Luffa-Ni/Al layered double hydroxide bio-nanocomposite for efficient ibuprofen removal from aqueous solution: Kinetic, equilibrium, thermodynamic studies and GEP modeling.

Luffa is a robust, renewable biomaterial known for its low mass, high specific strength, and non-toxicity, making it ideal for composite development. This study modified luffa to create the LF@ppy@LDH nanocomposite, combining luffa, polypyrrole, and layered double hydroxides to efficiently remove ibuprofen from water. Techniques like FE-SEM, EDX, FTIR, and XRD confirmed the modification.

A comparative study of black-box and white-box data-driven methods to predict landfill leachate permeability.

Due to the dynamic and complexity of leachate percolation within municipal solid waste (MSW), planning and operation of solid waste management systems are challenging for decision-makers. In this regard, data-driven methods can be considered robust approaches to modeling this problem. In this paper, three black-box data-driven models, including artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and support vector regression (SVR), and also three white-box data-driven models, including the M5 model tree (M5MT), classification and regression trees (CART), and group method of data handling (GMDH), were developed for modeling and predicting landfill leachate permeability ([Formula: see text]).