Smart Tocha is a computer vision solution that aims to increase the efficiency of burning gases in the torch of refineries. Applying machine learning techniques, Smart Tocha processes burning images continuously, classifies the current state of the torch and acts directly on the industrial control system. The system monitors and controls the steam flow in real time, unlike manual control. This artificial intelligence keeps burning safe for the environment, minimizing greenhouse gas emissions.

The solution caught attention in various media reports:

• Petrobras (in Portuguese)
• DI PUC-Rio (in Portuguese)

The Smart Tocha project was developed through a collaborative effort between the ExACTa PUC-Rio initiative and the research center of Petrobras, a leading Brazilian oil company. The source code is not provided due to intellectual property restrictions.

Technology stack: Python Azure Machine Learning Workspace Jupyter Notebook Stream Analytics Azure Functions PyTorch TensorFlow OpenCV

IAGO (Artificial Intelligence for Odor Management) is a machine learning project designed to take a proactive approach by alerting the refinery's operations and engineering team about the increased likelihood of emissions that may lead to community discomfort. By identifying patterns in operational variables and meteorological conditions, IAGO offers insights to assist refinery technicians in swiftly diagnosing issues and taking preventative measures to mitigate odor nuisances.

The Iago project was developed through a collaborative effort between the ExACTa PUC-Rio initiative and the research center of Petrobras, a leading Brazilian oil company. The source code is not provided due to intellectual property restrictions.

Technology stack: Python PowerBI Azure Machine Learning Workspace Jupyter Notebook Stream Analytics Azure Functions Logistic Regression