{"title":"提高植物微生物燃料电池性能的机器学习解决方案","authors":"Tuğba Gürbüz , M. Erdem Günay , N. Alper Tapan","doi":"10.1016/j.ijhydene.2024.06.417","DOIUrl":null,"url":null,"abstract":"<div><p>It is well known that numerous operational, material and design variables act upon the performance of a plant-based microbial fuel cell which is an emerging sustainable and versatile energy device like hydrogen fuel cells. However, due to the high complexity of these bioelectrochemical systems, new solutions are required to optimize performance and uncover hidden relationships between dominant fuel cell variables. For this purpose, a database of 229 observations was created for plant-based microbial fuel cells (PMFCs) with 159 descriptor variables and a target variable (maximum power density) based on experimental results from 51 recent publications. Then, some machine learning solutions like principal component analysis (PCA), classification trees and SHapley Additive exPlanations (SHAP) analysis were applied. The <span>PCA</span> indicated mainly two routes involving low and high chemical oxygen demand (COD) towards high maximum power density which consists of the plant family, wastewater type, support media, construction design, separator type, anode and cathode electrodes and light source. SHAP analysis revealed that the most important factors for high performance are operating temperature, natural light, soil support medium, and constructed wetland design. Finally, the classification tree successfully demonstrated nine routes towards high maximum power density which exclude the use of graphite plate cathode electrodes.</p></div>","PeriodicalId":337,"journal":{"name":"International Journal of Hydrogen Energy","volume":null,"pages":null},"PeriodicalIF":8.1000,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning solutions for enhanced performance in plant-based microbial fuel cells\",\"authors\":\"Tuğba Gürbüz , M. Erdem Günay , N. Alper Tapan\",\"doi\":\"10.1016/j.ijhydene.2024.06.417\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>It is well known that numerous operational, material and design variables act upon the performance of a plant-based microbial fuel cell which is an emerging sustainable and versatile energy device like hydrogen fuel cells. However, due to the high complexity of these bioelectrochemical systems, new solutions are required to optimize performance and uncover hidden relationships between dominant fuel cell variables. For this purpose, a database of 229 observations was created for plant-based microbial fuel cells (PMFCs) with 159 descriptor variables and a target variable (maximum power density) based on experimental results from 51 recent publications. Then, some machine learning solutions like principal component analysis (PCA), classification trees and SHapley Additive exPlanations (SHAP) analysis were applied. The <span>PCA</span> indicated mainly two routes involving low and high chemical oxygen demand (COD) towards high maximum power density which consists of the plant family, wastewater type, support media, construction design, separator type, anode and cathode electrodes and light source. SHAP analysis revealed that the most important factors for high performance are operating temperature, natural light, soil support medium, and constructed wetland design. Finally, the classification tree successfully demonstrated nine routes towards high maximum power density which exclude the use of graphite plate cathode electrodes.</p></div>\",\"PeriodicalId\":337,\"journal\":{\"name\":\"International Journal of Hydrogen Energy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.1000,\"publicationDate\":\"2024-07-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Hydrogen Energy\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0360319924026569\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Hydrogen Energy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0360319924026569","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Machine learning solutions for enhanced performance in plant-based microbial fuel cells
It is well known that numerous operational, material and design variables act upon the performance of a plant-based microbial fuel cell which is an emerging sustainable and versatile energy device like hydrogen fuel cells. However, due to the high complexity of these bioelectrochemical systems, new solutions are required to optimize performance and uncover hidden relationships between dominant fuel cell variables. For this purpose, a database of 229 observations was created for plant-based microbial fuel cells (PMFCs) with 159 descriptor variables and a target variable (maximum power density) based on experimental results from 51 recent publications. Then, some machine learning solutions like principal component analysis (PCA), classification trees and SHapley Additive exPlanations (SHAP) analysis were applied. The PCA indicated mainly two routes involving low and high chemical oxygen demand (COD) towards high maximum power density which consists of the plant family, wastewater type, support media, construction design, separator type, anode and cathode electrodes and light source. SHAP analysis revealed that the most important factors for high performance are operating temperature, natural light, soil support medium, and constructed wetland design. Finally, the classification tree successfully demonstrated nine routes towards high maximum power density which exclude the use of graphite plate cathode electrodes.
期刊介绍:
The objective of the International Journal of Hydrogen Energy is to facilitate the exchange of new ideas, technological advancements, and research findings in the field of Hydrogen Energy among scientists and engineers worldwide. This journal showcases original research, both analytical and experimental, covering various aspects of Hydrogen Energy. These include production, storage, transmission, utilization, enabling technologies, environmental impact, economic considerations, and global perspectives on hydrogen and its carriers such as NH3, CH4, alcohols, etc.
The utilization aspect encompasses various methods such as thermochemical (combustion), photochemical, electrochemical (fuel cells), and nuclear conversion of hydrogen, hydrogen isotopes, and hydrogen carriers into thermal, mechanical, and electrical energies. The applications of these energies can be found in transportation (including aerospace), industrial, commercial, and residential sectors.