| 胡瑞芬，王迪，程一启，等.基于BP神经网络的花生挤压膨化参数优化研究[J].中国油脂,2017,42(8):.[HU Ruifen, WANG Di, CHENG Yiqi, et al.Optimization of peanut extrusion parameters based on BP neural network[J].China Oils and Fats,2017,42(8):.] |
| 基于BP神经网络的花生挤压膨化参数优化研究 |
| Optimization of peanut extrusion parameters based on BP neural network |
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| DOI： |
| 中文关键词: 花生 挤压膨化；BP神经网络 |
| 英文关键词:peanut extrusion BP neural network |
| 基金项目: |
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| 摘要点击次数: 1277 |
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| 中文摘要: |
| 研究花生挤压膨化工艺参数对产品质量（粕残油率）的影响。通过建立BP神经网络模型，利用样本对其进行训练使其具有工艺参数-产品质量的映射能力，结合粒子群算法进行参数寻优，确定粕残油率最低时的最优参数组合。结果表明：建立了BP神经网络模型，相关的试验验证了仿真结果，表明BP神经网络模型在参数优化中的有效性和适应性；确定最优的参数组合为主轴转速55 r/min、模孔直径12 mm、套筒温度105?℃、喂料速度26 r/min、含水率11%和轴头间隙12 mm。在最优参数组合下，粕残油率为1.03%。模孔直径、主轴转速和套筒温度对产品质量的影响较大。 |
| 英文摘要: |
| The effects of peanut extrusion parameters on product quality (residual oil rate of the meal) were studied. Through the establishment of BP neural network model, the samples were trained to have the mapping ability of process parameters-product quality. Combined with PSO, the optimal parameter combination was determined when the residual oil rate of the meal was the lowest. The results showed that BP neural network model was established and the relevant experiments verified the simulation results, which showed that BP neural network model had effectiveness and adaptability in parameter optimization. The optimal parameter combination was obtained as follows: spindle speed 55 r/min, diameter of die orifice 12 mm, sleeve temperature 105?℃, screw speed 26 r/min, moisture content 11% and distance between die and screw 12 mm. Under these conditions, the residual oil rate of the meal was 1.03%. The diameter of die orifice, spindle speed and sleeve temperature had higher effects on the product quality. |
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