Nirjhar Bar

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.

Removal of chromium(VI) from aqueous solutions using rubber leaf powder: batch and column studies

AbstractChromium metal is found in industrial wastewater at a much higher concentration than the prescribed limit set by different regulatory authorities. Since chromium(VI) is very toxic and carcinogenic, it requires removal at source, that is, before its discharge to the water bodies. The present study is carried out for removal of Cr(VI) from aqueous solution by using locally available rubber leaf as a low-cost adsorbent in batch and continuous column mode. The effects of pH, adsorbent dose, contact time, initial metal ion concentration, and temperature on removal of Cr(VI) were studied in batch process. Different kinetic and isotherm models were examined and the model parameters were determined. The column studies were conducted to investigate the effects of flow rate, bed height, and initial metal ion concentration on removal efficiencies. The experimental data reflects reasonably with Thomas and Yoon–Nelson models in continuous mode.

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.

The use of artificial neural networks (ANN) for modeling of adsorption of Cr(VI) ions

AbstractIn this study, an artificial neural network (ANN) based techniques is applied for the prediction of the percentage removal of Cr(VI) ions from aqueous solution using eight different natural biosorbents. The effects of operating parameters such as initial pH, initial Cr(VI) ion concentration, adsorbent dosages, and contact time are studied to optimize the conditions for maximum removal of Cr(VI) ions. The ANN with a single hidden layer trained with Levenberg-Marquardt algorithm predicted the percentage removal of Cr(VI) ions from aqueous solution accurately.

Removal of Cr(VI) from Its Aqueous Solution Using Green Adsorbent Pistachio Shell: a Fixed Bed Column Study and GA-ANN Modeling

Long-term exposure of Cr(VI) causes severe health effects to the living beings. A continuous fixed bed experimental study is carried out by using pistachio shell as green and eco-friendly adsorbent for Cr(VI) adsorption. Effects of several operating parameters on Cr(VI) removal were investigated using the breakthrough curves (CtC0

Modeling of Gas Holdup and Pressure Drop Using ANN for Gas-Non-Newtonian Liquid Flow in Vertical Pipe

\frac{C_t}{C_0}

Prediction of pressure drop using artificial neural network for non-Newtonian liquid flow through piping components

versus time) and determination of saturation time (CtC0≤1).