Optimization of Energy Consumption of the Debutanizer Column, Using Genetic Algorithm

Abstract

In the light of the emissions associated with fossil fuel combustion and their associated health and environmental impacts, natural gas is becoming increasingly attractive when compared with other fuels. The separation of natural gas liquids (NGL) in gas processing is energy-intensive, which is performed by a series of column among them the debutanizer column. This latter is used to separates C4 cuts and lighter components from NGL, typically gasoline, requiring modeling and optimization to reduce energy consumption. This work presents a rigorous methodology for modeling and optimization energy consumption of debutanizer column. This methodology is to develop a statistical model that will be used to test the variation of the energy consumed by the process, depending on columns parameters that have a great influence on this consumption. An experimental design was implemented and the data required for modeling were obtained by numerical simulations studies of debutanizer column, basing on Peng-Robinson (PR) thermodynamic model. Simulation results were validated successfully and the resulting model will be operated, using a genetic algorithm. The genetic algorithm modeling allows us determining the optimal values of reflux ratio and pressure of column, which provide savings in energy consumption for NGL separation process.

Keywords

• Energy consumption
• Genetic algorithm
• distillation column
• NGL separation

References

1. Alam, T., Qamar, S., Dixit, A., & Benaida, M. (2020) Genetic Al-gorithm: Reviews, implementations, and applications. International Journal of Engineering Pedagogy (iJEP).
2. Chun, H. H., & Kim, Y. H. (2013). Application of a divided wall column for gas separation in floating liquefied natural gas plant. Korean Journal of Chemical Engineering, 30(7), 1473–1479.
3. Gutiérrez Antonio, C., & Briones Ramírez, A. (2009). Pareto front of ideal Petlyuk sequences using a multi objective genetic algorithm with constraints. Computers & Chemical Engineering, 33(2), 454-464.