Skip to main content

Advertisement

Springer Nature Link
Log in

Design of intelligent control system for printing and dyeing wastewater treatment under internet of things and deep learning

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

The study aims to improve the utilization rate of water resources and meet the new national requirements for wastewater discharge indexes. The study proposes an intelligent control system for printing and dyeing wastewater treatment based on the internet of things (IoT) and deep learning (DL). Specifically, IoT is used to accurately monitor the real-time data of sewage treatment equipment and quality, and a DL structure with multi-layer nonlinear mapping is constructed. The wastewater in a printing and dyeing factory is taken as the research object, and it is treated by the printing and dyeing wastewater treatment system with “iron–carbon micro electrolysis, biological contact oxidation (BCO), and advanced oxidation process (AOP)” as the main processes. IoT and DL are combined to establish an intelligent wastewater control system for the factory. The sewage treatment system is optimized using the back propagation neural network (BPNN) in DL. And then, the intelligent operation module based on DL is designed and implemented to manage the sewage treatment system. The results show that the removal rates of chemical oxygen demand (COD) and chromaticity by iron–carbon micro electrolysis process are 70.19% and 91.32%; those of COD and ammonia nitrogen by BCO are more than 80%; and those of COD and ammonia nitrogen by AOP are 62.2% and 25.6%. This shows that through DL of the collected data and analysis of the wastewater treatment law, predicting and controlling wastewater discharge can improve the accuracy rate and efficiency of wastewater treatment and provide technical support for wastewater treatment and control.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

References

  1. Wang T, Zhou Y, Bi C, Lu Y, Giesy JP (2017) Determination of water environment standards based on water quality criteria in China: limitations and feasibilities. J Environ Ences 57(7):127–136

    Google Scholar 

  2. Zhu LY, Chen YY, Liu J, Wang YW, Zhang YX (2020) Spatio-temporal evolution and relationship of water environment quality and phytoplankton community in Wenyu River. Huan jing ke xue = Huanjing kexue/[bian ji, Zhongguo ke xue yuan huan jing ke xue wei yuan hui “Huan jing ke xue” bian ji wei yuan hui.] 41(2):702–712

    Google Scholar 

  3. Lavanya S, Prasanth A, Jayachitra S, Shenbagarajan A (2021) A tuned classification approach for efficient heterogeneous fault diagnosis in IoT-enabled WSN applications. Measurement 183:109771

    Article  Google Scholar 

  4. Deogaonkar-Baride S, Wakode P, Rawat K (2021) Treatment of biorefractory organic compounds in dyeing and printing process textile wastewater with electron beam radiation. J Hazard Toxic Radioact Waste 25(2):04021007

    Article  Google Scholar 

  5. Shiguo G et al (2019) Application of polymeric ferric sulfate combined with cross-frequency magnetic field in the printing and dyeing wastewater treatment. Water Sci Technol J Int Assoc Water Pollut Res 80(8):1562–1570

    Article  Google Scholar 

  6. Prasanth A, Jayachitra S (2020) A novel multi-objective optimization strategy for enhancing quality of service in IoT-enabled WSN applications. Peer-to-Peer Netw Appl 4:1–16

    Google Scholar 

  7. Prasanth A, Pavalarajan S (2019) Implementation of efficient intra and inter-zone routing for extending network consistency in wireless sensor networks. J Circuits Syst Comput 29:2050129

    Article  Google Scholar 

  8. Yuan D et al (2018) Treatment performance and degradation process of contaminants in Vitamin B_(12) wastewater. Environ Eng Sci 35(7):673–683

    Article  Google Scholar 

  9. Shen Y, Xu Q, Shi J, Li M, Zhang Y (2017) Optimization and mechanism study of C.I. Acid Blue 25 wastewater degradation by ozone/Fenton oxidation process: response surface methodology, intermediate products and degradation pathway. Desalin Water Treat 65(FEB.):313–326

    Google Scholar 

  10. Nkudede E, Sulemana H, Zhang B, Zhu K, Kosiba AA (2021) Treatment of simulated printing and dyeing wastewater using ozone microbubble. E3S Web Conf 261(1):04005

    Article  Google Scholar 

  11. Yan Y (2020) A dynamic comparison sustainability study of standard wastewater treatment system in the straw pulp papermaking process and printing and dyeing papermaking process based on the hybrid neural network and emergy framework. Water 12:32

    Google Scholar 

  12. Zhou X, Luo R, Yao L (2018) Assessing integrated water use and wastewater treatment systems in China: a mixed network structure two-stage SBM DEA model. J Clean Prod 185(1):533–546

    Article  Google Scholar 

  13. Li P, Liu Z, Wang X, Guo Y, Wang L (2017) Enhanced decolorization of methyl orange in aqueous solution using iron–carbon micro-electrolysis activation of sodium persulfate. Chemosphere 180(Aug.):100–107

    Article  Google Scholar 

  14. Chen Y, Tao L, Wei C (2019) Enhanced removal of organic matter and typical disinfection byproduct precursors in combined iron–carbon micro electrolysis-UBAF process for drinking water pre-treatment. J Environ Sci 78(04):317–329

    Google Scholar 

  15. Ren D, Li S, Wu J, Fu L, Zhang S (2019) Remediation of phenanthrene-contaminated soil by electrokinetics coupled with iron/carbon permeable reactive barrier. Environ Eng Sci 36(9):1224–1235

    Article  Google Scholar 

  16. Zhao J, Gao J, Liu J (2020) Preparation of a new iron–carbon-loaded constructed wetland substrate and enhanced phosphorus removal performance. Materials 13(21):4739

    Article  Google Scholar 

  17. Han X et al (2019) Micro-polluted water treatment by biological contact oxidation process: aeration mode and bacteria community analysis. Environ Eng Sci 36(12):1491–1502

    Article  Google Scholar 

  18. Miklos DB, Remy C, Jekel M, Linden KG, Drewes JE, Huebner U (2018) Evaluation of advanced oxidation processes for water and wastewater treatment—a critical review. Water Res 139(AUG 1):118–131

    Article  Google Scholar 

  19. Huang N, Wang T, Wang WL, Wu QY, Li A, Hu HY (2017) UV/chlorine as an advanced oxidation process for the degradation of benzalkonium chloride: synergistic effect, transformation products and toxicity evaluation. Water Res 114(MAY1):246

    Article  Google Scholar 

  20. Li R, Li Z, Jiang Q, Zhong Z, Zhou M, Xing W (2020) Acid precipitation coupled membrane-dispersion advanced oxidation process (MAOP) to treat crystallization mother liquor of pulp wastewater. Chin J Chem Eng 28(7):1911–1917

    Article  Google Scholar 

  21. Wang T, Zhou Y, Cao S, Lu J, Zhou Y (2019) Degradation of sulfanilamide by Fenton-like reaction and optimization using response surface methodology. Ecotoxicol Environ Saf 172(MAY):334–340

    Article  Google Scholar 

  22. Kim YS, Kim SH (2017) Optimization of fermentation conditions for burdock vinegar using response surface methodology. J Korean Soc Food Sci Nutr 46(8):986–996

    Google Scholar 

  23. Gholamhossein et al (2019) Extraction of seed oil from Diospyros lotus optimized using response surface methodology. J For Res 30(02):333–343

    Google Scholar 

  24. Rahmani M, Atia GK (2017) Coherence pursuit: fast, simple, and robust principal component analysis. IEEE Trans Signal Process 65(23):6260–6275

    Article  MathSciNet  MATH  Google Scholar 

  25. Li H, Linderman GC, Szlam A, Stanton KP, Kluger Y, Tygert M (2017) Algorithm 971: an implementation of a randomized algorithm for principal component analysis. Acm Trans Math Softw 43(3):1–14

    Article  MathSciNet  MATH  Google Scholar 

  26. Lu C, Feng J, Chen Y, Liu W, Lin Z, Yan S (2020) Tensor robust principal component analysis with a new tensor nuclear norm. IEEE Trans Pattern Anal Mach Intell 42(4):925–938

    Article  Google Scholar 

  27. Zhou J et al (2019) Pretreatment of pig manure liquid digestate for microalgae cultivation via innovative flocculation-biological contact oxidation approach. Sci Total Environ 694(Dec.1):133720.1-133720.8

    Google Scholar 

  28. Sakai H, Matsushita K, Kitajima R (2019) Extraction of causal information from summaries of financial statements by automatic generation of training data. J Jpn Soc Fuzzy Theory Intell Inform 31(2):653–661

    Google Scholar 

  29. Uma KV (2018) Improving the classification accuracy of noisy dataset by effective data preprocessing. Int J Comput Appl 180(36):37–46

    Google Scholar 

  30. Chayangkoon N, Srivihok A (2021) Text classification model for methamphetamine-related tweets in Southeast Asia using dual data preprocessing techniques. Int J Electr Comput Eng 11(4):3617

    Google Scholar 

  31. Sano Y, Antabe R, Atuoye KN, Braimah JA, Galaa SZ, Luginaah I (2018) Married women’s autonomy and post-delivery modern contraceptive use in the Democratic Republic of Congo. BMC Women’s Health 18(1):49

    Article  Google Scholar 

  32. Huang B, Ma C (2019) The least squares solution of a class of generalized Sylvester-transpose matrix equations with the norm inequality constraint. J Global Optim 73(1):193–221

    Article  MathSciNet  MATH  Google Scholar 

  33. Eriksson-Bique S (2019) Characterizing spaces satisfying Poincaré inequalities and applications to differentiability. Geom Funct Anal 29(1):119–189

    Article  MathSciNet  MATH  Google Scholar 

  34. Garay-Rodríguez L, Zermeo B, Karla A, Leyva E, Moctezuma E (2018) Photocatalytic degradation of Bisphenol A: kinetic studies and determination of the reaction pathway. J Appl Res Technol 16(5):334–345

    Google Scholar 

  35. Wang W, Zhang J, Chen T, Sun J, Xie Z (2020) Preparation of TiO2-modified biochar and its characteristics of photo-catalysis degradation for enrofloxacin. Sci Rep 10(1):6588

    Article  Google Scholar 

  36. Irani M, Mohammadi T, Mohebbi S, Irani M, Mohammadi T, Mohebbi S (2017) Photocatalytic degradation of methylene blue with ZnO nanoparticles; a joint experimental and theoretical study. J Mex Chem Soc 60(4):218–225

    Article  Google Scholar 

  37. Park JH, Kim YJ (2018) Spread of multidrug-resistant Escherichia coli harboring integron via swine farm waste water treatment plant. Ecotoxicol Environ Saf 149(5):36–42

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Urban Construction, Nanjing Tech University, Nanjing City, 211816, China

    Tong Xu, Changrong Zhao & Zhaoyang You

  2. College of Finance, Nanjing Agricultural University, Nanjing City, 210095, China

    Guancheng Song

Authors
  1. Tong Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Changrong Zhao
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Guancheng Song
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Zhaoyang You
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Zhaoyang You.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, T., Zhao, C., Song, G. et al. Design of intelligent control system for printing and dyeing wastewater treatment under internet of things and deep learning. J Supercomput 78, 18023–18050 (2022). https://doi.org/10.1007/s11227-022-04524-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04524-1

Keywords