Harnessing nature and algorithms for green energy: the electrochemical property prediction of activated bamboo using data mining and metaheuristic tuning techniques
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Chao Pan
Abstract
The application of Machine Learning (ML) models coupled with metaheuristic optimization algorithms represents a potentially powerful development in the field of predictive modeling, as it relates to sustainable energy materials. In this study, the electrochemical performance of biomass material from bamboo for energy storage applications is explored, focusing on the prediction of power density. The central objective is to enhance model accuracy with a novel hybrid model of Kernel Extreme Learning Machine (KELM) and Slime Mould Algorithm (SMA), Sunflower Optimization (SFO), and Social Ski Driver (SSD). The optimal predictive performance was achieved with KELM-SFO with a test Root Mean Square Error (RMSE) of 10,421.05, Mean Absolute Error (MAE) of 4,654.07, and R-squared (R2) of 96.2 %. Early and fast plateauing of the SFO algorithm’s convergence curve indicated stable, early-stage optimization. In addition to filling a significant knowledge gap in ML-incorporated materials modeling, this work opens the door for future research on deep learning techniques, adaptive hybrid optimization algorithms, and real-time experimental validations to improve the electrochemical prediction efficiency in energy storage systems inspired by biomass.
Funding source: Hunan Provincial Natural Science Foundation Interdepartmental Joint Research Project
Award Identifier / Grant number: 2023JJ60170
Acknowledgments
This work was supported by Hunan Provincial Natural Science Foundation Interdepartmental Joint Research Project (Grant Number: 2023JJ60170).
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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 neccessary 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 “Chao Pan and Yanshu Liu”. Also, the first draft of the manuscript was written by Chao Pan. Yanshu Liu commented on previous versions of 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 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.
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Conflict of interest: The authors declare no competing of interests.
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Research funding: This work was supported by Hunan Provincial Natural Science Foundation Interdepartmental Joint Research Project (2023JJ60170).
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Data availability: The authors do not have permissions to share data.
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