- Rogelj J., Shindell D., Jiang K., Fifita S., Forster P., Ginzburg V., Handa C., Kheshgi H., Kobayashi S., Kriegler E., Mundaca L., Séférian R., Vilariño M.V. (2018) Mitigation pathways compatible with 1.5 °C in the context of sustainable development, Chapter 2, in press, pp. 93–174. [Google Scholar]
- Obersteiner M., Azar Ch., Kauppi P., Möllersten K., Moreira J., Nilsson S., Read P., Riahi K., Schlamadinger B., Yamagata Y., Yan J., van Ypersele J.-P. (2001) Managing climate risk, Science 294, 5543, 786–787. https://doi.org/10.1126/science.294.5543.786b. [Google Scholar]
- Moora H., Roos I., Kask U., Kask L., Ounapuu K. (2017) Determination of biomass content in combusted municipal waste and associated CO2 emissions in Estonia, Energy Proc. 128, 222–229. https://doi.org/10.1016/j.egypro.2017.09.059. [Google Scholar]
- Riber Christian., Petersen C., Christensen T.H. (2009) Chemical composition of material fractions in Danish household waste, Waste Manage. 29, 4, 1251–1257. https://doi.org/10.1016/j.wasman.2008.09.013. [Google Scholar]
- Xie N., Chen B., Tan C., Liu Z. (2017) Energy consumption and exergy analysis of mea-based and hydratebased CO2 separation, Indus. Eng. Chem. Res. 56, 51, 15094–15101. https://doi.org/10.1021/acs.iecr.7b03729. [Google Scholar]
- Rochelle G.T. (2009) Amine scrubbing for CO2 capture, Science 325, 5948, 1652–1654. https://doi.org/10.1126/science.1176731. [CrossRef] [PubMed] [Google Scholar]
- Mores P., Scenna N., Mussati S. (2012) CO2 capture using monoethanolamine (MEA) aqueous solution: Modeling and optimization of the solvent regeneration and CO2 desorption process, Energy 45, 1, 1042–1058. https://doi.org/10.1016/j.energy.2012.06.038. The 24th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy, ECOS 2011. [Google Scholar]
- Neveux T., Le Moullec Y., Corriou J.-P., Favre E. (2013) A rigorous optimization method of operating parameters for amine-based CO2 capture processes, Energy Proc. 37, 1821–1829. https://doi.org/10.1016/j.egypro.2013.06.060. [Google Scholar]
- Lee A.S., Eslick J.C., Miller D.C., Kitchin J.R. (2013) Comparisons of amine solvents for post-combustion CO2 capture: A multi-objective analysis approach, Int. J. Greenhouse Gas Cont. 18, 68–74. https://doi.org/10.1016/j.ijggc.2013.06.020. [Google Scholar]
- Øi L.E., Bråthen T., Berg C., Brekne S.K., Flatin M., Johnsen R., Moen I.G., Thomassen E. (2014) Optimization of configurations for amine based CO2 absorption using Aspen HYSYS, Energy Proc. 51, 224–233. https://doi.org/10.1016/j.egypro.2014.07.026. [Google Scholar]
- Mores P.L., Godoy E., Mussati S.F., Scenna N.J. (2014) A NGCC power plant with a CO2 post-combustion capture option. Optimal economics for different generationcapture goals, Chem. Eng. Res. Des. 92, 7, 1329–1353. https://doi.org/10.1016/j.cherd.2013.11.013. [Google Scholar]
- Wilhelm R., Esche E., Guetta Z., Menzel J., Thielert H., Repke J.-U. (2018) Model adaptation and optimization for the evaluation and investigation of novel amine blends in a pilot-plant scale CO2 capture process under industrial conditions, Chem. Eng. Trans. 69, 175–180. https://doi.org/10.3303/CET1869030. [Google Scholar]
- Chen Y.-H., Shen M.-T., Chang H., Ho C.-D. (2019) Control of solvent-based post-combustion carbon capture process with optimal operation conditions, Processes 7, 6, 366. https://doi.org/10.3390/pr7060366. [Google Scholar]
- Chung W., Lee J.H. (2020) Input–output surrogate models for efficient economic evaluation of amine scrubbing CO2 capture processes, Indus. Eng. Chem. Res. 59, 42, 18951–18964. https://doi.org/10.1021/acs.iecr.0c02971. [Google Scholar]
- Li F., Zhang J., Oko E., Wang M. (2017) Modelling of a post-combustion CO2 capture process using extreme learning machine, Int. J. Coal Sci. Technol. 40, 1, 33–40. https://doi.org/10.1007/s40789-017-0158-1. [Google Scholar]
- Li F., Zhang J., Shang C., Huang D., Oko E., Wang M. (2018) Modelling of a post-combustion CO2 capture process using deep belief network, Appl. Therm. Eng. 130, 997–1003. https://doi.org/10.1016/j.applthermaleng.2017.11.078. [Google Scholar]
- Chan V., Chan C. (2017) Learning from a carbon dioxide capture system dataset: Application of the piecewise neural network algorithm, Petroleum 3, 1, 56–67. https://doi.org/10.1016/j.petlm.2016.11.004. [Google Scholar]
- Plesu V., Bonet J., Bonet-Ruiz A.E., Chavarria A., Iancu P., Llorens J. (2018) Surrogate model for carbon dioxide equilibrium absorption using aqueous monoethanolamine, Chem. Eng. Trans. 70, 919–924. https://doi.org/10.3303/CET1870154. [Google Scholar]
- Nuchitprasittichai A., Cremaschi S. (2011) Optimization of CO2 capture process with aqueous amines using response surface methodology, Comput. Chem. Eng. 35, 8, 1521–1531. https://doi.org/10.1016/j.compchemeng.2011.03.016. [Google Scholar]
- Dyment J., Watanasiri S., Acid gas cleaning using amine solvents: Validation with experimental and plant data (white paper), Aspentech. https://bit.ly/2SfwBai. [Google Scholar]
- Peng D.-Y., Robinson D.B. (1976) A new two-constant equation of state, Indus. Eng. Chem. Fundamental 15, 1, 59–64. https://doi.org/10.1021/i160057a011. [Google Scholar]
- Zhang Y., Que H., Chen C.-C. (2011) Thermodynamic modeling for CO2 absorption in aqueous MEA solution with electrolyte NRTL model, Fluid Phase Equilibria 311, 67–75. https://doi.org/10.1016/j.fluid.2011.08.025. [Google Scholar]
- Øi L.E. (2010) CO2 removal by absorption: challenges in modelling, Mathematical and Computer Modelling of Dynamical Systems 16, 6, 511–533. https://doi.org/10.1080/13873954.2010.491676. [Google Scholar]
- Øi L.E. (2007) Aspen HYSYS simulation of CO2 removal by amine absorption from a gas based power plant, in: P. Bunus, D. Fritzson, C. Führer (eds.), The 48th Scandinavian Conference on Simulation and Modeling (SIMS2007) Conference, Göteborg, October 30–31st 2007, Linköping University, Lund University, Linköping University Electronic Press, Linköoping, Sweden. [Google Scholar]
- Mckay M.D., Beckman R.J., Conover W.J. (2000) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics 42, 1, 55–61. https://doi.org/10.1080/00401706.1979.10489755. [CrossRef] [Google Scholar]
- Morris M.D., Mitchell T.J. (1995) Exploratory designs for computational experiments, J. Stat. Plan. Infer. 43, 3, 381–402. https://doi.org/10.1016/0378-3758(94)00035-T. [Google Scholar]
- Paulson C., Ragkousis G. (2015) pykriging: A python kriging toolkit, https://doi.org/10.5281/zenodo.21389. [Google Scholar]
- Ibrahim M., Al-Sobhi S., Mukherjee R., AlNouss A. (2019) Impact of sampling technique on the performance of surrogate models generated with artificial neural network (ANN): A case study for a natural gas stabilization unit, Energies 12, 10, 1–2. https://doi.org/10.3390/en12101906. [CrossRef] [Google Scholar]
- Loeppky J.L., Sacks J., Welch W.J. (2009) Choosing the sample size of a computer experiment: A practical guide, Technometrics 51, 4, 366–376. https://doi.org/10.1198/TECH.2009.08040. [Google Scholar]
- Afzal A., Kim K.-Y., Seo J.W. (2017) Effects of Latin hypercube sampling on surrogate modeling and optimization, Int. J. Fluid Mach. Syst. 10, 240–253. https://doi.org/10.5293/IJFMS.2017.10.3.240. [Google Scholar]
- Andreasen A. (2020) Applied process simulation-driven oil and gas separation plant optimization using surrogate modeling and evolutionary algorithms, ChemEng. 4, 1–2. https://doi.org/10.3390/chemengineering4010011. [Google Scholar]
- AspenTech (2017) Aspen HYSYS customization, Ver. 10. Aspen Technology Inc. [Google Scholar]
- Aspelund A., Gundersen T., Myklebust J., Nowak M.P., Tomasgard A. (2010) An optimization-simulation model for a simple lng process, Comput. Chem. Eng. 34, 10, 1606–1617. https://doi.org/10.1016/j.compchemeng.2009.10.018. [Google Scholar]
- Caballero J.A., Grossmann I.E. (2008) An algorithm for the use of surrogate models in modular flowsheet optimization, AIChE J. 540, 10, 2633–2650. https://doi.org/10.1002/aic.11579. [Google Scholar]
- Olsen E., Hooghoudt J.-O., Maschietti M., Andreasen A. (2021) Optimization of an oil and gas separation plant for different reservoir fluids using an evolutionary algorithm, Energy Fuels 35, 5392–5406. https://doi.org/10.1021/acs.energyfuels.0c04284. [Google Scholar]
- Kim I.H., Dan S., Kim H., Rim H.R., Lee J.M., Yoon E.S. (2014) Simulation-based optimization of multistage separation process in offshore oil and gas production facilities, Indus. Eng, Chem. Res. 53, 21, 8810–8820. https://doi.org/10.1021/ie500403a. [Google Scholar]
- Krige D. (1951) A statitical approach to some mine valuation and allied problems on the Witwatersrand, Master Thesis, University of the Witwatersrand. [Google Scholar]
- Matheron G. (1963) Principles of geostatistics, Economic Geology 58, 8, 1246–1266. https://doi.org/10.2113/gsecongeo.58.8.1246. [Google Scholar]
- Jones D.R. (2001) A taxonomy of global optimization methods based on response surfaces, J Global Optim. 21, 4, 345–383. https://doi.org/10.1023/A:1012771025575. [Google Scholar]
- Ragkousis G.E., Curzen N., Bressloff N.W. (2016) Multi-objective optimisation of stent dilation strategy in a patient-specific coronary artery via computational and surrogate modelling, J Biomechanics 49, 2, 205–215. https://doi.org/10.1016/j.jbiomech.2015.12.013. [Google Scholar]
- Paulson C. (2017) The rapid development of bespoke sensorcraft: a proposed design loop for small unmanned aircraft, PhD thesis, Faculty of Engineering and the Environment Computational Engineering and Design Group, University of Southampton. [Google Scholar]
- Davis E., Ierapetritou M. (2007) A kriging method for the solution of nonlinear programs with black-box functions, AIChE J 53, 8, 2001–2012. https://doi.org/10.1002/aic.11228. [Google Scholar]
- Quirante N., Javaloyes J., Ruiz-Femenia R., Caballero J.A. (2015) Optimization of chemical processes using surrogate models based on a Kriging interpolation, in: K.V. Gernaey, J.K. Huusom, R. Ganii (eds.), 12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering, volume 37 of Computer Aided Chemical Engineering, Elsevier, NY, pp. 179–184. https://doi.org/10.1016/B978-0-444-63578-5.50025-6. [Google Scholar]
- Andreasen A., Rønn Rasmussen K., Mandø M. (2018) Plant wide oil and gas separation plant optimisation using response surface methodology, IFAC-PapersOnLine 51, 8, 178–184. https://doi.org/10.1016/j.ifacol.2018.06.374 [Google Scholar]
- Storn R., Price K. (1997) Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim. 11, 341–359. https://doi.org/10.1023/A:1008202821328. [Google Scholar]
- Wormington M., Panaccione C., Matney K.M., Keith Bowen D. (1999) Characterization of structures from x-ray scattering data using genetic algorithms, Philos. Trans. A Math. Phys. Eng. Sci. 357, 1761, 2827–2848. https://doi.org/10.1098/rsta.1999.0469. [Google Scholar]
- Lampinen J. (2002) A constraint handling approach for the differential evolution algorithm, Proceedings of the 2002 Congress on Evolutionary Computation CEC’02 (Cat. No.02TH8600) 2, 1468–1473. https://doi.org/10.1109/CEC.2002.1004459. [Google Scholar]
- Virtanen P., Gommers R., Oliphant T.E., Haberland M., Reddy T., Cournapeau D., Burovski E., Peterson P., Weckesser W., Bright J., van der Walt S.J., Brett M., Wilson J., Millman K.J., Mayorov N., Nelson A.R.J., Jones E., Kern R., Larson E., Carey C.J., Polat I., Feng Y., Moore E.W., Vander Plas J., Laxalde D., Perktold J., Cimrman R., Henriksen I., Quintero E.A., Harris C.R., Archibald A.M., Ribeiro A.H., Pedregosa F., van Mulbregt P. (2020) SciPy 1.0: Fundamental algorithms for scientific computing in python, Nat. Methods 17, 261–272. https://doi.org/10.1038/s41592-019-0686-2. [PubMed] [Google Scholar]
- Kraft D. (1994) Algorithm 733: TOMP–Fortran modules for optimal control calculations, ACM Trans. Math. Softw. 20, 3, 262–281. [Google Scholar]
- Byrd R.H., Hribar M.E., Nocedal J. (1999) An interior point algorithm for large-scale nonlinear programming, SIAM J. Optim. 9, 4, 877–900. https://doi.org/10.1137/S1052623497325107. [CrossRef] [MathSciNet] [Google Scholar]
- Powell M.J.D. (1994) A direct search optimization method that models the objective and constraint functions by linear interpolation, pp. 51–67. https://doi.org/10.1007/978-94-015-8330-5_4. [Google Scholar]
- Powell M.J.D. (1998) Direct search algorithms for optimization calculations, Acta Numer. 7, 287–336. https://doi.org/10.1017/S0962492900002841. [CrossRef] [Google Scholar]
- Harris C.R., Millman K.J., van der Walt S.J., Gommers R., Virtanen P., Cournapeau D., Wieser E., Taylor J., Berg S., Smith N.J., Kern R., Picus M., Hoyer S., van Kerkwijk M.H., Brett M., Haldane A., Del Río J.F., Wiebe M., Peterson P., Gérard-Marchant P., Sheppard K., Reddy T., Weckesser W., Abbasi H., Gohlke C., Oliphant T.E. (2020) Array programming with NumPy, Nature 58507825, 357–362. https://doi.org/10.1038/s41586-020-2649-2. [Google Scholar]
- McKinney Wes. (2010) Data structures for statistical computing in python, in: S. van der Walt, J. Millman (eds.), Proceedings of the 9th Python in Science Conference, pp. 51–56. [Google Scholar]
- Seabold S., Perktold J. (2010) Statsmodels: Econometric and statistical modeling with python, in: 9th Python in Science Conference, 2010. [Google Scholar]
- Deutsch J.L., Deutsch C.V. (2012) Latin hypercube sampling with multidimensional uniformity, J. Stat. Plan. Infer. 142, 3, 763–772. https://doi.org/10.1016/j.jspi.2011.09.016. [Google Scholar]
- Hammond M. (2020) pywin32. https://github.com/mhammond/pywin32. [Google Scholar]
- Hunter J.D., Matplotlib A. (2007) 2D graphics environment, Comput. Sci. Eng. 9, 3, 90–95. https://doi.org/10.1109/MCSE.2007.55. [Google Scholar]
- Agbonghae E.O., Hughes K.J., Ingham D.B., Ma L., Pourkashanian M. (2014) Optimal process design of commercial-scale amine-based CO2 capture plants, Indust. Eng. Chem. Res. 53, 38, 14815–14829. https://doi.org/10.1021/ie5023767. [Google Scholar]
- Zhang Y., Chen C.-C. (2013) Modeling CO2 absorption and desorption by aqueous monoethanolamine solution with aspen rate-based model, Energy Proc. 37, 1584–1596. https://doi.org/10.1016/j.egypro.2013.06.034. [Google Scholar]
- Mangalapally H.P., Hasse H. (2011) Pilot plant study of post-combustion carbon dioxide capture by reactive absorption: Methodology, comparison of different structured packings, and comprehensive results for monoethanolamine, Chem. Eng. Res. Des. 89, 8, 1216–1228. https://doi.org/10.1016/j.cherd.2011.01.013. [Google Scholar]
- Warudkar S.S., Cox K.R., Wong M.S., Hirasaki G.J. (2013) Influence of stripper operating parameters on the performance of amine absorption systems for post-combustion carbon capture: Part I. high pressure strippers, Int. J. Greenhouse Gas Cont. 16, 342–350. https://doi.org/10.1016/j.ijggc.2013.01.050. [Google Scholar]
- Knudsen J.N., Jensen J.N., Vilhelmsen P.-J., Biede O. (2009) Experience with CO2 capture from coal flue gas in pilot-scale: Testing of different amine solvents, Energy Procedia 1, 1, 783–790. https://doi.org/10.1016/j.egypro.2009.01.104. [CrossRef] [Google Scholar]
- Mangalapally H.P., Notz R., Hoch S., Asprion N., Sieder G., Garcia H., Hasse H. (2009) Pilot plant experimental studies of post combustion CO2 capture by reactive absorption with mea and new solvents, Energy Proc. 1, 1, 963–970. https://doi.org/10.1016/j.egypro.2009.01.128. [Google Scholar]
- GPSA (2012) GPSA Engineering Data Book (SI version), 13th edn., Gas Processors Suppliers Association, Tulsa, Oklahoma. [Google Scholar]
Open Access
Issue |
Oil Gas Sci. Technol. – Rev. IFP Energies nouvelles
Volume 76, 2021
|
|
---|---|---|
Article Number | 55 | |
Number of page(s) | 11 | |
DOI | https://doi.org/10.2516/ogst/2021036 | |
Published online | 30 August 2021 |
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