Dynamic modelling of a carbon-in-leach process with the regression network
Date
2004
Authors
Van Deventer J.S.J.
Kam K.M.
Van Der Walt T.J.
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Journal ISSN
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Abstract
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. © 2004 Elsevier Ltd. All rights reserved.
Description
Keywords
Leaching, Mathematical models, Regression analysis, Carbon-in-leach process, Non-parametric modelling, Regression networks, Carbon, leaching
Citation
Chemical Engineering Science
59
21
59
21