【摘要】 为了获得更高的精矿压滤脱水作业效率,需对压滤脱水过程的控制参数进行优化.研究了自动压滤机脱水过程的优化机理.采用了支持向量机(SVM)等机器学习方法建立了压滤脱水过程的仿真模型,提出了一套"循序寻优"的脱水过程控制参数寻优方法.结果表明,采用支持向量机方法建立的工业压滤脱水过程仿真模型仿真精度最高,对水分和处理能力的仿真相对误差分别是1.57%和3.81%;利用"循序寻优"方法获得的工业压滤脱水过程最优控制参数,不但可以保证生产指标的稳定,而且将压滤周期缩短到了原来的85%以下.更多还原

【Abstract】 In order to obtain the higher operating efficiency of concentrate pressure filtering,the optimization of controlling parameters for the pressure filter dewatering process was necessary.The principle and optimization mechanism of pressure filter dewatering processes were studied.The machine training methods including Support Vector Machines(SVM) were used to simulate the dewatering process of pressure filter,a controlling parameter optimization method,namely progressive optimization was proposed.The results show that the simulating accuracy of pressure filter dewatering process using the SVM model was highest,which the relative errors of cake moisture of 1.57% and capacity of 3.81% were achieved.Using the control parameters obtained by the progressive optimization method,the stable operating data can be kept and the pressure filtering cycle was only 85% of that by the previous method.更多还原