Authors

Abstract

To enhance the conveying efficiency of screw conveyors, reduce energy consumption during material transport, and improve particle integrity, this study proposes a multi-objective optimization framework integrating the NSGA-II algorithm with entropy-weighted TOPSIS. Discrete Element Method (DEM) simulations, conducted using EDEM software, and an optimal Latin hypercube sampling design were employed to systematically obtain high-fidelity data on mass flow rate and energy consumption under various operating conditions. A surrogate performance model relating key geometric and operational parameters — including pitch, inclination angle, and rotational speed — to mass flow rate and energy consumption was developed using least squares regression. Subsequently, the NSGA-II algorithm was applied to the surrogate model to generate a Pareto-optimal solution set. The entropy-weighted TOPSIS method was then used to rank and identify the optimal compromise solution from the Pareto frontier. Experimental validation of the optimized design demonstrated significant improvements: the mass flow rate increased by 15.77%, energy consumption decreased by 26.16%, and particle degradation was considerably reduced. These results provide practical, data-driven guidance for the rational design and energy-efficient operation of screw conveyors.

Abstract in Chinese

为了提高螺旋输送机的输送效率，减少螺旋输送机运输过程中的能量消耗，提高颗粒完整度。本文提出了一种基于NSGA-II算法和熵权TOPSIS的螺旋输送机多目标优化方法。基于离散元软件EDEM、最优拉丁超立方试验设计，得到螺旋输送机的质量流量、能量消耗数据。基于最小二乘法建立螺距、倾斜角度、螺旋转速与质量流量、能量消耗的性能指标模型。随后使用NSGA-II算法从性能指标模型中获参数的最佳值，获得Pareto前沿解集，基于熵权TOPSIS方法对Pareto前沿解集寻优，获得最优解。优化设计的实验验证表明，该设计取得了显著改进：质量流量提高了15.77%，能量消耗减少了26.16%。本研究结果为螺旋输送机的设计提供了一定的参考。