COMPUTATIONAL INTELLIGENCE MODELING AND CONTROL OF FLUE GAS EMISSION IN FBC PROCESS UDC 681

In this paper computationally intelligent modelling approach for fluidized bed combustion process has been considered, and also intelligent process control based on developed models. Applied adaptive neuro-fuzzy model structure provides for efficient combining of available expert knowledge with existing experimental data. On the basis of qualitative information on the desulphurization process models of the SO2 emission in fluidized bed combustion have been developed, which have been optimally tuned with measured data. Obtained results indicate that such approach can be successfully applied for economical and efficient reduction of SO2 in FBC by estimation of optimal process parameters and by design of intelligent control systems on the basis of defined emission model.