Prediction and Experimental Verification of CO₂ Adsorption on Ni/DOBDC Using a Genetic Algorithm–Back-Propagation Neural Network Model

A model combined a back-propagation neural network (BPNN) with a genetic algorithm (GA) based on experimental data as training samples was established to predict the CO₂ adsorption capacity for metal organic frameworks (MOFs) of Ni/DOBDC. The random function of the conventional BPNN model was modified by the GA–BPNN model for optimizing the initial weights and bias nodes. The amounts of adsorbed CO₂ and corresponding isosteric heat of adsorption on Ni/DOBDC were synchronously studied within a wide temperature range (25–145 °C) and pressure range (0–3.5 MPa). The predicted results of the proposed GA–BPNN model and those of theoretical models and a BPNN model were compared with the experimental data. The proposed model provided a more accurate prediction than those of the theoretical models and BPNN model. In particular, the theoretical models were invalid in the low-pressure range (0–0.1 MPa).