Given the rapid growth of our environment, it's crucial for us to plan multiple steps ahead, taking into account significant factors that cannot be overlooked, such as the emissions of factories and their impact on the environment. While this may not be a major concern for small factories with limited machinery, tracking every detail of every machine in large companies proves to be prohibitively expensive.

In this project, we propose utilizing digital twin technologies to monitor all necessary variables for maintaining a healthy and efficient environment. By incorporating a simple machine learning algorithm into the process, we can predict increases and decreases in emissions, enabling us to plan even further ahead