Abstract
Naphthalene is a major component of tar whose formation is a technical barrier in gasification systems. It can be used for hydrogen production via the steam reforming process. In this study, artificial neural network was used to model the steam reforming of naphthalene. The dataset will be developed by non-stoichiometric computation of the minimisation of Gibbs free energy method. The effect of temperature and steam-to-oil ratio (STOR) on the selectivity of hydrogen gas, carbon dioxide, carbon monoxide and methane in the product stream was investigated. Temperature and STOR increase favoured H2 production in the steam reforming process. At the threshold of temperature > 600° C and STOR > 4 kg/kg, optimal H2 selectivity is achieved. The coefficient of determination and root mean squared error for the model for all regimes (training, validation and testing) and all synthesis gas constituents was > 0.99 and < 1 mol%, respectively. Parity plots revealed that the predictions were accurate at both high and low levels of prediction. Paired samples correlation revealed a strong positive correlation between model predictions and the experimental values. The current approach is unfavourable in scenarios where quick predictions and preliminary estimations are required other investigations in product and process development.
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Abbreviations
- a li :
-
Number of gram atoms of element l in 1 mol of species i
- b l :
-
Total number of gram atoms of element l in the reaction mixture
- G :
-
Gibbs free energy (kJ/mol)
- \(\Delta H_{r}^{0}\) :
-
Change in enthalpy of reaction
- \(\Delta G_{i}^{0}\) :
-
Standard Gibbs free energy of the formation of species i (kJ/mol)
- K :
-
Total number of chemical species in the reaction mixture
- m :
-
Total number of atomic elements
- n :
-
Total number of moles of all species in the gas mixture (mol)
- n i :
-
Number of moles of species i (mol)
- P :
-
Pressure (bar and atm)
- R :
-
Gas constant (J/mol K)
- R 2 :
-
Coefficient of determination
- T :
-
Temperature (°C or K)
- μ i :
-
Chemical potential of species i (kJ/mol)
- y i :
-
Mole fraction of species i
- ANN:
-
Artificial neural networks
- ASPEN:
-
Advanced systems for process engineering
- L–M:
-
Levenberg–Marquardt
- MLP:
-
Multilayer perceptron
- MSE:
-
Mean square error
- nftool:
-
Neural fitting tool
- RMSE:
-
Root mean square error
- SPSS:
-
Statistical package for the social sciences
- SRK:
-
Soave–Redlich–Kwong equation of state
- STOR:
-
Steam-to-oil mass ratio (kg/kg)
References
Adeniyi AG, Ighalo JO (2018) Study of process factor effects and interactions in synthesis gas production via a simulated model for glycerol steam reforming. Chem Prod Process Model 14(1):1–11. https://doi.org/10.1515/cppm-2018-0034
Adeniyi AG, Ighalo JO (2019b) Hydrogen production by the steam reforming of waste lubricating oil. Indian Chem Eng 61(4):403–414. https://doi.org/10.1080/00194506.2019.1605847
Adeniyi AG, Ighalo JO, Otoikhian KS (2019a) Steam reforming of acetic acid: response surface modelling and study of factor interactions. Chem Prod Process Model 14(4):1–14. https://doi.org/10.1515/cppm-2019-0066
Adeniyi AG, Ighalo JO, Aderibigbe FA (2019b) Modelling of integrated processes for the pyrolysis and steam reforming of rice husk (Oryza sativa). SN Appl Sci 1(8):841–851. https://doi.org/10.1007/s42452-019-0877-6
Adeniyi AG, Ighalo JO, Odetoye TE (2019c) Response surface modelling and optimisation of biodiesel production from Avocado plant (Persea americana) oil. Indian Chem Eng 62(3):243–250. https://doi.org/10.1080/00194506.2019.1658546
Adeniyi AG, Ighalo JO (2019a) A review of steam reforming of glycerol. Chem Pap 73(11):2619–2635. https://doi.org/10.1007/s11696-019-00840-8
Adeniyi AG, Ighalo JO, Marques G (2020) Utilisation of machine learning algorithms for the prediction of syngas composition from biomass bio-oil steam reforming. Int J Sustain Energy. https://doi.org/10.1080/14786451.2020.1803862
Adhikari S, Fernando S, Haryanto A (2007a) A comparative thermodynamic and experimental analysis on hydrogen production by steam reforming of glycerin. Energy Fuels 21(4):2306–2310. https://doi.org/10.1021/ef070035l
Adhikari S, Fernando S, Gwaltney SR, To SF, Bricka RM, Steele PH, Haryanto A (2007b) A thermodynamic analysis of hydrogen production by steam reforming of glycerol. Int J Hydrogen Energy 32(14):2875–2880. https://doi.org/10.1016/j.ijhydene.2007.03.023
Bampenrat A, Meeyoo V, Kitiyanan B, Rangsunvigit P, Rirksomboon T (2010) Naphthalene steam reforming over Mn-doped CeO2–ZrO2 supported nickel catalysts. Appl Catal A 373(1–2):154–159. https://doi.org/10.1016/j.apcata.2009.11.008
Betiku E, Okunsolawo SS, Ajala SO, Odedele OS (2015) Performance evaluation of artificial neural network coupled with generic algorithm and response surface methodology in modeling and optimization of biodiesel production process parameters from shea tree (Vitellaria paradoxa) nut butter. Renew Energy 76:408–417. https://doi.org/10.1016/j.renene.2014.11.049
Czernik S, French R, Feik C, Chornet E (2002) Hydrogen by catalytic steam reforming of liquid byproducts from biomass thermoconversion processes. Ind Eng Chem Res 41(17):4209–4215. https://doi.org/10.1021/ie020107q
Da Silva AL, Müller IL (2011) Hydrogen production by sorption enhanced steam reforming of oxygenated hydrocarbons (ethanol, glycerol, n-butanol and methanol): thermodynamic modelling. Int J Hydrog Energy 36(3):2057–2075. https://doi.org/10.1016/j.ijhydene.2010.11.051
Dos Santos RG, Alencar AC (2020) Biomass-derived syngas production via gasification process and its catalytic conversion into fuels by Fischer Tropsch synthesis: A review. Int J Hydrogen Energy 45(36):18114–18132. https://doi.org/10.1016/j.ijhydene.2019.07.133
Dou X, Veksha A, Chan WP, Oh W-D, Liang YN, Teoh F, Mohamed DKB, Giannis A, Lisak G, Lim T-T (2019) Poisoning effects of H2S and HCl on the naphthalene steam reforming and water-gas shift activities of Ni and Fe catalysts. Fuel 241:1008–1018. https://doi.org/10.1016/j.fuel.2018.12.119
Faizollahzadeh Ardabili S, Najafi B, Shamshirband S, Minaei Bidgoli B, Deo RC, Chau K-W (2018) Computational intelligence approach for modeling hydrogen production: a review. Engineering Applications of Computational Fluid Mechanics 12(1):438–458. https://doi.org/10.1080/19942060.2018.1452296
Furusawa T, Tsutsumi A (2005a) Comparison of Co/MgO and Ni/MgO catalysts for the steam reforming of naphthalene as a model compound of tar derived from biomass gasification. Appl Catal A 278(2):207–212. https://doi.org/10.1016/j.apcata.2004.09.035
Furusawa T, Tsutsumi A (2005b) Development of cobalt catalysts for the steam reforming of naphthalene as a model compound of tar derived from biomass gasification. Appl Catal A 278(2):195–205. https://doi.org/10.1016/j.apcata.2004.09.034
Furusawa T, Saito K, Kori Y, Miura Y, Sato M, Suzuki N (2013) Steam reforming of naphthalene/benzene with various types of Pt-and Ni-based catalysts for hydrogen production. Fuel 103:111–121. https://doi.org/10.1016/j.fuel.2011.09.026
Gallegos-Suárez E, Guerrero-Ruiz A, Fernández-García M, Rodríguez-Ramos I, Kubacka A (2015) Efficient and stable Ni–Ce glycerol reforming catalysts: chemical imaging using X-ray electron and scanning transmission microscopy. Appl Catal B 165:139–148. https://doi.org/10.1016/j.apcatb.2014.10.007
Ghasemzadeh K, Ahmadnejad F, Aghaeinejad-Meybodi A, Basile A (2018) Hydrogen production by a PdAg membrane reactor during glycerol steam reforming: ANN modeling study. Int J Hydrogen Energy 43(15):7722–7730. https://doi.org/10.1016/j.ijhydene.2017.09.120
Ghosh A, Das P, Sinha K (2015) Modeling of biosorption of Cu (II) by alkali-modified spent tea leaves using response surface methodology (RSM) and artificial neural network (ANN). Appl Water Sci 5(2):191–199. https://doi.org/10.1007/s13201-014-0180-z
Goicoechea S, Ehrich H, Arias PL, Kockmann N (2015) Thermodynamic analysis of acetic acid steam reforming for hydrogen production. J Power Sources 279:312–322. https://doi.org/10.1016/j.jpowsour.2015.01.012
Heidari AA, Faris H, Aljarah I, Mirjalili S (2019) An efficient hybrid multilayer perceptron neural network with grasshopper optimization. Soft Comput 23(17):7941–7958. https://doi.org/10.1007/s00500-018-3424-2
Ighalo JO, Adeniyi AG (2020) Modelling of thermochemical energy recovery processes for switchgrass (Panicum virgatum). Indian Chem Eng. https://doi.org/10.1080/00194506.2020.1711535
Ighalo JO, Adeniyi AG (2019) Factor effects and interactions in steam reforming of biomass bio-oil. Chem Pap 74(5):1459–1470. https://doi.org/10.1007/s11696-019-00996-3
Ighalo JO, Adeniyi AG, Marques G (2020) Application of artificial neural networks in predicting biomass higher heating value: an early appraisal. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. https://doi.org/10.1080/15567036.2020.1809567
Ighalo JO, Adeniyi AG, Marques G (2021) Internet of things for water quality monitoring and assessment: a comprehensive review. In: Hassanien RBA, Darwish A (eds) Artificial intelligence for sustainable development: theory, practice and future applications. Springer, p 245–259. doi:https://doi.org/10.1007/978-3-030-51920-9_13.
Igwegbe CA, Mohmmadi L, Ahmadi S, Rahdar A, Khadkhodaiy D, Dehghani R, Rahdar S (2019) Modeling of adsorption of Methylene blue dye on Ho-CaWO4 nanoparticles using response surface methodology (RSM) and artificial neural network (ANN) techniques. MethodsX 6:1779–1797. https://doi.org/10.1016/j.mex.2019.07.016
Magnussen S, McRoberts RE, Tomppo EO (2009) Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories. Remote Sens Environ 113(3):476–488. https://doi.org/10.1016/j.rse.2008.04.018
Mei D, Wang Y, Liu S, Alliati M, Yang H, Tu X (2019) Plasma reforming of biomass gasification tars using mixed naphthalene and toluene as model compounds. Energy Convers Manage 195:409–419. https://doi.org/10.1016/j.enconman.2019.05.002
Menard S (2000) Coefficients of determination for multiple logistic regression analysis. Am Stat 54(1):17–24. https://doi.org/10.1080/00031305.2000.10474502
Meng J, Zhao Z, Wang X, Zheng A, Zhang D, Huang Z, Zhao K, Wei G, Li H (2018) Comparative study on phenol and naphthalene steam reforming over Ni–Fe alloy catalysts supported on olivine synthesized by different methods. Energy Convers Manage 168:60–73. https://doi.org/10.1016/j.enconman.2018.04.112
Meng J, Zhao Z, Wang X, Chen J, Zheng A, Huang Z, Wei G, Li H (2019) Steam reforming and carbon deposition evaluation of phenol and naphthalene used as tar model compounds over Ni and Fe olivine-supported catalysts. J Energy Inst 92(6):1765–1778. https://doi.org/10.1016/j.joei.2018.12.004
Nikoo MK, Amin NAS (2011) Thermodynamic analysis of carbon dioxide reforming of methane in view of solid carbon formation. Fuel Process Technol 92(3):678–691. https://doi.org/10.1016/j.fuproc.2010.11.027
Noichi H, Uddin A, Sasaoka E (2010) Steam reforming of naphthalene as model biomass tar over iron–aluminum and iron–zirconium oxide catalyst catalysts. Fuel Process Technol 91(11):1609–1616. https://doi.org/10.1016/j.fuproc.2010.06.009
Özşahin Ş (2012) The use of an artificial neural network for modeling the moisture absorption and thickness swelling of oriented strand board. BioResources 7(1):1053–1067
Parsland C, Ho PH, Benito P, Larsson A-C, Fornasari G, Brandin J (2019) Ba-Ni-Hexaaluminate as a new catalyst in the steam reforming of 1-methyl naphthalene and methane. Catal Lett. https://doi.org/10.1007/s10562-019-03042-9
PubChem. Naphthalene. 2020 [cited 2020 November 14th]. Available from: https://pubchem.ncbi.nlm.nih.gov/compound/Naphthalene
Qian K, Kumar A (2017) Catalytic reforming of toluene and naphthalene (model tar) by char supported nickel catalyst. Fuel 187:128–136. https://doi.org/10.1016/j.fuel.2016.09.043
Senseni AZ, Meshkani F, Fattahi SMS, Rezaei M (2017) A theoretical and experimental study of glycerol steam reforming over Rh/MgAl2O4 catalysts. Energy Convers Manage 154:127–137. https://doi.org/10.1016/j.enconman.2017.10.033
Speidel M, Fischer H (2016) Steam reforming of tars at low temperature and elevated pressure for model tar component naphthalene. Int J Hydrogen Energy 41(30):12920–12928. https://doi.org/10.1016/j.ijhydene.2016.05.023
Spragg J, Mahmud T, Dupont V (2018) Hydrogen production from bio-oil: a thermodynamic analysis of sorption-enhanced chemical loo** steam reforming. Int J Hydrogen Energy 43(49):22032–22045. https://doi.org/10.1016/j.ijhydene.2018.10.068
Umegaki T, Masuda A, Omata K, Yamada M (2008) Development of a high performance Cu-based ternary oxide catalyst for oxidative steam reforming of methanol using an artificial neural network. Appl Catal A 351(2):210–216. https://doi.org/10.1016/j.apcata.2008.09.019
Veksha A, Giannis A, Oh W-D, Chang VW-C, Lisak G, Lim T-T (2018) Catalytic activities and resistance to HCl poisoning of Ni-based catalysts during steam reforming of naphthalene. Appl Catal A 557:25–38. https://doi.org/10.1016/j.apcata.2018.03.005
Wang T, Chang J, Wu C, Fu Y, Chen Y (2005) The steam reforming of naphthalene over a nickel–dolomite cracking catalyst. Biomass Bioenerg 28(5):508–514. https://doi.org/10.1016/j.biombioe.2004.11.006
Wang X, Li S, Wang H, Liu B, Ma X (2008) Thermodynamic analysis of glycerin steam reforming. Energy Fuels 22(6):4285–4291. https://doi.org/10.1021/ef800487r
Wang Y, Yang H, Tu X (2019) Plasma reforming of naphthalene as a tar model compound of biomass gasification. Energy Convers Manage 187:593–604. https://doi.org/10.1016/j.enconman.2019.02.075
**e H, Yu Q, Wang K, Shi X, Li X (2014) Thermodynamic analysis of hydrogen production from model compounds of bio-oil through steam reforming. Environ Prog Sustain Energy 33(3):1008–1016. https://doi.org/10.1002/ep.11846
Zamaniyan A, Joda F, Behroozsarand A, Ebrahimi H (2013) Application of artificial neural networks (ANN) for modeling of industrial hydrogen plant. Int J Hydrogen Energy 38(15):6289–6297. https://doi.org/10.1016/j.ijhydene.2013.02.136
Zhang H, Zhu F, Li X, Xu R, Li L, Yan J, Tu X (2019) Steam reforming of toluene and naphthalene as tar surrogate in a gliding arc discharge reactor. J Hazard Mater 369:244–253. https://doi.org/10.1016/j.jhazmat.2019.01.085
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Igwegbe, C.A., Adeniyi, A.G. & Ighalo, J.O. ANN modelling of the steam reforming of naphthalene based on non-stoichiometric thermodynamic analysis. Chem. Pap. 75, 3363–3372 (2021). https://doi.org/10.1007/s11696-021-01566-2
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DOI: https://doi.org/10.1007/s11696-021-01566-2