New Approach in the Prediction of RDC Liquid‐Liquid Extraction Column Parameters

摘要: The liquid-liquid extraction process is well-known for its complexity and often entails intensive modeling computational efforts to simulate dynamic behavior. This paper presents a new application of the Genetic Algorithm (GA) predict parameters chemical pilot plant involving rotating disc contactor (RDC). In this process, droplet behavior dispersed phase has strong influence on mass transfer performance column. mechanism inside drops was modeled by Handlos-Baron circulating drop model with consideration effect forward mixing. Using method Numerical Analysis Group (NAG) software, axial dispersion coefficients in continuous these columns were optimized. order obtain RDC column parameters, least-square function differences between simulated experimental concentration profiles (SSD) 95 % confidence limit plug flow number unit prediction considered. minus sum square deviations GA justified it as successful optimization columns.