期刊
SEPARATION AND PURIFICATION TECHNOLOGY
卷 248, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.seppur.2020.117064
关键词
Semi-parametric model; Neural network; Film mass theory; Downstream processing; Digital twin
资金
- Austrian Research Promotion Agency (FFG) [859219]
Cross-flow ultrafiltration is a powerful tool used in bioprocesses to concentrate and separate biopharmaceuticals. However, there is a major challenge for its effective use: the fouling rate and decrease in the permeate flux vary substantially depending on the concentration and characteristics of the incoming feed, which causes variations in the process durations. We developed a hybrid model for use in predicting the flux evolution and duration of cross-flow ultrafiltration processes for various proteins, membrane types, input parameters, and filtration modes. The trained hybrid model is able to determine the process duration with an average normalized root-mean-square error of less than 6.2. It has superior adaptation to varying filtration characteristics compared to the mechanistic film theory model and can handle both batch- and fed-batch ultrafiltration using the same training set. In addition, the presented hybrid model can be used as a digital twin to simulate virtual processes with varying input parameters and different batch modes, which is a valuable tool for efficient process development or optimization.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据