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-  2018 

ELM模型水沙分离水力旋流器选型的研究
Separating Water and Sand by Hydrocy-clone of ELM Model Selection

Keywords: 极限学习机,水力旋流器,神经网络,预测,正交试验
adaptive algorithms
,combinatorial optimization,computer simulation,data processing,mathematical models,nonlinear analysis,nonlinear systems

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Abstract:

针对水力旋流器选型困难以及经验选型很难达到最佳分离效果的问题,以6个对分离效果影响较大的因素为研究对象,取L64(89)表前6列为数据变量组,建立ELM模型并与正交的结果做对比。结果表明:ELM模型与正交实验所反映的规律一致,正交实验只能给出一组最优组合,其分离效果为63.08%,ELM预测出的分离结果有4组值都和正交实验的最优组合接近或相等,分别为63.01%、62.78%、63.02%、63.08%。因此,在生产旋流器的过程中,以ELM模型预测的多组结果作为参考,可以节约选型后制造的成本。
Due to the difficulty in selecting hydrocyclone empirically to achieve the best separation effect, this paper discusses the influence of 6 factors on separation prevention. The first 6 columns of the L64 (89) table is choosen for various data groups, and the ELM model is established to compare the orthogonal results. The results show that the reflected ELM model and the orthogonal experiments can only give a set of optimal combinations, the separation effect is 63.08%, the prediction of the separation results on ELM with 4 sets of values and the optimal combination of orthogonal experiments is close to or equal to 63.01%, 62.78%, 63.02% and 63.08%, respectively. Therefore, in the process of producing a cyclone, the multi group results predicted by the ELM model can be used as a reference to save the cost of post selection

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