Assessing the Potential of Biosurfactant Production by Bacillus subtilis MTCC 2423 to Remediate the Zinc-Contaminated Soil: A Process Optimization Approach

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING(2024)

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摘要
Heavy metal contamination in soil due to rapid industrialization has imparted a severe threat to the terrestrial ecosystem. Mining activity has perturbed the fertility and increased the heavy metal concentration in soil. Here we explore an inexpensive and environmentally friendly tool, biosurfactant to restore edaphic factors. Here, we have synthetically prepared contaminated sandy soil and black cotton soil with zinc and investigated the potential of biosurfactants in decontaminating zinc (Zn) from soil samples (sandy soil and black cotton soil). The biosurfactant (64 dynes/cm) was produced by bacillus subtilis MTCC 2423 on an unconventional substrate (20% distillery spent wash) and used as a tool to decontaminate zinc (heavy metal) from soil. The column experimental study was performed to evaluate the heavy metal removal efficiency of the biosurfactant. Statistical tool, Box-Behnken design (BBD) with an artificial neural network linked genetic algorithm (ANN-GA) was adopted to optimize the three independent variables viz., pH, biosurfactant concentration (%), and heavy metal (Zn) concentration. Results reveal that the decontamination efficiency of biosurfactant varied from soil to soil. Highest efficiency was observed in sandy soil compared to black cotton soil. Maximum efficiency of 89.11% was achieved with an optimal level of pH = 5, biosurfactant concentration = 100%, and Zn concentration = 2750 ppm in sandy soil. Whereas a maximum of 36.03% of zinc was removed in black cotton soil with pH, biosurfactant, and zinc concentrations of 7, 55%, and 500 ppm, respectively.
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关键词
Artificial neural network linked genetic algorithm (ANN-GA),Box-Behnken design (BBD),Heavy metal,Spent wash
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