Pharmaceutical removal at low energy consumption using membrane capacitive deionization

Autonomous real-time control for membrane capacitive deionization

Artificial intelligence has been employed to simulate and optimize the performance of membrane capacitive deionization (MCDI), an emerging ion separation process. However, a real-time control for optimal MCDI operation has not been investigated yet. In this study, we aimed to develop a reinforcement learning (RL)-based control model and investigate the model to find an energy-efficient MCDI operation strategy. To fulfill the objectives, we established three long-short term memory models to predict applied voltage, outflow pH, and outflow electrical conductivity. Also, four RL agents were trained to minimize outflow concentration and energy consumption simultaneously. Consequently, actor-critic (A2C) and proximal policy optimization (PPO2) achieved the ion separation goal (<0.8 mS/cm) as they determined the electrical current and pump speed to be low. Particularly, A2C kept the parameters consistent in charging MCDI, which caused lower energy consumption (0.0128 kWh/m3) than PPO2 (0.0363 kWh/m3). To understand the decision-making process of A2C, the Shapley additive explanation based on the decision tree model estimated the influence of input parameters on the control parameters. The results of this study demonstrate the feasibility of RL-based controls in MCDI operations. Thus, we expect that the RL-based control model can improve further and enhance the efficiency of water treatment technologies.

Antiviral drugs against influenza: Treatment methods, environmental risk assessment and analytical determination

Over the past few years, antiviral drugs against influenza are considered emerging contaminants since they cause environmental toxicity even at low concentrations. They have been found in environmental matrices all around the world, showing that conventional treatment methods fail to remove them from water and wastewater. In addition, the metabolites and transformation products of these drugs can be more persistent than original in the environment. Several techniques to degrade/remove antiviral drugs against influenza have been investigated to prevent this contamination. In this study, the characteristics of antiviral drugs against influenza, their measurement by analytical methods, and their removal in both water and wastewater treatment plants (WWTPs) were presented. Different treatment methods, such as traditional procedures (biological processes, filtration, coagulation, flocculation, and sedimentation), advanced oxidation processes (AOPs), adsorption and combined methods, were assessed. Ecotoxicological effects of both the antiviral drug and its metabolites as well as the transformation products formed as a result of treatment were evaluated. In addition, future perspectives for improving the removal of antiviral drugs against influenza, their metabolites and transformation products were further discussed. The research indicated that the main tested techniques in this study were ozonation, photolysis and photocatalysis. Combined methods, particularly those that use renewable energy and waste materials, appear to be the optimum approach for the treatment of effluents containing antiviral drugs against influenza. In light of high concentrations or probable antiviral resistance, this comprehensive assessment suggests that antiviral drug monitoring is required, and some of those substances may cause toxicological effects.