Dynamic modelling of competitive elution of activated carbon in columns using neural networks

Deventer, J.S.J. van; Liebenberg, S.P.; Lorenzen, L.; Aldrich, Chris

doi:10.1016/0892-6875(95)00113-1

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…While both the Zadra and AARL processes are effective in stripping gold from activated carbon, those processes suffer from high-energy consumption, high capital costs for pressurized operations, long elution times and the use of concentrations of environmentally objectionable sodium cyanide (Banini and Stange, 1994;Belsak et al, 1990;Leibonberg and Van Deventer, 1997;Van Deventer et al, 1995;Van Deventer and Merwe, 1994). Thus a safe, rapid, ambient temperature process for stripping gold cyanide from activated carbon would be useful for subsequent recovery of metallic gold from aqueous solutions containing Au(CN) 2 À .…”

Section: Introductionmentioning

confidence: 96%

Gold recovery from loaded activated carbon using different solvents

Soleimani

Kaghazchi

2008

Journal of the Chinese Institute of Chemical Engineers

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Section: Introductionmentioning

confidence: 96%

Gold recovery from loaded activated carbon using different solvents

Soleimani

Kaghazchi

2008

Journal of the Chinese Institute of Chemical Engineers

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“…This is mainly due to the interaction of more number of adsorption process variables, and hence the resulting relationships are highly non-linear [11]. Adsorption isotherms are inadequate to accurately predict the extent of adsorption and reproduction of results.…”

Section: Introductionmentioning

confidence: 98%

“…Even the adsorption isotherms can be represented by neural networks [12]. So, it is preferable to use a non-parametric technique such as a back-propagation neural network to represent such an equilibrium relationship [11].…”

Section: Introductionmentioning

confidence: 99%

Prediction of biosorption efficiency for the removal of copper(II) using artificial neural networks

Prakash

Manikandan

Govindarajan

et al. 2008

Journal of Hazardous Materials

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“…are highly nonlinear (van Deventer et al 1995). Artificial neural networks (ANN) have proved to be an effective way of modelling nonlinear processes (Himmelblau 2008).…”

Section: Introductionmentioning

confidence: 99%

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

Garza-González

Alcalá-Rodríguez

Pérez-Elizondo

et al. 2011

Water Science and Technology

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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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Dynamic modelling of competitive elution of activated carbon in columns using neural networks

Cited by 11 publications

References 14 publications

Gold recovery from loaded activated carbon using different solvents

Gold recovery from loaded activated carbon using different solvents

Prediction of biosorption efficiency for the removal of copper(II) using artificial neural networks

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

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