Description:FIELD OF INVENTION
The present invention relates to the field of advanced gas blending system for optimizing the uniform mixing of two or more gaseous phase. More preferably, the present invention pertains to an AI-driven gas blending system for optimizing the gas blending process by dynamically adjusting the gas inflows using artificial intelligence enabling supply of low-carbon gas fuel blend thereby enhancing the fuel efficiency, safety and emergency response in gas supply management and method for producing the said gas blends. As the system is based on an AI technique, the model can continuously improve with its exposure to more and more data. The applicability and capability of the device can be enhanced.
BACKGROUD OF THE INVENTION
Mixing of Hydrogen gas in a fuel leads to a reduction in combustion duration, improve combustion stability and reduction in carbon emission. Therefore, hydrogen blending into natural gas pipelines is a pivotal strategy for decarbonizing energy systems. The most common ratio for mixing hydrogen into natural gas is between 0.1 to 25% by volume or more.
However, existing technologies for mixing of Hydrogen gas into a fuel gas face some significant limitations:
The prior art CN102311825A discloses the method for producing natural gas mixed with hydrogen gas by adjusting the hydrogen and the natural gas to a same pressure grade; but the said system achieves only ±1% accuracy, risking combustion inefficiencies.
US patent application no. US2023357657A1, US20130215704A1 and US patent US12297965B2, discloses a High-Pressure Gas Mixer. The said system discloses an automated blending of hydrogen gas with traditional fuels, such as natural gas, to produce a consistent stream of precision-blended flow with a single customizable, movable and modular unit that is automatically controlled. But the said systems failed to control the uniform mixing pattern in non-continuous supply pipeline.
The non-continuous /intermittent flow of gas blend in supply line creates fluctuation of pressure and that produces a non-uniform mixing of gases because fluctuating pressures alter upstream fluid flow dynamics, creating localized variations in velocity, density, and turbulence, which hinder the thorough and even diffusion of gases. This results in pockets of unevenly mixed or unmixed gas, compromising overall mixture quality. Therefore, the known mixing systems fails to providing homogeneous composition of Hydrogen gas and natural gas in the supply pipeline. These systems lack in mixing precision, safety and real time control on mixing phenomenon. Such uneven distribution of hydrogen within a natural gas stream, where hydrogen concentration varies across different locations and times, can lead to several issues like hydrogen embrittlement, intermittent NOx generation, increased leakage risk and reduced fuel efficiency.
Therefore, homogeneous mixing of hydrogen in natural gas is crucial for safe and efficient use of hydrogen-blended natural gas, particularly in pipelines having low-velocity gas streams and intermittent gas flow. Achieving a homogeneous mixture, where hydrogen gas molecules are evenly distributed, is challenging due to the pressure drop, density difference between hydrogen and natural gas, which can lead to stratification.
In view of the foregoing discussion, it is portrayed that there is a need to have an advance hydrogen gas mixing system that provides a precise mixing, safety, scalability, energy efficiency, dynamic mixing control with respective entire time of continuous mixing process. The inventors have developed an innovative gas blend mixing system using artificial intelligence. Recent advancements in artificial intelligence, particularly in machine learning and real-time data processing, offer opportunities to enhance valve operating performance by dynamically adjusting the gas inflow based on real-time operating conditions. Hence, the present invention system prevents uneven gas concentration distributions in supply pipeline to eliminate above said undesired effects of prior arts.

SUMMARY OF THE INVENTION
The present invention provides a gas blending system (100) for uniform and safe blending of Hydrogen and Natural gas streams. The system comprises two inlets for upstream gases, plurality of flow control valves, mixing chamber with static mixer, real-time array of sensors distributed in the mixing chamber, an AI controlling unit for continuous learning and optimization, an anomaly detection mechanism. The AI controlling unit employs machine learning algorithms, including neural networks and reinforcement learning, to analyze sensor data such as gas type, gas density, gas velocity, temperature and pressure and to determine the optimal upstream gas inflow for maximizing uniform mixing in mixing chamber and minimizing disruption.
The invention includes an anomaly detection mechanism that adjusts the gas inflow through a flow control valves and safety valves, enabling rapid and precise changes in gas inflow in mixing chamber thereby providing uniform mixing. The system is trained on historical and real-time data to predict optimal valve control for various gas mixing conditions, improving two gases mixing dynamics across a wide range of hydrogen mixing percentage in natural gas ranging from 0.01 to 20% or more. The invention also incorporates a safety diagnostic module to monitor safety parameters and adjust safety valves to prevent fire or leakage.
The present invention provides a gas blending system (100) for optimizing uniform and safe blending of Hydrogen and Natural gas streams comprising:
-hydrogen gas inlet stream (101) configured with plurality of flow control valves (103), plurality of safety valves (104) and plurality of process parameter measuring sensors (105),
-natural gas inlet stream (102) configured with plurality of flow control valves (106), and plurality of process parameter measuring sensors (107), wherein the gas inlet flow-control valves (103, 106) selected servo-driven valve;
-mixing chamber (108) contacting the two gas streams (101, 102) and configured with plurality of baffles and array of sensors (109) distributed across the chamber (108),
-gas blend outlet (110) to provide uniformly mixed gas blend and configured with blend gas process parameter measuring sensors (111),
-controlling unit (112) equipped with artificial intelligence (AI) to receive, process complex data from sensors, analyse, monitor and control the plurality of valves to optimise uniform and safe blending of gases; characterized in that;
-a data acquisition module (113) configured to collect data from plurality of sensors which is recorded based on type of sensors and measurement time points thereof;
-an analysing module (114) configured to evaluate process parameter variables and gas properties within a predetermined allowable range;
-a safety valve control module (115) configured to control valves operations;
-a Proportional-Integral-Derivative (PID) control module (116) configured with flow control valves (103) to precisely regulate gas flow,
-an Artificial Intelligence (AI) processing module (117) configured to predict the gas mixing pattern, dynamically adjust gas ratios by dynamically controlling flow control valves and detecting anomaly based on the sensor data;
-a system log unit (118) configured to securely storing pre-recorded data, including real-time data collected by sensors, anomaly detection events, and system logs for future analysis and reference, wherein the system log (118) records the logs events selected from gas ratios, process parameter, pressure spike, gas leak, shutdowns for compliance and diagnostics;
-a user interface (119) configured to enable communication between the system and external devices for remote monitoring and control.
Wherein, the artificial intelligence utilizes neural networks and reinforcement learning to process gas parameter data recorded by sensors at different locations and feedback loop for updating the AI module with real-time performance data, wherein the artificial intelligence employs trained unit on said data and deploys control module (115, 116) to control gas flow to obtained an uniformly blend of gases at the outlet (110) and ensure gas safety measures. The AI module (117) processes sensors data in multiple time intervals to account for accuracy in gases mixing dynamics, more preferably time points between 50 to 500 ms to adjust flow-control valves (103, 106) using PID control module (116) and detect anomalies to operate safety valves (104) using safety valve control module (115).
More precisely, the mixing chamber along with baffles operates at flow rates between 15–300 m³/h with Reynolds numbers between 32,841–168,628 and pressure drops between 0.01–0.04 bar and baffles are selected from helical, segmental, static and adjustable baffles.
The non-limiting examples of safety mechanisms valves (104) selected from electropneumatic solenoid valves, ball valve, pressure reducing valves, pressure relief valve, mechanical over pressure shutoff valves.
The non limiting examples of the sensors (105, 107 and 109) selected from micro-electromechanical (MEMS) systems based sensors that determines concentration of gas and other sensors for intrinsic as well extrinsic properties of gases including temperature, pressure, flow, velocity, density and combination thereof.
The present invention also provides a method for optimizing an uniform and safe blending of hydrogen gas and natural gas using gas blending system (100) as disclosed herein, the method comprising:
-Sourcing Hydrogen gas and natural gas from reservoir in inflow line having flow control valve and safety valves;
-Measuring the process parameters of system using sensors including leak detection and fire detection sensors;
-Feeding value for the hydrogen gas blend proportion in controller via user interfaces and receiving both gases in mixing chamber;
-Detecting the gas parameters using array of sensors and recording the real-time data;
-Adapting to changing conditions of plurality of safety valves using safety valve control module to control safety threats;
-Analysing the flow mixing pattern by calculating the Reynolds number at different location in mixing chamber and at different time interval using real-time data using analysis module (114);
-Maintains turbulent flow (Reynolds numbers 32,841–168,628) with pressure drops of 0.01–0.04 bar using PID control module (116) by operation of flow valves of both gases for precisely receiving the gases stream in mixing chamber; characterized in that the said module (116) controls the flow valves to achieve uniform mixing with accuracy ±1% using artificial intelligence (AI) for dynamic control on operating flow valves and finally obtaining blend of uniformly mixed gases at the outlet;
-Enabling communication between the system and external devices for remote monitoring and control using a user interface;
-Securely storing all collected data and system logs using a system log unit for future analysis and reference.
Therefore, the present invention provides an advanced hydrogen-natural gas blending system (100) that addresses prior art deficiencies through four key innovations:
1. Modular Mixing System:
• Configurable helical baffles (4–8, 50–200 mm long) and servo-driven valves (2–4 per module, ±0.01% positioning) ensure uniform mixing across flow rates from 15 to 300 m³/h of natural gas.
• Scalable for pipeline diameters of 40–500 mm, using stainless steel (A-182 Gr. F316, A-312 TP SS 316) for durability.
• Maintains turbulent flow (Reynolds numbers 32,841–168,628) with pressure drops of 0.01–0.04 bar.

2. MEMS-Based Monitoring:
• Multi-point sensor arrays (3 per 2 m section) monitor flow (±0.01 m/s, 0–12 m/s), composition (±0.2%, 0–20% H2), temperature (±0.5°C, 0–100°C), PID flow controller (±0.5 % of RD and ±0.1% of FS), pressure (±0.12 bar, 0–25 bar H2).
• Sensors are spaced 0.5 m apart, enabling real-time feedback for dynamic control.

3. AI-Driven Adaptive Control:
• Neural networks and reinforcement learning process sensor data every 100 ms to adjust blending ratios (±0.1%), predict anomalies (e.g., leaks at ±0.04% H2 LEL), and optimize power (31.55 W total).
• Predictive algorithms detect micro-leaks, pressure deviations, or flow irregularities before escalation.

4. Enhanced Safety Mechanisms:
• Predictive leak detection and emergency shutdown valves (close within 50 ms) respond to thresholds (e.g., Hydrogen gas pressure >8 bar, gas detection >40% LEL).
• Pressure safety mechanism involves safety valve (open at >8.5 bar) mitigate explosion risks.
The system of present disclosure outperforms prior art by achieving superior precision, safety, and efficiency, making it ideal for retrofitting natural gas pipelines, industrial hydrogen blending, and residential energy supply. It supports hydrogen concentrations of 0–20% or more in natural gas, complies with pipeline standards (e.g., ASME B31.12), and reduces carbon emissions by enabling scalable hydrogen integration.

BRIEF DESCRIPTION OF THE DRAWINGS
A scheme of the system and method of the present invention is given with reference to the accompanying drawings, which are given for an illustrative purpose, not limiting the scope of the invention.:
FIGURE NO. 1: Schematic diagram of the modified gas blending optimization system (100) as per present invention.
FIGURE NO. 2: Schematic diagram of the AI-driven gas blending system (100) showing the sensor array (105, 107 and 109), Controlling unit (112) with safety mechanism (115), AI processing module (117) and valve control module (116) integration. The diagram illustrates the flow of data from sensors to the AI control unit, the output to the valve control module (116) and safety mechanism (115), and the feedback loop therein as per present invention.
FIGURE NO. 3: A block diagram that illustrates a gas blending system (100) environment in which various embodiments of the method and the system may be implemented.
BRIEF DESCRIPTION OF THE INVENTION
The most immediate and specific application of the present invention is preparing uniform blend of Hydrogen and natural gas stream for supplying gas fuel blend to domestic premises, which is for cleaner energy production. The obtained gas blends of hydrogen-natural gas, in view of their uniformly molecular distribution, can be advantageously to improve combustion stability, complete combustion and ensuring the safe integration of hydrogen into existing natural gas infrastructure. Non-limiting examples of gases that can be mixed in present invention includes elemental gases, toxic gases or mixed gases.
The present invention provides a gas blending system (100) for optimizing uniform and safe blending of Hydrogen and Natural gas streams. More preferably, the system (100) provides uniform blend of two gases when mixed in a non-continuous supply line, wherein pipeline faces frequent pressure fluctuation. The non-continuous gas flow, also known as intermittent flow, can indeed disturb gases distribution due to issues like pressure drops, flow instability, and changes in flow patterns, which can impact the performance and accuracy of mixing percentage. This is particularly problematic for applications requiring a steady gas delivery, such as in continuous emission monitoring systems, welding, domestic use and the operation of automated equipment etc.
In one of the embodiment, the present invention provides a gas blending technologies for mixing two or more gases, specifically a system for blending hydrogen with natural gas in energy pipelines to reduce carbon emissions and method of gas blending. Hence, the present invention aims to fulfil the need by introducing an advance hydrogen gas mixing system comprising of inlets for gas supply configured with servo-driven valves to control the inlet flow rate, a mixing chamber along with plurality of adjustable baffles for manipulating and providing a turbulence flow pattern and AI -driven control and plurality of electromechanical sensors for radiant gas mixing prediction and mapping of molecular level diffusion of each gas phase using electromechanical sensors. By doing so, it addresses the limitations of conventional gas mixing apparatuses and provides an advanced, intelligent solution for uniform mixing of Hydrogen gas into the natural gas.
It has been now found that the system for blending gases having different density, and to achieve uniform distribution when mixing two gas blends, the process must overcome density differences that cause stratification, using strategies like controlling pressure drop, controlled velocity mixing, and mixing chamber design for enhancing fluid dynamics to promote diffusion and turbulent mixing in gas streams. Therefore, the present invention provides an advance gas mixing system comprising a mixing zone including static mixers and optimized injection strategies, that employed to enhance mixing and minimize stratification.
It is therefore an objective of the present invention of advance system for the precisely continuous mixing of hydrogen with natural gas, the system comprising mixing chamber along with baffles operates at flow rates between 15–300 m³/h with Reynolds numbers between 32,841–168,628 and pressure drops between 0.01–0.04 bar. The present invention combines a modular mixing architecture, microelectromechanical systems (MEMS) for real-time monitoring, and artificial intelligence (AI) for dynamic control, addressing precision, safety, scalability, and efficiency in sustainable energy distribution. As the system is based on an AI technique, the model can continuously improve with its exposure to more and more data. The applicability and capability of the device can be enhanced.
Referring to figure no. 1, a modified gas blending optimization system (100) is illustrated in accordance with an embodiment of the present invention disclosure. The system includes mixing chamber/blending station (108) configured with array of sensors (109). Wherein the sensors are distributed in the entire region of blending station. The number of sensors ranging from 3 to 20 or more are installed to cover the mixing chamber area to detect the intrinsic and extrinsic parameter of gases therein. The hydrogen gas inlet stream (101) and Natural gas inlet stream (102) are configured with flow control valves (103, 106) to control the inflow, sensors (105, 107) to measure the respective gas parameters. The blended gas outlet (110) is configured with process variable measurement and analysis sensors (111) for monitoring the percentage of blended natural gas and process variables.
The non-limiting examples of valves are electropneumatic solenoid valves, pressure reducing valves, pressure relief valve, mechanical over pressure shutoff valves, servo-driven valve.
The sensors are selected from micro-electromechanical (MEMS) systems based sensors that determines concentration of gas and other sensors for intrinsic as well extrinsic properties of gases including temperature, pressure, flow, velocity, density and combination thereof.
The non-limiting type of sensors and their requirement is disclosed below:
Flow Sensor: ±0.01 m/s accuracy, range 0–12 m/s.
Composition Sensor: ±0.2% H2 accuracy, range 0–20% H2 in natural gas.
Temperature Sensor: ±0.5°C accuracy, range 0–50°C.
Pressure Sensor: ±0.12 bar accuracy, range 0-25 bar.
In accordance to one more embodiment of the present invention, the safety valves (104) controlled using control module (112) and are configured with inlet stream to incorporate advanced safety mechanisms to mitigate hydrogen’s flammability risks, ensuring compliance with standards. The valves include automated shutoff valves, Solenoid valves operating close within 50 ms if thresholds are exceeded. The system also configured with flame arrestors and pressure-relief valves (open at >8.5 bar) to prevent escalation.
Referring to figure no. 2, the present invention provides an advance gas blending system (100) for optimization of uniform and safe blending of Hydrogen and Natural gas streams comprising a mixing chamber (108) having inlet, outlet and adjustable helical baffles, a plurality of gas inlets configured with plurality of flow-control valves (103, 106) and flow meters to receive a gases flow from a gases source and array of sensors (109).
In an embodiment, the controlling unit (112) is integrated to receive, process complex data from sensors, analyse, monitor and control the plurality of valves to optimise uniform and safe blending of two gases.
In an embodiment, a controlling unit (112) is equipped with artificial intelligence (AI) (117) to receive sensors data from data acquisition module (113) and analyze process parameter data, gas component mixing ratio data, and safety values in analysis module (114).
In a preferred embodiment, the controlling unit (112) equipped with data acquisition module (113) configured to collect data from plurality of sensors which is recorded based on type of sensors and measurement time points thereof;
analysing module (114) configured to evaluate process parameter variables and gas properties within a predetermined allowable range;
safety valve control module (115) configured to control valves operations using programmable logic controller (PLC);
Proportional-Integral-Derivative (PID) control module (116) configured with flow control valves (103) to precisely regulate gas flow,
Artificial Intelligence (AI) processing module (117) configured to predict the gas mixing pattern, dynamically adjust gas ratios by dynamically controlling flow control valves and detecting anomaly based on the sensor data;
system log unit (118) configured to securely storing pre-recorded data, including real-time data collected by sensors, anomaly detection events, and system logs for future analysis and reference,
user interface (119) configured to enable communication between the system and external devices for remote monitoring and control.
Characterized in that the AI system processes sensor data every milliseconds, or using time interval of 100 ms, or 10 ms or less to adjust valves. Wherein, the AI system (117) processes MEMS data using neural networks and reinforcement learning, enabling dynamic blending, anomaly prediction, and energy optimization. More specifically, it operates at 100 ms intervals, ensuring rapid response to changing conditions. AI module adjusts hydrogen gas flow through PID to maintain ±0.1% H2 ratio accuracy and detects micro-leaks (±0.05% H2 sensitivity), pressure spikes (>8 bar), or gas detection (>40% LEL).
Further, the system log unit (118) configured to securely storing pre-recorded intrinsic and extrinsic properties of corresponding gases, including real-time intrinsic properties data collected by sensors, anomaly detection events, and system logs for future analysis and reference and user interface (119) configured to enable communication between the system and external devices for remote monitoring and control.
Referring to Figure no. 3, illustrating the method for uniform and safe blending of gases using gas blending system (100) as provided in present invention.
The method and operation of system (100) briefly comprising following steps:
-Sourcing Hydrogen gas and natural gas from reservoir in inflow line having flow control valve, the inflow line comprising emergency shutoff valve, pressure regulating valve, flow measuring meter and pressure safety mechanism;
-Measuring the process parameters of system using sensors including leak detection and fire detection sensors;
-Feeding value for the hydrogen gas blend proportion in controller via user interfaces and receiving both gases in mixing chamber;
-Detecting the gas parameters using array of sensors and recording the real-time data;
-Adapting to changing conditions of plurality of safety valves using safety valve control module to control safety threats;
-Analysing the flow mixing pattern by calculating the Reynolds number at different location in mixing chamber and at different time interval using real-time data;
-Maintains turbulent flow (Reynolds numbers 32,841–168,628) with pressure drops of 0.01–0.04 bar using PID control module (116) by operation of flow valves of both gases for precisely receiving the gases stream in mixing chamber; characterized in that the said module (116) controls the flow valves to achieve uniform mixing with accuracy ±1% using artificial intelligence (AI) for dynamic control on operating valves and finally obtaining blend of uniformly mixed gases at the outlet; optionally recirculating the natural gas in inline flow stream using bypass valve for re-blending.
Finally, enabling communication between the system and external devices for remote monitoring and control using a user interface, and securely storing all collected data and system logs using a system log unit for future analysis and reference.

The following example have to be considered representative and non-limiting the scope of the invention.
Example 1: Configuration of system: A hydrogen-natural gas blending system integrates a modular mixer, MEMS-based sensors, and AI-driven control for precision mixing (±0.1%), safe, and scalable operation in pipelines of 40–500 mm or more.
Adjustable helical baffles and servo-driven valves ensure uniform mixing at flow rates from 15 to 300 m³/h of natural gas, with pressure drops of 0.01–0.04 bar. MEMS sensors process parameter measurement involving flow monitoring (±0.01 m/s), gas composition detection (±0.2%), temperature (±0.5°C), and pressure (±0.12 bar) and PID controller involving MSME flow controller.
AI algorithms, using neural networks and reinforcement learning, dynamically adjust ratios, predict anomalies (e.g., leaks at ±0.04% H2 LEL sensitivity), and optimize power.
Safety features include emergency shutdown valves, pressure safety mechanism, predictive leak detection, Flame Detection and flame arrestors, ensuring compliance with energy pipeline standards for industrial, residential, and grid applications. Pressure safety mechanism involves safety valve (open at >8.5 bar) that mitigate explosion risks.
Implementation Example 2: Modular Mixer Design: The mixer is a modular, scalable assembly designed for pipelines of 40–500 mm, constructed from corrosion-resistant stainless steel (A-182 Gr. F316 for flanges, A-312 TP SS 316 for pipes, SS 316 for elements). Its design ensures uniform blending while minimizing energy losses.
Components:
• Helical Baffles: 4–8 adjustable baffles per module, 50–200 mm long, 10–50 mm pitch, induce controlled turbulence for mixing. Baffles are repositionable via servo motors for flow rates of 15–300 m³/h.
• Servo-Driven Valves: 2–4 valves (50 mm diameter, ±0.01% positioning accuracy) control natural gas inflow.
• PID Controller: control hydrogen gas inflows, adjustable from 0.1–100% open.
• Flanges and Piping: 2” 150# SORF flanges and seamless Schedule 40 pipes ensure structural integrity at pressures of 3–8 bar.
• Pipe Diameters: 40, 50, 65, 80, 100 mm (extensible to 500 mm via modular scaling).
• Flow Rates: Tested at 26 and 53.4 m³/h (0.0072–0.0148 m³/s), designed for up to 300 m³/h.
• Velocities: 0.92–11.80 m/s, depending on pipe size and flow.
• Reynolds Numbers: 32,841–168,628, ensuring turbulent flow for efficient mixing.
• Pressure Drops: 0.01–0.04 bar, minimizing energy losses.

Pipe Dia (mm) Flow Rate (m³/h) Velocity (m/s) Reynolds No. Pressure Drop (bar)
40 26 5.75 82,103 0.01
40 53.4 11.80 168,628 0.04
50 26 3.68 65,683 0.01
65 53.4 4.47 103,771 0.04
100 26 0.92 32,841 0.01
100 53.4 1.89 67,451 0.04
Table No. 1: Gas Flow Dynamics

 Calculation Example (40 mm, 26 m³/h):
• Cross-Sectional Area (A) = π * (0.02 m)² = 0.001256637 m²
• Velocity (V) = Q / A = 0.007222222 m³/s / 0.001256637 m² = 5.747 m/s
• Reynolds Number = (ρ * V * D) / μ = (4 kg/m³ * 5.747 m/s * 0.04 m) / 0.0000112 kg/m·s = 82,103.74
• Pressure Drop: Estimated at 0.01 bar via Bernoulli’s principle, validated experimentally.

Advantages of the Invention:
• The present invention dynamically adjusts gas mixing ratios.
• The present invention dynamically maintains turbulent flow (Reynolds numbers 32,841–168,628) with pressure drops of 0.01–0.04 bar and minimizes the energy losses.
• The present invention predict anomalies (e.g., leaks at ±0.04% H2 LEL sensitivity).
• The present invention operates at optimize power (31.55 W).
• The present invention provides safety features include emergency shutdown valves, pressure safety mechanism, predictive leak detection, Flame Detection, vent valve and flame arrestors, ensuring compliance with energy pipeline standards for industrial, residential, and grid applications.
, Claims:We claim;
1. A gas blending system (100) for optimizing uniform and safe blending of Hydrogen and Natural gas streams comprising:
-hydrogen gas inlet stream (101) configured with plurality of flow control valves (103), plurality of safety valves (104) and plurality of process parameter measuring sensors (105),
-natural gas inlet stream (102) configured with plurality of flow control valves (106), and plurality of process parameter measuring sensors (107),
-mixing chamber (108) contacting the two gas streams (101, 102) and configured with plurality of baffles and array of sensors (109) distributed across the chamber (108),
-gas blend outlet (110) to provide uniformly mixed gas blend and configured with blend gas process parameter measuring sensors (111),
-controlling unit (112) equipped with artificial intelligence (AI) to receive, process complex data from sensors, analyse, monitor and control the plurality of valves to optimise uniform and safe blending of gases; characterized in that;
-a data acquisition module (113) configured to collect data from plurality of sensors which is recorded based on type of sensors and measurement time points thereof;
-an analysing module (114) configured to evaluate process parameter variables and gas properties within a predetermined allowable range;
-a safety valve control module (115) configured to control valves operations;
-a Proportional-Integral-Derivative (PID) control module (116) configured with flow control valves (103) to precisely regulate gas flow,
-an Artificial Intelligence (AI) processing module (117) configured to predict the gas mixing pattern, dynamically adjust gas ratios by dynamically controlling flow control valves and detecting anomaly based on the sensor data;
-a system log unit (118) configured to securely storing pre-recorded data, including real-time data collected by sensors, anomaly detection events, and system logs for future analysis and reference,
-a user interface (119) configured to enable communication between the system and external devices for remote monitoring and control.

2. The gas blending system (100) as claimed in claim 1, wherein the artificial intelligence utilizes neural networks and reinforcement learning to process gas parameter data recorded by sensors at different locations and feedback loop for updating the AI module with real-time performance data, wherein the artificial intelligence employs trained unit on said data and deploys control module (115, 116) to control gas flow to obtained an uniformly blend of gases at the outlet (110) and ensure gas safety measures.
3. The gas blending system (100) as claimed in claim 1, wherein the mixing chamber along with baffles operates at flow rates between 15–300 m³/h with Reynolds numbers between 32,841–168,628 and pressure drops between 0.01–0.04 bar.
4. The gas blending system (100) as claimed in claim 1, wherein baffles are selected from helical, segmental, static and adjustable baffles.
5. The gas blending system (100) as claimed in claim 1, wherein safety mechanisms valves (104) Selected from electropneumatic solenoid valves, ball valve, pressure reducing valves, pressure relief valve, mechanical over pressure shutoff valves.
6. The gas blending system (100) as claimed in claim 1, wherein gas inlet flow-control valves (103, 106) selected servo-driven valve.
7. The gas blending system (100) as claimed in claim 1, wherein the sensors (105, 107 and 109) selected from micro-electromechanical (MEMS) systems based sensors that determines concentration of gas and other sensors for intrinsic as well extrinsic properties of gases including temperature, pressure, flow, velocity, density and combination thereof.
8. The gas blending system (100) as claimed in claim 1, wherein the AI module (117) processes sensors data in multiple time intervals to account for accuracy in gases mixing dynamics, more preferably time points between 50 to 500 ms to adjust flow-control valves (103, 106) using PID control module (116) and detect anomalies to operate safety valves (104) using safety valve control module (115).
9. The gas blending system (100) as claimed in claim 1, wherein the system log (118) records the logs events selected from gas ratios, process parameter, pressure spike, gas leak, shutdowns for compliance and diagnostics.
10. A method for optimizing an uniform and safe blending of hydrogen gas and natural gas using gas blending system (100) as claimed in claim 1, the method comprising:
-Sourcing Hydrogen gas and natural gas from reservoir in inflow line having flow control valve and safety valves;
-Measuring the process parameters of system using sensors including leak detection and fire detection sensors;
-Feeding value for the hydrogen gas blend proportion in controller via user interfaces and receiving both gases in mixing chamber;
-Detecting the gas parameters using array of sensors and recording the real-time data;
-Adapting to changing conditions of plurality of safety valves using safety valve control module to control safety threats;
-Analysing the flow mixing pattern by calculating the Reynolds number at different location in mixing chamber and at different time interval using real-time data using analysis module (114);
-Maintains turbulent flow (Reynolds numbers 32,841–168,628) with pressure drops of 0.01–0.04 bar using PID control module (116) by operation of flow valves of both gases for precisely receiving the gases stream in mixing chamber; characterized in that the said module (116) controls the flow valves to achieve uniform mixing with accuracy ±1% using artificial intelligence (AI) for dynamic control on operating flow valves and finally obtaining blend of uniformly mixed gases at the outlet;
-Enabling communication between the system and external devices for remote monitoring and control using a user interface;
-Securely storing all collected data and system logs using a system log unit for future analysis and reference.