Centre for Process Integration, The Mill, University of Manchester, Manchester, UK.
Centre for Process Systems Engineering, South Kensington Campus, Imperial College London, London, UK.
Biotechnol Bioeng. 2019 Nov;116(11):2919-2930. doi: 10.1002/bit.27120. Epub 2019 Jul 26.
Model-based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low-quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics-based and data-driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high-quality data by correcting raw process measurements via a physics-based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data-driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re-fitting the simple kinetic model (soft sensor) using the data-driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed-batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open-loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application.
基于模型的在线优化由于建模复杂的生物行为、低质量的工业测量以及缺乏对正在进行的过程的可视化技术等挑战，尚未在生物工艺中得到广泛应用。本研究提出了一种创新的混合建模框架，该框架利用基于物理和数据驱动的建模方法来进行生物过程的在线监测、预测和优化。该框架首先通过基于物理的噪声滤波器（具有高拟合但低预测性能的通用简单动力学模型）对原始过程测量值进行校正，从而生成高质量的数据；然后构建一个预测性的数据驱动模型，以确定最佳控制动作并预测离散的未来生物过程行为。随后，通过使用数据驱动模型预测的离散未来数据点重新拟合简单动力学模型（软传感器），对连续的未来过程轨迹进行可视化，从而可以在任何操作时间准确地监测正在进行的过程。该框架通过与不同的在线优化方案相结合，用于最大化分批培养微藻叶黄素的生产，并与传统的开环优化技术进行了比较。使用所提出的框架获得的最佳结果与理论上的最佳生产结果相当，证明了其具有较高的预测和灵活性能，以及在工业应用中的潜力。
