基于粒子群算法的重介质分选产品结构优化

通过实际分配曲线的坐标平移，可以求得在不同分选密度下各密度级的分配率，用于预测轻、重产物的产率和灰分，从而计算吨原煤经济效益。采用粒子群优化算法可以求解达到吨原煤最大经济效益时的分选密度，得到最优产品结构。将粒子群优化算法用Matlab编制计算程序，并以实际生产数据测试，结果表明，用粒子群算法对多参数产品优化问题进行求解，收敛速度快、效果好，得到的产品结构优化结果能实现吨煤最大经济效益。

Optimization of dense medium separation product structure based on particle swarm optimization algorithm

The distribution rate of each density grade is obtained under different separation condition through translating the coordinates of the distribution curve to change separation density. Then the yield and ash content of light and heavy products can be forecasted to calculate the economic benefits of raw coal per ton. Using particle swarm optimization algorithm to solve the optimum separation density with which the greatest economic benefit of raw coal per ton can be achieved, and the best product structure is obtained accordingly. Using MATLAB to composite program and testing it with actual product data, it’s concluded that particle swarm optimization algorithm has the advantage of fast convergence and high efficiency while used to solve the problem of multi-parameter products optimization. With the optimal product structure, the maximum economic benefits of raw coal per ton can be achieved.