Optimization of Heavy Metals Biosorption via Artificial Neural Network: A Case Study of Cobalt (II) Sorption by Pseudomonas alcaliphila NEWG-2.

ABSTRACT: The definitive screening design (DSD) and artificial neural network (ANN) were conducted for modeling the biosorption of Co(II) by Pseudomonas alcaliphila NEWG-2. Factors such as peptone, incubation time, pH, glycerol, glucose, K₂HPO₄, and initial cobalt had a significant effect on the biosorption process. MgSO₄ was the only insignificant factor. The 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 ±

SUBMITTER: Elsayed A 

PROVIDER: S-EPMC9194897 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature