基于Hopfeild神经网络的油菜籽脱皮冷榨压榨系数识别

    Identification of coefficient of consolidation in cold pressing of rapeseed decorticated based on hopfeild neural networks

    • 摘要: 在油菜籽脱皮侧限排油一维冷榨试验基础上,采用半固态饱和物料和滤饼物料物理模型分别建立两个理论压榨比模型,运用Hopfeild神经网络方法识别压榨系数。识别结果表明:脱皮油菜籽在压力低于10 MPa段为半固态状饱和物理模型,在压力高于20 MPa段则为滤饼状物理模型,对整个压榨过程,采用分段计算物理模型可有效提高整体压榨模型精度。脱皮冷榨的压榨系数远小于未脱皮冷榨的压榨系数。

       

      Abstract: A visualization of testing apparatus has been developed to measure consolidation property of pore media under mechanical pressing. On the basis of consolidation experiments of cold pressing of rapeseed decorticated two models of pressing rate were developed, by using the physical models of semisolid saturation material and of cakes. Hopfeild neural networks were used to identify the coefficient of consolidation. The physical models of semisolid saturation material in case lower pressure(≤10 MPa) and of cakes in case high pressure(≥20 MPa) were proposed. For the sake of improving precision of mathematical simulation of uniaxial compression, the two physical models of rapeseed decorticated are used to replace single physical model in whole process of pressing. Coefficient of consolidation of cold pressing of rapeseed decorticated is far smaller than that of rapeseed.

       

    /

    返回文章
    返回