2. Introduction to Fault Detection
• Fault detection is crucial for safety and
efficiency in chemical processes.
• Traditional methods struggle with nonlinear
and dynamic systems.
• ANNs provide a robust solution by learning
complex patterns and detecting anomalies.
3. Why Use ANNs for Fault Detection?
• ANNs handle nonlinear and dynamic behavior
effectively.
• They learn from historical data, identifying
patterns linked to faults.
• Capable of real-time fault detection and
diagnosis.
4. Case Study: CSTR Fault Detection
• Continuous Stirred Tank Reactor (CSTR) used
in chemical production.
• Common faults: Temperature fluctuations,
sensor failures.
• ANN model developed to monitor and detect
these faults.
5. Working Process of ANN in Fault Detection
1. Data Collection: Historical data from CSTR
sensors.
2. Preprocessing: Noise reduction and
normalization.
3. ANN Training: Supervised learning using labeled
data.
4. Fault Detection: Real-time monitoring and
anomaly detection.
5. Diagnosis: Identifying fault type and severity.
6. Results and Advantages
• High accuracy in detecting faults with minimal
false alarms.
• Improved safety and reduced downtime in
CSTR operation.
• Generalizable to other chemical processes with
minimal modifications.
7. Conclusion
• ANNs offer an effective solution for fault
detection in complex chemical processes.
• The case study demonstrates significant
improvements in reliability and safety.
• Future scope includes integrating with IoT and
real-time analytics.
