The application of Monte Carlo methods to simulate the agglomeration of suspended nanoparticles is currently limited to specific agglomeration regimes with reduced accuracy in terms of the particle's physical residence time. The definition of specific particles persistent distance, its corresponding time step and subsequent probabilities for particle displacements may improve the accuracy of this method. To solve these issues, a new persistent distance and its corresponding time step based on Langevin dynamics simulations are introduced. Additionally, a probability of particle displacements, not restricted to a specific agglomeration regime, is introduced. All the modifications are validated by comparison with Langevin dynamics simulations. Finally, the above mentioned modifications considerably improve the accuracy of Monte Carlo methods to predict the dynamics and agglomeration of suspended nanoparticles. (C) 2020 Elsevier Inc. All rights reserved.