The Neural Network Model for Backflushing in Enzymatic Membrane Reactor

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.

采用反吹、微流控技术分析原油中的轻烃组分

利用程序升温蒸发进样器、反吹技术和微流控装置分析原油中的C5-C13轻烃组分，用一根HPPONA色谱柱分离，同时得到保留时间相同的火焰离子化检测信号和质谱检测信号，为原油中轻烃组分的色谱定性提供了新的有力工具。该方法中所用的色谱柱使用寿命长，分离效果好，对平湖4井油样中C5～C13馏分的分离得到了可用质谱定性的峰约290个（尚有不少混合峰存在），较难分离的物质对t-1，2-二甲基环戊烷与2-乙基戊烷得到了较好的分离，2-乙基戊烷峰得到非常好的质谱图，是目前研究原油中轻烃组分分析的有效手段。同时应用微流控装置后可在质谱仪不停机的条件下更换色谱柱。