Dataset Information

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 ± 2.1% of cobalt bioremoval. During the biosorption process, Fourier transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy, and scanning electron microscopy 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 531 cm^-1. The symmetric amino (NH₂) binding was also formed due to Co(II) sorption. Interestingly, throughout the revision of publications so far, no attempt has been conducted to optimize the biosorption of Co(II) by P. alcaliphila via DSD or ANN paradigm.

SUBMITTER: Elsayed A

PROVIDER: S-EPMC9194897 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

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

Elsayed Ashraf A Moussa Zeiad Z Alrdahe Salma Saleh SS Alharbi Maha Mohammed MM Ghoniem Abeer A AA El-Khateeb Ayman Y AY Saber WesamEldin I A WIA

Frontiers in microbiology 20220531

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 lac ...[more]

PMID: 35711743

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Project description:In comparison to physicochemical and chemical methods, microbial dye biosorption is regarded as an eco-effective and economically viable alternative and is a widely applied method due to its high efficiency and compatibility with the environment. Therefore, the idea of this study is to clarify to what extent the viable cells and the dry biomass of Pseudomonas alcaliphila NEWG-2 can improve the biosorption of methylene blue (MB) from a synthetic wastewater sample. The array of Taguchi paradigm has been conducted to ascertain five variables affecting the biosorption of MB by broth forms of P. alcaliphila NEWG. The data of MB biosorption were familiar to the predicted ones, indicating the precision of the Taguchi model's prediction. The maximum biosorption of MB (87.14%) was achieved at pH 8, after 60 h, in a medium containing 15 mg/ml MB, 2.5% glucose, and 2% peptone, with sorting the highest signal-to-noise ratio (38.80). FTIR spectra detected various functional groups (primary alcohol, α, β-unsaturated ester, symmetric NH2 bending, and strong C-O stretching) on the bacterial cell wall that participated in the biosorption of MB. Furthermore, the spectacular MB biosorption ability was validated by equilibrium isotherms and kinetic studies (the dry biomass form), which were derived from the Langmuir model (qmax = 68.827 mg/g). The equilibrium time was achieved in about 60 min, with 70.5% of MB removal. The biosorption kinetic profile might be adequately represented by pseudo-second order and Elovich models. The changes in the bacterial cells before and after the biosorption of MB were characterized using a scanning electron microscope. As realized from the aforementioned data, the bacterium is a talented, effective, eco-friendly, and low-cost bio-sorbent for the decolorization and remedy of an industrial effluent containing MB from an aqueous environment. The current outcomes in the biosorption of MB molecules promote the use of the bacterial strain as viable cells and/or dry biomass in ecosystem restoration, environmental cleanup, and bioremediation studies.

Dataset Information

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

Publications

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

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