Fuzzy-neural-net-based inferential control for a high-purity distillation column

✍ Scribed by R.F. Luo; H.H. Shao; Z.J. Zhang

• Publisher: Elsevier Science
• Year: 1995
• Tongue: English
• Weight: 794 KB
• Volume: 3
• Category: Article
• ISSN: 0967-0661
• DOI: 10.1016/0967-0661(94)00061-k

✦ Synopsis

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 to 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 FNN is constructed by distributed multi-networks, whose classification, online running and learning are based upon fuzzy set theory. Application of this method to an industrial high purity distillation column shows that the FNN-based inferential control is far superior to conventional composition control.