The compositional distribution in two-component aggregative mixing of initially bidisperse particle populations can be described by a Guassian-type function, which is determined by the mixing degree chi (assessed quantitatively by the mass-normalized power density of excess component A), and the overall mass fraction phi (a known value from the initial feeding condition) of component A. It is known that chi will reach a steady-state value chi(infinity) over time (factually, after attaining the self-preserving size distribution), and X-infinity is only relevant to phi, namely the feeding condition. However, the dynamic evolution of x before the attainment of a steady-state value is not exactly known. In this paper, the fast differentially-weighted Monte Carlo method for population balance modeling was used to predict the dependence of time-varied chi on initial feeding conditions through hundreds of systematically varied simulations. It is found that chi is subject to an exponential decay, largely depending on the ratio of steady-state mixing degree and its initial value (chi(infinity)/chi(0)) With the explored exponential formulas for the dynamic mixing degree, it is possible to attain an optimum control on the compositional distributions during two-component aggregation processes through selecting the initial feeding parameters, and the time needed for reaching a steady-state is investigated. (C) 2016 Elsevier Ltd. All rights reserved.