Modeling of CO2 loading in aqueous solutions of piperazine: Application of an enhanced artificial neural network algorithm

• An effective ANN to predict CO2 loading in aqueous PZ solutions is developed and tested.
• Extensive ranges of experimental conditions are satisfactorily covered.
• The proposed ANN is of high accuracy and outperforms the other correlations in the literature.

Carbon dioxide (CO2) separation by chemical absorption is widely regarded as the most effective method for its mitigation in natural gas streams or flue gas of the fossil fuel power plants. In this paper, CO2 loading (αCO2) in aqueous solution of piperazine (PZ: C4H10N2), a high reactive cyclic secondary diamine, is modeled over extensive ranges of operational conditions: temperature (287–395 K), PZ concentration (0.1–8 mol kg H2O−1) and CO2 partial pressure (0.0215–9510.3 kPa). To achieve this goal, a feed-forward back-propagation multilayer perceptron artificial neural network (FFANN) was developed and tested via employing the Levenberg–Marquardt training algorithm, enhanced through combination with Bayesian regularization technique. Regression analysis results (R2 = 0.9977) and comparison of the ANN predicted αCO2αCO2 values with corresponding experimental data (%AARD = 2.3927) as well as with some correlations in the literature, have revealed high prediction ability and robustness of the developed neural network.

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