Published

2020-04-01

Research on Control Method of Pollutant Total Amount of Water Quality based on Fuzzy Mathematics

Investigación sobre el método de control de la cantidad total de contaminantes de la calidad del agua basada en las matemáticas difusas

DOI:

https://doi.org/10.15446/esrj.v24n2.87248

Keywords:

System structure, Fuzzy mathematics, Total quaintly control of point source, Total quaintly control of unit, Water quality planning (en)
Estructura del sistema, Matemática difusa, Control total de la fuente puntual, Control total de la unidad, Planificación de la calidad del agua (es)

Downloads

Authors

  • Rui Liu Institute of Mathematics and Computer Science, Yan'an University, Yan'an, 716000, China

With the increase of pollutants discharged into the water, it is difficult to control the water environment pollution via reducing part of pollutant discharge. Therefore, the control method of pollutant total amount of water quality based on fuzzy mathematics is proposed. Firstly, a control framework and process of the pollutant total amount was built. The total amount of pollutant discharged into this region was controlled within a certain amount to achieve the predetermined environmental objective. Then, water pollution of different regions was evaluated via water quality model based on the fuzzy mathematics in the region or key protection domain with severe pollution and a concentrated pollution source, which makes the comprehensive evaluation of the water quality pollution more scientific. Finally, the control of pollutant total amount was completed via the optimized combination of point source control and unit control of total amount. Experimental results show that the method is scientific, objective and reasonable during controlling the pollutant discharge. It controls the pollutant total amount excellently.

Con el aumento de los contaminantes descargados en el agua, es difícil controlar la contaminación del medio ambiente mediante la reducción de parte de la descarga de contaminantes. Por lo tanto, se propone el método de control de la cantidad total de contaminante de la calidad del agua basado en matemáticas difusas. En primer lugar, se construyó un marco de control y un proceso de la cantidad total de contaminantes. La cantidad total de contaminantes descargados en esta región se controló dentro de una cierta cantidad para lograr el objetivo ambiental predeterminado. Luego, se evaluó la contaminación del agua de diferentes regiones mediante un modelo de calidad del agua basado en las matemáticas difusas de la región o en el dominio de protección clave con contaminación severa y una fuente de contaminación concentrada, lo que hace que la evaluación integral de la contaminación de la calidad del agua sea más científica. Finalmente, el control de la cantidad total de contaminantes se completó mediante la combinación optimizada del control de fuente puntual y el control de la unidad de la cantidad total. Los resultados experimentales muestran que el método es científico, objetivo y razonable durante el control de la descarga de contaminantes. Controla la cantidad total de contaminantes de forma excelente.

References

Abubakari, A., Raymond, S., Jo, H. S. (2017). Optimal Planar Array Architecture for Full-Dimensional Multi-user Multiple-Input Multiple-Output with Elevation Modeling. Electronics and Telecommunications Research Institute Journal, 39, 234-244.

Bodiga, V. L., Thokala, S., Kovur, S. M., & Bodiga, S. (2017). Zinc Dyshomeostasis in Cardiomyocytes after Acute Hypoxia/Reoxygenation. Biological Trace Element Research, 179, 117-129.

Brix, K. V., DeForest, D. K., Tear, L., Grosell, M., & Adams, W. J. (2017). Use of Multiple Linear Regression Models for Setting Water Quality Criteria for Copper: A Complementary Approach to the Biotic Ligand Model. Environmental Science & Technology, 51, 5182-5192.

Chen, C. H., Tsai, C. R., Liu, Y. H., Hung, W. L., & Wu, A. Y. (2017). Compressive Sensing (CS)-Assisted Low-Complexity Beamspace Hybrid Precoding for Millimeter-Wave MIMO Systems. IEEE Signal Processing Bulletin, 65, 1412-1424.

Eckley, C. S., Gustin, M., Miller, M. B., & Marsik, F. (2017). Scaling non-point-source mercury emissions from two active industrial gold mines: influential variables and annual emission estimates. Environmental Science & Technology, 45, 392-399.

Fan, L., Yuan, Y., Ying, Z., Lam, S. K., Liu, L., Zhang, X., Liu, H., & Gu, B. (2018). Decreasing farm number benefits the mitigation of agricultural non-point source pollution in China. Environmental Science and Pollution Research, 26, 464-472.

Gao, W., Wang, W. F., & Dimitrov, D. (2019). Toughness condition for a graph to be all fractional (g,f,n)-critical deleted. Filomat, 33(9), 2735-2746.

Ge, B., Wang, Z., Lin, W., Xu, X., Li, J., Ji, D., & Ma, Z. (2017). Air pollution over the North China Plain and its implication of regional transport: A new sight from the observed evidences. Environmental Pollution, 166, 29-38.

Huang, W., Kai, S., Qi, J., & Ning, J. (2017). Optimize the configuration of dynamic reactive power supply by using Voronoi diagram method of linear programming. IEEE Power Systems Reporting, 1-1.

Li, Y., Shuai, S., & Tong, S. (2017). Adaptive Fuzzy Control Design for Stochastic Nonlinear Switched Systems with Arbitrary Switchings and Unmodeled Dynamics. IEEE Transactions on Cybernetics, 47, 403-414.

Mohamad, D. A., & Boumahdaf, A. (2017). Semiparametric Bicomponent Mixed Model with Linear Constraints. IEEE Information Theory Bulletin, 64, 1-36.

Molinos-Senante, M., Maziotis, A., & Sala-Garrido, R. (2017). Assessment of the Total Factor Productivity Change in the English and Welsh Water Industry: a Färe-Primont Productivity Index Approach. Water Resources Management, 31, 2389-2405.

Read, E. K., Carr, L., Cicco, L. D., Dugan, H. A., Hanson, P. C., Hart, J., Kreft, J., Read, J. S., & Winslow, L. (2017). Water quality data for national‐scale aquatic research: The Water Quality Portal. Water Resources Research, 53, 1735-1745.

Shi, Y., Xu, G., Wang, Y., Engel, B. A., Peng, H., Zhang, W., Cheng, M., & Dai, M. (2017). Modelling hydrology and water quality processes in the Pengxi River basin of the Three Gorges Reservoir using the soil and water assessment tool. Agricultural water management, 182, 24-38.

Wang, D. A. (2019). Critical review of cellulose-based nanomaterials for water purification in industrial processes. Cellulose, 26, 687-701.

Wang, Y., Huang, Q., Lemckert, C., & Ma, Y. (2017). Laboratory and magnetic field evaluation of heavy metal pollution in Shilaodan beach. Marine Pollution Bulletin, 117, 291-301.

Wang, Y., Yang, J., Liang, J., Qiang, Y., Fang, S., Gao, M., Fan, X, Yang, G., Zhang, B., & Feng, Y. (2018). Analysis of the environmental behavior of farmers for non-point source pollution control and management in a water source protection area in China. General Environmental Science, 227, 1126-1135.

Yao, Y., Verginelli, I., & Suuberg, E. M. (2017). Two-dimensional analytical model of vapor intrusion involving vertical heterogeneity. Water Resources Research, 53, 4499-4513.

Yu, S., & Lu, H. (2018). Multi-level and step-by-step optimization of integrated watershed management for large-scale total water pollutant allocation. Environmental Geosciences, 77, 373.

Zhou, L., Li, H., & Sun, K. (2017). Teaching performance evaluation by means of a hierarchical multifactorial evaluation model based on type-2 fuzzy sets. Applied Intelligence, 46, 34-44.

How to Cite

APA

Liu, R. (2020). Research on Control Method of Pollutant Total Amount of Water Quality based on Fuzzy Mathematics. Earth Sciences Research Journal, 24(2), 191–199. https://doi.org/10.15446/esrj.v24n2.87248

ACM

[1]
Liu, R. 2020. Research on Control Method of Pollutant Total Amount of Water Quality based on Fuzzy Mathematics. Earth Sciences Research Journal. 24, 2 (Apr. 2020), 191–199. DOI:https://doi.org/10.15446/esrj.v24n2.87248.

ACS

(1)
Liu, R. Research on Control Method of Pollutant Total Amount of Water Quality based on Fuzzy Mathematics. Earth sci. res. j. 2020, 24, 191-199.

ABNT

LIU, R. Research on Control Method of Pollutant Total Amount of Water Quality based on Fuzzy Mathematics. Earth Sciences Research Journal, [S. l.], v. 24, n. 2, p. 191–199, 2020. DOI: 10.15446/esrj.v24n2.87248. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/87248. Acesso em: 15 jul. 2024.

Chicago

Liu, Rui. 2020. “Research on Control Method of Pollutant Total Amount of Water Quality based on Fuzzy Mathematics”. Earth Sciences Research Journal 24 (2):191-99. https://doi.org/10.15446/esrj.v24n2.87248.

Harvard

Liu, R. (2020) “Research on Control Method of Pollutant Total Amount of Water Quality based on Fuzzy Mathematics”, Earth Sciences Research Journal, 24(2), pp. 191–199. doi: 10.15446/esrj.v24n2.87248.

IEEE

[1]
R. Liu, “Research on Control Method of Pollutant Total Amount of Water Quality based on Fuzzy Mathematics”, Earth sci. res. j., vol. 24, no. 2, pp. 191–199, Apr. 2020.

MLA

Liu, R. “Research on Control Method of Pollutant Total Amount of Water Quality based on Fuzzy Mathematics”. Earth Sciences Research Journal, vol. 24, no. 2, Apr. 2020, pp. 191-9, doi:10.15446/esrj.v24n2.87248.

Turabian

Liu, Rui. “Research on Control Method of Pollutant Total Amount of Water Quality based on Fuzzy Mathematics”. Earth Sciences Research Journal 24, no. 2 (April 1, 2020): 191–199. Accessed July 15, 2024. https://revistas.unal.edu.co/index.php/esrj/article/view/87248.

Vancouver

1.
Liu R. Research on Control Method of Pollutant Total Amount of Water Quality based on Fuzzy Mathematics. Earth sci. res. j. [Internet]. 2020 Apr. 1 [cited 2024 Jul. 15];24(2):191-9. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/87248

Download Citation

CrossRef Cited-by

CrossRef citations5

1. Wenbing Jiang, Yihuo Jiang. (2021). Water pollution monitoring method after flood disaster based on big data technology. Arabian Journal of Geosciences, 14(7) https://doi.org/10.1007/s12517-021-06895-w.

2. Linfang Wang, Dexuan Dang, Yue Liu, Xinyuan Peng, Ruimin Liu. (2023). Dynamic Water Environment Capacity Assessment Based on Control Unit Coupled with SWAT Model and Differential Evolution Algorithm. Water, 15(10), p.1817. https://doi.org/10.3390/w15101817.

3. Qiao Zeng. (2023). Multi-source heterogeneous data fusion model based on fuzzy mathematics. Journal of Computational Methods in Sciences and Engineering, 23(4), p.2165. https://doi.org/10.3233/JCM-226796.

4. Yuxuan Liu, Yuqi Zhang, Peidong Su, Guangze Zhang, Peng Qiu, Lin Tang. (2022). Risk Prediction of Rock Bursts and Large Deformations in YL Tunnel of the Chongqing–Kunming High-Speed Railway. Frontiers in Earth Science, 10 https://doi.org/10.3389/feart.2022.892606.

5. Jiangbo Yu. (2021). Coordinated development of urban economy and total amount control of water environmental pollutants in the Yellow River basin. Arabian Journal of Geosciences, 14(8) https://doi.org/10.1007/s12517-021-06999-3.

Dimensions

PlumX

Article abstract page views

455

Downloads

Download data is not yet available.