Enhancing boron removal efficiency through a design of experiments approach with a customized hydrophobic deep eutectic solvent

Deep Eutectic Solvents (DESs) are promising sustainable solvents, offering a remarkable potential to simplify and improve the efficiency of extraction processes. Utilizing boron in various applications like agriculture, health, and industry is beneficial. However, excess concentration of boron poses a threat to plants and marine systems. Therefore, extracting boron is essential for productive and preventative purposes. Conventional boron removal from wastewater technologies are material and energy-intensive, and this formulates a serious challenge for industrial applications, necessitating more sustainable and feasible alternatives. This study optimizes the extraction procedure for boron via liquid–liquid extraction (LLE) using hydrophobic TMPD-based DES. The measurements of boron concentration in the aqueous phase before and after extraction were observed using inductively coupled plasma optical emission spectroscopy (ICP-OES). The extraction efficiency optimization was performed using the Design of Experiments (DoE) statistical methodology. Specifically, response surface methodology (RSM) with a box-Behnken design (BBD) was utilized to thoroughly examine the impact of each parameter (Temperature, pH, and feed-to-solvent ratio) on the desired outcome (Extraction Efficiency). The main objective is to fine-tune the operational parameters to reduce the experimental load for an efficient boron remediation process, which is more befitting for the industrial scale-up of DESs. Among the tested conditions, an extraction efficiency of 93.1 ± 0.6 % at an A/O mass ratio of 1.5 and a pH of 6 was identified as the optimum point, aligning with industrial scalability requirements. The recyclability of the boron-loaded DES was examined over three cycles, consistently maintaining efficiencies above 80 %. Additionally, the ability to regenerate the boron-loaded DES was also investigated, revealing a robust stripping efficiency of ≃98 %. The validated statistical model optimizes the boron extraction process and allows for further predictions for various conditions.