FUZZY-NEURAL-NET-BASED INFERENTIAL CONTROL FOR A HIGH-PURITY DISTILLATION COLUMN

Many industrial processes are difficult to control because the product quality cannot be measured rapidly and reliably. One solution to this problem is inferential control, which uses an inferential estimator t o infer unmeasurable process outputs from secondary measurements, and controls these outputs. This contribution proposes a new approach for designing a Fuzzy Neural-Net (FNN)-based inferential estimator. The FN N is constructed by distributed multi-networks, whose classification, online running and learning are based upon fuzzy set theory. Applicati on of this method to an industrial high purity distillation column sho ws that the FNN-based inferential control is far superior to conventio nal composition control.