Abstract
Inferential estimation involves the determination of difficult to measure process variables from easily accessible secondary information. In addition to the capital costs of instrumentation, the downward pressure on manpower costs and overheads involved in maintenance make soft sensing attractive for industrial applications. Viscosity control in a polymerization reactor is addressed. The neural network based inferential estimation, where the network is trained to predict the polymer viscosity from past torque and viscosity data, is investigated. Results from the offline training of a feedforward network are presented and new work on online viscosity estimation using B-Spline networks is described.
| Original language | English |
|---|---|
| Pages (from-to) | 1/1-1/6 |
| Journal | IEE Colloquium (Digest) |
| Issue number | 65 |
| Publication status | Published - 1995 |
| Externally published | Yes |
| Event | IEE Computing and Control Division Colloquium on Intelligent Measuring Systems for Control Applications - London, UK Duration: 4 Apr 1995 → 4 Apr 1995 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 9 Industry, Innovation, and Infrastructure
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