.png){kind=icon}
返回
Mechanism- and data-driven based dynamic hybrid modeling for multi-condition processes
.png){kind=icon}
PRE
AI
DOI：10.1016/j.chemolab.2025.105353.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In process industries, the complexity and variability of working conditions make it challenging to accurately measure product quality. While data-driven models have developed rapidly, they often overlook the underlying physical or chemical mechanisms. To address this, we propose a hybrid modeling approach that combines mechanism- and data-driven methods. Historical and current working condition data are processed through a hidden layer to extract features. The partial differential equation is discretized and approximated using the forward Euler method to derive mechanism-based quality variable values. These values are then combined with real data through a weighted mix to create anew label for dynamic regression. Additionally, a domain adaptation regularization term is introduced to align the distributions of different working conditions. Through analyses of three process industry datasets, we demonstrate that this method can predict unmeasurable variables with reasonable accuracy and exhibits stronger generalization ability compared to pure data-driven models.
Keyword:
Mechanism-driven
Data-driven
Multiple working condition
Dynamic time-varying
期刊
IF:
3.8
论文数:
4.6K
被引数:
1.2W
机构
引用论文
Soft sensor model development in multiphase/multimode processes based on Gaussian mixture regression基于高斯混合回归的多相/多模过程软测量模型开发
.png){kind=icon}
PREAI
分享收藏Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations物理信息神经网络: 一种用于解决涉及非线性偏微分方程的正反问题的深度学习框架
OAAI
OAAI分享收藏分享收藏Maximizing information from chemical engineering data sets: Applications to machine learning最大化来自化学工程数据集的信息: 机器学习的应用OAAI
OAAI分享收藏A hybrid driven approach to integrate surrogate model and Bayesian framework for the prediction of machining errors of thin-walled parts集成代理模型和贝叶斯框架的混合驱动方法，用于预测薄壁零件的加工误差
PREAI
分享收藏分享收藏
查看更多内容