Cfd and Neural Network-based Expert System for the Supervision of Boilers and Furnaces

CFD (Computational Fluid Dynamics) tools were used to build a "virtual" furnace, validated with experimental data. This model was used to simulate both normal and “faulty” behaviours, regarding parameters such as energy conversion efficiency, steam leakage and fouling. A database was developed comprising normal situations and simulated fault sets, characterized by virtual sensor outputs used in the evaluation of diagnostic parameters patterns to be processed and recognized by the diagnostic system. The database was processed using Neural Networks, with satisfactory results even in their most simple form (backpropagation networks) trained using standard algorithms. Pattern recognition was thus performed, identifying root causes of simulated anomalies. Interactions with related research areas and future proposed developments are also discussed.