Dynamic modelling of a carbon-in-leach process with the regression network

The regression network provides a connectionist framework in which both parametric and non-parametric modelling can be implemented. It is shown how mechanistic knowledge can be built directly within the connectionist structure that results in a semi-empirical network model. In doing so the inherent freedom of a specific model is restricted so that the generalisation performance of such a model improves accordingly. It is described how a semi-empirical regression network kinetic model is developed for the dynamic modelling of the carbon-in-leach (CIL) process for gold recovery. By providing for mechanistic knowledge in the connectionist structure and catering for poorly understood aspects of the process by use of non-parametric regions within the structure of the semi-empirical regression network, the regression network kinetic model displayed significant superiority in generalisation properties over other non-parametric regression models if evaluated during dynamic simulation runs.