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Design of an Intelligent Variable-Flow Recirculating Aquaculture System Based on Machine Learning Methods

文献类型：期刊论文

作者Chen, Fudi1,2,3; Du, Yishuai1,2,3; Qiu, Tianlong1,2,3; Xu, Zhe4; Zhou, Li1,2,3; Xu, Jianping1,2,3; Sun, Ming1,2,3,5; Li, Ye1,2,3; Sun, Jianming1,2,3,4,5
刊名APPLIED SCIENCES-BASEL
出版日期2021-07-01
卷号11期号:14页码:15
关键词recirculating aquaculture system variable-flow regulation model circulating pump-drum filter linkage working technique machine learning methods gene algorithm support vector machine
DOI10.3390/app11146546
通讯作者Sun, Jianming(jianming_sun@outlook.com)
英文摘要Featured Application The proposed classification models could be adapted to develop a recirculating aquaculture system with continuous variable-flow control technology. A recirculating aquaculture system (RAS) can reduce water and land requirements for intensive aquaculture production. However, a traditional RAS uses a fixed circulation flow rate for water treatment. In general, the water in an RAS is highly turbid only when the animals are fed and when they excrete. Therefore, RAS water quality regulation technology based on process control is proposed in this paper. The intelligent variable-flow RAS was designed based on the circulating pump-drum filter linkage working model. Machine learning methods were introduced to develop the intelligent regulation model to maintain a clean and stable water environment. Results showed that the long short-term memory network performed with the highest accuracy (training set 100%, test set 96.84%) and F1-score (training 100%, test 93.83%) among artificial neural networks. Optimization methods including grid search, cuckoo search, linear squares, and gene algorithm were proposed to improve the classification ability of support vector machine models. Results showed that all support vector machine models passed cross-validation and could meet accuracy standards. In summary, the gene algorithm support vector machine model (accuracy: training 100%, test 98.95%; F1-score: training 100%, test 99.17%) is suitable as an optimal variable-flow regulation model for an intelligent variable-flow RAS.
资助项目Key Program for International Cooperation on Scientific and Technological Innovation, Ministry of Science and Technology of the People's Republic of China[2017YFE0118300] ; National Key R&D Programs of China[2019YFD0900800] ; National Key R&D Programs of China[2019YFD0900502]
WOS研究方向Chemistry ; Engineering ; Materials Science ; Physics
语种英语
WOS记录号WOS:000675940200001
出版者MDPI
源URL[http://ir.qdio.ac.cn/handle/337002/175785]  
专题海洋研究所_实验海洋生物学重点实验室
通讯作者Sun, Jianming
作者单位1.Chinese Acad Sci, Ctr Ocean Megasci, Inst Oceanol, CAS, Qingdao 266071, Peoples R China
2.Chinese Acad Sci, Ctr Ocean Megasci, Inst Oceanol, Shandong Prov Key Lab Expt Marine Biol, Qingdao 266071, Peoples R China
3.Qingdao Natl Lab Marine Sci & Technol, Lab Marine Biol & Biotechnol, Qingdao 266071, Peoples R China
4.Dalian Huixin Titanium Equipment Dev Co Ltd, Dalian 116039, Peoples R China
5.Liaoning Ocean & Fisheries Sci Res Inst, Liaoning Prov Key Lab Marine Biol Resources & Eco, Dalian Key Lab Conservat Fishery Resources, Dalian 116023, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Fudi,Du, Yishuai,Qiu, Tianlong,et al. Design of an Intelligent Variable-Flow Recirculating Aquaculture System Based on Machine Learning Methods[J]. APPLIED SCIENCES-BASEL,2021,11(14):15.
APA Chen, Fudi.,Du, Yishuai.,Qiu, Tianlong.,Xu, Zhe.,Zhou, Li.,...&Sun, Jianming.(2021).Design of an Intelligent Variable-Flow Recirculating Aquaculture System Based on Machine Learning Methods.APPLIED SCIENCES-BASEL,11(14),15.
MLA Chen, Fudi,et al."Design of an Intelligent Variable-Flow Recirculating Aquaculture System Based on Machine Learning Methods".APPLIED SCIENCES-BASEL 11.14(2021):15.

入库方式： OAI收割

来源：海洋研究所

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