Modeling of pertinent parameters influence on the time dependent mass transfer coefficient of particulate matter under the sink effect

Abstract

One of the most crucial evaluation metrics for the performance of particle sink purification technology is the time-dependent mass transfer coefficient (TDMTC). Therefore, it is very helpful for designers and developers to accurately describe the functional relationship between different influence parameters and the TDMTC. In this paper, four influence parameters (the applied voltage (V), interelectrode distance (dc), porosity (P), and shape (n) of the collecting electrode) were considered, and then non-linear multiple regression (NLMR) and multi-gene genetic programming (MGGP) methods were used to establish prediction models of the TDMTC. Results showed that V and n were the most significant factors, followed by dc and P. Both multi-factor models could accurately predict the TDMTC under the sink effect with a maximum prediction error of 20% and 15%, respectively. Moreover, for particulate matter (PM) with different size fractions, MGGP models could improve the prediction accuracy by 5–10% compared to NLMR models.