Design of an Intelligent Variable-Flow Recirculating Aquaculture System Based on Machine Learning Methods
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 excret...
Main Authors: | , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-07-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/11/14/6546 |
id |
doaj-c99b98f614404f8f9b7c0525029ffcd1 |
---|---|
record_format |
Article |
spelling |
doaj-c99b98f614404f8f9b7c0525029ffcd12021-07-23T13:29:59ZengMDPI AGApplied Sciences2076-34172021-07-01116546654610.3390/app11146546Design of an Intelligent Variable-Flow Recirculating Aquaculture System Based on Machine Learning MethodsFudi Chen0Yishuai Du1Tianlong Qiu2Zhe Xu3Li Zhou4Jianping Xu5Ming Sun6Ye Li7Jianming Sun8CAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, ChinaCAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, ChinaCAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, ChinaDalian Huixin Titanium Equipment Development Co., Ltd., Dalian 116039, ChinaCAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, ChinaCAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, ChinaCAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, ChinaCAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, ChinaCAS and Shandong Province Key Laboratory of Experimental Marine Biology, Center for Ocean Mega-Science, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, ChinaA 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.https://www.mdpi.com/2076-3417/11/14/6546recirculating aquaculture systemvariable-flow regulation modelcirculating pump-drum filter linkage working techniquemachine learning methodsgene algorithm support vector machine |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Fudi Chen Yishuai Du Tianlong Qiu Zhe Xu Li Zhou Jianping Xu Ming Sun Ye Li Jianming Sun |
spellingShingle |
Fudi Chen Yishuai Du Tianlong Qiu Zhe Xu Li Zhou Jianping Xu Ming Sun Ye Li Jianming Sun Design of an Intelligent Variable-Flow Recirculating Aquaculture System Based on Machine Learning Methods Applied Sciences recirculating aquaculture system variable-flow regulation model circulating pump-drum filter linkage working technique machine learning methods gene algorithm support vector machine |
author_facet |
Fudi Chen Yishuai Du Tianlong Qiu Zhe Xu Li Zhou Jianping Xu Ming Sun Ye Li Jianming Sun |
author_sort |
Fudi Chen |
title |
Design of an Intelligent Variable-Flow Recirculating Aquaculture System Based on Machine Learning Methods |
title_short |
Design of an Intelligent Variable-Flow Recirculating Aquaculture System Based on Machine Learning Methods |
title_full |
Design of an Intelligent Variable-Flow Recirculating Aquaculture System Based on Machine Learning Methods |
title_fullStr |
Design of an Intelligent Variable-Flow Recirculating Aquaculture System Based on Machine Learning Methods |
title_full_unstemmed |
Design of an Intelligent Variable-Flow Recirculating Aquaculture System Based on Machine Learning Methods |
title_sort |
design of an intelligent variable-flow recirculating aquaculture system based on machine learning methods |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-07-01 |
description |
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. |
topic |
recirculating aquaculture system variable-flow regulation model circulating pump-drum filter linkage working technique machine learning methods gene algorithm support vector machine |
url |
https://www.mdpi.com/2076-3417/11/14/6546 |
work_keys_str_mv |
AT fudichen designofanintelligentvariableflowrecirculatingaquaculturesystembasedonmachinelearningmethods AT yishuaidu designofanintelligentvariableflowrecirculatingaquaculturesystembasedonmachinelearningmethods AT tianlongqiu designofanintelligentvariableflowrecirculatingaquaculturesystembasedonmachinelearningmethods AT zhexu designofanintelligentvariableflowrecirculatingaquaculturesystembasedonmachinelearningmethods AT lizhou designofanintelligentvariableflowrecirculatingaquaculturesystembasedonmachinelearningmethods AT jianpingxu designofanintelligentvariableflowrecirculatingaquaculturesystembasedonmachinelearningmethods AT mingsun designofanintelligentvariableflowrecirculatingaquaculturesystembasedonmachinelearningmethods AT yeli designofanintelligentvariableflowrecirculatingaquaculturesystembasedonmachinelearningmethods AT jianmingsun designofanintelligentvariableflowrecirculatingaquaculturesystembasedonmachinelearningmethods |
_version_ |
1721289588965638144 |