Dynamic inferential estimation using principal components regression (PCR)

✍ Scribed by M.K. Hartnett; G. Lightbody; G.W. Irwin

• Book ID: 104309569
• Publisher: Elsevier Science
• Year: 1998
• Tongue: English
• Weight: 439 KB
• Volume: 40
• Category: Article
• ISSN: 0169-7439
• DOI: 10.1016/s0169-7439(98)00021-5

✦ Synopsis

Principal components regression PCR is applied to the dynamic inferential estimation of plant outputs from highly cor-Ž . related data. A genetic algorithm GA approach is developed for the optimal selection of subsets from the available measurement variables, thereby providing a method of identifying nonessential elements. The theoretical link between principal Ž . components analysis PCA and state-space modelling is employed to identify a measurement equation involving the GAselected subset, which is then used for inferential estimation of the omitted variables. These techniques are successfully demonstrated for the inferential estimation of outputs from a validated industrial benchmark simulation of an overheads con-Ž . densor and reflux drum model OCRD .