Intelligent AI + IoT Safety System for Worker Protection in Cracker Factories

Intelligent AI + IoT Safety System for Worker Protection in Cracker Factories

M. Priyadharshan, B.Tech M.E (Ph.D)

Assistant Professor, Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology, Coimbatore, 641105, India

Madesh K

Artificial Intelligence and Data Science NIET, Coimbatore, Tamil Nadu madeshk1335@gmail.com

Navin Raja J

Artificial Intelligence and Data Science NIET, Coimbatore, Tamil Nadu navinraja402@gmail.com

Logeshwaran S

Artificial Intelligence and Data Science NIET, Coimbatore, Tamil Nadu logeshsbofficial@gmail.com

Abstract—The cracker manufacturing industry involves highly hazardous working environments where workers are exposed to risks such as gas leakage, fire outbreaks, smoke formation, and abnormal temperature rise. These hazards can lead to serious industrial accidents if they are not detected at an early stage.

Traditional safety systems in many factories rely on manual monitoring or basic alarm systems that cannot continuously observe environmental conditions or provide intelligent analysis of potential risks. Therefore, there is a need for an advanced safety monitoring system that can detect dangerous situations in real time and ensure worker protection. This project proposes an Intelligent Artificial Intelligence and Internet of Things based safety system designed specifically for worker protection in cracker factories. The system uses multiple environmental sensors including gas sensor, fire sensor, smoke sensor, and DHT11 temperature sensor to continuously monitor the surrounding environment. These sensors detect hazardous conditions such as gas leakage, fire presence, smoke generation, and temperature changes. The sensor data is collected and processed using the ESP32 microcontroller integrated with the INDUS Board Coin. The processed information is displayed locally on an LCD screen so that workers can easily observe the environmental conditions inside the factory. In addition, the ESP32 transmits the collected data to an IoT cloud platform using wireless communication. The cloud platform allows supervisors to remotely monitor the factory environment and receive alerts when unsafe conditions occur. Artificial Intelligence techniques can further analyze the sensor data to identify abnormal patterns and predict potential hazards before they become serious accidents. The proposed system provides real-time monitoring, early hazard detection, and intelligent safety alerts. By integrating sensors, IoT technology, and AI- based analysis, the system improves workplace safety and helps prevent accidents in cracker manufacturing industries. This solution offers an efficient, low-cost, and reliable approach to protect workers and enhance industrial safety management.

Index Terms—Artificial Intelligence, Internet of Things (IoT), Industrial Safety Monitoring, ESP32 Microcontroller, Gas Leakage Detection, Fire Detection, Smoke Monitoring, Temperature Monitoring, Worker Safety, Smart Industrial Systems.