| Abstract: | Low‐temperature desulphurization (LTD) is a low‐cost alternative to conventional wet scrubbing for removing sulphur dioxide from flue gas produced by power generating plants. A problem in the design of conventional controllers to achieve and maintain process conditions for optimal desulphurisation is the lack of mathematical models to characterise the complex desulphurization process and unexpected environ mental disturbances. The aim of the work reported in this paper was to replace a skilled human operator who could successfully regulate the LTD plant through manipulation of low‐level controllers with a competitive neurofuzzy system, which possesses both the learning ability of neural networks and the structural transparency of fuzzy logic systems. A hierarchical control structure was adopted whereby the competitive neurofuzzy method was used at the top level for co‐ordinating actions of low‐level conventional controllers. This approach would considerably simplify the task of designing the control system and has been shown to yield high‐level controllers with performances at least equalling that of expert operators, as demonstrated in this paper. Copyright © 2001 John Wiley & Sons, Ltd. |
