Optimization of a Gas–Liquid Dual-Impeller Stirred Tank Based on Deep Learning with a Small Data Set from CFD Simulation

Abstract

An optimization strategy based on deep learning was developed for dual-impeller design in a gas−liquid stirred tank. The optimization objective was to maximize the gas−liquid specific interfacial area and minimize power consumption. A small raw data set was obtained with time-consuming computational fluid dynamics (CFD) simulation for different dual-impeller designs. The noise injection method was employed as a data augmentation technique to generate the virtual data. Based on the augmented data set, feedforward neural network (FNN) was used to model the relationship between the six geometric variables of impellers and two objective functions, while Bayesian optimization was applied to tune the hyperparameters of FNN. A two-stage deep learning approach was introduced to enhance the prediction of power consumption based on the correlation between the specific interfacial area and power consumption. The deep learning model was used to calculate the entire solution space and, thereby, capture the Pareto optimal solutions. The proposed method validated by a data set of CFD simulations could achieve high prediction accuracy and robustness.

Application of Machine Learning Models in Coaxial Bioreactors: Classification and Torque Prediction

“…Research indicates that utilizing ANN models or evolutionary algorithms can address multi-objective optimization challenges. In fact, these methods are capable of determining the most efficient operating conditions, thereby optimizing performance while minimizing power consumption [26,27].…”