Experimental determination and model-based prediction of flooding points in a pilot-scale continuous liquid-liquid gravity separator

Liquid-liquid gravity phase separation is crucial in chemical, biotechnological, metallurgical, and recycling processes. However, fluctuations in the feed stream conditions of the separator significantly affect the coalescence of dispersed drops, leading to the accumulation of a dense-packed zone (DPZ) and flooding. In this study, we investigate the relationship between feed stream conditions, such as temperature, and flooding points in a pilot-scale DN200 liquid-liquid gravity separator. A temperature-controlled experimental setup enabling a temperature range of 20 degrees C to 50 degrees C was constructed with artificial-intelligence-assisted online measurements of separation curves, drop size distributions, and DPZ heights. Experiments were conducted with 1-octanol dispersed in water at dispersed phase fractions of 0.3 and 0.5. Experimental data show that temperaturedependent coalescence parameters, Sauter mean diameter d32, and phase fraction primarily influence flooding points. Further, we evaluated the prediction accuracy and consistency of two models from the literature, a lumped zero-dimensional model and the established Henschke model, which require solely feed stream data, geometry data, and physical properties. Both models underestimate experimental flooding points by a mean absolute percentage error and relative standard deviation MAPE +/- RSD of (21.5 +/- 12.2) % and (24.8 +/- 14.8) % for the Henschke and 0D model, respectively. Considering the experimental relative standard error of 8.2 % accounting for 95 % confidence, the prediction accuracy and consistency of both models are reasonable. This study suggests batch settling experiments and endoscope measurements in the feed stream of the liquid-liquid separator to predict its flooding point due to fluctuations in the feed.