Artificial neural network (ANN) approach for modelling of arsenic (III) biosorption from aqueous solution by living cells of Bacillus cereus biomass

Artificial neural network model is applied for the prediction of the biosorption capacity of living cells of Bacillus cereus for the removal of chromium (VI) ions from aqueous solution. The maximum biosorption capacity of living cells of Bacillus cereus for chromium (VI) was found to be 89.24% at pH 7.5, equilibrium time of 60 min, biomass dosage of 6 g/L, and temperature of 30 ± 2 °C. The biosorption data of chromium (VI) ions collected from laboratory scale experimental set up is used to train a back propagation (BP) learning algorithm having 4-7-1 architecture. The model uses tangent sigmoid transfer function at input to hidden layer whereas a linear transfer function is used at output layer. The data is divided into training (75%) and testing (25%) sets. Comparison between the model results and experimental data gives a high degree of correlation R2 = 0.984 indicating that the model is able to predict the sorption efficiency with reasonable accuracy. Bacillus cereus biomass is characterized using AFM and FTIR.

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Modeling of Cu(II) Adsorption from an Aqueous Solution Using an Artificial Neural Network (ANN)

Molecules ◽

10.3390/molecules25143263 ◽

2020 ◽

Vol 25 (14) ◽

pp. 3263

Author(s):

Taimur Khan ◽

Teh Sabariah Binti Abd Manan ◽

Mohamed Hasnain Isa ◽

Abdulnoor A.J. Ghanim ◽

Salmia Beddu ◽

...

Keyword(s):

Neural Network ◽

Aqueous Solution ◽

Artificial Neural Network ◽

Adsorption Capacity ◽

Contact Time ◽

Bet Surface Area ◽

Isotherm Models ◽

Order Kinetic ◽

Artificial Neural ◽

Artificial Neural Network Ann

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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Prediction of thermal conductivity of aqueous solution at high pressures by using artificial neural network

Chemical Industry and Chemical Engineering Quarterly ◽

10.2298/ciceq130729038a ◽

2014 ◽

Vol 20 (4) ◽

pp. 565-569

Author(s):

Ali Amooey ◽

Maryam Ahangarian ◽

Farshad Rezazadeh

Keyword(s):

Neural Network ◽

Thermal Conductivity ◽

Aqueous Solution ◽

Artificial Neural Network ◽

Regression Analysis ◽

High Pressures ◽

Ann Model ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Layer

The objective of this study is to predict thermal conductivity of aqueous solution with artificial neural network (ANN) model with three inputs (pressure, temperature and concentration). A feed forward artificial neural network with three neurons in its hidden layer is recommended to predict thermal conductivity and the accuracy of this method evaluated by regression analysis between the predicted and experimental value and it shows desired result.

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