中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 广州能源研究所
  3. 中国科学院广州能源研究所
A newly early warning model for anaerobic digestion systems: Based on an improved sparrow search algorithm combined with least square support vector machine

文献类型：期刊论文

作者Chen, Yushu1,2; Huang, Zetao1; Ma, Chongjian2,3; Li, Zuhao1; Zhang, Zhige1; Tan, Tao1,4; Chen, Yong1,4,5
刊名CHEMICAL ENGINEERING JOURNAL
出版日期2024-06-15
卷号490页码:9
关键词Anaerobic digestion Machine learning Optimization algorithm Early warning Carbon neutrality
ISSN号1385-8947
DOI10.1016/j.cej.2024.151743
通讯作者Zhang, Zhige(zhangzhige@scau.edu.cn) ; Tan, Tao(tantao@scau.edu.cn)
英文摘要Anaerobic digestion as an important means of organic waste treatment will play a key role in the realization of ecological civilization and the goal of double carbon. However, the instability of the system due to the high sensitivity to operating conditions restricts the economy and sustainability of the current commercial biogas projects in China. Machine learning as an early warning and control tool for many industrial systems is also applicable to anaerobic digestion systems. Existing studies focus on the biogas or methane yield prediction of the system, while there are few studies have considered the acid-bases indicators, which is crucial to the stability of the system. In this study, an improved sparrow search algorithm was developed, and after comparing its performance with selected optimization algorithms using CEC2017 test suite, combined with LSSVM, was applied to the prediction of eight different indicators of anaerobic systems, and eight datasets were validated. The results show that the optimization algorithm proposed in this study improves the performance of LSSVM and the model of ISSALSSVM shows excellent potential in the early warning and controlling of anaerobic digestion system.
WOS关键词OPTIMIZATION ; INSTABILITY
资助项目National Key Research and Development Program of China[2023YFC3905802] ; Guangdong Key Construction Discipline Research Capacity Enhance-ment Project[2022ZDJS047]
WOS研究方向Engineering
语种英语
WOS记录号WOS:001300424300001
出版者ELSEVIER SCIENCE SA
资助机构National Key Research and Development Program of China ; Guangdong Key Construction Discipline Research Capacity Enhance-ment Project
源URL[http://ir.giec.ac.cn/handle/344007/42720]  
专题中国科学院广州能源研究所
通讯作者Zhang, Zhige; Tan, Tao
作者单位1.South China Agr Univ, Inst Biomass Engn, Guangzhou 510642, Peoples R China
2.Shaoguan Univ, Sch Biol & Agr, Shaoguan 512005, Peoples R China
3.Guangdong Prov Key Lab Utilizat & Conservat Food &, Shaoguan 512005, Peoples R China
4.Nanjing Tech Univ, Sch Mech Engn, Nanjing 211816, Jiangsu, Peoples R China
5.Chinese Acad Sci, Guangzhou Inst Energy Convers, Guangzhou 510640, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Yushu,Huang, Zetao,Ma, Chongjian,et al. A newly early warning model for anaerobic digestion systems: Based on an improved sparrow search algorithm combined with least square support vector machine[J]. CHEMICAL ENGINEERING JOURNAL,2024,490:9.
APA Chen, Yushu.,Huang, Zetao.,Ma, Chongjian.,Li, Zuhao.,Zhang, Zhige.,...&Chen, Yong.(2024).A newly early warning model for anaerobic digestion systems: Based on an improved sparrow search algorithm combined with least square support vector machine.CHEMICAL ENGINEERING JOURNAL,490,9.
MLA Chen, Yushu,et al."A newly early warning model for anaerobic digestion systems: Based on an improved sparrow search algorithm combined with least square support vector machine".CHEMICAL ENGINEERING JOURNAL 490(2024):9.

入库方式： OAI收割

来源：广州能源研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。