Research article

Analyzing temperature distribution in pyrolysis systems using an atomic model

  • Received: 03 August 2023 Revised: 21 September 2023 Accepted: 13 October 2023 Published: 01 November 2023
  • Pyrolysis is a complex energy conversion reaction due to the multiple stages of the process, the interaction of kinetics, mass and heat transfer and thermodynamics. The feedstock, temperature, heating rate, residence time, and reactor design are only a few factors that might impact the final product during the pyrolysis process. This study focuses on the temperature analysis of pyrolysis with sheep manure as feedstock, which includes reactor, pipes and condenser. The examination of the temperature distribution within a pyrolysis system can contribute to the preservation of product quality, the maintenance of heat balance, and the enhancement of energy efficiency. Based on the analysis, the degradation temperature of sheep manure is between 210–500 ℃. Consequently, it is crucial to control the reactor temperature at a desirable temperature that aligns with the degradation temperature of sheep manure. To ensure optimal condensation and maximize bio-oil yield, it is also necessary to control the condenser temperature. This study aims to determine the characteristics of temperature changes in pyrolysis systems using atomic models. The atomic model was built in OpenModelica using the Modelica language. The atomic model was validated with experiment, and it was found that there was a significant difference in reactor temperature. Complex processes occur in the reactor where pyrolysis occurs and various factors can impact the temperature of the reaction. The temperature in the multistage condenser gradually decreases by 1–3 ℃. In the principle of condensation, this temperature drop is considered less than optimal because the cooling fluid in the pyrolysis condensation system is air coolant, which is entirely reliant on ambient temperature. The accuracy of the atomic model is evaluated using error analysis and the mean absolute percentage error (MAPE). A value of 13.6% was calculated using the MAPE. The atomic model can be applied because this value is still within the tolerance range.

    Citation: Ahmad Indra Siswantara, Illa Rizianiza, Tanwir Ahmad Farhan, M. Hilman Gumelar Syafei, Dyas Prawara Mahdi, Candra Damis Widiawaty, Adi Syuriadi. Analyzing temperature distribution in pyrolysis systems using an atomic model[J]. AIMS Energy, 2023, 11(6): 1012-1030. doi: 10.3934/energy.2023048

    Related Papers:

  • Pyrolysis is a complex energy conversion reaction due to the multiple stages of the process, the interaction of kinetics, mass and heat transfer and thermodynamics. The feedstock, temperature, heating rate, residence time, and reactor design are only a few factors that might impact the final product during the pyrolysis process. This study focuses on the temperature analysis of pyrolysis with sheep manure as feedstock, which includes reactor, pipes and condenser. The examination of the temperature distribution within a pyrolysis system can contribute to the preservation of product quality, the maintenance of heat balance, and the enhancement of energy efficiency. Based on the analysis, the degradation temperature of sheep manure is between 210–500 ℃. Consequently, it is crucial to control the reactor temperature at a desirable temperature that aligns with the degradation temperature of sheep manure. To ensure optimal condensation and maximize bio-oil yield, it is also necessary to control the condenser temperature. This study aims to determine the characteristics of temperature changes in pyrolysis systems using atomic models. The atomic model was built in OpenModelica using the Modelica language. The atomic model was validated with experiment, and it was found that there was a significant difference in reactor temperature. Complex processes occur in the reactor where pyrolysis occurs and various factors can impact the temperature of the reaction. The temperature in the multistage condenser gradually decreases by 1–3 ℃. In the principle of condensation, this temperature drop is considered less than optimal because the cooling fluid in the pyrolysis condensation system is air coolant, which is entirely reliant on ambient temperature. The accuracy of the atomic model is evaluated using error analysis and the mean absolute percentage error (MAPE). A value of 13.6% was calculated using the MAPE. The atomic model can be applied because this value is still within the tolerance range.



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    [1] Santos RM, Bakhshoodeh R (2021) Climate change/global warming/climate emergency versus general climate research: Comparative bibliometric trends of publications. Heliyon 7: 1–15. https://doi.org/10.1016/j.heliyon.2021.e08219 doi: 10.1016/j.heliyon.2021.e08219
    [2] Amjith LR, Bavanish B (2022) A review on biomass and wind as renewable energy for sustainable environment. Chemosphere 293: 133579. https://doi.org/10.1016/j.chemosphere.2022.133579 doi: 10.1016/j.chemosphere.2022.133579
    [3] Khoshnevisan B, Duan N, Tsapekos P, et al. (2021) A critical review on livestock manure biorefinery technologies: Sustainability, challenges, and future perspectives. Renewable Sustainable Energy Rev 135: 110033. https://doi.org/10.1016/j.rser.2020.110033 doi: 10.1016/j.rser.2020.110033
    [4] Erdogdu AE, Polat R, Ozbay G (2019) Pyrolysis of goat manure to produce bio-oil. Eng Sci Technol Int J 22: 452–457. https://doi.org/10.1016/j.jestch.2018.11.002 doi: 10.1016/j.jestch.2018.11.002
    [5] Aslila RD, Ledi EMS (2021) Livestock and animal health statistics 2021. Available from: https://pusvetma.ditjenpkh.pertanian.go.id/upload/statistik/1644549920.Buku_Statistik_2021.pdf.
    [6] Parthasarathy P, Al-Ansari T, Mackey HR, et al. (2022) A review on prominent animal and municipal wastes as potential feedstocks for solar pyrolysis for biochar production. Fuel 316: 123378. https://doi.org/10.1016/j.fuel.2022.123378 doi: 10.1016/j.fuel.2022.123378
    [7] Wu P, Zhang X, Wang J, et al. (2021) Pyrolysis of aquatic fern and macroalgae biomass into bio-oil: Comparison and optimization of operational parameters using response surface methodology. J Energy Institute 97: 194–202. https://doi.org/10.1016/j.joei.2021.04.010 doi: 10.1016/j.joei.2021.04.010
    [8] Khan S, Malviya R, Athankar KK (2022) Optimization and simulation of heat loss in pyrolysis reactor. Mater Today: Proc. https://doi.org/10.1016/j.matpr.2022.08.285 doi: 10.1016/j.matpr.2022.08.285
    [9] Aghbashlo M, Almasi F, Jafari A, et al. (2021) Describing biomass pyrolysis kinetics using a generic hybrid intelligent model: A critical stage in sustainable waste-oriented biorefineries. Renewable Energy 170: 81–91. https://doi.org/10.1016/j.renene.2021.01.111 doi: 10.1016/j.renene.2021.01.111
    [10] Atienza-Martínez M, Ábrego J, Gea G, et al. (2020) Pyrolysis of dairy cattle manure: Evolution of char characteristics. J Analytical Appl Pyrolysis 145: 104724. https://doi.org/10.1016/j.jaap.2019.104724 doi: 10.1016/j.jaap.2019.104724
    [11] Yuan X, He T, Cao H, et al. (2017) Cattle manure pyrolysis process: Kinetic and thermodynamic analysis with isoconversional methods. Renewable Energy 107: 489–496. https://doi.org/10.1016/j.renene.2017.02.026 doi: 10.1016/j.renene.2017.02.026
    [12] Akyurek Z (2021) Synergetic effects during co-pyrolysis of sheep manure and recycled polyethylene terephthalate. Polymers (Basel) 13: 2363. https://doi.org/10.3390/polym13142363 doi: 10.3390/polym13142363
    [13] Cantrell KB, Hunt PG, Uchimiya M, et al. (2012) Impact of pyrolysis temperature and manure source on physicochemical characteristics of biochar. Bioresour Technol 107: 419–428. https://doi.org/10.1016/j.biortech.2011.11.084 doi: 10.1016/j.biortech.2011.11.084
    [14] Naji A, Rechdaoui SG, Jabagi E, et al. (2023) Horse manure and lignocellulosic biomass characterization as methane production substrates. Fermentation 9. https://doi.org/10.3390/fermentation9060580 doi: 10.3390/fermentation9060580
    [15] Gajera B, Tyagi U, Sarma AK, et al. (2023) Pyrolysis of cattle manure: Kinetics and thermodynamic analysis using TGA and artificial neural network. Biomass Convers Biorefinery. https://doi.org/10.1007/s13399-023-04476-3 doi: 10.1007/s13399-023-04476-3
    [16] Guo M, Li H, Baldwin B, et al. (2020) Thermochemical Processing of Animal Manure for Bioenergy and Biochar. Animal Manure, 255–274. Available from: https://acsess.onlinelibrary.wiley.com/doi/10.2134/asaspecpub67.c21.
    [17] Yıldız Z, Kaya N, Topcu Y, et al. (2019) Pyrolysis and optimization of chicken manure wastes in fluidized bed reactor: CO2 capture in activated bio-chars. Proc Saf Environ Prot 130: 297–305. https://doi.org/10.1016/j.psep.2019.08.011 doi: 10.1016/j.psep.2019.08.011
    [18] Kostis Atsonios KDP, Bridgwater AV, Emmanuel Kakaras (2015) Biomass fast pyrolysis energy balance of a 1 kg/h test rig. Int J Thermodynamics 18: 267–275. https://doi.org/10.5541/ijot.5000147483 doi: 10.5541/ijot.5000147483
    [19] Daugaard DE, Brown RC (2003) Enthalpy for pyrolysis for several types of biomass. Energy Fuels 17: 934–939. https://doi.org/10.1021/ef020260x doi: 10.1021/ef020260x
    [20] Chaudhary A, Lakhani J, Dalsaniya P, et al. (2023) Slow pyrolysis of low-density Poly-Ethylene (LDPE): A batch experiment and thermodynamic analysis. Energy, 263. https://doi.org/10.1016/j.energy.2022.125810 doi: 10.1016/j.energy.2022.125810
    [21] Siswantara AI, Syafei MHG, Budiyanto MA, et al. (2023) Flow distribution analysis of a novel fcc system through experiment study and atomic model. EUREKA: Physics Eng, 52–67. https://doi.org/10.21303/2461-4262.2023.002813 doi: 10.21303/2461-4262.2023.002813
    [22] Escalante J, Chen W-H, Tabatabaei M, et al. (2022) Pyrolysis of lignocellulosic, algal, plastic, and other biomass wastes for biofuel production and circular bioeconomy: A review of thermogravimetric analysis (TGA) approach. Renewable Sustainable Energy Rev 169: 112914. https://doi.org/10.1016/j.rser.2022.112914 doi: 10.1016/j.rser.2022.112914
    [23] Carrier M, Hardie AG, Uras Ü, et al. (2012) Production of char from vacuum pyrolysis of South-African sugar cane bagasse and its characterization as activated carbon and biochar. J Analytical Appl Pyrolysis 96: 24–32. https://doi.org/10.1016/j.jaap.2012.02.016 doi: 10.1016/j.jaap.2012.02.016
    [24] Mong GR, Chong CT, Chong WWF, et al. (2022) Progress and challenges in sustainable pyrolysis technology: Reactors, feedstocks and products. Fuel 324: 124777. https://doi.org/10.1016/j.fuel.2022.124777 doi: 10.1016/j.fuel.2022.124777
    [25] Zhang K, Lu P, Guo X, et al. (2020) High-temperature pyrolysis behavior of two different rank coals in fixed-bed and drop tube furnace reactors. J Energy Institute 93: 2271–2279. https://doi.org/10.1016/j.joei.2020.06.010 doi: 10.1016/j.joei.2020.06.010
    [26] Deng B, Yuan X, Siemann E, et al. (2021) Feedstock particle size and pyrolysis temperature regulate effects of biochar on soil nitrous oxide and carbon dioxide emissions. Waste Manage 120: 33–40. https://doi.org/10.1016/j.wasman.2020.11.015 doi: 10.1016/j.wasman.2020.11.015
    [27] Al-Rumaihi A, Shahbaz M, McKay G, et al. (2022) A review of pyrolysis technologies and feedstock: A blending approach for plastic and biomass towards optimum biochar yield. Renewable Sustainable Energy Rev 167: 112715. https://doi.org/10.1016/j.rser.2022.112715 doi: 10.1016/j.rser.2022.112715
    [28] Yoichi Kodera, Kaiho M (2016) Model calculation of heat balance of wood pyrolysis. J Japan Institute Energy 95: 881–889. Available from: https://www.jstage.jst.go.jp/article/jie/95/10/95_881/_article.
    [29] Pourkarimi AH, Alizadehdakhel A, Nouralishahi A (2021) Bio-oil production by pyrolysis of Azolla filiculoides and Ulva fasciata macroalgae. Global J Environ Sci Manage (GJESM) 7: 331–346. https://doi.org/10.22034/GJESM.2021.03.02 doi: 10.22034/GJESM.2021.03.02
    [30] Tiller M (2001) Introduction to physical modeling with modelica. Part of the book series: The Springer International Series in Engineering and Computer Science (SECS, volume 615). Available from: https://link.springer.com/book/10.1007/978-1-4615-1561-6.
    [31] Fritzson P, Vadim EA (2017) Unified object-oriented language for systems modeling. Available from: https://modelica.org/documents/ModelicaSpec34.pdf.
    [32] Petrov A, Stroud T, Blackburn D, et al. (2023) An open-source power balance model for the estimation of tokamak net electrical power output. Fusion Eng Design 191: 113563. https://doi.org/10.1016/j.fusengdes.2023.113563 doi: 10.1016/j.fusengdes.2023.113563
    [33] Fritzson P (2003) Principles of object oriented modeling and simulation with Modelica. United States of America: IEEE Press-John Wiley & Sons Inc. Available from: https://www.ida.liu.se/ext/WITAS-eval/PELAB/modelicabookwitas-page-1-110.pdf.
    [34] Çengel YA (2007) Heat and mass transfer: Fundamentals & applications. Fifth Ed. New York: McGraw-Hill Education. Available from: https://www.academia.edu/30479689/Heat_and_Mass_Transfer_Fundamentals.
    [35] Papari S, Hawboldt K (2018) A review on condensing system for biomass pyrolysis process. Fuel Proc Technol 180: 1–13. https://doi.org/10.1016/j.fuproc.2018.08.001 doi: 10.1016/j.fuproc.2018.08.001
    [36] Qureshi KM, Abnisa F, Wan Daud WMA (2019) Novel helical screw-fluidized bed reactor for bio-oil production in slow-pyrolysis mode: A preliminary study. J Analytical Appl Pyrolysis 142: 104605. https://doi.org/10.1016/j.jaap.2019.04.021 doi: 10.1016/j.jaap.2019.04.021
    [37] Poddar S, Sarat Chandra Babu J (2021) Modelling and optimization of a pyrolysis plant using swine and goat manure as feedstock. Renewable Energy 175: 253–269. https://doi.org/10.1016/j.renene.2021.04.120 doi: 10.1016/j.renene.2021.04.120
    [38] Chen T, Deng C, Liu R (2010) Effect of selective condensation on the characterization of bio-oil from pine sawdust fast pyrolysis using a fluidized-bed reactor. Energy Fuels 24: 6616–6623. https://doi.org/10.1021/ef1011963 doi: 10.1021/ef1011963
    [39] Kim P, Weaver S, Labbé N (2016) Effect of sweeping gas flow rates on temperature-controlled multistage condensation of pyrolysis vapors in an auger intermediate pyrolysis system. J Analytical Appl Pyrolysis 118: 325–334. https://doi.org/10.1016/j.jaap.2016.02.017 doi: 10.1016/j.jaap.2016.02.017
    [40] Das P, Chandramohan VP, Mathimani T, et al. (2021) A comprehensive review on the factors affecting thermochemical conversion efficiency of algal biomass to energy. Sci Total Environ 766: 144213. https://doi.org/10.1016/j.scitotenv.2020.144213 doi: 10.1016/j.scitotenv.2020.144213
    [41] Tyass I, Khalili T, Rafik M, et al. (2023) Wind speed prediction based on statistical and deep learning models. Int J Renewable Energy Dev 12: 288–299. https://doi.org/10.14710/ijred.2023.48672 doi: 10.14710/ijred.2023.48672
    [42] Gulghane A, Sharma RL, Borkar P (2023) Performance analysis of ML-based prediction models for residential building construction waste. Asian J Civil Eng 24: 3265–3276. https://doi.org/10.1007/s42107-023-00708-z doi: 10.1007/s42107-023-00708-z
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