Biosorption of pb(II) from aqueous solution by citrus reticulate: adsorption studies, and modeling.

Removing toxic Pb(II) ions from aqueous solution by the peels of citrus reticulate (mandarin orange), a fruit industry waste, presents suitable scale-up possibilities. The Scanning Electron Microscope (SEM) and Brunauer-Emmett-Teller (BET) studies reflected that the mandarin orange peel powder had a porous surface area (32.46 m2g-1), average pore size and pore volume was 38.6 Å and 0.402 cm3g-1, respectively, favorable for binding Pb(II) ions. Fourier-transform infrared spectroscopy (FTIR) showed C-Br stretching, primary alcohol (C-O), phenolic O-H, and carbodimide N = C = N bands primarily helped to bind Pb(II) ions. The study evaluated and optimized the parametric influences of pH, adsorbate and biosorbent concentration, contact time and temperature on the removal efficiency of Pb(II) ions. A maximum of 97.08% Pb(II) was removed from 20 mg L-1 solution when 2.5 g L-1 adsorbent was present. The reaction obeyed the pseudo-second-order kinetic model. The intra-particle diffusion was involved in lead sorption. The Langmuir isotherm model resulted in an adsorption capacity of 23.04 mg g-1. 35.28% Pb(II) was removed in the 3rd adsorption-desorption cycle with 0.4 M HCl. The adsorption process was natural, impulsive and endothermic. The statistical investigation used Multiple Polynomial Regression (MPR) and Genetic Algorithm (GA). The analysis effectively forecasted the percentage removal at the optimized condition.

The results of toxic Pb(II) ion removal from aqueous solution by the peels of citrus reticulate (mandarin orange), a food industry waste, are reported. The maximum Pb(II) adsorption capacity of 23.04 mg/g. This work provides a new way to realize good adsorption capacity of Pb(II) by orange peel and accelerates to utilize for small and medium-sized industries in rural areas of 3^rd World Countries.

Effect of complex nutrients, amino acids, vitamins on Ni(II) biosorption from aqueous solution by Ni(II) resistant Saccharomyces cerevisiae AJ208.

The paper aims to establish and enhance the microorganism's successful growth, proper activity, and biosorption potency for Ni(II) biosorption from an aqueous solution using 5,000 mg/l Ni(II) resistant Saccharomyces cerevisiae AJ208. Complex nutrients, amino acids, and vitamins were added to the specifically optimized fermentation media as essential growth factors. Amino acids such as L-cysteine (0.0002 g/ml), L-Proline (0.0002 g/ml), L-Lysine (0.0002 g/ml), L-tryptophan (0.0001 g/ml) and L-Histidine (0.0003 g/ml) led to an increase of more than 87% biosorption. Vitamins such as, Ascorbic acids (0.01 × 10-8 g/ml), folic acids (0.01 × 10-8 g/ml), pyridoxine-HCl (0.01 × 10-8 g/ml),Thiamin-HCl (0.05 × 10-8 g/ml) promotes biosorption more than 91%. The Ni(II) bio-removal increased with complex nutrients like soybean meal, malt extract, and yeast extract at the concentration of 0.03, 0.4, 0.05 in g/ml, and nickel removal reached more than 85%. The multiple linear regression (MLR) and ANN application of the experimental data have predicted Ni(II) percentage removal well. This adsorption shows that the proposed Ni(II) removal process using complex nutrients is environmentally friendly and economically feasible.Novelty statement: This study evaluates a cost-effective approach to bioremediation of Ni(II) by using complex nutrients as a growth factor. Media enriched with complex nutrients is cheap than chemical media. Ni(II) Removal significant increased up to 87%, 88.34%, 96% with soybean meal, L-proline, and L-ascorbic acids at 3,000 mg/l initial Ni(II) concentration using newly developed 5,000 mg/l Ni(II) resistant Saccharomyces cerevisiae AJ208 and their NCBI accession number: MZ027228 (AJ208 ITS 1) and MZ027229 (AJ208 ITS 2).

Development of Ni(II) resistant S. cerevisiae and its application: Adsorption study and modeling.

The study aims to develop Ni(II) resistant Saccharomyces cerevisiae to decontaminate high Ni(II) concentrations from an aqueous system. Initially, two different microorganisms were taken: Bacillus circulans MTCC 3161, Saccharomyces cerevisiae. For these two strains, the experiments were carried out for successive screening for survival/tolerance, minimum inhibitory concentration (MIC), and biosorption capacity for Ni(II) from an aqueous solution. Ni(II) resistant Saccharomyces cerevisiae AJ208 showed a MIC of 5500 mg/L for Ni(II). Nucleotide sequences of Saccharomyces cerevisiae AJ208 were deposited in the Gene bank. All experiments were conducted to determine the effects of various physical conditions, such as pH, age and volume of inoculum, temperature, and incubation time, the volume of fermentation medium. The characterization of the Saccharomyces cerevisiae AJ208 was carried out using SEM-EDAX, FTIR. The Langmuir isotherm and pseudo-second-order kinetic models are well fitted with the experimental data. The Langmuir maximum adsorption capacity is 170.06 mg/g. The thermodynamic studies showed the mechanism of Ni(II) removal is an endothermic and spontaneous reaction. The experimental data have been analyzed using statistical method (MLR) and Genetic algorithm (GA). This study reports the highest Ni(II) resistant Saccharomyces cerevisiae AJ208 (5000 mg/L) and also the feasibility of Ni(II) removal from 3000 mg/L initial Ni(II) concentration into an aqueous solution, which could be of great interest as a potential reference strain for Ni(II) removal.

Pb(II) adsorption from aqueous solution by nutshells, green adsorbent: Adsorption studies, regeneration studies, scale-up design, its effect on biological indicator and MLR modeling.

In this paper, agricultural waste nutshells, such as walnut and almond shell, were utilized to treat Pb(II) containing aqueous solution. Lead(II) is a typical poisonous, commercial, water-pollutant, having multiple awful effects on the environment. The effluent of the different industrial wastewater cans is treated by using leftover and excess green waste. This finding is focused on the utilization of walnut and almond shells for Pb(II) removal. These green adsorbents are characterized using SEM, FTIR, pHpzc, and BET analyzer. The operating parameters are first optimized. The pseudo-2nd order kinetic, as well as the Langmuir isotherm model, have better applicability for both nutshells. Chemical sorption processes have been reported at higher temperatures, whereas at a lower temperature, it follows the physical sorption process. Elevated temperature helps to remove the metal ion more efficiently. The sorption process is spontaneous and endothermic for both nutshells. The desorption study shows that adsorbents can be used several times. Deadly effects of Pb(II) have been reported by the RBC count of Gallus gallus domesticus. It's been observed that the treated solution is somewhat less harmful. Application study using industrial effluent is successfully demonstrated. The scale-up design operation has been investigated. Statistical modeling has also been very successfully implemented using the data collected from the experiment. The study indicates that both nutshells have the potential for the removal of Pb(II).

Biosorption of chromium (VI) from aqueous solutions and ANN modelling.

The use of sustainable, green and biodegradable natural wastes for Cr(VI) detoxification from the contaminated wastewater is considered as a challenging issue. The present research is aimed to assess the effectiveness of seven different natural biomaterials, such as jackfruit leaf, mango leaf, onion peel, garlic peel, bamboo leaf, acid treated rubber leaf and coconut shell powder, for Cr(VI) eradication from aqueous solution by biosorption process. Characterizations were conducted using SEM, BET and FTIR spectroscopy. The effects of operating parameters, viz., pH, initial Cr(VI) ion concentration, adsorbent dosages, contact time and temperature on metal removal efficiency, were studied. The biosorption mechanism was described by the pseudo-second-order model and Langmuir isotherm model. The biosorption process was exothermic, spontaneous and chemical (except garlic peel) in nature. The sequence of adsorption capacity was mango leaf > jackfruit leaf > acid treated rubber leaf > onion peel > bamboo leaf > garlic peel > coconut shell with maximum Langmuir adsorption capacity of 35.7 mg g-1 for mango leaf. The treated effluent can be reused. Desorption study suggested effective reuse of the adsorbents up to three cycles, and safe disposal method of the used adsorbents suggested biodegradability and sustainability of the process by reapplication of the spent adsorbent and ultimately leading towards zero wastages. The performances of the adsorbents were verified with wastewater from electroplating industry. The scale-up study reported for industrial applications. ANN modelling using multilayer perception with gradient descent (GD) and Levenberg-Marquart (LM) algorithm had been successfully used for prediction of Cr(VI) removal efficiency. The study explores the undiscovered potential of the natural waste materials for sustainable existence of small and medium sector industries, especially in the third world countries by protecting the environment by eco-innovation.

Comparative study of adsorptive removal of Cr(VI) ion from aqueous solution in fixed bed column by peanut shell and almond shell using empirical models and ANN.

Cr(VI) is a toxic water pollutant, which causes cancer and mutation in living organisms. Adsorption has become the most preferred method for removal of Cr(VI) due to its high efficiency and low cost. Peanut and almond shells were used as adsorbents in downflow fixed bed continuous column operation for Cr(VI) removal. The experiments were carried out to scrutinise the adsorptive capacity of the peanut shells and almond shells, as well as to find out the effect of various operating parameters such as column bed depth (5-10 cm), influent flow rate (10-22 ml min-1) and influent Cr(VI) concentration (10-20 mg L-1) on the Cr(VI) removal. The fixed bed column operation for Cr(VI) adsorption the equilibrium was illustrated by Langmuir isotherm. Different well-known mathematical models were applied to the experimental data to identify the best-fitted model to explain the bed dynamics. Prediction of the bed dynamics by Yan et al. model was found to be satisfactory. Applicability of artificial neural network (ANN) modelling is also reported. An ANN modelling of multilayer perceptron with gradient descent and Levenberg-Marquardt algorithms have also been tried to predict the percentage removal of Cr(VI). This study indicates that these adsorbents have an excellent potential and are useful for water treatment particularly small- and medium-sized industries of third world countries. Almond shell represents better adsorptive capacity as breakthrough time and exhaustion time are longer in comparison to peanut shell.

Removal of Pb(II) ions from aqueous solution using water hyacinth root by fixed-bed column and ANN modeling.

Hyacinth root was used as a biosorbent for generating adsorption data in fixed-bed glass column. The influence of different operating parameters like inlet Pb(II) ion concentration, liquid flow rate and bed height on the breakthrough curves and the performance of the column was studied. The result showed that the adsorption efficiency increased with increase in bed height and decreased with increase in inlet Pb(II) ion concentration and flow rate. Increasing the flow rate resulted in shorter time for bed saturation. The result showed that as the bed height increased the availability of more number of adsorption sites in the bed increased, hence the throughput volume of the aqueous solution also increased. The adsorption kinetics was analyzed using different models. It was observed that maximum adsorption capacity increased with increase in flow rate and initial Pb(II) ion concentration but decreased with increase in bed height. Applicability of artificial neural network (ANN) modeling for the prediction of Pb(II) ion removal was also reported by using multilayer perceptron with backpropagation, Levenberg-Marquardt and scaled conjugate algorithms and four different transfer functions in a hidden layer and a linear output transfer function.