This research presents the possibility of using banana peel (arising from agricultural production waste) as biosorbent for removal of copper from simulated aqueous solution. Batch sorption experiments were performed as a function of pH, sorbent dose, and contact time. The optimal pH value of Copper (II) removal by banana peel was 6. The amount of sorbed metal ions was calculated as 52.632 mg/g. Sorption kinetic data were tested using pseudo-first order, and pseudo-second order models. Kinetic studies showed that the sorption followed a pseudo second order reaction due to the high correlation coefficient and the agreement between the experimental and calculated values of q_e. Thermodynamic parameters such as enthalpy change (ΔH

Kinetic and mechanism studies of the oxidation of oxalic acid by Cerium sulphate have been carried out in acid medium sulphuric acid. The uv- vis. Spectrophotometric technique was used to follow up the reaction and the selected wavelength to be followed was 320 nm. The kinetic study showed that the order of reaction is first order in Ce(IV) and fractional in oxalic acid. The effect of using different concentration of sulphuric acid on the rate of the reaction has been studied a and it was found that the rate decreased with increasing the acid concentration. Classical organic tests was used to identify the product of the oxidation reaction, the product was just bubbles of CO₂.

In this paper, the ability of using corn leaves as low-cost natural biowaste adsorbent material for the removal of Indigo Carmen (IC) dye was studied. Batch mode system was used to study several parameters such as, contact time (4 days), concentration of dye (10-50) ppm, adsorbent dosage (0.05-0.25) gram, pH (2-12) and temperature (30-60) ^oC. The corn leaf was characterized by Fourier-transform infrared spectroscopy device before and after the adsorption process of the IC dye and scanning electron microscope device was used to find the morphology of the adsorbent material. The experimental data was imputing with several isotherms where it fits with Freundlich (R² = 0.9

The present study intends to prepare nanofibers mat of polyacrylonitrile by electrospinning technique and investigates their adsorption capacity to Congo red dye from the aqueous solution, after characterize it by different techniques such as FTIR, SEM, EDA, XRD and BET. The influence factors on adsorption were studied including adsorbent dosage, initial concentration, contact time, pH and ionic strength. The results confirmed that the increasing in pH decreases the adsorption capacity. So, the optimum adsorbent dosage, initial concentration and contact time were 0.006 g, 25 mg/L and 150 min respectively. The isotherm models of Freundlich and Langmuir were applied on the experimental adsorption data to evaluate the maximum capacity and ener

The uptake of Cd(II) ions from simulated wastewater onto olive pips was modeled using artificial neural network (ANN) which consisted of three layers. Based on 112 batch experiments, the effect of contact time (10-240 min), initial pH (2-6), initial concentration (25-250 mg/l), biosorbent dosage (0.05-2 g/100 ml), agitation speed (0-250 rpm) and temperature (20-60ºC) were studied. The maximum uptake (=92 %) of Cd(II) was achieved at optimum parameters of 60 min, 6, 50 mg/l, 1 g/100 ml, 250 rpm and 25ºC respectively.

Tangent sigmoid and linear transfer functions of ANN for hidden and output layers respectively with 7 neurons were sufficient to present good predictions for cadmium removal efficiency with coefficient of correlatio

This study was aimed to investigate the response surface methodology (RSM) to evaluate the effects of various experimental conditions on the removal of levofloxacin (LVX) from the aqueous solution by means of electrocoagulation (EC) technique with stainless steel electrodes. The EC process was achieved successfully with the efficiency of LVX removal of 90%. The results obtained from the regression analysis, showed that the data of experiential are better fitted to the polynomial model of second-order with the predicted correlation coefficient (pred. R2) of 0.723, adjusted correlation coefficient (Adj. R2) of 0.907 and correlation coefficient values (R2) of 0.952. This shows that the predicted models and experimental values are in go