Published on Jun 05, 2023
The pressure on the Process Industries to improve yield, reduce wastage, eliminate toxins and above all increase profits makes it essential to increase the efficiency of process operations. One possible approach for achieving this is through the improvement of existing process monitoring and control systems.
Many process monitoring and control schemes are based upon a representation of the dynamic relationship between cause and effect variables. In such schemes, this representation is typically approximated using some form of linear dynamic model, such as finite impulse response (FIR), autoregressive with exogenous variable (ARX) and auto-regressive, moving average with exogeneous variable (ARMAX) models.
Once determined, the dynamic process model of the system can be integrated within a variety of process monitoring and control algorithms. In process control, for example, the model can be incorporated within a model based predictive control (MBPC) algorithm, such as Generalised Predictive Control.Alternatively, for process monitoring, the residuals (prediction errors) from such models can be analyzed to detect abnormal operation.
Such monitoring and control schemes have found widespread application in industry and have led to significant improvements in process operations. Unfortunately, the models employed within the schemes tend to be linear in form. Although linear models can provide acceptable performance for many systems, they may be unsuitable in the presence of significant non-linearities.
For such systems it may be beneficial to employ a model that reflects the non-linear relationship between cause and effect variables. Preliminary studies have indicated that artificial neural networks (ANNs) may provide a generic, non-linear solution for such systems. As with standard linear modelling techniques, ANNs are capable of approximating the dynamic relationships between cause and effect variables. In contrast to linear techniques however, ANNs offer the benefit of being able to capture non-linear relationships.
