Computers & Chemical Engineering, Vol.21, No.9, 951-963, 1997
A Generalized-Approach to Process State Estimation Using Hybrid Artificial Neural-Network Mechanistic Models
In this work, a hybrid model which combines mechanistic elements with Artificial Neural Networks (ANNs) is used as a basis for a generalised on-line state estimation technique. Balance equations, which are more clearly defined, constitute the mechanistic side of the model whilst the ANN element is exclusively applied to modelling the more complex non-linear rate relationships present. The black-box approach offered by ANNs avoids the call made on both manpower and laboratory resources in modelling such complex system features mechanistically. The standard extended Kalman filter algorithm is modified to accommodate the hybrid model and, along with the stochastic process and measurement noises, handles intrinsic ANN error explicitly. Application of the approach is demonstrated in a simulation case study based on a pilot scale process involving three tanks in series. Results demonstrate the effectiveness of both the estimation scheme and an inferential estimate-based control scheme for the system.