Innovative optimization for enhancing Pb biosorption from aqueous solutions using .

INTRODUCTION

Toxic heavy metal pollution has been considered a major ecosystem pollution source. Unceasing or rare performance of Pb to the surrounding environment causes damage to the kidney, nervous, and liver systems. Microbial remediation has acquired prominence in recent decades due to its high efficiency, environment-friendliness, and cost-effectiveness.

METHODS

The lead biosorption by was optimized by two successive paradigms, namely, a definitive screening design (DSD) and an artificial neural network (ANN), to maximize the sorption process.

RESULTS

Five physicochemical variables showed a significant influence ( < 0.05) on the Pb biosorption with optimal levels of pH 6.1, temperature 30°C, glucose 1.5%, yeast extract 1.7%, and MgSO.7HO 0.2, resulting in a 96.12% removal rate. The Pb biosorption mechanism using biomass was investigated by performing several analyses before and after Pb biosorption. The maximum Pb biosorption capacity of was 61.8 mg/g at a 0.3 g biosorbent dose, pH 6.0, temperature 30°C, and contact time 60 min. Langmuir's isotherm and pseudo-second-order model with R of 0.991 and 0.999 were suitable for the biosorption data, predicting a monolayer adsorption and chemisorption mechanism, respectively.

DISCUSSION

The outcome of the present research seems to be a first attempt to apply intelligence paradigms in the optimization of low-cost Pb biosorption using biomass, justifying their promising application for enhancing the removal efficiency of heavy metal ions using biosorbents from contaminated aqueous systems.