This paper projects the potential of waste beer yeast Saccharomyces cerevisiae in biosorbing lead from battery manufacturing industrial effluent. Experiments were carried out as a function of pH, biosorbent concentration, lead concentration and agitation speed. Specific lead uptake of 2.34 mg/g was recorded and the data gave good fits for Freundlich and Langmuir models with K f and Q max values of 0.5149 and 55.71 mg/g. The roles played by amines, carboxylic acids, phosphates, sulfhydryl group and lipids in lead biosorption were studied. Electrostatic attraction may be the mechanism of biosorption. The extent of contribution of the functional groups and lipids to lead biosorption was in the order: carboxylic acids > lipids > amines > phosphates. Blocking of sulfhydryl group did not have any significant effect on lead uptake.

Teaching-Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces [1]. This paper presents an, improved version of TLBO algorithm, called the Weighted Teaching-Learning-Based Optimization (WTLBO). This algorithm uses a parameter in TLBO algorithm to increase convergence rate. Performance comparisons of the proposed method are provided against the original TLBO and some other very popular and powerful evolutionary algorithms. The weighted TLBO (WTLBO) algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional TLBO and other algorithms as well.

Biosorption of manganese from its aqueous solution using yeast biomass Saccharomyces cerevisiae and fungal biomass Aspergillus niger was carried out. Manganese biosorption equilibration time for A. niger and S. cerevisiae were found to be 60 and 20 min, with uptakes of 19.34 and 18.95 mg/g, respectively. Biosorption increased with rise in pH, biomass, and manganese concentration. The biosorption equilibrium data fitted with the Freundlich isotherm model revealed that A. niger was a better biosorbent of manganese than S. cerevisiae.

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In searching for optimal solutions, teaching learning based optimization (TLBO) (Rao et al. 2011a; Rao et al. 2012; Rao & Savsani 2012a) algorithms, has been shown powerful. This paper presents an, improved version of TLBO algorithm based on orthogonal design, and we call it OTLBO (Orthogonal Teaching Learning Based Optimization). OTLBO makes TLBO faster and more robust. It uses orthogonal design and generates an optimal offspring by a statistical optimal method. A new selection strategy is applied to decrease the number of generations and make the algorithm converge faster. We evaluate OTLBO to solve some benchmark function optimization problems with a large number of local minima. Simulations indicate that OTLBO is able to find the near-optimal solutions in all cases. Compared to other state-of-the-art evolutionary algorithms, OTLBO performs significantly better in terms of the quality, speed, and stability of the final solutions.

Bioleaching of heavy metals from contaminated soil was carried out using indigenous sulfur oxidizing bacterium Acidithiobacillus thiooxidans. Experiments were carried out by varying sulfur/soil ratio from 0.03 to 0.33 to evaluate the optimum ratio for efficient bioleaching of heavy metals from soil. The influence of sulfur/soil ratio on the bioleaching efficiency was assessed based on decrease in pH, increase in oxidation-reduction potential, sulfate production and solubilization of heavy metals from the soil. Decrease in pH, increase in oxidation-reduction potential and sulfate production was found to be better with the increase in sulfur/soil ratio. While the final pH of the system with different sulfur/soil ratio was in the range of 4.1-0.7, oxidation reduction potential varied from 230 to 629 mV; sulfate production was in the range of 2,786-8,872 mg/l. Solubilization of chromium, zinc, copper, lead and cadmium from the contaminated soil was in the range of 11-99%. Findings of the study will help to optimize the ratio of sulfur/soil to achieve effective bioleaching of heavy metals from contaminated soils.