Hybrid intelligent modelling and simulation for cold tandem rolling process

Abstract

Dynamics of cold tandem rolling processes are difficult to be simulated with mechanism models due to their high non-linearities, multivariable and strong couplings, time-varying distributed parameters and so on. A novel hybrid intelligent dynamic modelling approach is proposed based on the combination of a linearised state space model derived from various mechanism equations, a case-based reasoning algorithm for multi-state space models selection, a genetic algorithm for optimisation of case attributes, an adaptive fractal filtering algorithm for the identification of state space model parameters, a neural network-based simulation error compensation model for the strip exit velocity. With actual data from a 2030 mm five-stand cold tandem rolling system of a steel plant, simulation experiments verify the effectiveness of the proposed approach. Furthermore, a rolling simulation system is developed based on the proposed model, and the virtual tandem rolling experiments with the simulation system also validate that the proposed model can accurately simulate the dynamics variation from different types of disturbances of cold tandem rolling processes.