Multi-Objective Optimization to Predict Minimum Temperature for Efficient BTEX Destruction to Minimize Fuel Gas Consumption in Sulfur Recovery Units

2018 ◽  
Author(s):  
Ramees K. Rahman ◽  
Salisu Ibrahim ◽  
Abhijeet Raj
Keyword(s):  
Minimum Temperature ◽  
Multi Objective Optimization ◽  
Sulfur Recovery ◽  
Fuel Gas ◽  
Multi Objective ◽  
Gas Consumption

Development of an Online Soft Analyzer for the Continuous Analysis of BTEX Emissions from the Furnace of Sulfur Recovery Units

2021 ◽  
Author(s):  
Satyadileep Dara ◽  
Salisu Ibrahim ◽  
Abhijeet Raj ◽  
Ibrahim Khan ◽  
Eisa Al Jenaibi
Keyword(s):  
Kinetic Model ◽  
Soft Sensor ◽  
Sulfur Recovery ◽  
Furnace Temperature ◽  
Fuel Gas ◽  
Gas Processing ◽  
Wide Range ◽  
Gas Consumption ◽  
Reaction Furnace ◽  
Co Emission

Abstract The oxidation of Benzene, Toluene, Ethylbenzene, and Xylenes (BTEX) in the furnace of SRUs at high temperature is an effective solution to prevent Claus catalyst deactivation in the downstream catalytic converters. However, the existing SRUs do not have the means to monitor BTEX emissions from Claus furnace due to lack of commercial online analyzers in the market. This often leads to excessive temperatures up to 1150 °C in the furnace to ensure BTEX destruction. Such high temperatures increase fuel gas consumption and CO emission and reduce sulfur recovery efficiency. To obtain continuous BTEX indication at the furnace exit, an online BTEX soft sensor model is developed to predict BTEX concentration at furnace exit. Subsequently, this soft sensor will be implemented in one of the SRUs of ADNOC Gas Processing. The BTEX soft sensor has been developed by constructing a compact kinetic model for aromatics destruction in the furnace based on the understanding of BTEX oxidation mechanisms derived using a detailed and well validated kinetic model developed previously. The kinetic model, including its rate parameters were incorporated into Hysys/Sulsim software, where both the reaction furnace and catalytic converters were simulated. The BTEX soft sensor has been validated with plant data from different ADNOC Gas Processing SRU trains under a wide range of feed conditions (particularly, with varying relative concentrations of H2S, CO2, and hydrocarbons in acid gas feed) in order to ensure its robustness and versatile predictive accuracy. The model predicts BTEX emissions from the reaction furnace under a wide range of operating conditions in the furnace with deviation not exceeding +/- 5 ppm. It also predicts the reaction furnace temperature (with a deviation of +/- 5%) and species composition from the furnace exit within a reasonable error margin. Presently, the model is in the process of being deployed in one of the SRUs of ADNO Gas Processing as an online soft sensor, where it can read the feed conditions, predict the BTEX exit concentration and write this value to the DCS. Thus, plant operators can monitor BTEX exit concentration on continuous basis and use it as a reliable basis to lower fuel gas co-firing rate in the furnace to achieve optimum furnace temperature that provide efficient BTEX destruction and low CO emission. The online soft analyzer, when deployed in SRU, will continuously predict BTEX emission from SRU furnace with high accuracy, which cannot be done experimentally in the plant or reliably using most of the existing commercial software. This approach can be used to seek favorable means of optimizing BTEX destruction to enhance sulfur recovery, while decreasing fuel gas consumption and carbon footprint in sulfur recovery units to reduce operating cost.

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