This paper studies the optimization of product yield for the microbial fermentation process in the presence of model-plant mismatch. The typical “two-step” optimization method involves modifying the model by adjusting its parameters and subsequently utilizing the updated model to optimize product output. However, due to estimability and overfitting problems, it is often impractical to update all available parameters. Therefore, we propose a robust batch-to-batch optimization method with global sensitivity analysis. First, the parameters to be identified are determined through global sensitivity analysis. Then, the robust batch-batch optimization method is employed to optimize the yield of fermentation products. Compared with previous parameter selection methods, global sensitivity analysis considers the complex correlation between parameters, allowing for a more comprehensive evaluation of the impact of multiple parameters and their interactions on the model. Furthermore, compared with previous studies, the robust batch-to-batch optimization method assigns weights to each variable in the identification objective function based on experience and model output variance, significantly reducing the uncertainty of the next optimal batch run. Simultaneously, the method is robust to the uncertainty in initial conditions, ensuring a more stable process when running in large batches. A penicillin fermentation case study verifies the convergence and robustness of the method.

中文翻译：

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对模式-植物错配下的微生物发酵过程进行稳健的批次间优化和全局灵敏度分析


本文研究了在模式植物错配的情况下微生物发酵过程的产品产量优化。典型的“两步”优化方法包括通过调整模型参数来修改模型，然后利用更新的模型来优化产品输出。但是，由于可估计性和过拟合问题，更新所有可用参数通常是不切实际的。因此，我们提出了一种具有全局敏感性分析的稳健的批次到批次优化方法。首先，通过全局敏感性分析确定要识别的参数。然后，采用稳健的 batch-batch 优化方法来优化发酵产物的产量。与以前的参数选择方法相比，全局敏感性分析考虑了参数之间的复杂相关性，从而可以更全面地评估多个参数及其交互作用对模型的影响。此外，与以前的研究相比，稳健的批次到批次优化方法根据经验和模型输出方差为识别目标函数中的每个变量分配权重，显著降低了下一个最佳批次运行的不确定性。同时，该方法对初始条件下的不确定性具有鲁棒性，确保在大批量生产时过程更加稳定。青霉素发酵案例研究验证了该方法的收敛性和稳健性。