Prediction of chromatographic behaviors with Langmuir-artificial neural network adsorption isotherm models

This research optimized the adsorption performance of rice husk char (RHC4) for copper (Cu(II)) from an aqueous solution. Various physicochemical analyses such as Fourier transform infrared spectroscopy (FTIR), field-emission scanning electron microscopy (FESEM), carbon, hydrogen, nitrogen, and sulfur (CHNS) analysis, Brunauer–Emmett–Teller (BET) surface area analysis, bulk density (g/mL), ash content (%), pH, and pHZPC were performed to determine the characteristics of RHC4. The effects of operating variables such as the influences of aqueous pH, contact time, Cu(II) concentration, and doses of RHC4 on adsorption were studied. The maximum adsorption was achieved at 120 min of contact time, pH 6, and at 8 g/L of RHC4 dose. The prediction of percentage Cu(II) adsorption was investigated via an artificial neural network (ANN). The Fletcher–Reeves conjugate gradient backpropagation (BP) algorithm was the best fit among all of the tested algorithms (mean squared error (MSE) of 3.84 and R2 of 0.989). The pseudo-second-order kinetic model fitted well with the experimental data, thus indicating chemical adsorption. The intraparticle analysis showed that the adsorption process proceeded by boundary layer adsorption initially and by intraparticle diffusion at the later stage. The Langmuir and Freundlich isotherm models interpreted well the adsorption capacity and intensity. The thermodynamic parameters indicated that the adsorption of Cu(II) by RHC4 was spontaneous. The RHC4 adsorption capacity is comparable to other agricultural material-based adsorbents, making RHC4 competent for Cu(II) removal from wastewater.

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Artificial Neural Network for predicting biosorption of methylene blue by Spirulina sp.

Water Science & Technology ◽

10.2166/wst.2011.279 ◽

2011 ◽

Vol 63 (5) ◽

pp. 977-983 ◽

Cited By ~ 5

Author(s):

M. T. Garza-González ◽

M. M. Alcalá-Rodríguez ◽

R. Pérez-Elizondo ◽

F. J. Cerino-Córdova ◽

R. B. Garcia-Reyes ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Methylene Blue ◽

Artificial Neural Network ◽

Removal Efficiency ◽

Sensitivity Analyses ◽

Isotherm Models ◽

Biosorption Capacity ◽

Initial Ph ◽

Artificial Neural

An artificial neural network (ANN) was used to predict the biosorption of methylene blue on Spirulina sp. biomass. Genetic and anneal algorithms were tested with different quantity of neurons at the hidden layers to determine the optimal neurons in the ANN architecture. In addition, sensitivity analyses were conducted with the optimised ANN architecture for establishing which input variables (temperature, pH, and biomass dose) significantly affect the predicted data (removal efficiency or biosorption capacity). A number of isotherm models were also compared with the optimised ANN architecture. The removal efficiency or the biosorption capacity of MB on Spirulina sp. biomass was adequately predicted with the optimised ANN architecture by using the genetic algorithm with three input neurons, and 20 neurons in each one of the two hidden layers. Sensitivity analyses demonstrated that initial pH and biomass dose show a strong influence on the predicted removal efficiency or biosorption capacity, respectively. When supplying two variables to the genetic algorithm, initial pH and biomass dose improved the prediction of the output neuron (biosorption capacity or removal efficiency). The optimised ANN architecture predicted the equilibrium data 5,000 times better than the best isotherm model. These results demonstrate that ANN can be an effective way of predicting the experimental biosorption data of MB on Spirulina sp. biomass.

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Use of Artificial Neural Network for Modeling and Prediction of Reactive Red Dye Removal from Wastewater Using Banana Peels Bio-sorbent

Revista de Chimie ◽

10.37358/rc.18.8.6447 ◽

2018 ◽

Vol 69 (8) ◽

pp. 1919-1926 ◽

Cited By ~ 1

Author(s):

Firas Hashim Kamar ◽

Farrah E. Niamat ◽

Ayad A. H. Faisal ◽

Ahmed A. Mohammed ◽

Aurelia Cristina Nechifor ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Dye Removal ◽

Isotherm Models ◽

Adsorption Efficiency ◽

Modeling And Prediction ◽

Initial Ph ◽

Artificial Neural ◽

Influential Variable ◽

Red Dye

Bio-sorption of red dye from aqueous solutions onto banana peels was investigated. Effects of initial pH, bio-sorbent dose, initial concentration, contact time, and temperature were studied and they found of 3, 0.4 g/100 mL, 50 mg/L, 100 min and 298 K respectively with removal efficiency of 93.44%. Artificial neural network was used for prediction of adsorption efficiency and its outputs showed a better fit than other traditional isotherm models. The negative values of DG� and DH� indicate that the bio-sorption of red dye was favored and exothermic. The sensitivity analysis signified that the pH was the most influential variable.

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