Experimental Optimization and Modeling of Sodium Sulfide Production from H2S-Rich Off-Gas via Response Surface Methodology and Artificial Neural Network

Abstract

The gas effluents of oil gas and petrochemical industries called off-gas have high H2S concentration that causes environmental pollution and equipment corrosion. Using a spray column the production of sodium sulfide (Na2S) by H2S reactive absorption was studied using Response Surface Methodology to design and optimize the process based on Central Composite Design. An Artificial Neural Network model was used to predict Na2S production. The maximum weight of 15.5% Na2S was achieved at optimum operational of conditions by a numerical and graphical analysis at an initial 19.3% w/w NaOH concentration scrubbing solution temperature of 40°C and liquid-to-gas volumetric ratio of 24.6 x 10-3 v/v. The results show that Na2S production from H2S-rich off-gas is a suitable and reasonable way to achieve Na2S besides removing the principal portion of H2S from off-gas.