(373e) Fast Monte Carlo Algorithms for Modeling and Optimization of Complex Particulate Processes

In this work, new algorithms ([1], [2])are presented for the simulation and optimization of complex particulate systems modeled by multi-dimensional population balance equations. These new fast Monte Carlo methodologies reduce the computational load of the MC simulation by orders of magnitude with negligible accuracy losses, when compared with exact MC algorithms. This improvement in simulation speed makes process optimization feasible ([3], [4]). First, the algorithms are described in detail including benchmark solutions. Then, the capabilities of these methodologies are demonstrated using the following study cases: (1) Fast precipitation in micelles (soft micro reactors), (2) Dynamics of simultaneous agglomeration and breakage and (3) Optimal nano-particle coating process by solving a dynamic optimization problem.

[1] Irizarry, R., Fast Monte Carlo Methodology for Multivariate Particulate Systems: Point Ensemble Monte Carlo, submitted to Chemical Engineering Science.

[2] Irizarry, R, t-PEMC: Very fast Monte Carlo solution of Multivariate Population Balance equation, submitted to Chemical Engineering Science.

[3] Irizarry, R. (2005), Ageneralized framework for solving dynamic optimization problems using the artificial chemical process paradigm: Applications to particulate processes and discrete dynamic systems, Chemical Engineering Science, 60, 5663-5681.

[4] Irizarry, R. (2006), Hybrid Dynamic Optimization using Artificial Chemical Process Method: Extended LARES-PR, Ind. Eng. Chem. Res., 45, 8400-8412.