Real-time calibration method for digital twin models in process industry
流程工业数字孪生模型的实时校准方法

摘要

流程工业中数字孪生模型的精度直接影响其在实时优化与故障预测中的应用效果。本文围绕“流程工业数字孪生模型的实时校准方法”展开研究，提出一种基于数据驱动与机理融合的动态校准策略，旨在提升模型对复杂工况变化的适应能力。通过构建多源数据采集框架与在线参数更新机制，实现模型输出与实际系统状态的持续逼近。实验结果表明，该方法能显著提升模型的实时性与准确性，为流程工业智能化提供有力支撑。

Abstract

The accuracy of digital twin models in the process industry directly affects their application effects in real-time optimization and fault prediction. This paper focuses on the research of "real-time calibration methods for digital twin models in process industry" and proposes a dynamic calibration strategy based on the integration of data-driven and mechanism-based approaches. The strategy aims to enhance the model's adaptability to complex working condition changes. By constructing a multi-source data acquisition framework and an online parameter update mechanism, the continuous approximation between model output and the actual system state is achieved. Experimental results show that this method can significantly improve the real-time performance and accuracy of the model, providing strong support for the intelligentization of the process industry.