INTEGRATED NEURAL NETWORK AND ASPEN PLUS MODEL FOR ENTRAINED FLOW GASIFICATION KINETICS INVESTIGATION Original scientific paper

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Dario Balaban
https://orcid.org/0000-0001-6235-3272
Jelena Lubura
https://orcid.org/0000-0003-1054-1698
Predrag Kojić
https://orcid.org/0000-0002-1842-3402

Abstract

Entrained flow gasification is a well-established technology, however, the main obstacle in process design is the 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 the AspenPlus gasification simulation, where sensitivity analysis was performed for different equivalence ratios (0.1—0.7) and gasification temperature (1200—1500°C) values. For analyzed materials, an optimal equivalence ratio range exists (usually 0.3—0.4), maximizing gasification efficiency. The obtained results were used in ANN development for each output variable (syngas composition, efficiency, heating value, and carbon conversion). Matlab algorithm was used for the determination of the 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 values of 0.35 and 0.41, respectively, at a temperature of 1200 °C. Yoon interpretation method was used for quantifying the relative impacts of each input variable on syngas content and gasification efficiency. The proposed approach represents a powerful tool that can facilitate the investigation of the entrained flow gasification and process design.

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INTEGRATED NEURAL NETWORK AND ASPEN PLUS MODEL FOR ENTRAINED FLOW GASIFICATION KINETICS INVESTIGATION: Original scientific paper. (2025). Chemical Industry & Chemical Engineering Quarterly, 31(3), 239-248. https://doi.org/10.2298/CICEQ240430032B

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References

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

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

[3] 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).

[4] 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.

[5] 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.

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

[7] 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.

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

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

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

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

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

[13] U. Arena, 32 (2012) 625—639. https://doi.org/10.1016/j.wasman.2011.09.025.

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

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

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

[17] 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.

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

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

[20] 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.

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

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

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

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

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

[26] 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.

[27] 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.

[28] 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.

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

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

[31] 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.

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

[33] 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.

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

[35] 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.

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

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

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

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

[40] 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.

[41] 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.

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

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

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

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