Uncertainty-Quantified Kinetic Model Discrimination for Photocatalytic Reactor Design: A Bootstrap Based Framework

Translating laboratory-scale photocatalytic batch data into continuous reactor design remains a critical challenge in reaction engineering, particularly when kinetic model selection is based solely on the coefficient of determination (R²) and parameter uncertainty is ignored. In this study, a data-driven framework is developed that integrates nonlinear kinetic model discrimination, bootstrap-based uncertainty quantification, and uncertainty propagation into plug-flow reactor (PFR) sizing. Time–concentration data from methylene blue degradation over a WO₃/BiVO₄ photocatalyst (C۰ = ۱۰ mg L⁻¹) were fitted to six nonlinear kinetic models using Particle Swarm Optimization (PSO). Model discrimination was performed using the bias-corrected Akaike Information Criterion (AICc) and Akaike weights, decisively selecting the Elovich model (ΔAICc > ۳۰ vs. pseudo-first-order; Akaike weight > ۰.۹۹). Parameter uncertainty was quantified via nonparametric bootstrap resampling (۲۰۰۰ replicates), yielding well-constrained ۹۵% confidence intervals (α: ۲.۰۲-۲.۲۸ mg g⁻¹ min⁻¹; β: ۰.۳۴-۰.۴۲ g mg⁻¹). The identified kinetics were embedded into a steady-state PFR model, and bootstrap parameter distributions were propagated to generate a reactor length design envelope for ۹۰% pollutant removal. The nominal reactor length was ۱.۲۵ m, with a ۹۵% confidence interval of ۱.۱۸-۱.۳۵ m (±۷%). This narrow design envelope demonstrates robust parameter identifiability and provides a statistically defensible basis for reactor sizing, moving beyond single-point deterministic estimates. The proposed methodology offers a transferable framework for integrating nonlinear kinetic discrimination and uncertainty quantification into photocatalytic reactor engineering.

کلیدواژه ها:

Photocatalytic reactor design ، Kinetic model ، Akaike Information Criterion ، Uncertainty quantification ، Bootstrap Resampling ، Optimization