Hghly stable, reusable, and multi-functional biocatalytic microparticles with Laccase (Lac) enzyme (Lac/particles) were synthesized for bisphenol A (BPA) removal from aqueous solution. The Lac/particles were prepared by encapsulating Lac enzymes into poly ethylene glycol (PEG) hydrogel via the UV assisted emulsion polymerization method followed by cross linking with glutaraldehyde (GA). The obtained Lac/particles were spherical and micron sized (137–535 μm), presenting high enzyme entrapment efficiency of 100%, high activity recovery of 18.9%, and great stability at various pHs (3–7) than the free Lac. The Lac/particles could adsorb the BPA into the catalytic particles in a short time, promoting contact between BPA and enzyme, and further enzymatically degrade them without the shaking process and independent surrounding buffer solution. The Lac/particles could be reused for another round BPA adsorption and biotranformation by maintaining over 90% of BPA removal efficiency after seven times reuse. The synergistic effects of adsorption and biocatalytical reaction of Lac/particles have significant values in high efficient and cost-effective BPA removal.

Membrane fouling remains a major challenge in full-scale membrane bioreactor (MBR) systems, reducing operational efficiency and increasing maintenance needs. This study introduces a predictive and analytic framework for membrane fouling by integrating artificial intelligence (AI)-driven feature engineering and explainable AI (XAI) using real-world data from an MBR treating food processing wastewater. The framework refines the target parameter to specific flux (flux/transmembrane pressure (TMP)), incorporates chemical oxygen demand (COD) removal efficiency to reflect biological performance, and applies a moving average function to capture temporal fouling dynamics. Among tested models, CatBoost achieved the highest predictive accuracy (R² = 0.8374), outperforming traditional statistical and other machine learning models. XAI analysis identified the food-to-microorganism (F/M) ratio and mixed liquor suspended solids (MLSSs) as the most influential variables affecting fouling. This robust and interpretable approach enables proactive fouling prediction and supports informed decision making in practical MBR operations, even with limited data. The methodology establishes a foundation for future integration with real-time monitoring and adaptive control, contributing to more sustainable and efficient membrane-based wastewater treatment operations. However, this study is based on data from a single full-scale MBR treating food processing wastewater and lacks severe fouling or cleaning events, so further validation with diverse datasets is needed to confirm broader applicability.