The Neural Network Model for Backflushing in Enzymatic Membrane Reactor  

作　　者：何志敏 董春华 齐崴 

机构地区：[1]Chemical Engineering Research Center,School of Chemical Engineering and Technology,Tianjin University,Tianjin 300072,China

出　　处：《Chinese Journal of Chemical Engineering》2005年第6期809-815,共7页中国化学工程学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China (No. 20306023).

摘　　要：In the enzymatic membrane reactor for separating casein hydrolysate, backflushing technology has been used to decrease the fouling of the membrane. Predication of the backflushing efficiency poses a complex non-linear problem as the system integrates enzymatic hydrolysis, membrane separation and periodic backflushing together. In this paper an alternative artificial neural network approach is developed to predict the backflushing efficiency as a function of duration and interval. A contour plot of backflushing performance is presented to model these effects, and the backflushing conditions have been optimized as duration of 10 s and interval of 10 min using this neural network. Also, simple neural networks are established to predict the time evolution of flux before and after backflushing. The results predicted by the models are in good agreement with the experimental data, and the average deviations for all the cases are well within ±5%. The neural network approach is found to be capable of modeling the backflushing with confidence.在为分开 caseinhydrolysate 的酶的膜反应堆， backflushing 技术被用来犯规 backflushing 效率姿势的 themembrane.Predication 减少系统一起集成酶的水解作用，膜分离和周期的 backflushing 的一个复杂非线性的问题。当持续时间和 backflushing 性能的 interval.A 等值线图表的一个函数被介绍为这些效果建模，在这篇论文，一条其他的人工的神经网络途径被开发预言 backflushing 效率，并且 backflushing 条件用这个神经网络作为持续时间 of10s 和 10 min 的间隔被优化了。另外，简单神经网络被建立在 backflushing 前后预言流动的时间进化。模型预言的结果在对试验数据的好同意，并且为所有盒子的平均偏差在神经网络途径被发现能够与信心为 backflushing 建模的 +-5%.The 以内好。

关 键 词：BACKFLUSHING neural network MODEL optimize PREDICT CASEIN 

分 类 号：O629.8[理学—有机化学]