This work proposes a parsimonious dynamic filtration model for superheated steam containing paper-derived dust, suitable for parameter identification, prediction, and future optimization under limited observability. The model is based on Darcy’s law, with the total resistance expressed as the sum of the intrinsic filter resistance and a time-dependent fouling contribution. Experimental data obtained from a dedicated superheated-steam filtration setup were used for parameter estimation and model validation under a single operating condition. Assuming a linear dust dosing rate, the model yields limited agreement with experimental data (R² = 0.24). By estimating the time-varying solid loading through minimization of the sum of squared errors between measured and predicted pressure drop, the agreement improves significantly (R² = 0.94). This demonstrates that uncertainties in the dust dosing rate strongly affect pressure drop predictions. The proposed model provides a foundation for extending the analysis to broader process conditions, enabling statistical characterization of fitted parameters, integration with a filter cleaning model, and the development of optimization strategies for long-term, energy-efficient operation of closed-loop superheated steam drying systems.