Autonomous Process Model Identification using Recurrent Neural Networks and Hyperparameter Optimization

Abstract

We demonstrate the application of automated machine learning to the problem of identifying dynamic process models using recurrent neural networks (RNNs). The general concept relies on continuous monitoring of input-output data from a plant and the processing of this data by a collection of algorithms. The data is first processed by a collaborative filtering system, which suggests a classification of system dynamics per output channel. The proposed classification and the number of input channels is used to initialize a search over RNN hyperparameters. The search algorithm uses subsets of historical data for training and validation to select the RNN architecture and determine the network parameters according to preselected objectives for balancing model accuracy and model compactness. The proposed approach is demonstrated on a simulated case study for online system identification of a chemical reactor, where the underlying dynamic characteristics of the simulated system are changed during the simulation as the system undergoes a number of disturbances and handles control tasks. Process models for the system in question are obtained via the automated machine learning approach and the models are updated as the system dynamics change. The results show good prediction accuracy of the models throughout the simulation representing changes in system dynamics.