Multi-objective optimization in combinatorial chemistry applied to the selective catalytic reduction of NO with C 3H 6

Abstract

A high-throughput approach, aided by multi-objective experimental design of experiments based on a genetic algorithm, was used to optimize the combinations and concentrations of a noble metal–free solid catalyst system active in the selective catalytic reduction of NO with C 3H 6. The optimization framework is based on PISA [S. Bleuler, M. Laumanns, L. Thiele, E. Zitzler, Proc. of EMO'03 (2003) 494], and two state-of-the-art evolutionary multi-objective algorithms—SPEA2 [E. Zitzler, M. Laumanns, L. Thiele, in: K.C. Giannakoglou, et al. (Eds.), Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001), International Center for Numerical Methods in Engineering (CIMNE), 2002, p. 95] and IBEA [E. Zitzler, S. Künzli, Conference on Parallel Problem Solving from Nature (PPSN VIII), 2004, p. 832]—were used for optimization. Constraints were satisfied by using so-called “repair algorithms.” The results show that evolutionary algorithms are valuable tools for screening and optimization of huge search spaces and can be easily adapted to direct the search towards multiple objectives. The best noble metal free catalysts found by this method are combinations of Cu, Ni, and Al. Other catalysts active at low temperature include Co and Fe.