CAJ | 학술논문

为了得到经济、高效的絮凝沉降参数，建立GA_SVM预测模型进行优化选择。在优选过程中，以供砂浓度、絮凝剂单耗和絮凝剂添加浓度作为输入因子，以沉降速度作为综合输出因子，通过室内试验，建立训练、验证样本集；建立支持向量机（ SVM ）回归预测模型，用训练集对模型进行训练，进而以验证集预测值的均方误差作为适应度函数，通过遗传算法（ GA）对SVM模型参数进行优化选择，应用优化得到的SVM模型对絮凝沉降参数进行预测、优化。以湖南某铅锌银矿为例，通过建立的GA_SVM模型对全尾砂絮凝沉降参数进行预测，优选出该矿最佳絮凝沉降参数为：供砂浓度20％-25％，絮凝剂单耗8g/t，添加浓度0.09％。经实验对比，该模型对絮凝沉降参数预测结果的相对误差能控制在5％左右，精确度较高，可以作为絮凝沉降参数优选的一种新思路。
위료득도경제、고효적서응침강삼수，건립GA_SVM예측모형진행우화선택。재우선과정중，이공사농도、서응제단모화서응제첨가농도작위수입인자，이침강속도작위종합수출인자，통과실내시험，건립훈련、험증양본집；건립지지향량궤（ SVM ）회귀예측모형，용훈련집대모형진행훈련，진이이험증집예측치적균방오차작위괄응도함수，통과유전산법（ GA）대SVM모형삼수진행우화선택，응용우화득도적SVM모형대서응침강삼수진행예측、우화。이호남모연자은광위례，통과건립적GA_SVM모형대전미사서응침강삼수진행예측，우선출해광최가서응침강삼수위：공사농도20％-25％，서응제단모8g/t，첨가농도0.09％。경실험대비，해모형대서응침강삼수예측결과적상대오차능공제재5％좌우，정학도교고，가이작위서응침강삼수우선적일충신사로。
A GA_SVM model was established to optimize the flocculating sedimentation parameters .The tailings concentration , flocculant consumption and flocculant concentration were used as the input parameters and the sedi -mentation speed was confirmed to be the synthesized output parameter .Some training and validating samples were established through indoor experiment .Then, for predicting flocculating sedimentation parameters , a support vector machine ( SVM) regression model was established .The mean square error of the value was made as a fitness func-tion.Then, the model parameters were optimized through the genetic algorithm ( GA) .GA_SVM model was used in some mine , and the results showed that the best tailings concentration , flocculant consumption and flocculant concentration are 20%~25%, 10g/t and 0.09%.Comparing with the experiment results , the relative error of prediction result can be controlled at about 5%.The application indicates that this mode makes good effect , and it provides a new method to optimize the flocculating sedimentation parameters .