Abstract
To address the challenges in modeling and optimization caused by nonlinear dynamic coupling and real-time measurement difficulties of key biological parameters in Pichia pastoris fermentation processes, this study proposes a soft-sensing method based on Adam-Fully Connected Neural Network inverse. Firstly, a non-deterministic mechanism model is constructed to characterize the dynamic coupling relationships among multiple variables in the fermentation process, and the reversibility of the system and the construction method of the inverse extended model are analyzed. Further, by leveraging the nonlinear fitting capabilities of the Fully Connected Neural Network to identify the inverse extended model, an adaptive learning rate optimization algorithm is introduced to dynamically adjust the learning rate of the Fully Connected Neural Network, thereby enhancing the convergence and robustness of the nonlinear system. Finally, a composite pseudo-linear system is formed by cascading the inverse model with the original system, achieving decoupling and the high-accuracy prediction of key parameters. Experimental results demonstrate that the proposed method significantly reduces prediction errors and enhances generalization capabilities compared to traditional models, validating the effectiveness of the proposed method in complex bioprocesses.