IOT - Based Gas Leakage Detection and Smart Valve Control with Real-Time Alerts and Automated Safety Management Integrated LPG Monitoring for Enhanced Residential and Commercial Safety

Abstract

It’s a great efficiency and affordability, liquefied petroleum gas (LPG) is used extensively in both residential and commercial settings. However, there are significant risks associated with gas leaks, including health risks, explosions, and fire incidents. An Internet of Things (IOT)-based gas leak detection, alert, and smart valve control system that guarantees proactive safety management and real-time monitoring is presented in this paper.The suggested system combines a Node MCU micro controller for data processing, a MQ-2 gas sensor for continuous leak detection, and Internet of Things connectivity for real- time alert notifications. The system automatically sounds alarms, notifies authorized users, and uses a relay-controlled smart valve to turn off the gas supply when the gas concentration rises above a predetermined safe threshold. Furthermore, a load cell with a HX711 module is used to track the weight of LPG cylinders and notify users when there is low gas. By effectively managing idle states, remote control overrides, and automatic shutoff, a clever decision-making algorithm guarantees safe operation. According to experimental results, compared to traditional systems, the suggested method increases leakage detection accuracy by 9.2%. Through intelligent automation, the system improves user convenience and safety in homes, restaurants, labs, and small businesses. It is scalable and dependable

Introduction

The text presents an IoT-based intelligent LPG gas leakage detection and safety system designed to improve safety in domestic and industrial environments. LPG is widely used but poses serious risks due to gas leaks, which can cause fires, explosions, and fatalities. Traditional safety methods rely on human detection or basic alarms, which are slow, unreliable, and unable to prevent gas flow during emergencies.

The proposed system uses IoT technology, sensors, and automation to provide real-time monitoring and automatic response. It integrates an MQ-2 gas sensor for detecting LPG leaks and a load cell (HX711) for monitoring cylinder weight. A NodeMCU microcontroller processes sensor data, normalizes it, and applies threshold-based logic to detect unsafe conditions.

When a gas leak is detected, the system automatically triggers a smart valve shut-off mechanism using a relay to stop gas flow immediately. It also sends instant alerts and updates through an IoT cloud dashboard, allowing remote monitoring via mobile or web applications. In addition, it provides LPG weight monitoring to warn users about low gas levels in advance.

The system improves safety by combining early leak detection, automatic gas cutoff, real-time alerts, and remote access, making it more reliable than conventional systems. However, limitations include dependency on internet and power supply, sensor accuracy issues, higher installation cost, and rule-based decision-making.

Conclusion

This research work has successfully designed and developed an IoT-Based Gas Leakage Detection, Alert, and Smart Valve Control System with the objective of enhancing LPG safety in practical settings. The proposed system is able to integrate gas leakage detection, automatic gas turn-off, LPG weight measurement, and remote IoT monitoring into a comprehensive safety solution. The system eliminates the need for human intervention and delays in response, which are the two most prominent causes of LPG- related accidents.Experimental verification also verifies that the system has a high level of accuracy in gas detection, fast response time, accurate control of the valves, and precise measurement of the weight of the LPG. The inclusion of a smart decision-making algorithm in the system ensures efficient functionality with minimal false alarms. The system is cost-effective, scalable, and can be applied to different scenarios, including residential, restaurant, laboratory, hospital, and small- scale industrial settings. Future improvements of the system may involve the development of mobile applications, cloud-based data analysis, and machine learning algorithms for predictive hazard detection.

References

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Copyright

Copyright © 2026 Dr. J Sathya Priya , Raghul S, Sri Sakthi Ram M . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.