Semi−industrial Experiment of Dynamic Thickening of Tailings Based on NSGA−II Algorithm and Its Multi−objective Optimization
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摘要:
尾砂浓密工艺是一种多输入、多输出、高时滞的复杂系统，探究不同因素影响下尾砂浓密多目标优化问题具有重要意义，为推进尾砂浓密工艺的精准控制和智能化发展提供参考。研制了一套尾砂动态浓密半工业智能实验装置，开展了尾砂动态浓密正交实验，考察泥层高度、进料流量和耙架转速对尾砂浓密多目标的影响；结合尾砂动态浓密半工业正交实验结果，建立了底流质量浓度、溢流浊度和耙架扭矩的多元回归模型，利用MATLAB 软件的通讯模块，实现了对尾砂动态浓密半工业实验效果的实时预测；结合矿山对尾砂浓密的实际需求，构建了基于NSGA−Ⅱ算法的尾砂动态浓密多目标优化模型，获得了优化后的尾砂动态浓密参数和浓密效果。研究结果表明：泥层高度和进料流量对尾砂浓密效果具有显著影响，泥层高度是影响浓密效果的最主要因素；尾砂浓密多元回归模型的预测差异在4.53%以内，模型拟合效果良好；多目标优化后的尾砂动态浓密参数为泥层高度0.30 m、进料流量0.91 m3/h、耙架转速3.80 r/min，优化后的尾砂浓密效果为底流质量浓度69.57%，溢流浊度40.41 NTU、耙架扭矩11.53 N·m。
Abstract:The tailings thickening process is a complex system with multiple inputs, multiple outputs, and high time delay. Exploring the multi−objective optimization problem of tailings thickening under different factors is of great significance, which provides reference for promoting precise control and intelligent development of tailings thickening process. A semi−industrial intelligent testing device for dynamic thickening of tailings was developed to conduct orthogonal experiments on dynamic thickening of tailings, and investigate the effects of mud layer height, feed flow rate, and rake speed on the multi−objective thickening of tailings; A multiple regression model was established for underflow concentration, overflow turbidity, and rake torque based on the results of semi−industrial orthogonal experiments for dynamic thickening of tailings. Using the communication module of MATLAB software, the real−time prediction of the semi−industrial test effect of dynamic thickening of tailings was achieved; Taking into account the actual demand of mines for tailings thickening, a multi−objective optimization model for dynamic tailings thickening based on NSGA−II algorithm was constructed. The optimized parameters and thickening effects of dynamic tailings thickening were obtained. The research results indicate that the height of the mud layer and the feed flow rate had a significant impact on the thickening effect of tailings. The height of the mud layer was the most important factor affecting the thickening effect. The prediction difference of the multiple regression model for tailings thickening process was within 4.53%, and the model fitting effect was good; The optimized parameters for tailings thickening after multi−objective optimization were mud layer height of 0.30 m, feed flow rate of 0.91 m3/h, and rake speed of 3.80 r/min. The optimized tailings thickening effect parameters were underflow mass concentration of 69.57%, overflow turbidity of 40.41 NTU, and rake torque of 11.53 N·m.
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表 1 正交实验因素水平
Table 1. Factor levels of orthogonal experiments
水平 因素 A泥层高度/m B进料流量/(m3·h−1) C耙架转速/(r·min−1) 1 0.15 0.97 1.50 2 0.20 1.08 2.00 3 0.30 1.19 2.50 
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表 2 尾砂动态浓密半工业正交实验结果
Table 2. Results of semi−industrial orthogonal test for dynamic thickening of tailings
编号 因素 指标 泥层
高度/m进料
流量
/(m3·h−1)耙架
转速
/(r·min−1)底流
质量
浓度/%溢流
浊度
/NTU耙架
扭矩
/(N·m)1 0.15 0.97 1.50 62.95 100.71 16.19 2 0.15 1.08 2.00 62.73 102.24 17.18 3 0.15 1.19 2.50 60.58 110.91 18.66 4 0.20 0.97 2.00 63.79 99.57 18.74 5 0.20 1.08 2.50 63.24 99.93 18.94 6 0.20 1.19 1.50 61.65 103.75 20.41 7 0.30 0.97 2.50 65.66 90.52 20.72 8 0.30 1.08 1.50 65.33 94.93 21.12 9 0.30 1.19 2.00 64.65 96.62 22.21
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表 3 正交实验的方差分析
Table 3. Analysis of variance in orthogonal experiments
指标 方差来源 离差平方和 自由度 均方 F值 P值 底流质量浓度 A 15.81 2 7.90 69.23 0.01 B 5.69 2 2.85 24.92 0.04 C 0.51 2 0.26 2.24 0.31 D空白 0.23 2 0.11 溢流浊度 A 174.64 2 87.32 10.75 0.08 B 73.35 2 36.68 4.52 0.18 C 1.49 2 0.74 0.09 0.92 D空白 16.24 2 8.12 耙架扭矩 A 24.08 2 12.04 91.16 0.01 B 5.62 2 2.81 21.26 0.04 C 0.06 2 0.03 0.24 0.81 D空白 0.26 2 0.13
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