Izvorni znanstveni članak
Continuum Regresion in Process Modeling Based on Plant Data
Dražen Slišković
Nedjeljko Perić
Ivan Petrović
Sažetak
Important process variables which give information about the final product quality cannot often be measured by a sensor but their value is determined based on laboratory analysis. In order to perform a continuous monitoring of a process variable and an efficient process control, it is necessary to estimate this difficult-to-measure process variable, i.e. to determine it on the basis of a mathematical model. However, to build an appropriate process model in many cases there are available only process measurement data stored in a process data base. This paper gives appropriate methodology for process modeling based on plant data. Regression methods based on input space projection into a latent subspace are proposed to build a model. The paper investigates, in particular, properties of continuum regression (CR). As neural networks present a good basis for data based model building, possibility of hybridization of multilayer perceptron (MLP) neural network with CR method is additionally investigated. The aim of that is to use good properties of both methods and to avoid their weaknesses in process model building based on plant data. Advantages of the proposed methods for process model building as compared to the usually used regression methods are demonstrated by the modeling of crude oil distillation process based on the measuring data available.
Ključne riječi
process modeling; plant data; difficult-to-measure process variable estimation; projection into a latent space; continuum regression; neural networks
Hrčak ID:
4398
URI
Datum izdavanja:
27.1.2006.
Posjeta: 1.640 *