Energy consumption, flow characteristics and energy-efficient design of cup-shape blade stirred tank reactors: Computational fluid dynamics and artificial neural network investigation

Abstract

Developing high-efficient and green energy-saving stirred tank reactors is of vital importance in liquid-liquid processing industries. Therefore, this work revealed the energy consumption, flow characteristics, and explored the energy-saving strategies of novel-designed Cup-shape Blade (CB) stirred tank reactors characterized by easy fabrication, low energy consumption and large pumping flow rate. Computational Fluid Dynamics (CFD) and Artificial Neural Network (ANN) were conducted to investigate the influences of geometrical parameters on liquid-liquid power number (NP), flow number (NQ) and flow patterns. The improved method for hyper-parameter specification indicates that the Bayesian regulation training algorithm and the two-hidden-layer scheme possess the best performance. Back Propagation Neural Network (BPNN) presents the best predictive accuracy for NP and NQ, and Learning Vector Quantization (LVQ) network shows the best classification capability of flow patterns. Analyses based on the ANN modeling and the “Local Interpretable Model-agnostic Explanations” method show the joint and complicated effects of geometrical parameters on energy consumption and flow characteristics. Procedures were proposed to ascertain the energy-efficiency blade configuration based on ANN.