In this work, a new approach for the auto-design of neural networkbased genetic algorithm (GA) has been adopted to manipulate the products of an absorption column in the Khangiran gas refinery located in northeastern Iran. The experimental input data included gas flow rate, gas pressure, gas temperature, amine temperature, amine flow rate. In order to construct a GA–artificial neural network (ANN)-based model, the H2S flow rate and dirty amine flow rate were selected for the output. The proposed method was assessed by the data taken from a case study in the Khangiran gas refinery. Design of topology and parameters of the neural networks as decision variables was first achieved by a trial-and-error procedure followed by genetic algorithms, which enhances the effectiveness of the forecasting scheme. The results reveal that the testing results from the model were in good agreement with the experimental data.

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