Fermentation Pilot Plant, Novonesis A/S, Bagsværd, Denmark.
Department of Chemical and Biochemical Engineering, Process and Systems Engineering Centre (PROSYS), Technical University of Denmark, Kongens Lyngby, Denmark.
Biotechnol Bioeng. 2024 May;121(5):1609-1625. doi: 10.1002/bit.28670. Epub 2024 Mar 7.
Digitalization has paved the way for new paradigms such as digital shadows and digital twins for fermentation processes, opening the door for real-time process monitoring, control, and optimization. With a digital shadow, real-time model adaptation to accommodate complex metabolic phenomena such as metabolic shifts of a process can be monitored. Despite the many benefits of digitalization, the potential has not been fully reached in the industry. This study investigates the development of a digital shadow for a very complex fungal fermentation process in terms of microbial physiology and fermentation operation on pilot-scale at Novonesis and the challenges thereof. The process has historically been difficult to optimize and control due to a lack of offline measurements and an absence of biomass measurements. Pilot-scale and lab-scale fermentations were conducted for model development and validation. With all available pilot-scale data, a data-driven soft sensor was developed to estimate the main substrate concentration (glucose) with a normalized root mean squared error (N-RMSE) of 2%. This robust data-driven soft sensor was able to estimate accurately in lab-scale (volume < 20× pilot) with a N-RMSE of 7.8%. A hybrid soft sensor was developed by combining the data-driven soft sensor with a mass balance to estimate the glycerol and biomass concentrations on pilot-scale data with N-RMSEs of 11% and 21%, respectively. A digital shadow modeling framework was developed by coupling a mechanistic model (MM) with the hybrid soft sensor. The digital shadow modeling framework significantly improved the predictability compared with the MM. The contribution of this study brings the application of digital shadows closer to industrial implementation. It demonstrates the high potential of using this type of modeling framework for scale-up and leads the way to a new generation of in silico-based process development.
数字化为发酵过程开辟了新的范例，如数字阴影和数字孪生，为实时过程监测、控制和优化打开了大门。通过数字阴影，可以实时监测模型对过程代谢转变等复杂代谢现象的适应性。尽管数字化带来了许多好处，但在工业界，其潜力尚未得到充分发挥。本研究旨在探讨在 Novonesis 针对非常复杂的真菌发酵过程的微生物生理学和发酵操作方面，开发数字阴影的情况，以及其中的挑战。由于缺乏离线测量和生物量测量，该过程历史上一直难以优化和控制。进行了中试规模和实验室规模的发酵，以开发和验证模型。利用所有可用的中试规模数据，开发了一种数据驱动的软传感器，以估计主要基质浓度（葡萄糖），归一化均方根误差（N-RMSE）为 2%。该稳健的数据驱动软传感器能够在实验室规模（体积<20×中试规模）内准确估计，N-RMSE 为 7.8%。通过将数据驱动软传感器与质量平衡相结合，开发了一种混合软传感器，以在中试规模数据上估计甘油和生物量浓度，N-RMSE 分别为 11%和 21%。通过将机理模型（MM）与混合软传感器耦合，开发了数字阴影建模框架。与 MM 相比，数字阴影建模框架显著提高了预测能力。本研究的贡献使数字阴影的应用更接近工业实施。它展示了使用这种建模框架进行放大的巨大潜力，并为新一代基于计算机的过程开发开辟了道路。
