Neural Network Modeling of Organics Removal by Activated Carbon Cloths
Catherine Faur, Pierre Le Cloirec · Journal of Environmental Engineering · 2001
The adsorption of organic compounds onto an activated carbon cloth is studied in a dynamic reactor. An experimental design is carried out to investigate the influence of operating conditions (initial concentration C 0 , flow velocity U 0 , and bed thickness Z ) and adsorbate's characteristics. A slow intraparticular diffusion is shown by flattened breakthrough curves, and adsorption capacities are high and range between 50 and 250 mg g − 1 . The transfer zone Z 0 , assessed by the Adams and Bohart equation, is low (3 mm). All experimental results are modeled by a neural network to take into account the specific diffusion of cloths. Parameters related to the adsorbate-adsorbent affinity in a batch reactor are consequently introduced in the input layer of the neural network (intraparticular coefficient K w and Freundlich parameters K f and l / n ), added to operating conditions whose influence was shown ( C 0 , U 0 , and Z ) and time t . The statistical quality of the neural network modeling is high ( r 2 = 0.956). Furthermore, the Garson connection weight method allows the relative influence of input neurons to be determined. This analysis confirms the influence of parameters relative to adsorbant-adsorbate affinity.