experimental optimization and modeling of sodium sulfide production from h2s-rich off-gas via response surface methodology and artificial neural network

The existence of hydrogen sulfide (H2S) in the gas effluents of oil, gas and petrochemical industries causes environmental pollution and equipment corrosion. These gas streams, called off-gas, have high H2S concentration, which can be used to produce sodium sulfide (Na2S) by H2S reactive absorption. Na2S has a wide variety of applications in chemical industries. In this study, the reactive absorption process was performed using a spray column. Response Surface Methodology (RSM) was applied to design and optimize experiments based on Central Composite Design (CCD). The individual and interactive effects of three independent operating conditions on the weight percent of the produced Na2S (Y) were investigated by RSM: initial NaOH concentration (10-20% w/w), scrubbing solution temperature (40-60 °C) and liquid-to-gas volumetric ratio (15 × 10−3 to 25 × 10−3). Furthermore, an Artificial Neural Network (ANN) model was used to predict Y. The results from RSM and ANN models were compared with experimental data by the regression analysis method. The optimum operating conditions specified by RSM resulted in Y of 15.5% at initial NaOH concentration of 19.3% w/w, scrubbing solution temperature of 40 °C and liquid-to-gas volumetric ratio of 24.6 × 10−3 v/v.