Wang Bo, Ma Wenyu, Jiang Hui, Huang Shaowen
School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel). 2025 Jun 30;25(13):4105. doi: 10.3390/s25134105.
To address the challenges in modeling and optimization caused by nonlinear dynamic coupling and real-time measurement difficulties of key biological parameters in 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.
为解决发酵过程中关键生物参数的非线性动态耦合和实时测量困难所导致的建模与优化挑战，本研究提出一种基于Adam-全连接神经网络逆模型的软测量方法。首先，构建一个非确定性机理模型来表征发酵过程中多个变量之间的动态耦合关系，并分析系统的可逆性和逆扩展模型的构建方法。进一步地，利用全连接神经网络的非线性拟合能力来识别逆扩展模型，引入自适应学习率优化算法动态调整全连接神经网络的学习率，从而增强非线性系统的收敛性和鲁棒性。最后，通过将逆模型与原系统级联形成复合伪线性系统，实现关键参数的解耦和高精度预测。实验结果表明，与传统模型相比，所提方法显著降低了预测误差并增强了泛化能力，验证了所提方法在复杂生物过程中的有效性。
