Tuning multi-objective optimization algorithms for cyclone dust separators

Abstract

Cyclone separators are filtration devices frequently used in industry, e.g., to filter particles from flue gas. Optimizing the cyclone geometry is a demanding task. Accurate simulations of cyclone separators are based on time consuming computational fluid dynamics simulations. Thus, the need for exploiting cheap information from analytical, approximative models is evident. Here, we employ two multi-objective optimization algorithms on such cheap, approximative models to analyze their optimization performance on this problem. Under various limitations, we tune both algorithms with Sequential Parameter Optimization (SPO) to achieve best possible results in shortest time. The resulting optimal settings are validated with different seeds, as well as with a different approximative model for collection efficiency. Their optimal performance is compared against a model based approach, where multi-objective SPO is directly employed to optimize the Cyclone model, rather than tuning the optimization algorithms. It is shown that SPO finds improved parameter settings of the concerned algorithms and performs excellently when directly used as an optimizer.

Tuning Multi-Objective Evolutionary Algorithms on Different Sized Problem Sets

“…Zaefferer et al [21] also performed tuning on a real-world multi-objective problem. They tuned two algorithms with Sequential Parameter Optimization (SPO) [15] for the cyclone (dust separator) optimization problem.…”

Optimization via multimodel simulation

“…Optimization can be performed on this model, e.g., to improve the collection efficiency for a given pressure drop. Even multiobjective optimization techniques can be applied (Elsayed and Lacor 2012; Zaefferer et al 2014). …”