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Bioremoval of Heavy Metals Through the use of an Indigenous Bacterial Isolate

Afghari, Amir Hossein | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 58241 (06)
  4. University: Sharif University of Technology
  5. Department: Chemical and Petroleum Engineering
  6. Advisor(s): Soheila Yaghmaei; Bagheri Lotfabad, Tayebeh
  7. Abstract:
  8. In this study, the biosorption process of zinc ions from aqueous solutions by the biomass of a native Bacillus paralicheniformis strain, isolated from the industrial wastewater of a textile factory in the Kashan region, was investigated and optimized. The objective was to develop an effective and eco-friendly biotechnological approach for the removal of heavy metals from industrial effluents. Due to its inherent resistance to harsh environmental conditions and high tolerance to metal ions, this strain was identified as a potent biosorbent. Attributes such as flocculation capability and surface adhesiveness facilitated biomass separation and reduced operational costs. Optimal conditions for Zn(II) biosorption were determined as biomass concentration of 2500 ppm, initial zinc concentration of 100 ppm, neutral pH (7), and contact time of 2 to 4 minutes between dead biomass and zinc ions. These parameters yielded a shorter equilibrium time and higher efficiency compared to similar studies. The maximum adsorption capacity under optimal conditions was measured at 22.24 mg Zn per gram of dead, dried biomass, indicating significant performance. Moreover, live biomass demonstrated enhanced adsorption kinetics and removal efficiency due to its metabolic activity, making it particularly suitable for bioremediation applications. An Artificial Neural Network (ANN) model utilizing the Levenberg–Marquardt algorithm was employed to predict the biosorption behavior, exhibiting high accuracy in forecasting adsorption capacity and removal percentage (with a suitable R² value). This highlights the pivotal role of artificial intelligence in the analysis and design of bioremediation processes. Zinc concentration was quantified via ICP-OES analysis and compared against a cost-effective electrical conductivity method. Innovations of this research include the use of a less-studied native strain, achievement of equilibrium in a remarkably short time, and a high adsorption capacity. The current study proposes further improvements such as investigating adsorption mechanisms at the surface level, conducting pilot-scale trials, and exploring multi-metal biosorption systems. The results demonstrate the considerable potential of bacterial biomass in treating wastewater contaminated with heavy metals, particularly zinc
  9. Keywords:
  10. ZINC ; Industrial Wastewaters ; Artificial Neural Network ; Heavy Metals ; Bioremediation ; Bioabsorption ; Bacillus Paralicheniformis

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