The factors of water quality in the intensive marine stichopus japonicus aquaculture process are changing with seasons, so water temperature, salinity, pH value and nitrite were selected as auxiliary variables to measure the concentration of ammonia nitrogen. FCM (Fuzzy C-means) algorithm was adopted to classify them. Based on the EM (Expectation Maximization) algorithm, fuzzy sub-models of ammonia nitrogen concentration were constructed around each operating point, and finally the fuzzy sub-models were combined according to the posterior distribution of the characteristics of the sampling data. Based on the data collected at Xinyulong Marine Biological Seed Technology Co., Ltd, in Dalian China, the ammonia nitrogen concentration prediction model was tested and verified.
AnyadikeC., MbajiorguC. and AjahG., Review of aquacultural production system models, Niger. J. Technol.35 (2016), 448–457.
2.
IsnansetyoA., GetsuS., SeguchiM., et al., Independent Effects of Temperature, Salinity, Ammonium Concentration and pH on Nitrification Rate of the Ariake Seawater Above Mud Sediment, HAYATI Journal of Biosciences21(1) (2014), 21–30.
3.
Carol E. Bower, Ionization of Ammonia in Seawater: Effects of Temperature, pH, and Salinity, Journal of the Fisheries Research Board of Canada35 (1978), 1012–1016.
4.
Černe,G., DovžanD. and Škrjanc,I., Short-term load forecasting by separating daily profiles and using a single fuzzy model across the entire domain, IEEE Trans. Ind. Electron.65(9) (2018), 7406–7415.
5.
ChiuS.L., Fuzzy model identification based on cluster estimation, Journal of Intelligent and Fuzzy Systems2(3) (1994), 267–278.
6.
ChanghuiD., DeyanK., YanhongS., et al., A soft-sensing approach to on-line predicting ammonia nitrogen based on RBF neural networks, International Conferences on Embedded Software and Systems978(1) (2009), 454–458.
7.
DovžanD. and Škrjanc,I., Recursive fuzzy c-means clustering for recursive fuzzy identification of time-varying processes, ISA Trans.50(2) (2011), 159–169.
8.
DovžanD., LogarV. and Škrjanc,I., Implementation of an evolving fuzzy model (eFuMo) in a monitoring system for a waste-water treatment process, IEEE Trans. Fuzzy Syst.23(5) (2015), 1761–1776.
9.
FerreiraN., BonettiC. and SeiffertW., Hydrological and water quality indices as management tools in marine shrimp culture, Aquaculture318 (2011), 425–433.
10.
McLachlanG., KrishnanT. The EM Algorithm and Extensions, John Wiley & Sons, New York. 2008.
11.
McLachlanG., PeelD. Finite Mixture Models. John Wiley & Sons, New York. 2000.
12.
QingxiH., KeesingJ.K. and DongyanL., A review of sea cucumber aquaculture, ranching, and stock enhancement in China, Reviews in Fisheries Science & Aquaculture24(4) (2016), 326–341.
13.
Hong-GuiH., Xiao-LongW., LuZ., et al. Intelligent modeling approach to predict effluent quality of wastewater treatment process. Wastewater and Water Quality. 2018.
14.
JuanH., HuiL., FanW. and WeijianC., Design of water quality monitoring system for aquaculture ponds based on NB-IoT, Aquacultural Engineering90 (2020), 102088.
15.
XingJ., BiaoH. and ShookD.S., Multiple model LPV approach to nonlinear process identification with EM algorithm, Journal of Process Control21 (2011), 182–193.
16.
JinwuJ. and LanyingW., Analysis of ammonia nitrogen content in water based on weighted least squares support vector machine algorithm, Journal of Software Engineering and Applications09 (2016), 45–51.
17.
KangL., WeiW. and shaohanL., Soft measurement of ammonia nitrogen concentration based on GA-SCN, Dec, IEEE International Symposium on Product Compliance Engineering - Asia, Shengzhen (2018), 5–7.
18.
KunningL., YongZ., YuanbiaoZ., et al., Determination of ammonia nitrogen in natural waters: Recent advances and applications, Trends in Environmental Analytical Chemistry24 (2019), 1–13.
19.
NúñezA., De Schutter,B., SaezD. and Škrjanc,I., Hybrid-fuzzy modeling and identification, Applied Soft Computing17 (2014), 67–78.
20.
NimaS., YujiaZ. and BiaoH., A review of the expectation maximization algorithm in data-driven process identification, Journal of Process Control73 (2019), 123–136.
21.
TakagiT. and SugenoM., Fuzzy identification of systems and its applications,to modeling and control, IEEE Trans. Syst. Man Cybern15(1) (1985), 116–132.
22.
ChuW., CancanJ., TianmingG., et al., Improvement of fish production and water quality in a recirculating aquaculture pond enhanced with bacteria-microalgae association, Aquaculture547 (2022), 737420.
23.
WeiW., KangL., KeweiC. and DonggangH., Analysis of ammonia nitrogen concentration using stochastic configuration networks, Chinese Automation Congress30 (2018), 903–906.
24.
XingR., MaW., ShaoY., et al., Factors that affect the growth and photosynthesis of the filamentous green algae, Chaetomorpha valida, in static sea cucumber aquaculture ponds with high salinity and high pH, PeerJ.7 (2019), e6468.
25.
HuihuiY., LingY., DaoliangL., et al., A hybrid intelligent soft computing method for ammonia nitrogen pr ediction in aquaculture, Information Processing in Agriculture (8) (2021), 64–74.
26.
HongbiaoZ. and JunfeiQ., Soft sensing of effluent ammonia nitrogen using rule automatic formation-based adaptive fuzzy neural network, Desalination and Water Treatment14 (2019), 132–142.
