Machine learning-driven multi-objective optimization of straight-blade paddle mixers for efficient mixing in melt-cast explosives systems.

ABSTRACT: In the production of melt-cast explosives, efficient mixing in straight-blade paddle mixers is critical to product performance. Taking the classical trinitrotoluene-cyclotrimethylenetrinitramine (TNT-RDX) system as an example, this paper presents a machine learning-based multi-objective optimization algorithm that addresses the conflicting requirements of maximizing mean flow velocity and minimizing maximum pressure difference during mixing. By training random forest regression models on historical experimental and simulation data, the algorithm accurately predicts mixing outcomes from structural and operational parameters (e.g., blade number, width, length, angle, and rotation speed). These surrogate models are coupled with the nondominated sorting genetic algorithm II (NSGA-II) multi-obj

SUBMITTER: Li C 

PROVIDER: S-EPMC12816029 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature