ESTIMATION OF DISTILLATION COMPOSITIONS FROM MULTIPLE TEMPERATURE-MEASUREMENTS USING PARTIAL-LEAST-SQUARES REGRESSION

This paper addresses the use of temperature measurements to estimate product compositions in distillation columns. A simple linear multivariate calibration procedure based on steady-state data is used, which requires minimal modeling effort. It is found that these principal-component-regression (PCR) and partial-least-squares (PLS) estimators perform well, even for multicomponent mixtures, pressure variations, and nonlinearity caused by changes in operating conditions. The use of weighting functions, additional factors, and logarithmic transformations improve the estimates and counteract nonlinearities, provided there is not too much noise on the temperatures. In the paper we also compare more generally regression methods based on singular-value decomposition (SVD; generalized least squares), PCR, and PLS.