Machine learning framework for oxytetracycline removal using nanostructured cupric oxide supported on magnetic chitosan alginate biocomposite.

This research proposes a machine learning controlled method for removing the antibiotic oxytetracycline (OTC) from liquids through the use of nanostructured cupric oxide (CuO) nanoparticles. These nanoparticles are attached to magnetic chitosan/alginate biocomposites (CuO-M-CAB). The dataset consisted of 42 experimentally generated samples, systematically created using key parameters (X1: antibiotic concentration, X2: pH, X3: reaction time, and X4: adsorbent concentration). Prior to applying machine learning models, preprocessing steps were performed, including normalization using Min-Max Scaling to confine all features within the [0,1] range, outlier detection and removal of anomalous values, correlation analysis to avoid redundancy and multicollinearity, and data splitting into training and testing sets at an 80:20 ratio, along with K-fold cross-validation (k = 5) for robust model evaluation. The study assesses the accurate predictions of specific models, including Tikhonov Regularization, Yandex Boosting, and Particle Swarm Optimization (PSO), to improve removal efficiency and provide analysis of the components influencing the method. The Tikhonov model demonstrates high accuracy with R