Product quality estimation using multi-rate sampled data

Abstract This paper investigates different approaches to develop soft sensors from multi-rate sampled data. The data lifting approach consists of two steps, identifying a model with a slow/lifted sampling period and extracting a fast model. Approaches based on direct extraction and linear regression are briefly reviewed, followed by reformulating the task as an unconstrained optimization problem. An illustrative example concerning design of a free lime soft sensor for cement kiln systems is presented. Using data collected from a simulation system based on first principles models, a weighted partial least squares (WPLS) approach for soft sensor development is compared with data lifting techniques. Case studies reveal the superior performance of the WPLS approach. In addition the product quality for cement kiln systems can be estimated reasonably well, demonstrating the potential to be used for effective quality control.