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Multicomponent Biosorption of Heavy Metals Using Fluidized Bed of Alga
Abstract
Biosorption of lead, cadmium, copper and arsenic ions was studied using algal biomass. Mixture of green (Chlorophyta) and blue-green (Cyanophyta) algae were used in this study as biosorbent of heavy metals.
A series of experiments were carried out in a batch reactor to obtain equilibrium isotherms data for biosorption of single, binary, ternary and quaternary metals solutions. The biosorption of these metals is based on ion exchange mechanism accompanied by the release of light metals such as calcium, magnesium, potassium and sodium. Experimental parameters included pH, initial metal concentrations and temperature were studied. The optimum pH values were 3 for Pb2+, 4 for Cu2+, 5 for As3+ and Cd2+, respectively. Fourier Transformation Infrared Spectroscopy analysis was used to find the effects of functional groups of algae in biosorption process. The results showed that Pb2+ made a greater change in the functional groups of algal biomass due to the high affinity to this metal.
An ion exchange model was found suitable for describing the biosorption process. The affinity constant (K) sequence calculated for single system was KPb>KAs>KCu>KCd; these values reduced in binary, ternary and quaternary system. The experimental data showed that the biosorption of the four metals well fitted the pseudo-second order kinetics model.
A fluidized bed reactor was adopted for the continuous removal of metals. The minimum fluidization velocities of beds were found equal to 2.27 and 3.64 mm/s for particles of 0.4-0.6 and 0.6-1 mm diameters, respectively. A series of experiments were carried out on the fluidized bed reactor to obtain the breakthrough curves data for biosorption of single, binary, ternary and quaternary metals ions.
The experimental breakthrough data of four metal ions were fitted well the theoretical model. Pb2+ showed the largest breakthrough time compared with other metals demonstrating high affinity, while Cd2+ had the shortest breakthrough time. Different operating conditions were used, static bed height (correspondent to 50,100 and 150 g of algal biomass), superficial velocity (1.1Umf and 1.5 Umf) and particle diameter (0.4-0.6 and 0.6-1 mm). The variation of liquid superficial velocity from 1.1Umf to 1.5Umf resulted decreasing in biosorption capacity. The variation of particles diameter causes a small variation in biosorption capacity. The biosorption capacity order for the four systems was: Pb2+>As3+>Cu2+>Cd2+.
In this study, the removal of the lead and copper (as a single and binary system) from a contaminated solution was examined in batch and continuous mode operation by the adsorption on the locally synthesized new composite adsorbent. This adsorbent was prepared from the loading of Mg/Fe Layered Double Hydroxide (in an optimum molar ratio equal to 3/1) on the low cost local adsorbent prepared from physical activation of date palm leaf base (in an optimum dose equal to 0.5 g/50 mL) and characterized by using the Fourier Transform Infrared spectroscopy (FTIR), Scanning Electron Microscope (SEM), Energy Dispersive Spectroscopy (EDS), X-Ray Diffraction Analysis (XRD) and Specific Surface Area (BET).
Adsorption studies of the solutions were examined in batch mode through experiments replicated with different values of parameters such as contact time, agitation speed, pH, initial concentrations of contaminant and adsorbent dosages; the optimum value of they were to be (1 hr, 200 rpm, 6, 5 mg/L and 0.2 g/50 mL) respectively dependent on the higher removal efficiency obtained. While for the continuous system, the effect of magnetic field exposure distance, flow rate, pH, Pb+2 and Cu+2 initial concentration and bed height on the breakthrough curve were studied in fixed and fluidized bed continuous systems; the optimum values of these parameters were to be 15 cm, 6 L/hr, 6, 5 mg/L and 20 cm respectively. The adsorption studies of the Pb+2 and Cu+2 ions in binary systems were done by using the same optimum conditions of the single systems experiments in both systems of batch and continuous; the results showed that the lead is high affinity to adsorbent than copper.
Four isotherm models were conducted and the Freundlich model gives better prediction for the adsorption processes due to its lower Sum Square Errors (3.56 for Cu+2 and 2.77 for Pb+2) and higher determination coefficient (0.991 and 0.994 for Cu+2 and Pb+2 respectively) comparing with the other four isotherm models. Both first order and second order kinetic models were applied, and it found that the lead and copper adsorption process follows the pseudo-second-order kinetics model. From this results, it can concluded that the adsorption process was a chemisorption process.
For the adsorption contaminants from aqueous solution in different system, the artificial neural network were studied for the normalization (C/C◦). The mean square error was ≤ 0.0595 and the yielded correlation coefficient was ≥ 0.966 for this normalization. Adsorption efficiency prediction by the transfer functions consist of a tangent sigmoid and a linear at hidden and output layer respectively with neurons in the range (2–16) depended on adsorbent and sorbate kind. The influence of each variable was specified by the experimental data ranges indicated by the sensitivity analysis depending on the artificial neural network and founded that the exposure distance for the magnetic field was the most influential system for removing contaminants.
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