Identification of Slow-Rate Integrated Measurement Systems Using Expectation–Maximization Algorithm

Despite the fact that slow-rate integrated measurement (SRTM), a gradual accumulation of material during a period of time, is a well-known method of sampling in industrial systems, especially chemical processes, the problem of the identification of SRTM systems has not been addressed so far. In this regard, system identification of the processes having SRTM will be presented in this work. By selecting finite impulse response (FIR) and autoregressive exogenous (ARX) models for the systems, parameters of them will be accurately estimated in the framework of the expectation-maximization (EM) algorithm. For this purpose, the instantaneous values of the output at the fast-rate sampling time are assumed to be latent variables. Applying the EM algorithm leads to some high-dimensional integrals, for which Monte Carlo simulation is adopted. The effectiveness of the proposed method is illustrated by both a simulation study and implementation on a laboratory-scale pH neutralization pilot plant.