Tasks
- Optimize data acquisition from multiple real-time sensors monitoring the aniline synthesis process.
- Develop a Deep Learning algorithm to detect reactor issues using multisensor data.
Aniline reactor predictive maintenance
Challenges
Solutions
- A complex Neural Network model was developed to perform sensor data fusion and predict reactor hard stop.
Outcomes
- The developed Neural Network model introduced additional control capabilities and improved process visibility, resulting in a 40% reduction in reactor failures.
Technologies Used
PyTorch
LSTM
CNN
