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Computational optimization of catalyst distributions at the nano-scale

Henrik Ström (Institutionen för tillämpad mekanik, Strömningslära ; Institutionen för energi och miljö, Energiteknik)
Applied Energy (0306-2619). Vol. 185 (2017), Part 2, Special Issue, p. 2224-2231.
[Artikel, refereegranskad vetenskaplig]

Catalysis is a key phenomenon in a great number of energy processes, including feedstock conversion, tar cracking, emission abatement and optimizations of energy use. Within heterogeneous, catalytic nano-scale systems, the chemical reactions typically proceed at very high rates at a gas-solid interface. However, the statistical uncertainties characteristic of molecular processes pose efficiency problems for computational optimizations of such nano-scale systems. The present work investigates the performance of a Direct Simulation Monte Carlo (DSMC) code with a stochastic optimization heuristic for evaluations of an optimal catalyst distribution. The DSMC code treats molecular motion with homogeneous and heterogeneous chemical reactions in wall-bounded systems and algorithms have been devised that allow optimization of the distribution of a catalytically active material within a three-dimensional duct (e.g. a pore). The objective function is the outlet concentration of computational molecules that have interacted with the catalytically active surface, and the optimization method used is simulated annealing. The application of a stochastic optimization heuristic is shown to be more efficient within the present DSMC framework than using a macroscopic overlay method. Furthermore, it is shown that the performance of the developed method is superior to that of a gradient search method for the current class of problems. Finally, the advantages and disadvantages of different types of objective functions are discussed.

Nyckelord: Optimization; DSMC; Catalysis; Stochastic optimization; Nanoscale



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Denna post skapades 2015-10-31. Senast ändrad 2017-01-20.
CPL Pubid: 225120

 

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