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. The NF membrane demonstrated its capability to concentrate wastewater at a high recovery value of 95 % and 99.7 ± 0.2 % rejection to azithromycin. The subsequent electrochemical oxidation process completely degraded azithromycin in the concentrate within 30 min and reduced total organic carbon by 95 % in 180 min. Such integrated treatment approach minimized the electrochemically-treated volume through a low-energy membrane approach and enhanced mass transfer towards the electrodes, therefore driving the process toward zero-liquid-discharge objectives. Overall, our integrated approach holds promises for cost-effective and sustainable removal of trace pharmaceutical compounds and other organics in pharmaceutical wastewater.

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). Kinetic modelling indicated that adsorption was the dominant process, while concomitant electrochemical degradation of complex organics first converted them to short-chain acids, followed by their full mineralization. In-situ electrochemical regeneration of SAC, taking place at the same time as the treatment, is a key feature of our process, enhancing its performance and ensuring its stable operation over time, while eliminating cleaning downtimes altogether. The energy consumption of this innovative process was remarkably low at 8.0×10-3 kWh gTOC-1. This study highlights the potential of SAC for treating hard-to-treat effluents by concurrent adsorption and mineralization of organics.

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. We used limited sets of lab-collected data to obtain satisfactory model performance over four iterations by introducing human expert experience in the online learning process. Four membranes under fabrication conditions guided by the model exceeded the present upper bound for mono/divalent ion selectivity and permeance of the polymeric membranes. Moreover, we obtained new mechanistic insights into the membrane design through feature analysis of the model. Our work demonstrates a ML approach that represents a paradigm shift for high-performance polymeric membranes design.

Default predicted no-effect target concentrations for antibiotics in the absence of data for the protection against antibiotic resistance and environmental toxicity.

The pharmaceutical manufacturing industry, via the AMR Industry Alliance, has developed and implemented steps to help minimize the potential impact of pharmaceutical manufacturing on the spread of antimicrobial resistance (AMR). One of these steps was to publish predicted no-effect concentrations (PNECs) to serve as targets for antibiotic manufacturing wastewater effluent risk assessments aimed to help protect environmental receptors and to mitigate against the spread of antibiotic resistance. Concentrations below which adverse effects in the environment are not expected to occur (PNECs) were first published in 2018 and are updated annually. The current list now stands at 125 antibiotics; however, it is recognized that this list does not encompass all manufactured antibiotics. Therefore, a statistical evaluation of currently available data was conducted and a default PNEC of 0.05 µg/L for antibiotics in the absence of other data was derived. Integr Environ Assess Manag 2022;18:863-867. © 2022 Merck, Sanofi, Johnson & Johnson Services, Inc, F.Hoffmann-La Roche Ltd, Teva Pharmaceuticals, GlaxoSmithKline, Novartis Pharma AG, and Pfizer lnc. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).