Procedures and Guidelines for Inputting and Output Smoothening of Kinetic Monte Carlo Distributions. Issue 5 (20th February 2022) - De Keer, Lies; Edeleva, Mariya; Figueira, Freddy L.; Reyes, Pablo; D'hooge, Dagmar R.; Van Steenberge, Paul H.M.
Abstract: Population balance modeling is very popular in several branches of modern science, including (bio)chemical engineering, geography, and demography. A growing trend is the use of stochastic solvers (e.g., kinetic Monte Carlo), but care should be taken upon inputting and outputting distributed data for the populations involved. In the present contribution, several procedures and guidelines are formulated facilitating such data treatment. The solutions are exemplified through polymerization and polymer modification case studies, taking the molar mass or chain length as core stochastic variable. It is shown that rediscretization is often necessary, as experimentally measured distributions are not directly interpretable at the input side of stochastic solvers. This rediscretization should take place both for the abscissa and ordinate values of the distributions. As stochastic solvers often display noisy data as model output, attention is paid to the smoothening of the output distributions as well. It is highlighted that the kernel‐smoothening method is very promising, as it can reliably tackle postprocessing of nonsymmetric distributions. Guidelines are formulated regarding the correct interpretation of the distribution tail both for the input and output modifications. Abstract : A growing trend is the use of stochastic solvers in population balance modeling. However, limited attention is paid to inputting and outputting distributed data, which is not as straightforward as one would think. In this work, several procedures and guidelines are formulated. These are exemplified through polymerization and polymer modification case studies, taking the molar mass as core stochastic variable.