EARLY FAULT DETECTION IN A DISTILLATION COLUMN: AN INDUSTRIAL APPLICATION OF KNOWLEDGE-BASED NEURAL MODELLING

One of the most widespread misconceptions about neural networks is the fact that they are "black boxes" which (i) do not make any use of prior knowledge of the process to be modelled, and (ii) cannot be "understood" by the expert of the process. We show that, on the contrary, neural networks can be used as "grey box models", and that the designer can take full advantage of the mathematical knowledge which may exist on the process. Using a knowledge-based neural model, we have been able to design a real-time distillation column simulator, implemented on a PC, which allows the early detection of faults. The neural network is a dynamic model (recurrent neural net) with 102 state variables, presumably the largest recurrent neural network ever trained and implemented for industrial purposes.