Department of Chemical and Biochemical Engineering, Technical University of Denmark, Lyngby, Denmark.
Molecular Quantum Solutions ApS, Copenhagen, Denmark.
Adv Biochem Eng Biotechnol. 2021;176:1-34. doi: 10.1007/10_2020_142.
The bio-manufacturing industry, along with other process industries, now has the opportunity to be engaged in the latest industrial revolution, also known as Industry 4.0. To successfully accomplish this, a physical-to-digital-to-physical information loop should be carefully developed. One way to achieve this is, for example, through the implementation of digital twins (DTs), which are virtual copies of the processes. Therefore, in this paper, the focus is on understanding the needs and challenges faced by the bio-manufacturing industry when dealing with this digitalized paradigm. To do so, two major building blocks of a DT, data and models, are highlighted and discussed. Hence, firstly, data and their characteristics and collection strategies are examined as well as new methods and tools for data processing. Secondly, modelling approaches and their potential of being used in DTs are reviewed. Finally, we share our vision with regard to the use of DTs in the bio-manufacturing industry aiming at bringing the DT a step closer to its full potential and realization.
生物制造行业与其他流程行业一样，现在有机会参与最新的工业革命，也称为工业 4.0。要成功实现这一目标，应该仔细开发物理到数字到物理的信息循环。实现这一目标的一种方法是，例如，通过实施数字孪生 (DT)，这是流程的虚拟副本。因此，本文的重点是了解生物制造行业在处理这种数字化范例时面临的需求和挑战。为此，突出强调并讨论了 DT 的两个主要构建块，即数据和模型。因此，首先，检查了数据及其特征和收集策略，以及用于数据处理的新方法和工具。其次，回顾了建模方法及其在 DT 中的潜在用途。最后，我们分享了我们对 DT 在生物制造行业中使用的看法，旨在使 DT 更接近其全部潜力和实现。
