Natural gas processing is currently facing economic and environmental challenges due to abrupt changes in oil prices and the development of an alternate source of energy. Therefore it is essential to optimize the processing unit to make it profitable and environmentally friendly. Sustainable optimization of an industrial natural gas treatment plant is carried out using the NSGA-II algorithm for the methyl diethanol amine (MDEA) process to optimize CO2 removal along with payback period and damage index. This multi-objective optimization study includes seven decision variables such as temperature and pressure of feed gas, feed flow rate, temperature and pressure of regenerator feed, lean amine temperature, and MDEA concentration. Three separate two-objective optimization study problems are developed and applied for retrofitted case and base case studies. Two different ProMax models are developed and validated the model by using actual plant data. All the retrofitted cases and base cases are solved and the Pareto optimal fronts are obtained. Trade-offs between different objectives are illustrated for all the problems. The lean vapor compression process can facilitate maximum H2S removal of 99.75% and maximum CO2 removal of 98.52% simultaneously by maintaining a DI value of 476. TOPSIS method is used to rank and find the best optimal solution. Optimization study for uncertain CO2 concentration (4%mole) in the feed gas is also analyzed and compared with normal feed conditions. The machine learning approach is used to obtain the predictions of selected objective functions for all the problem cases using the decision tree method.
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