Artificial neural networks can be used as a fault diagnostic tool in chemical process
industries. Connection strengths representing correlation between inputs (sensor
measurements) and outputs (faults) are made to learn by the network using the back
propagation algorithm. Results are presented for diagnostic faults in an ammonia–water
packed distillation column. First, a 6-4-6 network architecture (six input nodes corresponding
to the state variables and six output nodes corresponding to the six malfunctions) was …