用物理约束神经网络构建精馏塔动态数字孪生，实时精准预测组分与温度变化。
Physics-Informed Neural Network Digital Twin for Dynamic Tray-Wise Modeling of Distillation Columns under Transient Operating Conditions
- 将热力学平衡与物料能量守恒融入神经网络损失函数，实现物理一致性建模。
- 在8小时瞬态数据上，对组分预测的RMSE达0.00143，比最优纯数据模型提升44.6%。
- 适合工业过程实时监控、控制优化与异常检测，尤其适用于复杂动态系统。
数字孪生技术结合基于Aspen仿真的物理信息机器学习，为工业过程监测、控制与优化提供变革性能力。本文提出一种物理信息神经网络（PINN）数字孪生框架，用于二元精馏塔在瞬态工况下的动态逐板建模。模型架构通过物理残差项将汽液平衡（修正拉乌尔定律）、逐板质量与能量守恒及麦克卡伯-蒂尔方法直接嵌入神经网络损失函数。模型在基于Aspen HYSYS生成的高保真合成数据集上训练与评估，该数据集包含8小时瞬态操作的961个时间戳测量值，对应16个传感器流股的二元HX/TX精馏系统。采用自适应损失权重方案平衡数据拟合与物理一致性。相比五种数据驱动基线（LSTM、普通MLP、GRU、Transformer、DeepONet），所提PINN在HX摩尔分数预测上达到RMSE 0.00143（R²=0.9887），较最优纯数据模型降低44.6%，且严格满足热力学约束。在瞬态扰动下预测的逐板温度与组成分布表明，该数字孪生能准确捕捉进料板响应、回流比变化及压力波动等动态行为。结果确立了该PINN数字孪生在工业精馏过程实时软传感、模型预测控制与异常检测中的可靠基础。
原文摘要 · Abstract (English)
Digital twin technology, when combined with physics-informed machine learning with simulation results of Aspen, offers transformative capabilities for industrial process monitoring, control, and optimization. In this work, the proposed model presents a Physics-Informed Neural Network (PINN) digital twin framework for the dynamic, tray-wise modeling of binary distillation columns operating under transient conditions. The architecture of the proposed model embeds fundamental thermodynamic constraints, including vapor-liquid equilibrium (VLE) described by modified Raoult's law, tray-level mass and energy balances, and the McCabe-Thiele graphical methodology directly into the neural network loss function via physics residual terms. The model is trained and evaluated on a high-fidelity synthetic dataset of 961 timestamped measurements spanning 8 hours of transient operation, generated in Aspen HYSYS for a binary HX/TX distillation system comprising 16 sensor streams. An adaptive loss-weighting scheme balances the data fidelity and physics consistency objectives during training. Compared to five data-driven baselines (LSTM, vanilla MLP, GRU, Transformer, DeepONet), the proposed PINN achieves an RMSE of 0.00143 for HX mole fraction prediction (R^2 = 0.9887), representing a 44.6% reduction over the best data-only baseline, while strictly satisfying thermodynamic constraints. Tray-wise temperature and composition profiles predicted under transient perturbations demonstrate that the digital twin accurately captures column dynamics including feed tray responses, reflux ratio variations, and pressure transients. These results establish the proposed PINN digital twin as a robust foundation for real-time soft sensing, model-predictive control, and anomaly detection in industrial distillation processes.
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