Performance Evaluation of Different Clustering Techniques and Parameters of Hybrid PSO- and GA-ANFIS on Optimization and Prediction of Biomethane Yield of Alkali-Pretreated Groundnut Shells Article Swipe
Kehinde O. Olatunji
,
Stephen Oladipo
,
Daniel M. Madyira
,
Yanxia Sun
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s12649-024-02674-2
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s12649-024-02674-2
The study focuses on optimizing biomethane yield in the anaerobic digestion of alkali-pretreated groundnut shells, involving varied input parameters. Biomethane optimization will improve the economy of the technology, which will assist in managing the environmental challenges of fossil fuel combustion. Traditional methods prove challenging, inaccurate, and uneconomical, necessitating efficient optimization models. This research hybridizes particle swarm optimization (PSO) and genetic algorithms (GA) with adaptive neuro-fuzzy inference system (ANFIS) models, assessing input parameters’ influence on biomethane yield through renowned performance metrics. Comparing the best model in the hybrid analysis, encompassing pretreatments A-E, the PSO-ANFIS (RMSE = 1.1719, MADE = 0.6525, MAE = 0.9314, Theil’s U = 0.1844, and SD = 0.7737) outperformed the GA-ANFIS (RMSE = 1.9338, MADE = 0.9318, MAE = 1.6557, Theil’s U = 0.2734, SD = 1.0598), using the same cluster radius of 0.50. Furthermore, compared to the GA-ANFIS model, the PSO-ANFIS model demonstrated significant improvements across various metrics: RMSE by 39.40%, MADE by 29.97%, MAE by 43.75%, Theil’s U by 32.56%, and SD by 27.00%. Results indicate that the PSO-ANFIS model outperforms the GA-ANFIS model, emphasizing the importance of suitable clustering algorithms and precise parameter adjustment for optimal performance in predicting biomethane yield from pretreated lignocellulose feedstocks. Graphical Abstract
Related Topics
Concepts
Adaptive neuro fuzzy inference system
Particle swarm optimization
Biogas
Mean squared error
Cluster analysis
Mathematics
Yield (engineering)
Biotechnology
Machine learning
Fuzzy logic
Statistics
Engineering
Computer science
Mathematical optimization
Artificial intelligence
Waste management
Materials science
Biology
Fuzzy control system
Metallurgy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s12649-024-02674-2
- https://link.springer.com/content/pdf/10.1007/s12649-024-02674-2.pdf
- OA Status
- hybrid
- Cited By
- 6
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401329938
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401329938Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s12649-024-02674-2Digital Object Identifier
- Title
-
Performance Evaluation of Different Clustering Techniques and Parameters of Hybrid PSO- and GA-ANFIS on Optimization and Prediction of Biomethane Yield of Alkali-Pretreated Groundnut ShellsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-08-05Full publication date if available
- Authors
-
Kehinde O. Olatunji, Stephen Oladipo, Daniel M. Madyira, Yanxia SunList of authors in order
- Landing page
-
https://doi.org/10.1007/s12649-024-02674-2Publisher landing page
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https://link.springer.com/content/pdf/10.1007/s12649-024-02674-2.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s12649-024-02674-2.pdfDirect OA link when available
- Concepts
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Adaptive neuro fuzzy inference system, Particle swarm optimization, Biogas, Mean squared error, Cluster analysis, Mathematics, Yield (engineering), Biotechnology, Machine learning, Fuzzy logic, Statistics, Engineering, Computer science, Mathematical optimization, Artificial intelligence, Waste management, Materials science, Biology, Fuzzy control system, MetallurgyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
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2025: 6Per-year citation counts (last 5 years)
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55Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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