Search

The increasing e-waste volumes represent a great challenge in the current waste management landscape, primarily due to the massive production and turnover of electronic devices and the complexity of their components and constituents. Traditional strategies for e-waste treatment focus on metal recovery through costly, energetically intensive, and environmentally hazardous processes, such as pyrometallurgical and hydrometallurgical approaches, often neglecting other e-waste constituents. As efforts are directed towards creating a more sustainable and circular economic model, biobased alternative approaches to these traditional techniques have been increasingly investigated. This critical review focuses on recent advances towards sustainable e-waste treatment, exclusively considering studies using e-waste sources. It addresses, from a critical perspective, approaches using inactive biomass, live biomass, and biogenic compounds, showcasing the diversity of strategies and discussing reaction parameters, advantages and disadvantages, challenges, and potential for valorization of generated by-products. While ongoing research focuses on optimizing operational times and metal recovery efficiencies, bioprocessing approaches still offer significant potential for metal recovery from e-waste. These approaches include lower environmental impact by reducing energy consumption and effluent treatments and the ability to recover metals from complex e-waste streams, paving the way for a more circular economy in the electronics industry. [ABSTRACT FROM AUTHOR]

In this paper, the application of artificial intelligence, especially neural networks, in the field of water treatment is comprehensively reviewed, with emphasis on water quality prediction and chemical dosage optimization. It begins with an overview of machine learning and deep learning concepts relevant to water treatment. Key advances and challenges in using neural networks for coagulation processes are thoroughly analyzed, including the automation of coagulant dosing, dosage level optimization, and efficiency comparisons of modeling approaches. Applications of neural networks in predicting pollutant levels and supporting water quality monitoring are explored. The review identifies avenues for improving coagulation-based modeling with neural networks, such as enhancing data quality, employing feature engineering, refining model selection criteria, and improving cross-validation methods. The necessity of continuous monitoring and adaptive optimization strategies is emphasized. Challenges such as the complexity of coagulation processes, feedback control signal acquisition, and model adaptability from simulations to real-world settings are discussed. Cost control and resource management in water treatment are also highlighted, emphasizing the optimized chemical dosage to reduce expenses while maintaining water quality compliance. In summary, this review provides valuable insights into the current state of neural network applications in water treatment and highlights key areas for further research and development. Integrating AI into coagulation processes has the potential to enhance the efficiency and sustainability of drinking water treatment. [ABSTRACT FROM AUTHOR]

Filtration is one of the important technologies for separating suspended particles. Under the condition of gravity compression, the filtration density can be increased and the separation effect of suspended particles can be improved. Considering the complex composition and the difficulty in degrading dye in industrial wastewater, a gravity compression aeration system with a modified polyester fibre ball (denoted as MPFB) was evaluated for the separation of dye from water. Congo red azo dye solution (0–40 mg/L) was selected as the model treatment compound. The MPFB was prepared by adjusting the concentrations of alkali (Quality score 0–25%), β-cyclodextrin (0∼80 g/L), reaction temperature (40–90°C), and silane coupler concentration (Concentration fractions 0–0.8%). We used Fourier transform infrared spectroscopy (FTIR), Scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS) to characterise the MPFB. The separation was affected significantly by adsorption conditions such as MPFB dose and pH. The lower the MPFB dose, the higher the expected adsorption capacity. For the treatment of a dye solution at 500 mg/L, 100% removal was achieved with 48 g/L MPFB, at pH 8 during adsorption under non-circulation aeration. For 24 h of reaction, the system could reach the maximum adsorption capacity of 11.2 mg/g, which followed the pseudo-first order kinetics model and the intraparticle diffusion model. We discovered that circulation aeration provided the best adsorption and electrostatic and hydrogen bonding were the dominant components of adsorption. Overall, the system is a promising technology and has the potential to treat large volumes of dye wastewater. [ABSTRACT FROM AUTHOR]