Electrochemical oxidation of a membrane-distillation concentrate for the treatment of real pharmaceutical wastewater.

This study presents the first investigation of the electrochemical oxidation of a real membrane-distillation (MD) concentrate for the integrated treatment of highly concentrated pharmaceutical wastewater (PWW). The coupling of electro-Fenton and anodic oxidation applied to a real MD retentate, concentrated by a factor of 1.6 compared to the original PWW, reduced the total organic carbon (TOC) concentration from 23,460 to 12,199 mg/L in 24 h (mineralization efficiency of 48%).

Sequential high-recovery nanofiltration and electrochemical degradation for the treatment of pharmaceutical wastewater.

The presence of antibiotics in aquatic ecosystems poses a significant concern for public health and aquatic life, owing to their contribution to the proliferation of antibiotic-resistant bacteria. Effective wastewater treatment strategies are needed to ensure that discharges from pharmaceutical manufacturing facilities are adequately controlled. Here we propose the sequential use of nanofiltration (NF) for concentrating a real pharmaceutical effluent derived from azithromycin production, followed by electrochemical oxidation for thorough removal of pharmaceutical compounds.

A new approach of simultaneous adsorption and regeneration of activated carbon to address the bottlenecks of pharmaceutical wastewater treatment.

This study proposes a sustainable approach for hard-to-treat wastewater using sintered activated carbon (SAC) both as an adsorption filter and as an electrode, allowing its simultaneous electrochemical regeneration. SAC improves the activated carbon (AC) particle contact and thus the conductivity, while maintaining optimal liquid flow. The process removed 87 % of total organic carbon (TOC) from real high-load (initial TOC of 1625 mg/L) pharmaceutical wastewater (PWW), generated during the manufacturing of azithromycin, in 5 h, without external input of chemicals other than catalytic amounts of Fe(II).

Machine Learning Guided Polyamide Membrane with Exceptional Solute-Solute Selectivity and Permeance.

Designing polymeric membranes with high solute-solute selectivity and permeance is important but technically challenging. Existing industrial interfacial polymerization (IP) process to fabricate polyamide-based polymeric membranes is largely empirical, which requires enormous trial-and-error experimentations to identify optimal fabrication conditions from a wide candidate space for separating a given solute pair. Herein, we developed a novel multitask machine learning (ML) model based on an artificial neural network (ANN) with skip connections and selectivity regularization to guide the design of polyamide membranes.