Removal of the antidepressant flupentixol from aqueous solution using jujube oil-based emulsion liquid membrane through hybrid optimization of RSM and ANN

Recently, membrane-based separation gains significant attention among available water treatment technologies. Among the advanced techniques for wastewater treatment, emulsion liquid membranes (ELM) stand out, since it is environmentally friendly. In the present work, flupentixol (FL), an emergent micropollutant and antidepressant, was selected as the model pharmaceutical pollutant for removal using ELM system containing jujube oil (JJO-ELM). The primary effects and interactions of key independent variables, surfactant concentration (2–3 v/v %), carrier concentration (3–5 v/v %), concentration of stripping agent (HCl), (0.5–1.5 N), FL concentration (50–150 mg/L), and phase ratio (1:1–1:3 for water:JJO, respectively) were optimized using the Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) design components. Optimized results showed that the ideal condition for the independent parameters, surfactant concentration; carrier concentration, HCl concentration, flupentixol concentration, and a phase ratio, respectively, were determined to be 2.41% v/v, 3.70% v/v, 0.52 N, 143.84 mg/L, and 1:1.5. The ideal condition could result in a maximum extraction efficiency of 97.40% FL. Further, the effectiveness of FL stripping was examined by adjusting the H 2 SO 4 concentration. Investigations were made into the mechanism of flupentixol extraction by JJO-ELM. The feed forward neural network (FNN) model, which has an output layer with one (flupentixol extraction) neuron and an input layer with five neurons, was used to simulate the extraction data. Clinical trial number Not applicable.