A hierarchical fuzzy approach for flux prediction and optimization of milk microfiltration
A Hierarchical Fuzzy System (HFS), due to the dimensionality problem of ordinary fuzzy systems, was utilized for prediction and optimization of the permeation flux during the milk microfiltration process. A data-driven hybrid identification method based on a genetic and sequential quadratic programming (SQP) algorithm was applied to determine the fuzzy system parameters. The developed model was identified as a powerful tool for highly accurate flux prediction with the mean relative error of 1.5%. Comparison of HFS versus the well-known mathematical models revealed the robustness of the developed model. The optimum operating conditions for maximizing flux were evaluated by HFS and a genetic algorithm. The influences of trans-membrane pressure, temperature, cross-flow velocity and presence of fat globules (skim or whole milk) on membrane performance were elucidated at optimum conditions. (C) 2012 Elsevier Ltd. All rights reserved.
flux optimization milk microfiltration hierarchical fuzzy model