Abstract
In this investigation, differential evolution (DE) algorithm with the fuzzy inference system (FIS) are combined and the DE algorithm is employed in FIS training process. Considered data in this study were extracted from simulation of a 2D two-phase reactor in which gas was sparged from bottom of reactor, and the injected gas velocities were between 0.05 to 0.11 m/s. After doing a couple of training by making some changes in DE parameters and FIS parameters, the greatest percentage of FIS capacity was achieved. By applying the optimized model, the gas phase velocity in x direction inside the reactor was predicted when the injected gas velocity was 0.08 m/s.
Highlights
IntroductionDifferential evolution (DE) algorithm with the fuzzy inference system (FIS) are combined and the differential evolution (DE) algorithm is employed in FIS training process
In this investigation, differential evolution (DE) algorithm with the fuzzy inference system (FIS) are combined and the DE algorithm is employed in FIS training process
DE algorithm is applied in the FIS training step in order to reach the best FIS Capacity
Summary
Differential evolution (DE) algorithm with the fuzzy inference system (FIS) are combined and the DE algorithm is employed in FIS training process. The form and shape of these columns are simple, with no moving element These reactors are featured with economical operation costs, easy maintenance, and desirable mass/heat transfer flux. Local/global parameters (flow pattern, phase velocities, gas phase hold-up, turbulence, and bubble size) have a direct and complicated effect on the design variables. Bubble column (BC) reactors are usually utilized in industrial work like heterogeneous churn turbulent flow pattern[4]. There have been many empirical correlations developed to design such systems Such correlations normally possess a limited validity domain as to operating conditions, geometries, or physical properties. There is a need to develop new computation models to simulate bubble column reactors with a wider validity range compatible with both homogeneous/heterogeneous flow patterns. Computational fluid dynamics (CFD) technique is employed as a reliable procedure to find local/global properties through bubbly flows[7] and eliminate the limits of the conventional scale-up method through costly experimental measurements
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