Integrated neural network and Aspen Plus model for entrained flow gasification kinetics investigation

Original scientific paper

Authors

  • Dario Balaban University of Novi Sad, Faculty of Technology Novi Sad, Bulevar cara Lazara 1, 21000 Novi Sad, Serbia and University of East Sarajevo, Faculty of Technology Zvornik, Karakaj 34A, 75400 Zvornik, Republic of Srpska, Bosnia and Herzegovina https://orcid.org/0000-0001-6235-3272
  • Jelena Lubura University of Novi Sad, Faculty of Technology Novi Sad, Bulevar cara Lazara 1, 21000 Novi Sad, Serbia https://orcid.org/0000-0003-1054-1698
  • Predrag Kojić University of Novi Sad, Faculty of Technology Novi Sad, Bulevar cara Lazara 1, 21000 Novi Sad, Serbia https://orcid.org/0000-0002-1842-3402

DOI:

https://doi.org/10.2298/CICEQ240430032B

Keywords:

Syngas, optimization, simulation, machine learning

Abstract

Entrained flow gasification is a well-established technology, however, the main obstacle in process design is complex gasification mechanism, since numerous phenomena at extreme process conditions take place simultaneously. This study is focused on integrated thermodynamic and artificial neural network approach (ANN) for entrained flow gasification kinetics investigation. Data on 102 feedstock materials composition was used in AspenPlus gasification simulation, where sensitivity analysis was performed for different equivalence ratio (0.1-0.7) and gasification temperature (1200-1500°C) values. For analyzed materials, optimal equivalence ratio range exist (usually 0.3-0.4), maximizing gasification efficiency. Obtained results were used in ANN development for each output variable (syngas composition, efficiency, heating value and carbon conversion). Matlab algorithm was used for determination of optimal number of neurons (1-20 range) in each ANN. High R2 values (>0.99) for all models suggested good agreement between simulated and predicted values. Genetic algorithm-based optimization studies for maximization of hydrogen content and cold gas efficiency result in mean ER value of 0.35 and 0.41, respectively, at temperature of 1200°C. Yoon interpretation method was used for quantifying relative impacts of each input variable on syngas content and gasification efficiency. Proposed approach represents a powerful tool which can facilitate investigation of entrained flow gasification and process design.

References

H. Ritchie, P. Rosado, M. Roser, Energy Production and Consumption, https://ourworldindata.org/energy-production-consumption [accessed 7 May 2024].

IEA, World Energy Outlook 2022, https://www.iea.org/reports/world-energy-outlook-2022 [accessed 7 May 2024].

M.N. Dudin, E.E. Frolova, O.V. Protopopova, O. Mamedov, S.V. Odintsov, 6 (2019) 1704. https://doi.org/10.9770/jesi.2019.6.4(11).

C.-F. Schleussner, J. Rogelj, M. Schaeffer, T. Lissner, R. Licker, E.M. Fischer, R. Knutti, A. Levermann, K. Frieler, W. Hare, Nat Clim Chang 6 (2016) 827–835. https://doi.org/10.1038/nclimate3096.

L. Mundaca, L. Neij, A. Markandya, P. Hennicke, J. Yan, Appl Energy 179 (2016) 1283–1292. https://doi.org/10.1016/j.apenergy.2016.08.086.

H.B. Goyal, D. Seal, R.C. Saxena, 12 (2008) 504–517. https://doi.org/10.1016/j.rser.2006.07.014.

V. Kirubakaran, V. Sivaramakrishnan, R. Nalini, T. Sekar, M. Premalatha, P. Subramanian, 13 (2009) 179–186. https://doi.org/10.1016/j.rser.2007.07.001.

V. Tola, A. Pettinau, Appl Energy 113 (2014) 1461–1474. https://doi.org/10.1016/j.apenergy.2013.09.007.

J. Parraga, K.R. Khalilpour, A. Vassallo, 92 (2018) 219–234. https://doi.org/10.1016/j.rser.2018.04.055.

R. Rauch, J. Hrbek, H. Hofbauer, Wiley Interdiscip Rev Energy Environ 3 (2014) 343–362. https://doi.org/10.1002/9781118957844.ch7.

A. Molino, S. Chianese, D. Musmarra, 25 (2016) 10–25. https://doi.org/10.1016/j.jechem.2015.11.005.

M. Lapuerta, J.J. Hernández, A. Pazo, J. López, 89 (2008) 828–837. https://doi.org/10.1016/j.fuproc.2008.02.001.

U. Arena, 32 (2012) 625–639. https://doi.org/10.1016/j.wasman.2011.09.025.

J.J. Hernández, G. Aranda-Almansa, A. Bula, 91 (2010) 681–692. https://doi.org/10.1016/j.fuproc.2010.01.018.

M. Puig-Arnavat, J.C. Bruno, A. Coronas, 14 (2010) 2841–2851. https://doi.org/10.1016/j.rser.2010.07.030.

E. Henrich, F. Weirich, Environ Eng Sci 21 (2004) 53–64. https://doi.org/10.1089/109287504322746758.

T. Xu, Y. Wu, S. Bhattacharya, Int J Min Sci Technol 31 (2021) 473–481. https://doi.org/10.1016/j.ijmst.2021.03.001.

F. Qian, X. Kong, H. Cheng, W. Du, W. Zhong, Ind Eng Chem Res 52 (2013) 1819–1828. https://doi.org/10.1021/ie301630x.

M. Vascellari, R. Arora, C. Hasse, 118 (2014) 369–384. https://doi.org/10.1016/j.fuel.2013.11.004.

G.-S. Liu, H.R. Rezaei, J.A. Lucas, D.J. Harris, T.F. Wall, 79 (2000) 1767–1779. https://doi.org/10.1016/S0016-2361(00)00037-5.

A. Tremel, H. Spliethoff, 107 (2013) 170–182. https://doi.org/10.1016/j.fuel.2013.01.062.

M. Vascellari, D.G. Roberts, D.J. Harris, C. Hasse, 152 (2015) 58–73. https://doi.org/10.1016/j.fuel.2015.01.038.

I. Adeyemi, I. Janajreh, Renew Energy 82 (2015) 77–84. https://doi.org/10.1016/j.renene.2014.10.073.

M.A. Kibria, P. Sripada, S. Bhattacharya, 196 (2020) 117073. https://doi.org/10.1016/j.energy.2020.117073.

M. Al-Zareer, I. Dincer, M.A. Rosen, 115 (2016) 1–18. https://doi.org/10.1016/j.cherd.2016.09.009.

M. Pérez-Fortes, A.D. Bojarski, E. Velo, J.M. Nougués, L. Puigjaner, 34 (2009) 1721–1732. https://doi.org/10.1016/j.energy.2009.05.012.

Y. Lu, Z. Li, M. Zhang, C. Huang, Z. Chen, Energy Convers Manag 245 (2021) 114627. https://doi.org/10.1016/j.enconman.2021.114627.

Z. Dai, X. Gong, X. Guo, H. Liu, F. Wang, Z. Yu, 87 (2008) 2304–2313. https://doi.org/10.1016/j.fuel.2007.12.005.

D. Barba, M. Prisciandaro, A. Salladini, G.M. Di Celso, 90 (2011) 1402–1407. https://doi.org/10.1016/j.fuel.2010.12.022.

Ö.Ç. Mutlu, T. Zeng, Chem Eng Technol 43 (2020) 1674–1689. https://doi.org/10.1002/ceat.202000068.

M. Ozonoh, B.O. Oboirien, A. Higginson, M.O. Daramola, Renew Energy 145 (2020) 2253–2270. https://doi.org/10.1016/j.renene.2019.07.136.

H.O. Kargbo, J. Zhang, A.N. Phan, Appl Energy 302 (2021) 117567. https://doi.org/10.1016/j.apenergy.2021.117567.

M. Puig-Arnavat, J.A. Hernández, J.C. Bruno, A. Coronas, Biomass Bioenergy 49 (2013) 279–289. https://doi.org/10.1016/j.biombioe.2012.12.012.

D. Baruah, D.C. Baruah, M.K. Hazarika, Biomass Bioenergy 98 (2017) 264–271. https://doi.org/10.1016/j.biombioe.2017.01.029.

R. Mikulandrić, D. Lončar, D. Böhning, R. Böhme, M. Beckmann, Energy Convers Manag 87 (2014) 1210–1223. https://doi.org/10.1016/j.enconman.2014.03.036.

S. Ascher, W. Sloan, I. Watson, S. You, Appl Energy 320 (2022) 119289. https://doi.org/10.1016/j.apenergy.2022.119289.

F. Kartal, U. Özveren, 209 (2020) 118457. https://doi.org/10.1016/j.energy.2020.118457.

S. Safarian, S.M.E. Saryazdi, R. Unnthorsson, C. Richter, 213 (2020) 118800. https://doi.org/10.1016/j.energy.2020.118800.

J. Chen, J. Liang, Z. Xu, E. Jiaqiang, Energy Convers Manag 226 (2020) 113497. https://doi.org/10.1016/j.enconman.2020.113497.

H. Shahbeik, W. Peng, H.K.S. Panahi, M. Dehhaghi, G.J. Guillemin, A. Fallahi, H. Amiri, M. Rehan, D. Raikwar, H. Latine, 167 (2022) 112833. https://doi.org/10.1016/j.rser.2022.112833.

M. Shahbaz, T. Al-Ansari, M. Inayat, S.A. Sulaiman, P. Parthasarathy, G. McKay, 134 (2020) 110382. https://doi.org/10.1016/j.rser.2020.110382.

A. Ozyuguran, A. Akturk, S. Yaman, 214 (2018) 640–646. https://doi.org/10.1016/j.fuel.2017.10.082.

A. Gómez-Barea, P. Ollero, B. Leckner, 103 (2013) 42–52. https://doi.org/10.1016/j.fuel.2011.04.042.

Y. Yoon, G. Swales Jr, T.M. Margavio, 44 (1993) 51–60. https://doi.org/10.1057/jors.1993.6.

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26.09.2024

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Copyright (c) 2023 Dario Balaban, Jelena Lubura, Predrag Kojić

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How to Cite

Integrated neural network and Aspen Plus model for entrained flow gasification kinetics investigation: Original scientific paper. (2024). Chemical Industry & Chemical Engineering Quarterly. https://doi.org/10.2298/CICEQ240430032B

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