采用自制的油泥分离剂通过热化学分离法处理聚驱油田现场产生的含聚油泥。采用正交实验得到的最佳工艺参数为:剂泥比2.0 m L/g,反应温度80℃,反应时间30 min,搅拌转速500 r/min,在此工艺条件下原油回收率为92.08%。利用支持向量机运算法...采用自制的油泥分离剂通过热化学分离法处理聚驱油田现场产生的含聚油泥。采用正交实验得到的最佳工艺参数为:剂泥比2.0 m L/g,反应温度80℃,反应时间30 min,搅拌转速500 r/min,在此工艺条件下原油回收率为92.08%。利用支持向量机运算法(SVM)建立模型,分析了各工艺参数之间的交互作用,得出优化后的含聚油泥处理工艺参数为:剂泥比2.5 m L/g,反应温度80℃,反应时间34 min,搅拌转速530 r/min,理论上的最高原油回收率为94.76%。对于模型优选出的工艺参数进行了5组验证实验,平均原油回收率达94.50%。采用优选工艺参数处理3种不同来源的含聚油泥,原油回收率均高于90%。展开更多
In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(S...In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.展开更多
文摘采用自制的油泥分离剂通过热化学分离法处理聚驱油田现场产生的含聚油泥。采用正交实验得到的最佳工艺参数为:剂泥比2.0 m L/g,反应温度80℃,反应时间30 min,搅拌转速500 r/min,在此工艺条件下原油回收率为92.08%。利用支持向量机运算法(SVM)建立模型,分析了各工艺参数之间的交互作用,得出优化后的含聚油泥处理工艺参数为:剂泥比2.5 m L/g,反应温度80℃,反应时间34 min,搅拌转速530 r/min,理论上的最高原油回收率为94.76%。对于模型优选出的工艺参数进行了5组验证实验,平均原油回收率达94.50%。采用优选工艺参数处理3种不同来源的含聚油泥,原油回收率均高于90%。
基金Projects(61471370,61401479)supported by the National Natural Science Foundation of China
文摘In order to improve measurement accuracy of moving target signals, an automatic target recognition model of moving target signals was established based on empirical mode decomposition(EMD) and support vector machine(SVM). Automatic target recognition process on the nonlinear and non-stationary of Doppler signals of military target by using automatic target recognition model can be expressed as follows. Firstly, the nonlinearity and non-stationary of Doppler signals were decomposed into a set of intrinsic mode functions(IMFs) using EMD. After the Hilbert transform of IMF, the energy ratio of each IMF to the total IMFs can be extracted as the features of military target. Then, the SVM was trained through using the energy ratio to classify the military targets, and genetic algorithm(GA) was used to optimize SVM parameters in the solution space. The experimental results show that this algorithm can achieve the recognition accuracies of 86.15%, 87.93%, and 82.28% for tank, vehicle and soldier, respectively.
