A Volatile Organic Compounds Intelligent Monitoring and Warning Model Based on Deep Learning Algorithm

Volatile Organic Compounds (VOCs) consist of diverse compounds that exhibit challenging predictability trends using conventional mechanism models. Within many industrial parks, VOCs present difficulties due to their multiple emission points and dispersed sources, making manual monitoring and data collection at each point labor and resource-intensive as well as highly inefficient. Consequently, the journey towards accomplishing large-scale and reliable VOCs monitoring and warning systems, as well as their effective implementation in industrial parks, remains a considerable endeavor. For the purpose of effectively predicting the changing trend of VOCs concentration in industrial parks to achieve the goal of intelligent early warning of possible pollution events, this study aims to develop corresponding machine learning methods in combination with data processing methods as well as cutting-edge machine learning methods including long short-term memory neural network (LSTM) to develop corresponding machine learning models and attain intelligent early warning of pollutants by predicting VOCs concentration and combining this with pollution factor warning standards.