We show how a multiobjective bare-bones particle swarm optimization can be used for a process parameter tuning and performance enhancement of a natural gas sweetening unit. This has been made through maximization of hydrocarbon recovery and minimization of the total energy of the process as the two objectives of the optimization. A trade-off exists between these two objectives as illustrated by the Pareto front. This algorithm has been applied to a sweetening unit that uses the Benfield HiPure process. Detailed models of the natural gas unit are developed in ProMax process simulator and integrated to the multi-objective optimization developed in visual basic environment (VBA). In this study, the solvent circulation rates, stripper pressure and reboiler duties are considered as the decision variables while hydrogen sulfide and carbon dioxide concentrations in the sweetened gas are considered as process constraints. The upper and lower bounds of the decision variables are obtained through a parametric sensitivity analysis of the models. The Pareto sets show a significant improvement in hydrocarbon recovery and a decent reduction in the heat consumption of the process. 1. Introduction As global energy demand rises, natural gas now plays an important strategic role in world energy supply. It is the cleanest and most hydrogen rich of all the hydrocarbon energy sources and it has high energy efficiencies for power energy. Natural gas resources exploited and discovered are plentiful; however, they contain complex contaminants such as CO2, H2S, Mercaptans, and other sulfur compounds. Excessive amounts of these contaminants in natural gas streams will lead to low gas heating value and/or cause serious environmental hazards to the consumers. In LNG plants, large amounts of carbon dioxide and sulfur compounds may affect the quality of LNG products or pose serious operational problems in the cryogenic columns [1, 2]. Therefore, one of the major purposes of the sweetening units is to purify raw natural gas to meet both sale gas and liquefaction specifications [3]. Unpredicted changes in reservoir conditions, combined with the tough market competition, have forced the natural gas sweetening business to adopt sophisticated optimization techniques to improve the purity of their products, increase production capacity, and minimize total energy requirement for units’ operations. Gas sweetening processes use complex facilities whose design and operation basically depend on many parameters including gas composition, flow rate, circulation rates, absorber temperatures and
References
[1]
S. A. Ebenezer and J. S. Gudmundsson, Removal of carbondioxide from natural gas for LNG production [Ph.D. thesis], Norwegian University of Science and Technology, Trondheim, Norway, 2005.
[2]
A. Kohl and R. Nielsen, Gas Purification, Gulf, Houston, Tex, USA, 1997.
[3]
R. Hubbard, “The role of gas processing in the natural-gas value chain,” Journal of Petroleum Technology, vol. 61, no. 8, Article ID 118535, pp. 65–71, 2009.
[4]
B. Hassan and T. Shamim, “Parametric and exergetic analysis of a power plant with CO2 and capture using chemical looping combustion,” in Proceedings of the International Conference on Clean and Green Energy, Singapore, 2012.
[5]
R. Mohammad, L. Schneiders, and J. Neiderer, “Carbondioxide capture from power plants: part I : a parametric study of the technical performance based on monoethanolamine,” International Journal of Greenhouse Gas Control, vol. 1, no. 1, pp. 37–46, 2007.
[6]
D. Wolbert, X. Joulia, B. Koehret, and L. T. Biegler, “Flowsheet optimization and Ootimal sensitivity analysis using exact derivatives,” Internal Report PA, 15213, Engineering Design Research Center, Carnegie Mellon University, Pittsburgh, Pa, USA, 1993.
[7]
A. Aroonwilas, A. Chakma, P. Tontiwachwuthikul, and A. Veawab, “Mathematical modelling of mass-transfer and hydrodynamics in CO2 absorbers packed with structured packings,” Chemical Engineering Science, vol. 58, no. 17, pp. 4037–4053, 2003.
[8]
K. Y. Park and T.-W. Kang, “Computer simulation of H2S and CO2 absorption processesabsorption processes,” Korean Journal of Chemical Engineering, vol. 12, no. 1, pp. 29–35, 1995.
[9]
M. R. Rahimpour and A. Z. Kashkooli, “Enhanced carbon dioxide removal by promoted hot potassium carbonate in a split-flow absorber,” Chemical Engineering and Processing, vol. 43, no. 7, pp. 857–865, 2004.
[10]
M. R. Rahimpour and A. Z. Kashkooli, “Modeling and simulation of industrial carbon dioxide absorber using amine-promoted potash solution,” Iranian Journal of Science and Technology B, vol. 28, no. 6, pp. 653–666, 2004.
[11]
K. Y. Park and T. F. Edgar, “Simulation of the hot carbonate process for removal of CO2 and H2S from medium Btu gas,” Energy progress, vol. 4, no. 3, pp. 174–181, 1984.
[12]
P. Patil, Z. Malik, and M. Jobson, “Retrofit design for gas sweetening processes,” in Proceedings of the IChemE Symposium, 152, pp. 460–467, 2006.
[13]
J. C. Polasek and J. A. Bullin, “Design and optimization of integrated amine sweetening, claus sulfur and tail gas cleanup units by computer simulation,” Internal Report, Bryan Research & Engineering, Bryan, Tex, USA, 1990.
[14]
J. C. Polasek, J. A. Bullin, and S. T. Donnely, “Alternative flow schemes to reduce capital and operating costs of amine sweetening units,” Internal Report, Bryan Research & Engineering, Bryan, Tex, USA, 2006.
[15]
R. H. Weiland, M. Rawal, and R. G. Rice, “Stripping of carbondioxide from monoethanolamine solutions in a packed column,” AIChE Journal, vol. 28, no. 6, pp. 963–973, 1982.
[16]
H. Pierreval and L. Tautou, “Using evolutionary algorithms and simulation for the optimization of manufacturing systems,” IIE Transactions, vol. 29, no. 3, pp. 181–189, 1997.
[17]
E. Zitzler and L. Thiele, “Multiobjective optimization using evolutionary algorithms: a comparatice case study,” in Proceedings of the 5th International Conference on Parallel Problem Solving from Nature (PPSN '98), Amsterdam, The Netherlands, 1998.
[18]
S. Dehuri and S.-B. Cho, “Multi-criterion Pareto based particle swarm optimized polynomial neural network for classification: a review and state-of-the-art,” Computer Science Review, vol. 3, no. 1, pp. 19–40, 2009.
[19]
A. Konak, D. Coit, W. Smith, and E. Alice, “Multi-objective optimization using genetic algorithm: a tutorial,” Reliability Engineering & System Safety, vol. 91, pp. 992–100, 2006.
[20]
L. Mian, S. Azarm, and V. Aute, “A multi-objective genetic algorithm for robust design optimization,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '05), pp. 771–778, New York, NY, USA, June 2005.
[21]
W. Wu, Y. Liou, and Y. Zhou, “Multiobjective optimization of a hydrogen production system with low CO2 emissions,” Industrial and Engineering Chemistry Research, vol. 51, no. 6, pp. 2644–2651, 2012.
[22]
R. T. Marler and J. S. Arora, “The weighted sum method for multi-objective optimization: new insights,” Structural and Multidisciplinary Optimization, vol. 41, no. 6, pp. 853–862, 2010.
[23]
R. T. Marler and J. S. Arora, “Survey of multi-objective optimization methods for engineering,” Structural and Multidisciplinary Optimization, vol. 26, no. 6, pp. 369–395, 2004.
[24]
E. Bernier, F. Maréchal, and R. Samson, “Multi-objective design optimization of a natural gas-combined cycle with carbon dioxide capture in a life cycle perspective,” Energy, vol. 35, no. 2, pp. 1121–1128, 2010.
[25]
H. Li, F. Maréchal, M. Burer, and D. Favrat, “Multi-objective optimization of an advanced combined cycle power plant including CO2 separation options,” Energy, vol. 31, no. 15, pp. 3117–3134, 2006.
[26]
M. M. Montazer-Rahmati and R. Binaee, “Multi-objective optimization of an industrial hydrogen plant consisting of a CO2 absorber using DGA and a methanator,” Computers and Chemical Engineering, vol. 34, no. 11, pp. 1813–1821, 2010.
[27]
L. Sun and H. H. Lou, “A strategy for multi-objective optimization under uncertainty in chemical process design,” Chinese Journal of Chemical Engineering, vol. 16, no. 1, pp. 39–42, 2008.
[28]
O. O. Shadiya, V. Satish, and K. A. High, “Process enhancement through waste minimization and multiobjective optimization,” Journal of Cleaner Production, vol. 31, pp. 137–149, 2012.
[29]
K. E. Parsopoulos and M. N. Vrahatis, “Recent approaches to global optimization problems through particle swarm optimization,” Natural Computing, vol. 1, pp. 230–235, 2002.
[30]
R. Poli, J. Kennedy, and T. Blackwell, “Particle swarm optimization,” Swarm Intelligence, vol. 1, pp. 33–35, 2007.
[31]
M. Reyes-Sierra and C. A. Coello, “Multi-objective particel swarm optimizers: a survey of the state-of-the-art,” International Jounal of Computing Intelligenece Research, vol. 2, pp. 287–300, 2006.
[32]
J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conefercence on Neural Networks, vol. 4, pp. 1942–1948, December 1995.
[33]
H. Zhang, D. Kennedy, G. Rangaiah, and A. Bonilla-Petriciolet, “Novel bare-bones particle swarm optimization and its performance for modeling vapor-liquid equilibrium data,” Fluid Phase Equilibria, vol. 301, no. 1, pp. 33–45, 2011.
[34]
H. E. Benson and R. W. Parrish, “HiPure process removes CO2/H2S,” Hydrocarbon Processing, vol. 53, no. 4, pp. 81–82, 1974.
[35]
Bryan Research and Engineering, “ProMax V3.2,” 2010, http://www.bre.com/home.aspx.
[36]
J. Kennedy, “Bare bones particle swarms,” in Proceedings of the IEEE Swarm Intelligence Symposium, pp. 80–87, 2003.
