LPG Gas Leak Detection System and LPG Fire Classification Based on Internet of Things and Artificial Intelligence with Telegram Bot as a Monitoring Tool

Abstract

LPG gas leaks pose a serious threat in industrial kitchens as they can cause costly fires, both in terms of material and safety. To improve safety, an accurate detection system is required. This research focuses on developing an LPG gas leak detection system and LPG fire classification with Internet of Things and Artificial Intelligence technology. Supported by Telegram Bot as an emergency notification monitoring tool, this system uses MQ-2 sensors to detect LPG gas leaks and ESP32-Cam to classify LPG fires along with Pretrained-model technology such as Cascade Fire Detection on OpenCV Cloud Server. As the output of this system, the use of PWM control and automation oversees regulating the Exhaust Fan according to the detected leakage. FreeRTOS is also used for system task efficiency, and Port Forwarding with Ngrok Local Server allows public access to the ESP32-Cam. System testing was conducted by Black-Box testing, then evaluating the performance of the MQ-2 sensor against 400 ppm and 1500 ppm thresholds for LPG testing distances in open kitchens and closed kitchens, as well as analyzing system response and delay via HTTP protocol. The results demonstrated the system's success in detecting gas leaks, classifying LPG fires and facilitating emergency communication.