate personalized TDS control profiles.
[0023] In an aspect, the microcontroller is further configured to generate direction signals based on AI-inferred control parameters; transmit said signals to the stepper motor via the motor driver circuit; and control the actuation of the stepper motor in discrete increments. The precision-controlled valve actuation instruction is computed based on adaptive stepper control logic that considers flow dynamics and valve hysteresis, ensuring precise regulation under changing hydraulic conditions. The user interface is configured to display real-time TDS data, system performance metrics, and convergence status, and optionally allows manual override of control parameters.
[0024] Another aspect of the present disclosure pertains to a method for regulating the Total Dissolved Solids (TDS) level in a system using an AI-driven control mechanism includes the steps of receiving, at a cloud-based server, real-time sensor data from a microcontroller, where the sensor data comprises TDS level, water pressure, and flow rate; processing the received sensor data using a trained machine learning model stored on the cloud-based server to compute control parameters for adjusting a stepper motor coupled to a precision-controlled valve; transmitting the computed control parameters from the cloud-based server to the microcontroller for execution; receiving user-defined TDStargets and personalization preferences from a user interface associated with the system; updating the machine learning model on the cloud-based server based on historical sensor data, valve actuation outcomes, and feedback data from the system; and generating and refining personalized TDS control profiles for specific users, wherein the cloud-based server dynamically adapts blending strategies based on environmental and usage patterns. This AI-based architecture not only regulates TDS dynamically but evolves with environmental trends and user behaviour, removing manual intervention and enhancing water safety and personalization. The system further comprises triggering an alert or safety shutoff when the resultant TDS value cannot be brought to a permissible tolerance band for a predetermined duration.
[0025] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which numerals represent like components.

BRIEF DESCRIPTION OF DRAWINGS
[0026] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in, and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure, and, together with the description, explain the principles of the present disclosure.
[0027] FIG. 1 illustrates an exemplary architecture of a system for regulating the TDS level of water in a household or industrial water purification setup, in accordance with an embodiment of the present disclosure.
[0028] FIG. 2 illustrates an exemplary architecture of the module diagram of a system for regulating the TDS level of water in a household or industrial water purification setup, in accordance with an embodiment of the present disclosure.
[0029] FIG. 3 illustrates an exemplary view of a flow diagram of the proposed method for regulating the Total Dissolved Solids (TDS) level in a system (100) using an AI-driven control mechanism, in accordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION
[0030] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
[0031] The proposed present disclosure enables the regulation of Total Dissolved Solids (TDS) in a water system (100) using an AI-driven control method. A microcontroller (104) collects real-time data from sensors measuring TDS, pressure, and flow rate, and sends it to a cloud-based server (114). A trained machine learning model processes this data to compute control signals for a stepper motor (106) that adjusts a precision valve (110) to regulate water blending. User-defined TDS targets are received via a user interface (112), and the system adapts over time by updating the model using historical data and user feedback. Personalized TDS control profiles are generated based on environmental and usage patterns. Additionally, the system triggers alerts or shutoff if TDS exceeds a critical threshold, ensuring safety and reliability. The system is implementable using standard hardware and software components without undue experimentation.
[0032] The manner in which the proposed system works is described in further detail in conjunction with FIGs. 1 to 3. It may be noted that these figures are only illustrative and should not be construed to limit the scope of the subject matter in any manner.
[0033] FIG. 1 illustrates an exemplary architecture of a system for regulating the TDS level of water in a household or industrial water purification setup, in accordance with an embodiment of the present disclosure.
[0034] In an embodiment, referring to FIG. 1, a smart AI-driven Total Dissolved Solids (TDS) Control system 100 (interchangeably referred to as a system 100, hereinafter), tailored for integration into household and industrial drinking water filtration systems. The system 100 embodies a closed-loop intelligent control mechanism that dynamically adjusts the water TDS concentration to match a user-specified target, delivering high precision and adaptability across varying input water conditions.
[0035] In an embodiment, the system 100 can include a plurality of sensors 102 (interchangeably referred to as sensors 102, hereinafter) may be configured to provide real-time data related to TDS, pressure, and flow to a microcontroller 104 can refer a group of different sensor types installed within the system 100, each designed to monitor specific water quality and system parameters in real time and transmit that information to a central controller (e.g., a microcontroller 104). The sensors 102 include:
1. At least one TDS sensor 102A: The TDS sensor 102A is specifically designed to measure the Total Dissolved Solids (TDS) in water. It may be positioned at multiple points in the system 100 such as the feed line, permeate line, and concentrate line to track how the TDS level changes throughout the water purification process.
2. At least one pressure sensor 102B: The pressure sensor 102B monitors hydraulic pressure within the system 100. Detecting pressure levels is critical to ensure that the system 100 operates within safe and optimal pressure ranges, which also affects filtration performance and system integrity.
3. At least one flow sensor 102C: The flow sensor 102C measures the flow rate of water through the system 100. Monitoring flow helps in controlling output volume, efficiency, and detecting blockages or anomalies in operation.
[0036] In an embodiment, together, the sensors 102 collect essential real-time data and send it to the controller 104, which uses the input to regulate operations particularly for adjusting valves or motors to maintain desired TDS levels and ensure the system runs efficiently and safely.
[0037] In an embodiment, the system 100 can include a microcontroller 104 is operably coupled to the sensors 102 and serves as the central processing unit within the system 100. The system 100 may be configured to receive real-time data from the TDS sensor 102A, pressure sensor 102B, and flow sensor 102C. Upon receiving this data, the microcontroller 104 analyzes the input or forwards it to a cloud-based server 114 for advanced processing. Based on the computed control parameters received from the cloud-based server 114 or pre-programmed logic in standalone operation, the microcontroller 104 executes control commands to regulate the Total Dissolved Solids (TDS) level. This includes actuating a stepper motor 106 that adjusts a precision-controlled valve 110, thereby modifying water blending or flow to maintain the user-defined TDS target. The microcontroller 104 can ensure synchronized operation between sensors, actuators, and the control logic to enable dynamic, real-time water quality regulation.
[0038] In an embodiment, the system can include a stepper motor 106 that is electrically coupled to the microcontroller 104 via a motor driver circuit 108. The motor driver circuit 108 may be configured to act as an interface between the microcontroller 104 and the stepper motor 106. The stepper motor 106 receives digital control signals from the microcontroller 104 and converts them into actuation signals that drive the stepper motor 106 with precise motion control. The stepper motor 106 is, in turn, mechanically coupled to a precision-controlled valve 110.
[0039] In an embodiment, the motor driver circuit 108 can serve as an essential interface between the microcontroller 104 and the stepper motor 106. Its primary function is to receive low-power digital control signals, such as step and direction commands, from the microcontroller 104 and convert them into higher-power electrical signals suitable for driving the stepper motor 106 coils.
[0040] In an embodiment, the motor driver circuit 108 can manage the precise sequencing and current regulation required to control the stepper motor’s movement accurately, enabling the stepper motor 106 to rotate in discrete steps. By doing so, it can ensure the stepper motor 106 can execute fine, controlled adjustments to the precision-controlled valve 110 position, thereby modulating the water blending ratio to achieve the desired Total Dissolved Solids (TDS) level. The motor driver circuit 108 is designed to interface between the microcontroller and the stepper motor, enabling safe and efficient translation of control signals into motor actuation. It incorporates protection and amplification elements to ensure reliable operation under varying load conditions.
[0041] In an embodiment, the precision-controlled valve 110 may be designed to modulate the mixing ratio between a high-TDS water stream and a low-TDS water stream. By adjusting the valve position using the stepper motor 106, the system 100 can dynamically regulate the blending of water sources, thereby controlling the Total Dissolved Solids (TDS) level of the output water. This precise actuation mechanism allows for fine-tuned TDS adjustments based on real-time sensor data and user-defined preferences.
[0042] In an embodiment, the system can feature a user interface 112 that is communicatively coupled to the microcontroller 104 through a cloud-based server 114. This means the user interface 112, typically a mobile app or web portal connects to the system 100 remotely over the internet via the cloud-based server 114.
[0043] In an embodiment, the user interface 112 can allow users to input their desired target TDS values and set personalized control preferences for the water purification system 100. The inputs are sent securely to the cloud-based server 114, which processes and routes this information to the microcontroller 104 in real time.
[0044] In an embodiment, the cloud-based server 114 (interchangeably referred to as server 114, hereinafter) can act as an intermediary and central hub that not only facilitates two-way communication between the user interface 112 and the microcontroller 104 but also hosts the AI or machine learning models. It receives sensor data from the microcontroller 104, performs advanced data processing and analysis, and sends back control commands or update dparameters to the microcontroller 104. This cloud connection enables real-time system monitoring, remote control, and adaptive tuning of the water TDS regulation based on user preferences and environmental data.
[0045] FIG. 2 illustrates an exemplary architecture of the module diagram of a system for regulating the TDS level of water in a household or industrial water purification setup, in accordance with an embodiment of the present disclosure.
[0046] In an exemplary embodiment, referring to FIG. 2, a system 100 may include one or more processors 204 (interchangeably referred to as processors 204, hereinafter). The processor 204 may be implemented as one or more microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions. Among other capabilities, the processors 204 may be configured to fetch and execute computer-readable instructions stored in a memory 206 of the system 200. The memory 206 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory 206 may include any non-transitory storage device, including, for example, volatile memory such as Random Access Memory (RAM), or non-volatile memory such as Erasable Programmable Read-Only Memory (EPROM), flash memory, and the like.
[0047] The system 200 may include an interface(s) 208. The interface(s) 208 may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 208 may facilitate communication to/from the system 100. The interface(s) 208 may also provide a communication pathway for one or more components of the system 100. Examples of such components include, but are not limited to, processing unit/engine(s) 210 and a database 202.
[0048] In an embodiment, the processing unit/engine(s) 210 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) 210. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) 210 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) 210 may include a processing resource (for example, one or more processors 204), to execute such instructions.
[0049] In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) 210. In such examples, the system 200 may include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system 200 and the processing resource. In other examples, the processing engine(s) 210 may be implemented by electronic circuitry.
[0050] In an embodiment, the database 202 may include data that may be either stored or generated as a result of functionalities implemented by any of the components of the processor 204 or the processing engine 210. In an embodiment, the database 202 may be separate from the system 200. Moreover, the database 202 is a centralized data storage component within the system 200, designed to store and manage all relevant information related to water quality control.
[0051] In an embodiment, the database 202 can include real-time and historical sensor data such as Total Dissolved Solids (TDS) measurements, pressure readings, and flow rates collected from the sensors 102. Furthermore, the database 202 maintains user-defined preferences and target TDS values, system performance logs, valve actuation records, and feedback data. By organizing this data efficiently, the database 202 enables the AI and machine learning models to access historical trends and patterns, facilitating accurate predictive analysis and adaptive control of the water purification process. The database 202 supports continuous learning by storing updates generated during system operation, thus helping to improve control precision and personalize water quality regulation for different users and environments.
[0052] In an exemplary embodiment, the processing engine 210 may include one or more engines selected from any of a sensor monitoring engine 212, a data acquisition and processing engine 214, an AI Interface engine 216, a close loop feedback engine 218, a cloud based learning and optimization engine 220, and a stepper motor control engine 222.
[0053] In an embodiment, the sensor monitoring engine 212 is a dedicated software module within the system 100 responsible for continuously collecting and processing real-time data from the sensors 102, including the TDS sensor, pressure sensor, and flow sensor. The engine 212 ensures accurate and timely acquisition of water quality and system performance parameters by managing sensor communication, data validation, filtering, and error checking. By maintaining a reliable stream of high-quality sensor data, the sensor monitoring engine 212 provides the foundational input necessary for the AI-driven control algorithms to make informed decisions. It detects anomalies or sudden changes in sensor readings and can trigger alerts or initiate corrective actions, thereby supporting the overall stability and responsiveness of the TDS regulation system.
[0054] In an embodiment, the data acquisition and processing engine 214 is a core component responsible for collecting raw sensor data from the sensor monitoring engine 212 and performing initial processing to prepare the data for analysis. This includes tasks such as signal conditioning, filtering noise, normalization, and aggregation of TDS, pressure, and flow measurements. The engine 214 transforms raw sensor inputs into meaningful, clean datasets that accurately reflect current water quality and system conditions. It also formats and organizes this data for efficient use by the AI model and control algorithms. By ensuring high-quality, real-time data availability, the data acquisition and processing engine 214 enables precise and adaptive control of the stepper motor and valve to maintain the desired TDS level.
[0055] In an embodiment, the AI Interface engine 216 can act as the critical communication bridge between the system’s real-time data processing components and the embedded artificial intelligence (AI) model. Its primary function is to interpret processed sensor data and user inputs, and then transmit this information to the AI model for analysis and decision-making. The AI Interface Engine 216 receives control parameters and predictions generated by the AI, such as valve adjustment steps or motor actuation commands, and translates them into actionable instructions for the microcontroller 104 and motor driver circuits 108. It also facilitates continuous feedback by sending system performance data back to the AI model for learning and optimization. This engine 216 ensures seamless integration of AI-driven intelligence into the TDS regulation system, enabling adaptive, precise, and personalized water quality control.
[0056] In an embodiment, the close loop feedback engine 218 is a crucial component that enables the system 100 to continuously monitor and adjust the water quality control process in real time. It receives post-actuation sensor data such as updated TDS levels, pressure, and flow measurements, after the precision-controlled valve has been adjusted by the stepper motor 106. By comparing the actual water quality outcomes with the user-defined target TDS values, the feedback engine calculates any deviations or errors and relays this information back to the AI Interface Engine 216 and control algorithms. This continuous feedback loop allows the system 100 to dynamically refine valve adjustments, correct inaccuracies, and improve future control decisions, ensuring precise convergence toward the desired water quality while adapting to changing input conditions and system behaviours.
[0057] In an embodiment, the cloud based learning and optimization engine 220 is a powerful remote processing unit responsible for analyzing aggregated historical and real-time sensor data collected from multiple systems. Leveraging advanced machine learning algorithms, this engine 220 continuously trains andrefines predictive models that optimize the control strategies for regulating Total Dissolved Solids (TDS) levels. By processing extensive datasets including conductivity variations, flow dynamics, valve actuation outcomes, and user preferences, the engine 220 generates improved control parameters and adaptive algorithms. These optimized models are then sent back via the cloud to the local system to enhance the precision and efficiency of valve adjustments. This continuous learning process enables the system to evolve, providing personalized, robust, and energy-efficient water quality management tailored to varying environmental conditions and user needs.
[0058] In an exemplary embodiment, the stepper motor control engine 222 is a dedicated subsystem responsible for translating AI-generated control signals into precise mechanical movements of the stepper motor 106. It receives digital actuation commands such as step counts and direction signals from the microcontroller 104 or the AI interface engine 216 and converts these into controlled electrical pulses through the motor driver circuit. This engine 222 manages the incremental and accurate positioning of the precision-controlled valve, ensuring that the valve modulates the mixing ratio between high-TDS and low-TDS water streams with fine granularity. By precisely controlling the stepper motor’s operation, the Stepper Motor Control Engine 222 enables smooth, responsive adjustments that maintain the desired TDS levels while compensating for mechanical factors like valve hysteresis and flow variations.
[0059] FIG. 3 illustrates an exemplary view of a flow diagram of the proposed method for regulating the Total Dissolved Solids (TDS) level in a system (100) using an AI-driven control mechanism, in accordance with some embodiments of the present disclosure.
[0060] As illustrated, referring to FIG. 3, a method 300 for regulating the Total Dissolved Solids (TDS) level in a system 100 using an AI-driven control mechanism. The method 300 can include multiple steps, including:
[0061] At step 302, the method 300 may involve receiving, at a cloud-based server 114, real-time sensor data transmitted from a microcontroller 104, the sensor data including measurements of total dissolved solids (TDS) level, water pressure, and flow rate, thereby enabling continuous monitoring of water quality and system conditions for further processing and control.
[0062] Continuing further, at step 304, the method 300 may involve processing the received sensor data at the cloud-based server 114 by applying a trained machine learning model stored therein, where the processing involves analyzing the sensor data to compute control parameters that determine the precise adjustments required for actuating a stepper motor 106 mechanically coupled to a precision-controlled valve 110.
[0063] Continuing further, at step 306, the method 300 may involve transmitting the computed control parameters from the cloud-based server 114 to the microcontroller 104, thereby enabling the microcontroller 104 to execute control commands for adjusting the stepper motor 106 and modulating the precision-controlled valve 110 accordingly.
[0064] Continuing further, at step 308, the method 300 may involve receiving, at the cloud-based server 114 or the microcontroller 104, user-defined total dissolved solids (TDS) target values and personalization preferences through a user interface 112 associated with the system 100, enabling customization of the TDS regulation parameters according to individual user requirements.
[0065] Continuing further, at step 310, the method 300 may involve updating the machine learning model stored on the cloud-based server 114 by incorporating historical sensor data, valve actuation outcomes, and feedback data received from the system 100, thereby refining the model’s predictive accuracy and improving future control parameter computations.
[0066] Continuing further, at step 312, the method 300 may involve generating and continuously refining personalized TDS control profiles for specific individual users.
[0067] Working Example: Suppose the user desires a TDS level of 150 ppm for drinking water. The input water has a fluctuating TDS between 250–400 ppm. The mobile application sends the target TDS to the cloud, which routes the command to the machine through the server. The AI model evaluates the real-time sensor readings - including TDS (e.g., 320 ppm), pressure, and flow rate, infers an optimal stepper motor correction (e.g., 38 steps clockwise to increase the low-TDS flow), and actuates accordingly. Data from the pressure and flow sensors help the model assess whether the flow conditions are optimal for adjustment and ensure that the valve movement aligns with actual hydraulic behavior.
[0068] After a brief stabilization period, the TDS sensor reports the new level (e.g., 160 ppm), and a minor backward correction (e.g., 3 steps counterclockwise) is issued. This iterative adjustment continues until the output stabilizes close to the desired 150 ppm. Over time, the model learns from these behaviors, factoring in the influence of pressure and flow variations thereby reducing convergence time and improving stability.
[0069] Product Integration Perspective: The entire solution is embedded in a compact PCB & a dedicated app, enabling seamless integration into modern water purifiers, dispensers, or filtration systems. The use of standard communication protocols and low-power microcontrollers ensures scalability for both domestic and industrial deployment. The final equilibrium is reached through a closed feedback-based control strategy; however, the control itself is dynamic and relies on many real-time inputs.
[0070] In summary, this present disclosure provides an AI-driven system for regulating Total Dissolved Solids (TDS) in water filtration setups. It uses real-time sensor data, cloud-based machine learning, and a motorized valve to precisely control water quality according to user-defined targets via a mobile interface. The system continuously learns and adapts to changing conditions, ensuring consistent, personalized water purification with minimal manual human intervention.
[0071] While the foregoing describes various embodiments of the disclosure, other and further embodiments of the disclosure may be devised without departing from the basic scope thereof. The scope of the disclosure is determined by the claims that follow. The described embodiments, versions, or examples are provided solely to enable those skilled in the art to make and use the disclosure in conjunction with their general knowledge and commonly available information.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0072] The present disclosure offers a major advantage over conventional mechanical TDS regulators which are static and slow to adapt. The incorporation of AI enables predictive adjustments based on contextual water behavior, making the regulation process both responsive and self-improving.
[0073] The present disclosure dynamically adjusts TDS levels based on continuous sensor feedback, ensuring consistent water quality despite fluctuations in input water conditions.
[0074] The present disclosure allows for customized water mineral content tailored to individual health needs and preferences.
[0075] The present disclosure reduces human error and improving control over water blending processes.
[0076] The present disclosure minimizes water wastage and reduces strain on filtration membranes.
[0077] The present disclosure enhances user convenience and enables ongoing system updates and learning.
[0078] The present disclosure updates an AI model using historical and real-time data, improving performance and adapting to changing environmental and usage patterns.
[0079] The present disclosure reduces the need for manual intervention and technician visits, lowering operational costs.
[0080] The present disclosure can trigger alerts or shutoffs when TDS levels exceed safe thresholds, ensuring user safety.
, Claims:1. A system (100) for regulating the TDS level of water in a household or industrial water purification setup, comprising:
a plurality of sensors (102) configured to provide real-time data related to TDS, pressure, and flow to a controller, comprising:
at least one TDS sensor (102A) configured to measure the TDS level of water at one or more locations comprising feed, permeate, and concentrate lines;
at least one pressure sensor (102B) configured to detect hydraulic pressure; and
at least one flow sensor (102C) configured to monitor water flow rate to the controller.
a microcontroller (104) operably coupled to the plurality of sensors, configured to receive sensor data in real time and execute control commands for TDS adjustment;
a stepper motor (106) electrically coupled to the microcontroller (104) via a motor driver circuit (108), wherein the motor driver circuit (108) configured to receive digital control signals from the microcontroller (104) and convert them into actuation signals for the stepper motor (106);
a precision-controlled valve (110) mechanically coupled to the stepper motor (106), wherein the precision-controlled valve (110) configured to modulate a mixing ratio between a high-TDS water stream and a low-TDS water stream to control the output TDS level; and
a user interface (112) communicatively coupled to the microcontroller (104) via a cloud-based server (114), wherein the user interface (112) configured to receive user-defined target TDS values and transmit control preferences to the microcontroller (104).
2. The system (100) as claimed in claim 1, further comprises an artificial intelligence (AI) model trained using historical sensor data comprising conductivity variation, pressure, and flow trends.
3. The system (100) as claimed in claim 2, wherein the AI model computes a control profile comprising:
a delta between a measured TDS level and the user-specified target TDS level;
an inference of required valve adjustment steps based on trained regression parameters that consider valve hysteresis and flow dynamics; and
an adaptive actuation instruction for the stepper motor (106).
4. The system (100) as claimed in claim 1, wherein the stepper motor (106) executes incremental valve adjustments in response to the AI-generated control profile, such that the water TDS level is dynamically modulated to converge toward the user-specified target.
5. The system (100) as claimed in claim 1, wherein the cloud-based server (114) configured to:
receive real-time sensor data from the microcontroller (104), comprising total dissolved solids (TDS), pressure, and flow measurements;
process said data to generate predictive control parameters using a trained machine learning model;
transmit inference results to the microcontroller (104) to drive the stepper motor (106) actuation for controlling the precision-controlled valve (110); and
receive and store user preferences via the user interface (112) to generate personalized TDS control profiles.
6. The system (100) as claimed in claim 1, wherein the microcontroller (104) configured to:
generate direction signals based on AI-inferred control parameters;
transmit said signals to the stepper motor (106) via the motor driver circuit (108); and
control the actuation of the stepper motor (106) in discrete increments.
7. The system (100) as claimed in claim 1, wherein the precision-controlled valve (110) actuation instruction is computed in terms of absolute or relative step counts based on flow dynamics and compensation for valve hysteresis.
8. The system (100) as claimed in claim 1, the user interface (112) configured to display real-time TDS data, system performance metrics, and convergence status, and optionally allows manual override of control parameters.
9. A method (300) for regulating the Total Dissolved Solids (TDS) level in a system (100) using an AI-driven control mechanism, comprising the steps of:
receiving (302), at a cloud-based server (114), real-time sensor data from a microcontroller (104), wherein the sensor data comprises TDS level, water pressure, and flow rate;
processing (304) the received sensor data using a trained machine learning model stored on the cloud-based server (114) to compute control parameters for adjusting a stepper motor (106) coupled to a precision-controlled valve (110);
transmitting (306) the computed control parameters from the cloud-based server (114) to the microcontroller (104) for execution;
receiving (308) user-defined TDS targets and personalization preferences from a user interface (112) associated with the system (100);
updating (310) the machine learning model on the cloud-based server (114) based on historical sensor data, valve actuation outcomes, and feedback data from the system (100); and
generating and refining (312) personalized TDS control profiles for specific users, wherein the cloud-based server (114) dynamically adapts blending strategies based on environmental and usage patterns.