Over the last few years rapid progress has been made in adopting well-known process modeling techniques from chemicals to biologics manufacturing. The main challenge has been analytical methods as engineers need quantitative data for their workflow. Industrialization 4.0, Internet of Things, artificial intelligence and machine learning activities up to big data analysis have taken their share in solving fundamental problems like component- or at least group-specific evaluation of spectroscopic data. Besides, concerning inline analytics methods included in process analytical technology concepts the key technology has been the generation of decisive validated digital twins based on process models. This review aims to summarize the methodology to achieve a holistic understanding of process models, control and optimization by means of digital twins using the example of recent work published in this field. © 2021 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).

中文翻译：

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过程分析技术是连续生物制造中数字孪生的关键推动力

在过去几年中，在采用从化学品到生物制品制造的众所周知的过程建模技术方面取得了快速进展。主要挑战是分析方法，因为工程师需要为其工作流程提供定量数据。工业化 4.0、物联网、人工智能和机器学习活动一直到大数据分析，都在解决诸如光谱数据的组件或至少特定组的评估等基本问题方面发挥了作用。此外，关于流程分析技术概念中包含的在线分析方法，关键技术是基于流程模型生成具有决定性的经过验证的数字双胞胎。本综述旨在总结实现对过程模型的整体理解的方法，使用该领域最近发表的工作示例，通过数字双胞胎进行控制和优化。© 2021 作者。John Wiley & Sons Ltd 代表化学工业协会 (SCI) 出版的《化学技术和生物技术杂志》。