Prediction of syngas production in the gasification process of biomass employing adaptive neuro-fuzzy inference system along with meta-heuristic algorithms
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Heng Wang
,
Shuming Cao
Abstract
Compared to fossil fuels, biomass fuels have minimal sulfur content, lower ash production, and significantly reduced emissions. The global need to reduce dependence on imported energy sources and preserve dwindling fossil fuel reserves underscores the importance of utilizing alternative energy resources. Biomass, with its abundant availability, presents a promising source for syngas production, even though the gasification procedure requires substantial energy due to its endothermic nature. Challenges related to the efficiency of biomass gasification and compliance with environmental standards have hindered economic viability. Much attention has been focused on predictive modeling of biomass gasification procedures to address these issues, necessitating robust frameworks capable of predicting parameters under varying operating conditions. This article introduces two hybrid frameworks, which are combined versions of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Artificial Rabbits Optimization (ARO) and Crystal Structure Algorithm (CSA), based on proximate biomass values to predict elemental compositions (N2 and H2). These intelligent hybrid frameworks, trained with 70 % of biomass data, were further validated and tested with the remaining 15 % portions of the database. The frameworks were assessed based on some known performance metrics, namely, root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R 2), RMSE-observations standard deviation ratio (RSR), and Nash-Sutcliffe Efficiency (NSE). Developed single and two hybrid frameworks compared and obtained outcomes revealed that both introduced optimizers efficiently promoted N2 and H2 estimation by ANFIS, especially CSA. R 2 values for ANCS were a maximum of 0.993 in both targets’ predictions. Also, minimum RMSE values of 1.007 and 1.470 related to N2 and H2 prediction emphasized the accuracy of ANCS, which is capable of being used in real-world applications.
Acknowledgments
We would like to take this opportunity to acknowledge that there are no individuals or organizations that require acknowledgment for their contributions to this work.
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Research ethics: Research involving Human Participants and Animals: The observational study conducted on medical staff needs no ethical code. Therefore, the above study was not required to acquire ethical code.
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Informed consent: This option is not necessary due to that the data were collected from the references.
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Author contributions: All authors contributed to the study’s conception and design. Data collection, simulation and analysis were performed by “Heng Wang, Shuming Cao and Xi Liu”. The first draft of the manuscript was written by “Heng Wang ”and all authors commented on previous versions of the manuscript. All authors have read and approved the manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: During the preparation of this work, the authors used ChatGPT by OpenAI and Grammarly in order to assist with language refinement and ensure clarity and coherence in the manuscript, and perform grammar and spell checks. After using these tools/services, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.
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Conflict of interests: The authors declare no competing interests.
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Research funding: No funding.
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Data availability: The authors do not have permission to share data.
References
1. Miller, B, Croft, J. Planet has only until 2030 to stem catastrophic climate change, experts warn. World 2018;8:1–3.Suche in Google Scholar
2. Wuebbles, D. US Global change research program: climate science special report (CSSR). Monographic reports and papers 2016.Suche in Google Scholar
3. Petković, B, Petković, D, Kuzman, B. Adaptive neuro fuzzy predictive models of agricultural biomass standard entropy and chemical exergy based on principal component analysis. Biomass Convers Biorefinery 2020:1–11. https://doi.org/10.1007/s13399-020-00767-1.Suche in Google Scholar
4. Sikarwar, VS, Peela, NR, Vuppaladadiyam, AK, Ferreira, NL, Mašláni, A, Tomar, R, et al.. Thermal plasma gasification of organic waste stream coupled with CO2-sorption enhanced reforming employing different sorbents for enhanced hydrogen production. RSC Adv 2022;12:6122–32. https://doi.org/10.1039/d1ra07719h.Suche in Google Scholar PubMed PubMed Central
5. Sikarwar, VS, Mašláni, A, Hlína, M, Fathi, J, Mates, T, Pohořelý, M, et al.. Thermal plasma assisted pyrolysis and gasification of RDF by utilizing sequestered CO2 as gasifying agent. J CO2 Util 2022;66:102275. https://doi.org/10.1016/j.jcou.2022.102275.Suche in Google Scholar
6. Ahmed, TY, Ahmad, MM, Yusup, S, Inayat, A, Khan, Z. Mathematical and computational approaches for design of biomass gasification for hydrogen production: a review. Renew Sustain Energy Rev 2012;16:2304–15. https://doi.org/10.1016/j.rser.2012.01.035.Suche in Google Scholar
7. Boerrigter, H, Rauch, R. Review of applications of gases from biomass gasification. ECN Biomassa, Kolen En Milieuonderzoek 2006;20:211–30.Suche in Google Scholar
8. Masoumi, F, Najjar-Ghabel, S, Safarzadeh, A, Sadaghat, B. Automatic calibration of the groundwater simulation model with high parameter dimensionality using sequential uncertainty fitting approach. Water Supply 2020;20:3487–501. https://doi.org/10.2166/ws.2020.241.Suche in Google Scholar
9. Shiri, N, Shiri, J, Kazemi, MH, Xu, T. Estimation of CO2 flux components over northern hemisphere forest ecosystems by using random forest method through temporal and spatial data scanning procedures. Environ Sci Pollut Res 2022:1–15. https://doi.org/10.1007/s11356-021-16501-x.Suche in Google Scholar PubMed
10. Karimi, S, Nazemi, AH, Sadraddini, AA, Xu, TR, Bateni, SM, Fard, AF. Estimation of forest leaf area index using meteorological data: assessment of heuristic models. J Environ Inform 2020;36:119–32. https://doi.org/10.3808/jei.202000430.Suche in Google Scholar
11. Cohce, MK, Rosen, MA, Dincer, I. Efficiency evaluation of a biomass gasification-based hydrogen production. Int J Hydrogen Energy 2011;36:11388–98. https://doi.org/10.1016/j.ijhydene.2011.02.033.Suche in Google Scholar
12. Balat, M, Balat, M, Kırtay, E, Balat, H. Main routes for the thermo-conversion of biomass into fuels and chemicals. Part 2: gasification systems. Energy Convers Manag 2009;50:3158–68. https://doi.org/10.1016/j.enconman.2009.08.013.Suche in Google Scholar
13. Kırtay, E. Recent advances in production of hydrogen from biomass. Energy Convers Manag 2011;52:1778–89. https://doi.org/10.1016/j.enconman.2010.11.010.Suche in Google Scholar
14. Alauddin, ZABZ, Lahijani, P, Mohammadi, M, Mohamed, AR. Gasification of lignocellulosic biomass in fluidized beds for renewable energy development: a review. Renew Sustain Energy Rev 2010;14:2852–62. https://doi.org/10.1016/j.rser.2010.07.026.Suche in Google Scholar
15. Weerachanchai, P, Horio, M, Tangsathitkulchai, C. Effects of gasifying conditions and bed materials on fluidized bed steam gasification of wood biomass. Bioresour Technol 2009;100:1419–27. https://doi.org/10.1016/j.biortech.2008.08.002.Suche in Google Scholar PubMed
16. Fowler, P, Krajačić, G, Lončar, D, Duić, N. Modeling the energy potential of biomass–H2RES. Int J Hydrogen Energy 2009;34:7027–40. https://doi.org/10.1016/j.ijhydene.2008.12.055.Suche in Google Scholar
17. Obernberger, I, Thek, G. Combustion and gasification of solid biomass for heat and power production in Europe-State-of-the-art and relevant future developments. Vilamoura, Portugal: Proc. 8th Eur. Conf. Ind. Furn. Boil., Vilamoura Portugal; 2008.Suche in Google Scholar
18. Aggarwal, M, Ydstie, BE, White, LR, Seminar, P. Modeling biomass gasification. Present. PSE Semin. 2008;420.Suche in Google Scholar
19. Iliuta, I, Leclerc, A, Larachi, F. Allothermal steam gasification of biomass in cyclic multi-compartment bubbling fluidized-bed gasifier/combustor – New reactor concept. Bioresour Technol 2010;101:3194–208. https://doi.org/10.1016/j.biortech.2009.12.023.Suche in Google Scholar PubMed
20. Phillips, J. Different types of gasifiers and their integration with gas turbines. Gas Turbine Handb 2006;1.Suche in Google Scholar
21. Maniatis, K. Progress in biomass gasification: an overview. In: Progress in thermochemical biomass conversion. Blackwell Sci Ltd;2001, vol. 10:9780470694954 p.10.1002/9780470694954.ch1Suche in Google Scholar
22. Kolb, T. Country report Germany for IEA task 33 – thermal gasification of 2011.Suche in Google Scholar
23. Mikulandrić, R, Lončar, D, Böhning, D, Böhme, R, Beckmann, M. Artificial neural network modelling approach for a biomass gasification process in fixed bed gasifiers. Energy Convers Manag 2014;87:1210–23. https://doi.org/10.1016/j.enconman.2014.03.036.Suche in Google Scholar
24. Sedaghat, B, Tejani, GG, Kumar, S. Predict the maximum dry density of soil based on individual and hybrid methods of machine learning. Adv Eng Intell Syst 2023;2(3):95–106. https://doi.org/10.22034/aeis.2023.414188.1129.Suche in Google Scholar
25. Akbarzadeh, MR, Ghafourian, H, Anvari, A, Pourhanasa, R, Nehdi, ML. Estimating compressive strength of concrete using neural electromagnetic field optimization. Materials 2023;16:4200. https://doi.org/10.3390/ma16114200.Suche in Google Scholar PubMed PubMed Central
26. Gómez-Barea, A, Leckner, B. Modeling of biomass gasification in fluidized bed. Prog Energy Combust Sci 2010;36:444–509. https://doi.org/10.1016/j.pecs.2009.12.002.Suche in Google Scholar
27. Dixon, R, Pike, AW, Donne, MS. The ALSTOM benchmark challenge on gasifier control. Proc Inst Mech Eng Part I J Syst Control Eng 2000;214:389–94. https://doi.org/10.1243/0959651001540744.Suche in Google Scholar
28. Sikarwar, VS, Mašláni, A, Van Oost, G, Fathi, J, Hlína, M, Mates, T, et al.. Integration of thermal plasma with CCUS to valorize sewage sludge. Energy 2024;288:129896. https://doi.org/10.1016/j.energy.2023.129896.Suche in Google Scholar
29. Sikarwar, VS, Reichert, A, Pohorely, M, Meers, E, Ferreira, NL, Jeremias, M. Equilibrium modeling of thermal plasma assisted co-valorization of difficult waste streams for syngas production. Sustain Energy Fuels 2021;5:4650–60. https://doi.org/10.1039/d1se00998b.Suche in Google Scholar
30. Ozbas, EE, Aksu, D, Ongen, A, Aydin, MA, Ozcan, HK. Hydrogen production via biomass gasification, and modeling by supervised machine learning algorithms. Int J Hydrogen Energy 2019;44:17260–8. https://doi.org/10.1016/j.ijhydene.2019.02.108.Suche in Google Scholar
31. Bahadar, A, Kanthasamy, R, Sait, HH, Zwawi, M, Algarni, M, Ayodele, BV, et al.. Elucidating the effect of process parameters on the production of hydrogen-rich syngas by biomass and coal Co-gasification techniques: a multi-criteria modeling approach. Chemosphere 2022;287:132052. https://doi.org/10.1016/j.chemosphere.2021.132052.Suche in Google Scholar PubMed
32. George, J, Arun, P, Muraleedharan, C. Assessment of producer gas composition in air gasification of biomass using artificial neural network model. Int J Hydrogen Energy 2018;43:9558–68. https://doi.org/10.1016/j.ijhydene.2018.04.007.Suche in Google Scholar
33. Shenbagaraj, S, Sharma, PK, Sharma, AK, Raghav, G, Kota, KB, Ashokkumar, V. Gasification of food waste in supercritical water: an innovative synthesis gas composition prediction model based on Artificial Neural Networks. Int J Hydrogen Energy 2021;46:12739–57. https://doi.org/10.1016/j.ijhydene.2021.01.122.Suche in Google Scholar
34. Puig-Arnavat, M, Bruno, JC. Artificial neural networks for thermochemical conversion of biomass. In: Recent adv. Thermo-chemical convers. Biomass. Elsevier; 2015:133–56 pp.10.1016/B978-0-444-63289-0.00005-3Suche in Google Scholar
35. Antonopoulos, I-S, Karagiannidis, A, Gkouletsos, A, Perkoulidis, G. Modelling of a downdraft gasifier fed by agricultural residues. Waste Manage 2012;32:710–8. https://doi.org/10.1016/j.wasman.2011.12.015.Suche in Google Scholar PubMed
36. Jang, J-SR, Sun, C-T, Mizutani, E. Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence. IEEE Trans Automat Contr 1997;42:1482–4. https://doi.org/10.1109/tac.1997.633847.Suche in Google Scholar
37. Bakyani, AE, Sahebi, H, Ghiasi, MM, Mirjordavi, N, Esmaeilzadeh, F, Lee, M, et al.. Prediction of CO2 – oil molecular diffusion using adaptive neuro-fuzzy inference system and particle swarm optimization technique. Fuel 2016;181:178–87. https://doi.org/10.1016/j.fuel.2016.04.097.Suche in Google Scholar
38. Cabalar, AF, Cevik, A, Gokceoglu, C, Baykal, G. Neuro-fuzzy based constitutive modeling of undrained response of Leighton Buzzard Sand mixtures. Expert Syst Appl 2010;37:842–51.10.1016/j.eswa.2009.05.085Suche in Google Scholar
39. Wang, L, Cao, Q, Zhang, Z, Mirjalili, S, Zhao, W. Artificial rabbits optimization: a new bio-inspired meta-heuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell 2022;114:105082. https://doi.org/10.1016/j.engappai.2022.105082.Suche in Google Scholar
40. Talatahari, S, Azizi, M, Tolouei, M, Talatahari, B, Sareh, P. Crystal structure algorithm (CryStAl): a metaheuristic optimization method. IEEE Access 2021;9:71244–61. https://doi.org/10.1109/access.2021.3079161.Suche in Google Scholar
Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cppm-2024-0043).
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Artikel in diesem Heft
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Artikel in diesem Heft
- Frontmatter
- Research Articles
- Comparative study of deterministic and stochastic optimization algorithms applied to the absorption of CO2 by alkanolamine solution
- Modeling the kinetics, energy consumption and shrinkage of avocado pear pulp during drying in a microwave assisted dryer
- Study of municipal solid waste treatment using plasma gasification by application of Aspen Plus
- Numerical analysis of segregation of microcrystalline cellulose powders from a flat bottom silo with various orifice positions
- Prediction of syngas production in the gasification process of biomass employing adaptive neuro-fuzzy inference system along with meta-heuristic algorithms
- Industrial high saline water desalination by activated carbon in a packed column- an experimental and CFD study
- Dual-loop PID control strategy for ramp tracking and ramp disturbance handling for unstable CSTRs
- A control perspective on hybrid membrane/distillation propane/propylene separation process
- Prediction of surface heating effect on non-equilibrium homogeneous condensation in supersonic nozzle using CFD method
- Modeling the emitted carbon dioxide and monoxide gases in the gasification process using optimized hybrid machine learning models
