我国化工过程强化技术理论与应用研究进展

化工过程强化技术被认为是解决化学工业"高能耗、高污染和高物耗"问题的有效技术手段,可望从根本上变革化学工业的面貌。经过多年的基础研究和技术开发,我国在化工过程强化技术方面形成了自己的特色与优势。本文综述了我国在超重力技术、膜过程耦合技术、微化工技术、磁稳定床技术、等离子体技术、离子液体技术、超临界流体技术、微波辐射技术等典型化工过程强化技术方面的进展。

关于化工强化技术分析探究

化学工业在我国国民经济中仍然占有不可或缺的支柱地位。在地球资源日趋枯竭,环境污染日益严重的今天,我国化学工业迫切需要向资源节约型和环境友好型发展模式转变,而针对复杂化工体系利用过程强化技术来推动和促进这一转变过程则是化学工业的必由之路。通过过程强化技术开发新型、高效的生产工艺,或对传统工艺进行改造和升级,使过程的能耗、物耗和废物排放大幅度减少,必将从根本上变革化学工业的面貌。文章对我国在超重力技术、膜过程耦合技术、微化工技术、磁稳定床技术、等离子体技术、离子液体技术、超临界流体技术、微波辐射技术等进行了阐述。

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.