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真空冷冻干燥能够最好的保证干燥后产品质量,但干燥成本高已成为真空冷冻干燥技术大规模工业应用的技术瓶颈。因此利用BP神经网络理论对真空冷冻干燥过程进行了模拟研究,结果表明,BP神经网络能较精确的模拟真空冷冻干燥过程。采用人参切片干燥正交试验结果对BP神经网络进行训练后,对真空冷冻干燥工艺条件进行了预测和优化,预测值与试验实测值的相对误差较小,表明用BP神经网络理论模拟真空冷冻干燥过程具有较高的准确性。
Vacuum freeze-drying can best ensure the quality of products after drying, but the high cost of drying has become the technical bottleneck of large-scale industrial application of vacuum freeze-drying technology. Therefore, the BP neural network theory is used to simulate the vacuum freeze-drying process. The results show that the BP neural network can simulate the vacuum freeze-drying process more accurately. After the BP neural network was trained by the results of orthogonal test of ginseng slice drying, the process conditions of vacuum freeze-drying were predicted and optimized. The relative error between the predicted value and the experimental value was small, which indicated that the BP neural network theory was used to simulate the vacuum freeze-drying The process has high accuracy.