Innovative optimization for enhancing Pb²⁺ biosorption from aqueous solutions using Bacillus subtilis.

Area of Science:

• Environmental Microbiology
• Bioremediation
• Chemical Engineering

Background:

• Heavy metal pollution, particularly lead (Pb²⁺), poses significant environmental and health risks.
• Microbial remediation offers an efficient, eco-friendly, and cost-effective solution for heavy metal contamination.
• Bacillus subtilis is explored as a biosorbent for lead removal from aqueous systems.

Purpose of the Study:

• To optimize lead (Pb²⁺) biosorption using Bacillus subtilis biomass.
• To maximize the efficiency of the biosorption process through intelligent optimization paradigms.
• To investigate the mechanism and capacity of Pb²⁺ biosorption by B. subtilis.

Main Methods:

• Utilized definitive screening design (DSD) and artificial neural network (ANN) for process optimization.
• Investigated the influence of physicochemical variables (pH, temperature, nutrients) on Pb²⁺ biosorption.
• Analyzed biosorption mechanisms using isotherm and kinetic models.

Main Results:

• Achieved a 96.12% Pb²⁺ removal rate under optimal conditions (pH 6.1, 30°C, 1.5% glucose, 1.7% yeast extract, 0.2% MgSO₄.7H₂O).
• Determined maximum biosorption capacity of 61.8 mg/g with B. subtilis biomass.
• Langmuir isotherm and pseudo-second-order kinetics models accurately described the biosorption process, indicating monolayer adsorption and chemisorption.

Conclusions:

• Intelligent optimization paradigms (DSD and ANN) effectively enhanced Pb²⁺ biosorption by B. subtilis.
• B. subtilis biomass shows significant potential for low-cost, high-efficiency removal of lead from contaminated water.
• This study validates the application of biosorbents and AI in environmental remediation of heavy metals.