Analysis of the Interrelationship between Water Color and Coagulant Dose and the Possibility of Taking it into Account in Mathematical Modeling
https://doi.org/10.18412/1816-0395-2024-11-15-21
Abstract
The character of water chromaticity change in a water source, which is the main one for organizing water supply of a large urban agglomeration, is analyzed. It is shown that the correlation analysis characterizes the interrelationship between the coagulant dose and water chromaticity as noticeable (on the Cheddock scale), which predetermines the necessity to take into account water chromaticity as a parameter influencing the choice of coagulant dose. Variation series were constructed, empirical and theoretical functions of water chromaticity distribution by months were obtained. The possibility of modeling the reagent dose on the change of chromaticity taking into account specific conditions of practically constant influence of seasonal and random factors in the selected periods has been achieved. It is concluded that the chromaticity index makes a significant contribution to the value of the predicted value of coagulant dose.
About the Authors
A.V. Yalaletdinova
Ufa State Petroleum Technological University
Russian Federation
Russian Federation
Cand. Sci. (Eng.), Associate Professor
P.V. Serebryakov
SUE RB “Ufavodokanal”
Russian Federation
Russian Federation
Chief. Engineer
M.Yu. Vozhdaeva
SUE RB “Ufavodokanal”
Russian Federation
Russian Federation
Dr. Sci. (Chem.), Head of the Central Chemical and Bacteriological Laboratory of the Center for Analytical Water Quality Control
E.A. Mazlova
Gubkin Russian State University of Oil and Gas (NRU)
Russian Federation
Russian Federation
Dr. Sci. (Eng.), Professor
I.G. Shaikhiev
Kazan National Research Technological University
Russian Federation
Russian Federation
Dr. Sci. (Eng.), Head of Department
E.A. Kantor
Ufa State Petroleum Technological University
Russian Federation
Russian Federation
Dr. Sci. (Chem.), Professor
References
1. Опекунова М.Г., Никулина А.Р., Опекунов А.Ю., Кукушкин С.Ю., Федькина М.Ю. Скрининг ключевых биогеохимических индикаторов экологического состояния крупных промышленных агломераций (на примере Московского района Санкт-Петербурга). Экология и промышленность России. 2024. Т. 28. № 3. С. 44—49. https://doi.org/10.18412/1816-0395-2024-3-44-49.
2. Wu Yue, He Peiran, Zhang Jian, Wei Ning, Zhang Zhongguo, Zhang Fengshan, Cheng Hongshun. Treatment of Deinking Wastewater by Enhanced Coagulation-Flat Turning Cross-flow Ultrafiltration Process. Chinese Journal of Environmental Engineering. 2022. Vol. 16. No. 5. P. 1497—1505. https://doi.org/10.12030/j.cjee.202201139.
3. Маркушина Л.А. Эффективность экологических инициатив при оценке состояния окружающей среды города. Экономический вестник РГУ "Terra economica". 2010. Т. 3. № 2. С. 59—65.
4. Meyer T., Lei Y.D., Wania F. Transport of polycyclic aromatic hydrocarbons and pesticides during snowmelt within an urban watershed. Water Research. 2011. Vol.45. No. 3. P. 1147—1156. https://doi.org/10.1016/j.watres.2010.11.004.
5. Juncosa R., Arango J.L., Vбzquez R. Physicochemical Parameters in the Generation of Turbidity Episodes in a Water Supply Distribution System. Water. 2022. Vol. 14. P. 3383. https://doi.org/10.3390/w14213383
6. Xu Y., Feng L., Hou X., Wang J., Tang J. Fourdecade dynamics of the water color in 61 large lakes on the Yangtze Plain and the impacts of reclaimed aquaculture zones. Science of The Total Environment. 2021. Vol. 781. P. 146688. https://doi.org/10.1016/j.scitotenv.2021.146688.
7. Malakootian M., Fatehizadeh A. Color removal from water by coagulation/caustic soda and lime. Iranian Journal of Environmental Health Science and Engineering. 2010. Vol. 7. No. 3. P. 267—272.
8. Бигалиев А.Б., Синтюрина А.В., Бияшева З.М. К вопросу о патогенном действии бензапирена, как загрязнителя окружающей среды (обзор). Вестник КазНУ. Серия экологическая. 2009. № 1 (24). С. 14—21.
9. Rosińska A., Dąbrowska L. Influence of type and dose of coagulants on effectiveness of PAH removal in coagulation water treatment. Water Science and Engineering. 2021. Vol. 14. No. 3. P. 193—200. https://doi.org/10.1016/j.wse.2021.08.004.
10. Jr-Lin Lin, Aldeno Rachmad Ika. Minimization of halogenated DBP precursors by enhanced PACl coagulation: The impact of organic molecule fraction changes on DBP precursors destabilization with Al hydrates. Science of The Total Environment. 2020. Vol. 703. P. 134936. https://doi.org/10.1016/j.scitotenv.2019.134936.
11. Linde J.C., Fosso-Kankeu E., Waanders F., Gericke G. Investigation of effective chemical flocculation conditions for the treatment of reverse osmosis reject water from coal power plant: a case study. Desalination and Water Treatment. 2020. Vol. 193. P. 251—265.
12. Dnyaneshwar W., Alka K. Applications of cascade feed forward neural network for modelling of coagulant dose in a drinking water treatment plant: comparative study. Groundwater and Water Quality. 2022. P. 191—198. https://doi.org/10.1007/978-3-031-09551-1_14.
13. Елисеева И.И., Курышева С.В., Костеева Т.В., Пантина И.В. Эконометрика. Учебник. М., Финансы и статистика. 2007. 576 с.
14. Кремер Н.Ш. Теория вероятностей и математическая статистика. Учебник для вузов. 3-е изд., перераб. И доп. М., ЮНИТИ-ДАНА. 2009. 551 с.
15. Ялалетдинова А.В., Еникеева Л.В., Вождаева М.Ю., Кантор Е.А. Статистические характеристики взаимосвязи мутности и расходов воды в реке, вызванных попусками водохранилища. Теоретическая и прикладная экология. 2018. № 1. С. 33—42. https://doi.org/10.25750/1995-4301-2018-1-033-042.
Review
For citations:
Yalaletdinova A., Serebryakov P., Vozhdaeva M., Mazlova E., Shaikhiev I., Kantor E. Analysis of the Interrelationship between Water Color and Coagulant Dose and the Possibility of Taking it into Account in Mathematical Modeling. Ecology and Industry of Russia. 2024;28(11):15-21. (In Russ.) https://doi.org/10.18412/1816-0395-2024-11-15-21
Views:
221
Email this article 