LONG-TERM PREDICTIONS OF CHEMICAL PROCESSES USING RECURRENT NEURAL NETWORKS - A PARALLEL TRAINING APPROACH

Multilayer feedforward networks have been used successfully for nonlinear system identification by using them as discrete-time dynamic models. In the past, feedforward networks have been adapted as one-step-ahead predictors; however, in model predictive control the model has to be iterated to predict many time steps ahead into the future. Therefore, the feedforward network is chained to itself to go as far as needed in the future, and this chaining may result in large errors. As an alternative to using the one-step-ahead approach, a feedforward network is chained to itself during the training. This training procedure is referred to as a parallel identification method since the network is in parallel with the system to be identified. A feedforward network used in the parallel approach results in an external recurrent network. The learning algorithm for external recurrent networks is derived using ordered derivatives. In this paper, the network is used to identify the dynamic behavior of a biological wastewater treatment plant and a catalytic reformer in a petroleum refinery. For long-term predictions, this parallel training of the neural network leads to much better results than the conventional training.