Mechanism-Guided Residual Lifting and Control Consistent Modeling for Pneumatic Drying Processes

TL;DR

This paper presents a hybrid modeling framework combining mechanistic and data-driven approaches for pneumatic drying processes, achieving high accuracy, interpretability, and stability suitable for control applications.

Contribution

It introduces a unified physics-guided residual lifting method with control-consistent correction, integrating mechanistic models with residual learning and stability constraints for improved process modeling.

Findings

Achieved MAE of 0.016% for moisture prediction

Validated on 63,000 samples from 10 industrial batches

Residuals exhibit white-noise characteristics with reduced low-frequency spectral energy

Abstract

Pneumatic drying processes in industries such as agriculture, chemicals,and pharmaceuticals are notoriously difficult to model and control due to multi-source disturbances,coupled stage dynamics, and significant measurement delays. Traditional modeling paradigms often fail to simultaneously deliver accuracy, interpretability, and closed-loop applicability. To address this challenge, this paper introduces a unified hybrid modeling framework, termed Physics-Guided Residual Lifting with Control-Consistent Correction,which integrates a transient mechanistic model with a stability-constrained data-driven component. The framework covers the complete process chain of drying, transport, and winnowing. On the mechanistic level, the model unifies mass transfer dynamics using the partial pressure difference of water vapor, incorporates water activity clamping and latent heat corrections for bound water, and ensures energy closure with moisture-dependent specific heat. On the data-driven level,we propose an orthogonal residual learning scheme. It leverages intermediate states from the mechanistic model as proxy variables to construct a physics-inspired dictionary, preventing parameter compensation and overfitting during ridge regression. Furthermore, to ensure suitability for predictive control, a Control-Consistent Extended Dynamic Mode Decomposition with stability constraints is employed to learn the residual dynamics, for which we provide boundedness proofs and stability guarantees. The framework was validated on 10 industrial batches, comprising 63,000 samples. On unseen test data, the hybrid model achieved a Mean Absolute Error of 0.016% for outlet moisture and 0.015 {\deg}C for outlet temperature, with values improving to 0.986 and 0.995, respectively. The resulting prediction residuals exhibit white-noise characteristics, with significantly reduced spectral energy at low frequencies.