Pressure swing adsorption (PSA) is an energy-efficient technology for gas separation, while the multiobjective optimization of PSA is a challenging task. To tackle this, we propose a hybrid optimization framework (TSEMO + DyOS), which integrates two steps. In the first step, a Bayesian stochastic multiobjective optimization algorithm (i.e., TSEMO) searches the entire decision space and identifies an approximated Pareto front within a small number of simulations. Within TSEMO, Gaussian process (GP) surrogate models are trained to approximate the original full process models. In the second step, a gradient-based deterministic algorithm (i.e., DyOS) is initialized at the approximated Pareto front to further refine the solutions until local optimality. Therein, the full process model is used in the optimization. The proposed hybrid framework is efficient, because it benefits from the coarse-to-fine function evaluations and stochastic-to-deterministic searching strategy. When the result is far away from the optima, TSEMO can efficiently approximate a trade-off curve as good as a commonly used evolutional algorithm, i.e., Nondominated Sorting Genetic Algorithm II (NSGA-II), while TSEMO only uses around 1/16th of CPU time of NSGA-II. This is because the GP-based surrogate model is utilized for function evaluations in the initial coarse search. When the result is near the optima, the searching efficiency of TSEMO dramatically decreases, while DyOS can accelerate the searching efficiency by over 10 times. This is because, in the proximity of optima, the exploitation capacity of DyOS is significantly higher than that of TSEMO.

Catalyst leaching is a major impediment to the development of commercially-viable processes conducted in a liquid-phase. To date, there is no reliable technique that can accurately identify the extent and dynamics of the leaching process in a quantitative manner. In this work, a tandem flow-reactor system has been developed, which allowed us to distinguish between surface-catalyzed reactions from those occurring in solution by comparing%conversion at the exit of each reactor (S1, S2) corresponding to predominance of heterogeneous/homogeneous reactions (spatial) and two different residence times (temporal). A multiscale model is subsequently established to quantify the two types of reaction rate and simulate the catalyst leaching from a cross-coupling catalyst, PdEncat™ 30; including: (1) a multi-particle sizes model for catalyst scale; and (2) a dispersion model for reactor scale. The results show that catalyst leaching occurs via more than one process, and that the homogeneous Pd-catalyst (leached from the immobilized catalyst and dissolved in the flow) dominates the reaction and possesses a much higher activity than the heterogeneous (immobilized) Pd-catalyst. Additionally, the change of leached Pd stream inside reactors can be predicted along with the axial direction and the reaction time through the reactor-scale dispersion model.