Multi-objective optimization of key parameters of stirred tank based on ANN-CFD

Abstract

In order to balance the maximum mixing efficiency and minimum energy consumption of stirred tanks, this study proposes a four-stage optimization framework, integrating all: CFD model, ANN data-prediction model, multi-objective optimization model, and multi-criteria decision making model. With the stirred tank reactor as the modeling and simulation context, a data-prediction model, GA-GABP, is developed firstly. Second, an optimization model is established targeting energy consumption, fluid mixing degree, and suspension uniformity: the predictions of GA-GABP are optimized using the NSGA II, and finally, based on the Pareto front, weights are determined using the entropy weighting method, and the TOPSIS algorithm is employed to decide the final optimization scheme. Compared to the base case, the optimized scheme Opt1 shows a 52.49% reduction in energy consumption, a 1.35% increase in fluid mixing degree, and a 72.31% improvement in suspension uniformity. This demonstrates the framework's effectiveness in balancing multiple conflicting objectives.