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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.
In order to obtain higher efficiency of pressure filtration and dewatering of concentrate, the control parameters of filter press dewatering process need to be optimized.The optimization mechanism of automatic filter press dewatering process is studied.The machine learning method such as support vector machine (SVM) is used to establish The simulation model of filter press dewatering process is proposed and a set of optimization method of “sequence optimization” is proposed. The results show that the simulation model of industrial filter press dewatering process established by support vector machine has the highest simulation accuracy, The relative error of simulation on moisture content and processing capacity is 1.57% and 3.81%, respectively. The optimal control parameters of industrial filter press dehydration obtained by the method of “sequential optimization” can not only ensure the stability of the production index, Shorten the cycle to 85% of the original.