Use of inverse repeat sequence (IRS) for identification in chemical process systems

Use of a pseudo random binary sequence as an input signal for identificatio n of finite impulse response models of chemical process systems has been qu ite popular. In this paper, we show the utility of another binary signal, t he inverse repeat sequence (IBS) signal in identification of chemical proce sses. The attractiveness of IRS lies in the fact that, by its design, even- order kernels in the process response are canceled out. This reduces the ef fect of nonlinearity in identification. The improvements that can be derive d by using IRS are shown through the aid of continuous stirred tank reactor and fluidized catalytic cracking unit; case studies. Fm ther, the utility of the identified models in the model predictive control framework is also demonstrated.