Artificial neural network optimization for methyl orange adsorption onto polyaniline nano-adsorbent: Kinetic, isotherm and thermodynamic studies

Tanzifi, Marjan; Hosseini, Seyyed Hossein; Kiadehi, Asieh Dehghani; Olazar, Martı́n; Karimipour, Kianoush; Rezaiemehr, Reza; Ali, Imran

doi:10.1016/j.molliq.2017.08.122

Cited by 155 publications

(45 citation statements)

References 53 publications

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“…We optimized the structure and parameters of the ANN model based on the minimum RMSE between the projected and desired output data. 40 The hidden neurons were dened to gradually increase from two, and the RMSE values of the neural networks were calculated. Because of the randomness of the neural network training, ANN training was performed at each learning rate for 200 times.…”

Section: Optimization Of the Model Structurementioning

confidence: 99%

Appraisal of Cu(ii) adsorption by graphene oxide and its modelling via artificial neural network

et al. 2019

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Section: Optimization Of the Model Structurementioning

confidence: 99%

Appraisal of Cu(ii) adsorption by graphene oxide and its modelling via artificial neural network

et al. 2019

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“…where R is the universal gas constant (J mol −1 ) and T is the absolute temperature in Kelvin and 'β' is associated to the adsorption free energy (E). Adsorption energy is calculated with the following equation (Tanzifi et al 2017):…”

Section: Equilibrium Modelingmentioning

confidence: 99%

Valorization of aquaculture waste in removal of cadmium from aqueous solution: optimization by kinetics and ANN analysis

Aditya

Hossain

2018

Appl Water Sci

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Cadmium is one of the most hazardous heavy metal concerning human health and aquatic pollution. The removal of cadmium through biosorption is a feasible option for restoration of the ecosystem health of the contaminated freshwater ecosystems. In compliance with this proposition and considering the efficiency of calcium carbonate as biosorbent, the shell dust of the economically important snail Bellamya bengalensis was tested for the removal of cadmium from aqueous medium. Following use of the flesh as a cheap source of protein, the shells of B. bengalensis made up of CaCO 3 are discarded as aquaculture waste. The biosorption was assessed through batch sorption studies along with studies to characterize the morphology and surface structures of waste shell dust. The data on the biosorption were subjected to the artificial neural network (ANN) model for optimization of the process. The biosorption process changed as functions of pH of the solution, concentration of heavy metal, biomass of the adsorbent and time of exposure. The kinetic process was well represented by pseudo second order (R 2 = 0.998), and Langmuir equilibrium (R 2 = 0.995) had better fits in the equilibrium process with 30.33 mg g −1 of maximum sorption capacity. The regression equation (R 2 = 0.948) in the ANN model supports predicted values of Cd removal satisfactorily. The normalized importance analysis in ANN predicts Cd 2+ concentration, and pH has the most influence in removal than biomass dose and time. The SEM and EDX studies show clear peaks for Cd confirming the biosorption process while the FTIR study depicts the main functional groups (-OH, C-H, C=O, C=C) responsible for the biosorption process. The study indicated that the waste shell dust can be used as an efficient, low cost, environment friendly, sustainable adsorbent for the removal of cadmium from aqueous solution.

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“…The input layer acquires information from outside sources and by‐passes to the hidden layer for commerce out. Prior to in‐going the hidden layer, the input magnitudes are weighted autonomously and the all the data handing out are completed by the hidden layer, leading to production of output sustained on the whole the weighted values from the input layer modified by a sigmoid transfer function . The experimental time, temperature, pH, o , p ‐DDD concentration, and the quantity of river sediment were measured as input data for the model.…”

Section: Methodsmentioning

confidence: 99%

“…Prior to in-going the hidden layer, the input magnitudes are weighted autonomously and the all the data handing out are completed by the hidden layer, leading to production of output sustained on the whole the weighted values from the input layer modified by a sigmoid transfer function. [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] The experimental time, temperature, pH, o,p-DDD concentration, and the quantity of river sediment were measured as input data for the model. The uptake effectiveness data were put in and measured as output.…”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

Stereoselective uptake and degradation of (±)‐o,p‐DDD pesticide stereomers in water‐sediment system

2018

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The environmental stereoselective uptake and degradation of (±)-o,p-DDD pesticide stereomers in water-sediment system are described. The results were analyzed by artificial neural network model. The optimized experimental parameters were concentration of o,p-DDD streamers (7.0 μg L ), experimental time (60 min), pH (6), dose (5.0 g L ), and temperature (25°C). The maximum uptake and degradation were 87% and 85% and 33.0% and 30.5% for (-)- and (+)-stereomers of o,p-DDD in 15-day time. Both uptake and degraded phenomenon showed first-order rate reaction. Thermodynamic variables indicated exothermic nature of uptake and degradation processes. The uptake and degradation were slightly higher for (-)-stereomer than (+)-stereomer of o,p-DDD. It was assumed that both uptake and degradation processes are accountable for the removal of the streomers of o,p-DDD from earth's ecosystem, but the uptake is responsible for major contribution. The magnitudes of relative errors obtained by artificial neural network model were in the range of ±0.2 to 3.5, indicating good applicability of the experimental data. The results are very useful to control the environmental contamination due to the chiral o,p-DDD pesticide as its two enantiomers have different ecological toxicities.

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Artificial neural network optimization for methyl orange adsorption onto polyaniline nano-adsorbent: Kinetic, isotherm and thermodynamic studies

Cited by 155 publications

References 53 publications

Appraisal of Cu(ii) adsorption by graphene oxide and its modelling via artificial neural network

Appraisal of Cu(ii) adsorption by graphene oxide and its modelling via artificial neural network

Valorization of aquaculture waste in removal of cadmium from aqueous solution: optimization by kinetics and ANN analysis

Stereoselective uptake and degradation of (±)‐o,p‐DDD pesticide stereomers in water‐sediment system

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