Gasification process prediction using a novel and reliable metaheuristic algorithm coupled with the K-nearest neighbors
-
Yuan Li
Abstract
The present work introduces a new method for forecasting the formation of CH4 and C2Hn gases in the gasification of biomass. The K-nearest neighbors (KNN) algorithm is utilized as the base model, while two innovative optimization techniques, Artificial Rabbits Optimization (ARO) and Smell Agent Optimization (SAO), are employed to enhance the overall performance and achieve optimal results. The goal of this investigation is to create a prediction model that can reliably and accurately anticipate the amount of CH4 and C2Hn gases produced during the gasification of biomass. By combining the strengths of the KNN algorithm with the optimization capabilities of ARO and SAO, the proposed approach aims to overcome existing limitations in gasification process predictions. The experimental results demonstrate the effectiveness of the combined approach in accurately predicting the gasification process and estimating the quantities of CH4 and C2Hn gases produced. The integration of ARO and SAO with the KNN algorithm enables better optimization of the model, leading to improved accuracy and reliability in predicting gasification outcomes. Additionally, the effectiveness of the suggested models was thoroughly evaluated and assessed utilizing performance evaluators. Remarkably, the KNSA (combination of KNN and SAO) model achieved the highest R 2 values of 0.994 and 0.995 for CH4 and C2Hn, correspondingly, which demonstrates the effectiveness of the suggested methods. The conclusion of this study contributes to the field of biomass gasification, as it proposed a new methodology that used the KNN algorithm as a base model, further improving its performance through the implementation of new optimization techniques. Further optimizations of the gasification process may now be opened, and a set of insights may be derived from the research for curiosity-driven scholars and practitioners in renewable energy production.
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.
-
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 an ethical code.
-
Informed consent: This option is not necessary due to that the data were collected from the references.
-
Author contributions: All authors contributed to the study’s conception and design. Data collection, simulation and analysis were performed by “Yuan LI and Mingjing CAO”. Also, the first draft of the manuscript was written by Yuan LI. Mingjing CAO commented on previous versions of the manuscript.
-
Use of Large Language Models, AI and Machine Learning Tools: During the preparation of this work, the authors used Large Language Models, AI, and Machine Learning tools for tasks such as language refinement, data analysis, or figure generation, with all outputs being reviewed and validated by the authors to ensure accuracy and originality. After using these tools/services, the authors reviewed and edited the content and take full responsibility for the content of the published article.
-
Conflict of interest: The authors declare no competing interests.
-
Research funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
-
Data availability: The authors do not have permission to share data.
References
1. Ascher, S, Sloan, W, Watson, I, You, S. A comprehensive artificial neural network model for gasification process prediction. Appl Energy 2022;320:119289. https://doi.org/10.1016/j.apenergy.2022.119289.Suche in Google Scholar
2. Yucel, O, Aydin, ES, Sadikoglu, H. Comparison of the different artificial neural networks in prediction of biomass gasification products. Int J Energy Res 2019;43:5992–6003. https://doi.org/10.1002/er.4682.Suche in Google Scholar
3. Dang, Q, Zhang, X, Zhou, Y, Jia, X. Prediction and optimization of syngas production from a kinetic-based biomass gasification process model. Fuel Process Technol 2021;212:106604. https://doi.org/10.1016/j.fuproc.2020.106604.Suche in Google Scholar
4. Tungalag, A, Lee, B, Yadav, M, Akande, O. Yield prediction of MSW gasification including minor species through ASPEN plus simulation. Energy 2020;198:117296. https://doi.org/10.1016/j.energy.2020.117296.Suche in Google Scholar
5. Xiao, G, Ni, M, Chi, Y, Jin, B, Xiao, R, Zhong, Z, et al.. Gasification characteristics of MSW and an ANN prediction model. Waste Manag 2009;29:240–4. https://doi.org/10.1016/j.wasman.2008.02.022.Suche in Google Scholar PubMed
6. Brown, BW, Smoot, LD, Smith, PJ, Hedman, PO. Measurement and prediction of entrained‐flow gasification processes. AIChE J 1988;34:435–46. https://doi.org/10.1002/aic.690340311.Suche in Google Scholar
7. Mutlu, AY, Yucel, O. An artificial intelligence based approach to predicting syngas composition for downdraft biomass gasification. Energy 2018;165:895–901. https://doi.org/10.1016/j.energy.2018.09.131.Suche in Google Scholar
8. Sreejith, CC, Muraleedharan, C, Arun, P. Performance prediction of steam gasification of wood using an ASPEN PLUS thermodynamic equilibrium model. Int J Sustain Energy 2014;33:416–34. https://doi.org/10.1080/14786451.2012.755977.Suche in Google Scholar
9. Ayub, Y, Zhou, J, Ren, J, Shi, T, Shen, W, He, C. High-dimensional model representation-based surrogate model for optimization and prediction of biomass gasification process. Int J Energy Res 2023;2023. https://doi.org/10.1155/2023/7787947.Suche in Google Scholar
10. Khan, MNA, Haq, ZU, Ullah, H, Naqvi, SR, Ahmed, U, Zaman, M, et al.. Prediction of hydrogen yield from supercritical gasification process of sewage sludge using machine learning and particle swarm hybrid strategy. Int J Hydrogen Energy 2023.Suche in Google Scholar
11. Gong, X, Lu, W, Guo, X, Dai, Z, Liang, Q, Liu, H, et al.. Pilot-scale comparison investigation of different entrained-flow gasification technologies and prediction on industrial-scale gasification performance. Fuel 2014;129:37–44. https://doi.org/10.1016/j.fuel.2014.03.030.Suche in Google Scholar
12. Fang, Y, Ma, L, Yao, Z, Li, W, You, S. Process optimization of biomass gasification with a Monte Carlo approach and random forest algorithm. Energy Convers Manag 2022;264:115734. https://doi.org/10.1016/j.enconman.2022.115734.Suche in Google Scholar
13. Elmaz, F, Yücel, Ö, Mutlu, AY. Predictive modeling of biomass gasification with machine learning-based regression methods. Energy 2020;191:116541. https://doi.org/10.1016/j.energy.2019.116541.Suche in Google Scholar
14. Chejne, F, Hernandez, JP. Modelling and simulation of coal gasification process in fluidised bed. Fuel 2002;81:1687–702. https://doi.org/10.1016/s0016-2361(02)00036-4.Suche in Google Scholar
15. Cerinski, D, Baleta, J, Mikulčić, H, Mikulandrić, R, Wang, J. Dynamic modelling of the biomass gasification process in a fixed bed reactor by using the artificial neural network. Clean Eng Technol 2020;1:100029. https://doi.org/10.1016/j.clet.2020.100029.Suche in Google Scholar
16. Li, J, Li, L, Tong, YW, Wang, X. Understanding and optimizing the gasification of biomass waste with machine learning. Green Chem Eng 2023;4:123–33. https://doi.org/10.1016/j.gce.2022.05.006.Suche in Google Scholar
17. Ozonoh, M, Oboirien, BO, Higginson, A, Daramola, MO. Performance evaluation of gasification system efficiency using artificial neural network. Renew Energy 2020;145:2253–70. https://doi.org/10.1016/j.renene.2019.07.136.Suche in Google Scholar
18. Mikulandrić, R, Lončar, D, Böhning, D, Böhme, R. Biomass gasification process modelling approaches. In: 8th Conference on sustainable development of energy, water and environment systems–SDEWES conference; 2013:22–7 pp.Suche in Google Scholar
19. La Villetta, M, Costa, M, Massarotti, N. Modelling approaches to biomass gasification: a review with emphasis on the stoichiometric method. Renew Sustain Energy Rev 2017;74:71–88. https://doi.org/10.1016/j.rser.2017.02.027.Suche in Google Scholar
20. Pan, J, Shahbeik, H, Shafizadeh, A, Rafiee, S, Golvirdizadeh, M, Ghafarian Nia, SA, et al.. Machine learning optimization for enhanced biomass-coal co-gasification. Renew Energy 2024;229:120772. https://doi.org/10.1016/j.renene.2024.120772.Suche in Google Scholar
21. Bongomin, O, Nzila, C, Mwasiagi, JI, Maube, O. Exploring insights in biomass and waste gasification via ensemble machine learning models and interpretability techniques. Int J Energy Res 2024;2024:6087208. https://doi.org/10.1155/2024/6087208.Suche in Google Scholar
22. Cong, W. Machine learning and LSSVR model optimization for gasification process prediction. Multiscale Multidiscip Model, Exp Des 2024;7:5991–6018. https://doi.org/10.1007/s41939-024-00552-x.Suche in Google Scholar
23. Gupta, R, Ouderji, ZH, Uzma, Yu, Z, Sloan, WT, You, S. Machine learning for sustainable organic waste treatment: a critical review. npj Mater Sustain 2024;2:5. https://doi.org/10.1038/s44296-024-00009-9.Suche in Google Scholar
24. Prabowo, B, Umeki, K, Yan, M, Nakamura, MR, Castaldi, MJ, Yoshikawa, K. CO2–steam mixture for direct and indirect gasification of rice straw in a downdraft gasifier: laboratory-scale experiments and performance prediction. Appl Energy 2014;113:670–9. https://doi.org/10.1016/j.apenergy.2013.08.022.Suche in Google Scholar
25. 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
26. Chavan, PD, Sharma, T, Mall, B, Rajurkar, B, Tambe, S, Sharma, B, et al.. Development of data-driven models for fluidized-bed coal gasification process. Fuel 2012;93:44–51. https://doi.org/10.1016/j.fuel.2011.11.039.Suche in Google Scholar
27. Pandey, DS, Das, S, Pan, I, Leahy, JJ, Kwapinski, W. Artificial neural network based modelling approach for municipal solid waste gasification in a fluidized bed reactor. Waste Manag 2016;58:202–13. https://doi.org/10.1016/j.wasman.2016.08.023.Suche in Google Scholar PubMed
28. Baruah, D, Baruah, DC, Hazarika, MK. Artificial neural network based modeling of biomass gasification in fixed bed downdraft gasifiers. Biomass Bioenergy 2017;98:264–71. https://doi.org/10.1016/j.biombioe.2017.01.029.Suche in Google Scholar
29. Puig-Arnavat, M, Hernández, JA, Bruno, JC, Coronas, A. Artificial neural network models for biomass gasification in fluidized bed gasifiers. Biomass Bioenergy 2013;49:279–89. https://doi.org/10.1016/j.biombioe.2012.12.012.Suche in Google Scholar
30. Imandoust, SB, Bolandraftar, M. Application of k-nearest neighbor (knn) approach for predicting economic events: theoretical background. Int J Eng Res Appl 2013;3:605–10.Suche in Google Scholar
31. Al-Shehri, H, Al-Qarni, A, Al-Saati, L, Batoaq, A, Badukhen, H, Alrashed, S, et al.. Student performance prediction using support vector machine and k-nearest neighbor. In: 2017 IEEE 30th canadian conference on electrical and computer engineering (CCECE). IEEE; 2017:1–4 pp.10.1109/CCECE.2017.7946847Suche in Google Scholar
32. Khan, MMR, Siddique, MAB, Sakib, S. Non-intrusive electrical appliances monitoring and classification using K-nearest neighbors. In: 2019 2nd International conference on innovation in engineering and technology (ICIET). IEEE; 2019:1–5 pp.10.1109/ICIET48527.2019.9290671Suche in Google Scholar
33. Abu Alfeilat, HA, Hassanat, AB, Lasassmeh, O, Tarawneh, AS, Alhasanat, MB, Eyal Salman, HS, et al.. Effects of distance measure choice on k-nearest neighbor classifier performance: a review. Big Data 2019;7:221–48. https://doi.org/10.1089/big.2018.0175.Suche in Google Scholar PubMed
34. 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
35. Meadows, OA, Mu’Azu, MB, Salawudeen, AT. A smell agent optimization approach to capacitated vehicle routing problem for solid waste collection. In: 2022 IEEE Nigeria 4th international conference on disruptive technologies for sustainable development (NIGERCON). IEEE; 2022:1–5 pp.10.1109/NIGERCON54645.2022.9803009Suche in Google Scholar
36. Bankole, AT, Moses, SO, Ibitoye, TY. Smell agent optimization based supervisory model predictive control for energy efficiency improvement of a cold storage system. In: 2022 IEEE Nigeria 4th international conference on disruptive technologies for sustainable development (NIGERCON). IEEE; 2022:1–5 pp.10.1109/NIGERCON54645.2022.9803096Suche in Google Scholar
37. Sakalli, E, Temirbekov, D, Bayri, E, Alis, EE, Erdurak, SC, Bayraktaroglu, M. Ear nose throat-related symptoms with a focus on loss of smell and/or taste in COVID-19 patients. Am J Otolaryngol 2020;41:102622. https://doi.org/10.1016/j.amjoto.2020.102622.Suche in Google Scholar PubMed PubMed Central
38. Salawudeen, AT, Mu’azu, MB, Sha’aban, YA, Adedokun, EA. On the development of a novel smell agent optimization (SAO) for optimization problems. In: 2nd International conference on information and communication technology and its applications (ICTA 2018). Minna; 2018.Suche in Google Scholar
39. Botchkarev, A. Performance metrics (error measures) in machine learning regression, forecasting and prognostics: properties and typology. arXiv preprint arXiv:1809.03006, 2018.Suche in Google Scholar
40. Ascher, S, Wang, X, Watson, I, Sloan, W, You, S. Interpretable machine learning to model biomass and waste gasification. Bioresour Technol 2022;364:128062. https://doi.org/10.1016/j.biortech.2022.128062.Suche in Google Scholar PubMed
© 2025 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Research Articles
- An improvement of level control of non-linear horizontal tank process using sliding mode controller
- Numerical investigation of inlet pressure effects on condensation flow regime in a supersonic nozzle
- Effects of tapered helical obstacles on heat transfer in tubes
- Gasification process prediction using a novel and reliable metaheuristic algorithm coupled with the K-nearest neighbors
- Evaluating the ionic liquids, commercial solvents, and pressure-swing for efficient azeotropic separation
- A synergistic approach to CO2 sequestration: evaluating trapping mechanisms in saline aquifers
- Diffusion modeling and optimization of drying dynamics of ogbono seed (Irvingea gabonensis): empirical insights into energy indices and process conditions
- Production of polyphenol extracts with antioxidant activity from olive pomace: process modeling and optimization
Artikel in diesem Heft
- Frontmatter
- Research Articles
- An improvement of level control of non-linear horizontal tank process using sliding mode controller
- Numerical investigation of inlet pressure effects on condensation flow regime in a supersonic nozzle
- Effects of tapered helical obstacles on heat transfer in tubes
- Gasification process prediction using a novel and reliable metaheuristic algorithm coupled with the K-nearest neighbors
- Evaluating the ionic liquids, commercial solvents, and pressure-swing for efficient azeotropic separation
- A synergistic approach to CO2 sequestration: evaluating trapping mechanisms in saline aquifers
- Diffusion modeling and optimization of drying dynamics of ogbono seed (Irvingea gabonensis): empirical insights into energy indices and process conditions
- Production of polyphenol extracts with antioxidant activity from olive pomace: process modeling and optimization
