Realization of Sewage Treatment Control System based on Fieldbus

doi:10.23940/ijpe.20.03.p6.375382

Abstract

Abstract: In view of the fact that there is no intelligent monitoring device in the existing sewage treatment process, this paper proposes using Siemens S7-300 series PLC as the core controller of the sewage control system. This paper also designs the PLC multi-computer communication based on the Profibus communication protocol, and the field bus sewage treatment control system based on the communication between the main controller and the power meter based on the Modbus protocol. Finally, a sewage treatment control system based on fieldbus is built, and the designed system is applied to the control process of sewage treatment in the CASS process, which greatly reduces the labor intensity of workers and the cost of sewage treatment.

Key words: Multi-Teachers, cooperative education, new energy power generation, control technology

Beiyi Wang, Xiaohong Zhang, and Haibin Wu. Realization of Sewage Treatment Control System based on Fieldbus [J]. Int J Performability Eng, 2020, 16(3): 375-382.

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References

1. J. H. Xie, “Technical Research on Computer Remote Monitoring System,”Computer Programming Skills and Maintenance, No. 8, pp. 116-117, 2018.
2. C. M. Wu, Q. Geng, “The Transformer Station Remote Monitoring System Based on ARM/GPRS Network,” IEEE International Conference on Computational Intelligence and Natural Computing, 2010.
3. Y. L. Yang, “Design of mine sewage treatment control system based on PLC,”Coal Technology, No. 1, pp. 71-73, 2014.
4. 4.J. Sun, “Simulation Design of Wastewater Treatment Control System Based on PLC,” Journal of Huaihai Institute of Technology, Vol. 28, No. 2, pp. 20-24, 2019.
5. J. T. Tang, P. Zheng, H. Y. Liu, et al.“Large-scale sewage treatment control system based on PLC,”Instrument technology and sensors, No. 6, pp. 48-52, 2015.
6. H. L. Liang, Q. F. Bai, et al.“Design of sewage treatment control system for small and medium-sized towns based on PLC,” Mechanical Engineering and Automation, No. 1, pp. 12-13, 2013.
7. W. Lu.“SBR Sewage Treatment Control System Based on PLC,”Equipment Manufacturing Technology, No. 7, pp. 186-188, 2013.
8. D. S. Su, “Design of sewage treatment system control system based on PLC,” Mechanical and electrical engineering technology, Vol. 28, No. 4, pp. 65-68, 2013.
9. Y. H. Xin, H. C. Yuan, “Progress in application of remote monitoring technology,” Journal of North China Institute of Water Conservancy and Hydroelectric Power, Vol. 30, No. 4, pp. 78-79, 2009.
10. J. X. Wang, S. F. Yang, Y. J. Shi, et al.“The development status and trend of remote monitoring technology,”Foreign electronic measurement technology, No. 4, pp. 9-12, 2005.
11. D. Y. Qiu, Y. L. Fan, Y. Li, et al.“Research on sewage treatment monitoring system,” Journal of North China University of Water Resources and Electricity, Vol. 29, No. 2, pp. 80-82, 2008.
12. H. P. Fang, K. L. Fang, “The Design of Remote Embedded Monitoring System based on Internet,”International Conference on Measuring Technology and Mechatronics Automation, pp. 852-854, 2010.
13. L. Y. Wang, “Analysis and Discussion on sewage treatment process effect of small and medium-sized wastewater treatment plants,”Guide of Sci-tech Magazine, No. 27, pp. 393-393, 2011.
14. C. Chandhok, S. Chaturvedi, A. Khurshid, “An approach to image segmentation using K-means clustering algorithm,” International Journal of Information Technology, Vol. 1, No. 1, pp. 11-17, 2012.
15. Y. Lin, Y. Li, X. Yin, et al.“Multisensor Fault Diagnosis Modeling Based on the Evidence Theory,” IEEE Transactions on Reliability, Vol. 67, No. 2, pp. 513-521, 2018.
16. Y. Lin, X. Zhu, Z. Zheng, et al.“The individual identification method of wireless device based on dimensionality reduction and machine learning,” Journal of Supercomputing, Vol. 75, No. 6, pp. 3010-3027, 2019.
17. Y. Tu, Y. Lin, J. Wang, et al.“Semi-Supervised Learning with Generative Adversarial Networks on Digital Signal Modulation Classification,” CMC-Computers Materials & Continua, Vol. 55, No. 2, pp. 243-254, 2018.
18. Y. Lin, C. Wang, J. X. Wang, Z. Dou, “A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks,” Sensors, Vol. 16, No. 10, pp. 1675, 2016.
19. M. G. Khoshkholgh, K. Navaie, H. Yanikomeroglu, “Achievable Capacity in Hybrid DS-CDMA/OFDM Spectrum-Sharing,” IEEE Transactions on Mobile Computing, Vol. 9 No. 6, pp. 765-777, 2010.
20. J. Zhang, H. B. Zhu, “On power allocation for a cognitive radio network with hybrid spectrum sharing,” Science China Information Sciences, Vol. 54, No. 11, pp. 2425-2434, 2011.