Optimization of Heavy Metals Biosorption via Artificial Neural Network: A Case Study of Cobalt (II) Sorption by Pseudomonas alcaliphila NEWG-2 - Elsayed, Ashraf; Moussa, Zeiad; Alrdahe, Salma Saleh; Alharbi, Maha Mohammed; Ghoniem, Abeer A.; El-khateeb, Ayman Y.; Saber, WesamEldin I. A. (Frontiers in Microbiology, 2022)
The definitive screening design (DSD) and artificial neural network (ANN) were conducted for modeling the bio-sorption of Co(II) by Pseudomonas alcaliphila NEWG-2. Peptone, incubation time, pH, glycerol, glucose, K2HPO4, and initial cobalt had a significant effect on the bio-sorption process. MgSO4 was the only insignificant factor. DSD model was invalid and could not forecast the prediction of Co(II) removal, owing to the significant lack-of-fit (P˂0.0001). Decisively, the prediction ability of ANN was accurate with a prominent response for training R2 =0.9779) and validation (R2 =0.9773), and lower errors. Applying the optimal levels of the tested variables obtained by the ANN model led to 96.32±2.1% of cobalt bio-removal. Fourier transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy, and scanning electron microscopy during the bio-sorption process confirmed the sorption of Co(II) ions by P. alcaliphila. FTIR indicated the appearance of a new stretching vibration band formed with Co(II) ions at wavenumbers of 562, 530, and 531cm-1. The symmetric amino (NH2) binding was also formed as Co(II) sorption. Interestingly, throughout the revision of publications so far, no attempt has been conducted to optimize the bio-sorption of Co(II) by P. alcaliphila via DSD or ANN paradigms.