This paper examines the possibility to use a single neural network to model and predict a wide array of standard adsorption isotherm behaviour. Series of isotherm data were generated from the four most common isotherm equations (Langmuir, Freundlich, Sips and Toth) and the data were fitted with a unique neural network structure. Results showed that a single neural network with a hidden layer having three neurons, including the bias neuron, was able to represent very accurately the adsorption isotherm data in all cases. Similarly, a neural network with four hidden neurons, including the bias, was able to predict very accurately the temperature dependency of adsorption data.
NomenclatureA constant for Freundlich equation (kg ethanol/ kg adsorbent) (m 3 /kg ethanol) 1/n b constant for Langmuir, Toth and Sips equations; temperature dependent variable for the TD-Toth equation (m 3 /kg adsorbate) b o constant for the TD-Toth equation (m 3 / kg adsorbate) c fluid adsorbate concentration (kg adsorbate/m 3 ) n constant for the Freundlich and Sips equations (dimensionless)
SUMMARYBatch adsorption experiments were conducted to examine the liquid-phase adsorption of ethanol from ethanol-water solutions. Experiments performed established the kinetic and equilibrium behaviour of the various adsorbents in solution. The experiments with the ZSM-5 adsorbents indicate that the silica to alumina ratio had little effect on the ethanol-water separation at low ethanol concentrations. In general, ZSM-5 adsorbents were outperformed by the activated carbon adsorbents, which showed higher adsorption capacities. The capacity of activated carbon adsorbents correlated strongly with cumulative pore volume and Brunauer, Emmet and Teller (BET) surface area.Particle size was found to be the most influential factor in the ethanol uptake rate. The large pellets showed sluggish kinetics when compared to their powdered counterparts. When considering kinetic performance and adsorption capacity XTRUSORB A754 and M-30 activated carbon show the most potential for the selective adsorption of ethanol. The adsorbent screening performed herein applies to the energy efficient production of bio-ethanol via adsorption.
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