Prediction of the parameter “turbidity” at the outlet of sand filters fed by wastewater
https://doi.org/10.26897/1997-6011-2023-5-60-65
Abstract
The work was carried out according to an innovative methodology that combines the approach of regression of Gaussian processes with the Broyden-Fletcher-Goldfarb optimization algorithm with limited memory in order to predict the turbidity parameter of water at the outlet of sand-filled filters used in micro-irrigation systems. The order of significance of the variables involved in predicting the “turbidity” parameter at the outlet of filters with sand filler has been established. In particular, the input variable “Turbidity” can be considered the most important parameter in making a forecast. The methodology applied in the work can be successfully applied to other filtration processes with the same or different types of filter media, but the characteristics of each filter and experiment must always be taken into account.
About the Authors
T. A. ShatashviliRussian Federation
Shatashvili Tamara Alexandrovna, candidate of physical-mathematical sciences, associate professor
283048, Donetsk, st. Shchorsa, 31
M. Yu. Badekin
Russian Federation
Badekin Maxim Yurievich, senior lecturer, Author ID: 201633
283001, Donetsk, st. Universitetskaya, 24
N. N. Ivakhnenko
Russian Federation
Ivakhnenko Natalya Nikolaevna, candidate of physical-mathematical sciences, associate professor; Author ID: 836861
127434, Moscow, st. Timiryazevskaya, 49
N. A. Konoplin
Russian Federation
Konoplin Nikolai Aleksandrovich, candidate of physical-mathematical sciences, associate professor; Author ID: 580233
127434, Moscow, st. Timiryazevskaya, 49
References
1. Capra A., Scicolone B. Emitter and filter tests for wastewater reuse by drip irrigation. Agricultural water management. 2004 Vol. 68. No. 2. P. 135-149. – DOI 10.1016/j.agwat.2004.03.005.
2. Ivakhnenko N.N., Badekin M.Yu. Wastewater treatment by chromatographic processes. Modern development of Russia in the conditions of the new digital economy: materials of the II International scientific and practical conference, Krasnodar, April 19-20, 2018. Krasnodar: Range-B, 2018. P. 353-356.
3. Madramootoo C., Lee P.S., Gopalakrishnan M. International commission on irrigation and drainage (ICID): its objectives, achievements and plans // Irrigation and Drainage. 2009 Vol. 58. No S1. P. S22-S31. DOI– 10.1002/ird.475.
4. Hastie T., Tibshirani R., Friedman J. The elements of statistical learning: data mining, inference, and prediction. New York: springer, 2009. Vol. 2. P. 758.
5. Hawari A.H., Elamine M., Benamor A., Hasan S.W., Ayari M.A., Electorowicz M. Fuzzy logic-based model to predict the impact of flow rate and turbidity on the performance of multimedia filters. Journal of Environmental Engineering. 2017. Vol. 143. No 9. P. 04017065-1-04017065-9. – DOI 10.1061/(ASCE)EE.1943-7870.0001262.
6. Koltsov V.B., Potemkin A.Ya., Konoplin N.A., Soshina T.M., Prishchep V.L. Physical and chemical modeling of technological processes – a modern way to create new resource-saving technologies // Journal Prirodoobustrojstvo. 2010. No. 3. P. 98-102.
7. Kong D., Chen Y., Li N. Force-based tool wear estimation for milling process using Gaussian mixture hidden Markov models // The International Journal of Advanced Manufacturing Technology. 2017 Vol. 92. P. 2853-2865. – DOI 10.1007/s00170-017-0367-1
8. Ko J., Fox D. Learning GP-Bayes Filters via Gaussian process latent variable models. Autonomous Robots. 2011 Vol. 30. P. 3-23. – DOI 10.1007/s10514-010-9213-0
9. Lawrence N. Probabilistic non-linear principal component analysis with Gaussian process latent variable models. Journal of Machine Learning Research, 2010. Vol. 6, P. 1783-1816.
10. Rabiner L., Rosenberg A., Levinson S. Considerations in dynamic time warping algorithms for discrete word recognition. IEEE Transactions on Acoustics, Speech and Signal Processing. 1978 Vol. 26. P. 575-582.
Review
For citations:
Shatashvili T.A., Badekin M.Yu., Ivakhnenko N.N., Konoplin N.A. Prediction of the parameter “turbidity” at the outlet of sand filters fed by wastewater. Prirodoobustrojstvo. 2023;(5):60-65. (In Russ.) https://doi.org/10.26897/1997-6011-2023-5-60-65