RANSAC guided modeling and evolutionary optimization of UV/sulfite for ofloxacin removal

A new hierarchical model to elucidate the photo-mediated activation process of sulfite species in selective photodegradation of a fluoroquinolone antibiotic, ofloxacin, is introduced in this work, through the combination of RANSAC based high-order stochastically governed multi-outputs predictive modeling and an evolutionary parametric optimization, a set of coupled process parameters controlling the ofloxacin removal, biochemical oxygen demand (BOD) and chemical oxygen demand (COD) removals are determined. Systematic assessment of UV/sulfite system was performed to identify the effect of pH, UV intensity, sulfite concentration, starting drug concentration, contact time, and molar ratio of sulfite/ofloxacin, and strong predictive fidelity of COD and ofloxacin removal, and the complex BOD reduction behavior with moderate deviations under extreme conditions. RANSACRegressor was accurately captured non-linear and multivariate relations. Quantitative evaluation confirmed the stability of the model when using training and test data sets, that UV Intensity and initial ofloxacin concentration are the most important factors influencing the pollutant degradation and organic load reduction, whereas pH, contact time and sulfite/ofloxacin molar ratio are secondary but significant factors. Moreover, the evolutionary optimization protocol could identify the output-specific optimum settings, an increased UV intensity and sulfite concentration (when removal of ofloxacin and COD took place), compared with a decreased sulfite concentration and relatively alkaline pH (when removal of BOD took place). When combined, this hybrid photochemical-computational model gives improved predictive insight of the photon-induced sulfite activation mechanism and offer a robust predictive and operational framework of optimization of advanced oxidation in aqueous matrices to which antibiotics are added.