🤖 AI Summary
This study addresses the challenges of parameter estimation and limited generalizability in kinetic models for itaconic acid fermentation across varying agitation speeds and bioreactor scales. To overcome these limitations, the authors introduce, for the first time, generative conditional flow matching (CFM) into bioprocess modeling, leveraging multi-condition batch experimental data to calibrate model parameters. Compared to conventional nonlinear regression and direct deep learning (DDL) approaches, CFM substantially enhances both parameter estimation accuracy and cross-scale predictive performance. Scale-up experiments demonstrate excellent agreement between CFM-predicted concentration profiles and experimental measurements, confirming the method’s reliability and data efficiency in modeling dynamic biological processes.
📝 Abstract
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.