THE DYNAMIC MODELING OF ILL-DEFINED PROCESSING OPERATIONS USING CONNECTIONIST NETWORKS

This paper proposes a new methodology to model ill-defined processing operations using neural nets (NNs). A process with many variables (lar ge dimensionality) cannot be modelled adequately if limited process da ta are available. This problem of multidimensionality is addressed and an approach suggested to reduce the dimensionality using NNs. An NN i s trained on process data for the global variable space, whereafter th e first-order partial derivatives of the process are estimated with th e NN and used to perform perturbation analyses. As a result, the varia ble space can be subdivided into subspaces with reduced dimensionality . The final product of this modelling methodology is a combined model of phenomenological expressions and NNs. The model can be incorporated successfully in a dynamic simulator of the process. A new approach to conduct modelling on the basis of continuous data collected directly from an industrial processing unit is also proposed. The modelling met hodology is applied to a typical processing operation, i.e. the carbon -in-leach (CIL) process.