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Parameter optimization of double‐blade normal milk processing and mixing performance based on RSM and BP‐GA

机译:基于RSM和BP-GA的双叶片正常牛奶加工和混合性能参数优化

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摘要

Abstract Temperature stability was taken as the evaluation index of processing performance, and the three factors that influence normal milk processing and mixing performance were optimized by response surface analysis and BP‐GA neural network algorithm. Analysis results showed the influence order of the factors on temperature stability was as follows: shape > height > rotating speed. In the optimization by response surface methodology (RSM), when rotating speed was 30 r/min, height was 31 mm, and blade shape was a full trapezoid, predicted value and actual value of variable coefficient were 0.0046 and 0.0044 respectively, with relative error of 4.5%. In the optimization by BP‐GA neural network algorithm, when rotating speed was 34 r/min, height was 25 mm, and blade shape was a full trapezoid, the predicted value and actual value of variable coefficient were 0.0036 and 0.0035 respectively, with relative error of 2.9%. The predicted root‐mean‐square error of the model by the BP‐GA neural network algorithm was 0.0013, determination coefficient was 0.9960, and relative percent deviation was 8.4961, which showed better performance than the RSM model. Thus, the BP‐GA neural network algorithm has better fitting performance, and then, the optimal working parameter combination was confirmed, which could provide reference to improving double‐blade normal milk processing and mixing device design and milk processing quality.
机译:摘要温度稳定性取作的加工性能的评价指标,以及影响正常奶加工和混合性能这三个因素进行响应面分析和BP-GA神经网络算法进行了优化。分析结果表明:对温度稳定性的因素的影响顺序为:形状>高度>转速。在响应面法(RSM),当旋转速度为30转/分的优化,高度为31毫米,和刀片形状是一个完整的梯形,预测值和可变系数的实际值分别为0.0046和0.0044,相对误差的4.5%。在由BP-GA神经网络算法,当旋转速度为34转/分的优化,高度为25mm,和刀片形状是一个完整的梯形,预测值和可变系数的实际值分别为0.0036和0.0035,相对误差2.9%。由BP-GA神经网络算法的模型的预测根均方误差为0.0013,决定系数为0.9960,和相对百分比偏差为8.4961,其显示出比RSM模型更好的性能。因此,BP-GA神经网络算法具有更好的装配性能,然后,最佳工作参数组合被证实,其可以提供参考改善双叶片正常牛奶加工和混合装置设计和牛奶加工质量。

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