Scale-Dependent hydroxyl radical generation and energy efficiency in vortex diode hydrodynamic cavitation: machine learning insights toward industrial-scale applications in water treatment.

Hydrodynamic cavitation (HC) reactors are increasingly applied in the remediation of organic pollutants in water, leveraging intense shear and hydroxyl radical (OH•) generation to accelerate degradation processes. However, scaling up HC devices remains a challenge in environmental engineering due to poorly understood effects at different scales of operation. Vortex-based HC (VDs) provide superior cavitation efficiency compared to traditional orifice and venturi devices and therefore this study examines the scale-dependent generation of OH• in VDs. Coumarin dosimetry was employed as the quantification method for OH• generation. Available published experimental datasets were analysed: (i) varying inlet pressures from 100 to 400 kPa for throat diameters of 6 and 12 mm, and (ii) throat diameters ranging from 6 to 38 mm (nominal capacities of 5 to 200 L/min) at a fixed pressure drop of 280 kPa. After normalization of the available data, seven machine learning (ML) models were trained to establish relationships between operating conditions and OH• generation performance. eXtreme Gradient Boosting (XGB) and artificial neural networks (ANN) outperformed the others, with higher R