文章基本信息

标题：Machine-learning for biopharmaceutical batch process monitoring with limited data
本地全文：下载
作者：Aditya Tulsyan ; Christopher Garvin ; Cenk Undey 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：18
页码：126-131
DOI：10.1016/j.ifacol.2018.09.287
语种：English
出版社：Elsevier
摘要：AbstractCommercial biopharmaceutical manufacturing comprises of multiple distinct processing steps that require effective and efficient monitoring of many variables simultaneously in real-time. This article addresses the problem of real-time statistical batch process monitoring (BPM) for biopharmaceutical processes with limited production history; herein, referred to as the ‘Low-N’ problem. In this article, we propose an approach to transition from a Low-N scenario to a Large-N scenario by generating an arbitrarily large number ofin silicobatch data sets. The proposed method is a combination of hardware exploitation and algorithm development. To this effect, we propose a Bayesian non-parametric approach to model a batch process, and then use probabilistic programming to generate an arbitrarily large number of dynamicin silicocampaign data sets. The efficacy of the proposed solution is elucidated on an industrial process.
关键词：KeywordsProcess monitoringLow-N problemBiopharmaceutical manufacturing