Application of artificial neural networks for simulation of experimental CO2 absorption data in a packed column

The generalization performances of the Back Propagation Multi-Layer Perceptron (BPMLP) and the Radial Basis Function (RBF) neural networks were compared together by resorting to several sets of experimental data collected from a pilot scale packed absorption column. The experimental data were obtained from an 11 cm diameter packed tower filled with 1.8 m ¼ inch ceramic Rashig rings. The column was used for separation of carbon dioxide from air using various concentrations and flow rates of Di-Ethanol Amine (DEA) and Methyl Di-Ethanol Amine (MDEA) solutions. Two in-house efficient algorithms were employed for optimal training of both neural networks. The simulation results indicated that the RBF networks can perform more adequately than the MLP networks for filtering the noise (measurement errors) and capturing the true underlying trend which is essential for a reliable generalization performance.

► Extensive data are presented for packed absorption of CO2 from air by various solvents.
► Conventional software’s provided inadequate concentration profiles across packed bed.
► RBF networks generalized better than MLP networks due to their solid theoretical foundations.