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An easy-to-use implementation of a Differential Evolution Based Stochastic Optimizer (DEBSO) for nonlinear, multi-modal problems is presented. Using two case studies, we demonstrate that DEBSO is (1) more effective and (2) less sensitive to user defined initial guess values, in finding the global optimum, as compared to that of a gradient based deterministic optimizer. Results from using DEBSO for construction of empirical catalyst maps from pulsator data and estimation of parameters in a diesel oxidation catalyst model are also presented. The effectiveness and efficiency of DEBSO has been compared to other evolution-based optimizers in Appendix A.

Experiments were conducted to determine the effect of accelerated aging on LNT-SCR system performance. The systems were aged and evaluated on an engine dynamometer using a DW12 2.2L I4 Diesel. The aging consisted of three modes, including sulfur exposure, desulfation (deSOx), and DPF regeneration conditions. Two systems were aged using different desulfation protocols and then evaluated over steady-state cycling conditions. The sulfur release characteristics were measured. Overall system gross NOx conversion was robust to the aging protocol; however, there were significant differences in the individual component performance depending on the nature of the aging conditions. LNT NOx conversion was significantly lower for the case where maximum temperatures reached only 730°C, as opposed to the more severe aging where temperatures reached 800°C.