REAL-TIME INTELLIGENT LPG LEAKAGE AND FIRE PROTECTION SYSTEM WITH DIGITAL TWIN VISUALIZATION IN LABVIEW

Abstract

Liquefied petroleum gas leakage and fire hazards require rapid detection, reliable decision-making, and automated mitigation to protect residential and commercial environments. Sensor-based safety platforms are increasingly integrated with graphical monitoring tools to strengthen real-time hazard awareness. Existing domestic safety systems frequently detect hazards without synchronized visualization, risk classification, and automated isolation. The objective is to develop a LabVIEW-based intelligent LPG leakage and fire protection system with digital twin visualization and automated emergency response. An MQ-6 gas sensor and IR flame sensor were interfaced with an Arduino/NI myRIO acquisition platform, and LabVIEW was used for threshold processing, graphical monitoring, data logging, voice alerting, SMTP notification, and actuator control. Solenoid valve isolation, relay-based power shutdown, exhaust ventilation, and buzzer activation were configured as closed-loop safety actions. The gas sensing module achieved 95% detection accuracy, while flame detection reached 94% accuracy. Hazard identification and safety activation were completed in less than 2 s during tested warning and emergency conditions, and the combined system accuracy was approximately 94.5%. Compared with reference systems reporting 85–92% gas detection and 80–90% fire detection accuracy, the proposed platform provides faster coordinated response and clearer operational state awareness. The architecture supports practical deployment by linking detection, decision logic, isolation, ventilation, and user notification in one interface. Future research should extend cloud connectivity, machine-learning prediction, sensor calibration, and long-duration reliability testing under variable humidity and ventilation conditions