Artificial Neural Network Approach for Modeling of Mercury Adsorption from Aqueous Solution by Sargassum Bevanom Algae

In this study, the adsorption of mercury ions by sargassum bevanom (s. bevanom) in batch condition was investigated. sem was used to study the surface morphology of the biosorbent. the optimum operating parameters such adsorbent dosage, contact time, and ph, were obtained as:a biomass dose of 0.4 g in 100 ml of mercury solution, contact time of 90 min and ph 7. three equations morris – weber, lagergren and pseudo second order are tested to verify the kinetics of the adsorption process. the data are well explained by the model of weber morris. the langmuir, freundlich, temkin, and dubinin–radushkevich were subjected to sorption data to estimate sorption capacity; the langmuir model indicated better performance in the fitting of equilibrium data. also, the thermodynamic parameters indicated that the adsorption of mercury by s. bevanom was spontaneous and endothermic. artificial neural networks (ann) was used to predict the adsorption efficiency for the removal of mercury ions; the ann model could estimate the behavior of mercury removal process.