Simultaneous energy and environment-based optimization and retrofit of TEG dehydration process: An industrial case study

Abstract

Carbon dioxide emissions (CO2) during the dehydration process of natural gas are of important concerns as this gas negatively affects the climate and environment in general. Dehydration process also encounters many heating, cooling and pumping units, which leads to high energy consumption. Reducing these emissions along with minimizing the utilized energy while keeping the high production is a complex problem that can be solved by multi objective optimization (MOO). This study focuses on minimizing CO2 emissions, energy consumption (ENG) along with water content in the gas (WT). This means that the performance of the plant is improved from operational, environmental and energy point of view. The process is simulated with ProMax 4.0 and approved to be valid with the real plant data. Non-dominated sorting genetic algorithm (NSGA-II) was used for attaining the Pareto fronts for the decided MOO cases. The affecting decision variables and limitations are decided based on the capacity of the plant and industrial practice. Two bi-objective cases and a tri-objective case are considered, which are; minimizing CO2 emissions and WT (case 1), minimizing ENG and WT (case 2) and minimizing WT, ENG and CO2 emissions simultaneously (case 3). An attempt to retrofit the current process is also proposed and the cases are carried out with the modified process. Results showed noticeable improvements and enhancements in the given process.