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Uncertainty-Quantified Kinetic Model Discrimination for Photocatalytic Reactor Design: A Bootstrap Based Framework | ||
| Chemical Process Design | ||
| مقاله 8، دوره 5، شماره 2، اسفند 2026، صفحه 89-109 اصل مقاله (1.69 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22111/cpd.2026.55388.1088 | ||
| نویسنده | ||
| Masoud Khajenoori* | ||
| Catalytic Process Design and Simulation Laboratory, Department of Chemical Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran. | ||
| چکیده | ||
| 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 (C0 = 10 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 > 30 vs. pseudo-first-order; Akaike weight > 0.99). Parameter uncertainty was quantified via nonparametric bootstrap resampling (2000 replicates), yielding well-constrained 95% confidence intervals (α: 2.02-2.28 mg g⁻¹ min⁻¹; β: 0.34-0.42 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 90% pollutant removal. The nominal reactor length was 1.25 m, with a 95% confidence interval of 1.18-1.35 m (±7%). 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. | ||
| کلیدواژه‌ها | ||
| Photocatalytic reactor design؛ Kinetic model؛ Akaike Information Criterion؛ Uncertainty quantification؛ Bootstrap resampling؛ Optimization | ||
| مراجع | ||
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