Predictive modeling of mass-transfer technological plant using an algorithm of alternating conditional expectations

The task of predictive modelling under conditions of nonlinearity of a mass-transfer plant (MTP) on the basis of 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 analytical model of the object. That is taking into account the physico-chemical characteristics of the MTP. The proposed approach is illustrated using synthetic data and experimental data.