Neurofuzzy control of a low-temperature desulphurization plant

摘要

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 com- petitive neurofuzzy system, which possesses both the learning ability of neural networks and the structural transparency of fuzzy logic systems.