Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London, UK.
Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, China.
Biotechnol Bioeng. 2019 Nov;116(11):2971-2982. doi: 10.1002/bit.27131. Epub 2019 Aug 8.
The development of digital bioprocessing technologies is critical to operate modern industrial bioprocesses. This study conducted the first investigation on the efficiency of using physics-based and data-driven models for the dynamic optimisation of long-term bioprocess. More specifically, this study exploits a predictive kinetic model and a cutting-edge data-driven model to compute open-loop optimisation strategies for the production of microalgal lutein during a fed-batch operation. Light intensity and nitrate inflow rate are used as control variables given their key impact on biomass growth and lutein synthesis. By employing different optimisation algorithms, several optimal control sequences were computed. Due to the distinct model construction principles and sophisticated process mechanisms, the physics-based and the data-driven models yielded contradictory optimisation strategies. The experimental verification confirms that the data-driven model predicted a closer result to the experiments than the physics-based model. Both models succeeded in improving lutein intracellular content by over 40% compared to the highest previous record; however, the data-driven model outperformed the kinetic model when optimising total lutein production and achieved an increase of 40-50%. This indicates the possible advantages of using data-driven modelling for optimisation and prediction of complex dynamic bioprocesses, and its potential in industrial bio-manufacturing systems.
数字化生物处理技术的发展对于现代工业生物处理至关重要。本研究首次调查了基于物理和数据驱动的模型在长期生物处理动态优化中的效率。更具体地说，本研究利用预测动力学模型和最先进的数据驱动模型来计算分批操作中生产微藻叶黄素的开环优化策略。鉴于光强度和硝酸盐流入率对生物量生长和叶黄素合成的关键影响，将它们用作控制变量。通过采用不同的优化算法，计算了几种最优控制序列。由于模型构建原理和复杂的工艺机制不同，基于物理的模型和数据驱动的模型产生了相互矛盾的优化策略。实验验证证实，与基于物理的模型相比，数据驱动模型预测的结果更接近实验结果。与之前的最高记录相比，这两种模型都成功地将叶黄素的细胞内含量提高了 40%以上；然而，在优化总叶黄素产量方面，数据驱动模型优于动力学模型，实现了 40-50%的增长。这表明在复杂动态生物处理的优化和预测中使用数据驱动建模的可能优势，以及其在工业生物制造系统中的潜力。
