Physics-Informed Neural Network Digital Twin for Dynamic Tray-Wise Modeling of Distillation Columns under Transient Operating Conditions

Abstract

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.

Figures (10)

• Figure 1: Temporal evolution of eight key sensor channels across the 8-hour simulation horizon. The dataset captures rich transient dynamics including oscillations in reboiler hold-up and monotonic reflux ratio ramp.
• Figure 2: Pearson correlation heatmap for all 18 sensor and target variables. Strong inter-pressure correlations ($\rho > 0.99$) and moderate composition--holdup couplings are evident.
• Figure 3: Methodology workflow for PINN-based digital twin of a distillation column
• Figure 4: Architecture of the proposed Physics-Informed Neural Network (PINN) for distillation column modeling, showing the column process (left), neural network with 16 sensor inputs and predicted outputs $x_{\mathrm{HX}}$, $x_{\mathrm{TX}}$, $T$, and $P$ (middle), and the physics-informed loss combining data loss with governing thermodynamic and transport equations (right).
• Figure 5: PINN architecture for the distillation column digital twin. The network combines four hidden layers (256--256--128--64, Swish activation) with physics residual constraints on VLE, mass balance, and energy balance embedded in the composite loss function $L_{\mathrm{total}}$.