用深度学习提升衣康酸生产模型参数估计精度
Deep Learning for Model Calibration in Simulation of Itaconic Acid Production
- 采用生成式条件流匹配方法优化动力学参数估计
- 该方法预测浓度曲线与非线性回归结果接近，误差更小
- 适合需要跨尺度、多工况建模的生物过程研究
本研究利用深度学习方法，基于不同搅拌速度和反应器规模下的真实批次实验数据，估算衣康酸生产过程的动力学参数。对比了直接深度学习（DDL）与生成式条件流匹配（CFM）两种策略，并以非线性回归作为基准方法。结果显示，相较于DDL，CFM在所有条件下均表现出更高精度，其预测的浓度曲线与非线性回归结果高度一致，偏差更小；在放大实验中，CFM模型也展现出更强的泛化能力和鲁棒性。研究表明，CFM可可靠预测不同操作条件和尺度下的系统行为，为动态生物过程模型的参数估计提供了一种灵活且数据高效的框架。
原文摘要 · Abstract (English)
In this study, deep learning is used to estimate kinetic parameters for modeling itaconic acid production based on real batch experiments conducted at different agitation speeds and reactor scales. Two deep learning strategies, namely direct deep learning (DDL) and generative conditional flow matching (CFM) are compared and benchmarked against nonlinear regression as a reference method. Compared with DDL, CFM consistently yields more accurate results. The concentration profiles predicted by CFM closely match those obtained from nonlinear regression, whereas DDL results in larger deviations. Similar behavior is observed in the scale-up experiments, where the CFM model again generalizes better and is more robust than the direct approach. These findings demonstrate that CFM can reliably predict system behavior across different operating conditions and scales, offering a flexible and data-efficient framework for parameter estimation in dynamic bioprocess models.
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