深锥浓密机底流浓度预测与外部结构参数优化

针对困扰支持向量机(SVM)模型参数选择问题，结合遗传算法(GA)，建立了深锥浓密机底流放砂浓度的GA-SVM预测模型，研究了不同结构参数状态下底流浓度的变化规律，进行了深锥浓密机的外部结构参数优化选择。以司家营铁矿为例，在最优底流放砂浓度为72%的条件下，经外部结构参数优化后的深锥浓密机锥高10 m、锥角为30°，系统稳定可靠、底流连续均匀，动力荷载较同类矿山降低约15%，压耙停机故障降低80%。

Underflow concentration prediction and external structure parameter optimization of deep cone thickener

To overcome the difficulty of choosing appropriate external structure parameters for support vector machine(SVM)models, the genetic algorithm(GA)is introduced and a GA-SVM optimal prediction model of underflow concentration is built. The change laws of thickener underflow concentration are discussed under different parameters, and the structure parameters of deep cone thickener are optimized. Sijiaying iron mine is taken as an example, and the results show that with the optimal underflow concentration of 72%, the optimized external structure parameters of deep cone thickener are 10 m high and 30 degree cone. The optimized deep cone thickener in Sijiaying runs steady with continuous underflow concentration flowing. Compared with other similar thickeners, its energy load and fault probability are reduced by 15% and 80% respectively.