Predictive Modeling of Mass-Transfer of Plant Using an Algorithm of Alternating Conditional Expectations

Abstract

The problem of predictive modeling under condition of the nonlinearity of the mass−transfer plant (MTP) based on the experimental data is considered. To analyze the structural identifiability of the process under study and identify factors that affect the accuracy of the structural identifiability index with an unknown model structure, a technique based on an alternating conditional expectation (ACE) algorithm with a threshold value for the structural identifiability index of the MTP model is proposed. The threshold value of the structural identifiability index is determined based on the rigorous model of the plant, i.e., taking into account the physicochemical characteristics of the MTP. The proposed approach is illustrated using synthetic data and experimental data.