ACCURATE SIMPLISTIC PREDICTIVE MODELING OF NONLINEAR DYNAMIC PROCESSES

In this article, a new simplistic way of predictive modeling of proces s variables in nonlinear dynamic processes is introduced. This approac h, which is semi-empirical, is demonstrated on a simulated continuous stirred tank reactor. Model development uses a first-order-plus-dead-t ime structure and only two Or three input changes for determining the coefficients. This approach is evaluated for a variety of situations w hich include measured output, unmeasured output, extrapolation beyond the input range, various levels of dead time, various levels of measur ement error, large dynamics, and various levels of nonlinear behavior. In the situation of unmeasured output, the proposed approach is very accurate and in the other cases it is extremely accurate and far super ior to linear regression and artificial neural network models. (C) 199 8 Elsevier Science Ltd.