Bayesian framework for kinetic identification and robust optimization of a g-C₃N₄ photocatalytic tubular reactor for tetracycline degradation: source code and dataset

Abstract

This deposit contains the complete Python source code, curated kinetic dataset, and pre-computed results supporting the manuscript: Gamoura M., Kaabi I., Chebli D. (2026). Bayesian kinetic identification and robust multi-objective optimization of a g-C₃N₄ photocatalytic tubular reactor for tetracycline degradation. Submitted to Journal of Water Process Engineering. Contents:- code/ : Python scripts implementing the 1D axial-flow reactor model with P1 radiative transfer (LVRPA), Bayesian MCMC parameter identification (emcee), global Sobol sensitivity analysis (SALib), and deterministic + robust NSGA-II multi-objective optimization (pymoo).- data/ : Curated n=30 kinetic dataset extracted from 94 g-C₃N₄/tetracycline photocatalysis papers (2018–2025), with the TC_PMR_v1 extraction schema.- results/ : Pre-computed Bayesian posterior trace (ArviZ NetCDF), deterministic and robust Pareto archives, and Sobol sensitivity indices.- tables/ : CSV versions of the five tables in the manuscript. All results in the manuscript are reproducible from this deposit by running the scripts in the order documented in README.md. License: Code under MIT License; Data under Creative Commons Attribution 4.0.