Simulation of CO2 capture using sodium hydroxide solid sorbent in a fluidized bed reactor by a multi-layer perceptron neural network

Various investigations have been conducted in order to decrease worldwide carbon dioxide in recent decades. Presently, CO2 capture applying solid sorbent has attracted attentions as a manner in which the energy consumption is relatively low. In this study, a feed forward multi-layer perceptron neural network has been developed to predict the ratio of output to input of carbon dioxide concentration (Cout/Cin) in a fluidized bed reactor applied for CO2 capture using sodium hydroxide solid sorbent over operational conditions: temperature (25-40 °C), CO2 volume percentage (1-2%), air flow rate (14-16 m3/hr) and time (0-420 s). The ANN was trained by the Levenberge-Marquardt algorithm, enhanced through the combination with Bayesian regularization technique. Regression analysis results (R2 = 0.9838) and comparison of the ANN predicted Cout/Cin values with corresponding experimental data (%AARD = 1.9217) have shown high prediction ability and robustness of the developed neural network.