Cost-effective framework for design optimization using metamodel and evolutionary algorithm: with a case study of bio-filtration system

A cost-effective approach using metamodel-based design optimization has been employed to determine the optimal configuration of input parameters for a desired output in an industrial engineering problem, with a case study of a bio-filtration system. Inhomogeneous multiphase flow around a simplified bio-filtration system under backwashing conditions was simulated based on parameter sampling through the design of experiments. Metamodeling was conducted along with a sensitivity analysis of input parameters for desired outputs, which helped filter out unimportant parameters and achieve computational benefits during the modeling process. Finally, optimization was performed using the metamodel and an evolutionary algorithm to determine the optimal parameter configuration for the desired outputs, i.e., evenly distributed filter-bed mass flow. The numerical approach can yield well-predicted results with significantly reduced computational time compared to full computational fluid dynamics simulations, potentially reducing the number of expensive simulations required for various industrial engineering problems.