DOI: https://doi.org/10.32515/2664-262X.2019.2(33).214-221

The Structure of Monitoring and Identification by Oil Pollution

Olena Holyk, Ihor Volkov, Mohammad Ismail

About the Authors

Olena Holyk, Associate Professor, PhD in Technics (Candidate of Technics Sciences), Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine

Ihor Volkov, Instructor, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine

Mohammad Ismail, Doctoral Student, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine

Abstract

In different countries, scientists pay attention the method of monitoring, identification and water purification from oil pollutions. The problem of oil pollution is not only relevant for oil producing countries. Oil spills can occur anywhere in the world. The authors of this article propose to develop the robot with artificial intelligence that would monitor, identify and purify water resources from oil pollution in the mode of real time. Previous studies have shown that it is advisable to use biological methods to water purification from oil pollution. To date, scientists have already developed preparations containing a consortium of microorganisms to purify water resources from oil and petroleum products. Microorganisms are able to adapt to large doses of oil. As a result of the biological treatment of petroleum contamination, such microorganisms in the environment remain bacterial protein (which does not require further disposal) and non-toxic oil decay products. The products of the activity of bacteria and the bacteria themselves are easily absorbed by the native microflora, giving the basis for the formation of humus or forming bottom silt. The purpose of this work is to investigate installations for the biological treatment of water resources from oil and petroleum products. In order to achieve this goal, the structure of the scheme of general analysis of oil pollution is proposed in the article. This scheme contains blocks of comparison, determination of the type and amount of contamination. The results are processed using statistical and mathematical analysis methods. The following algorithm is proposed. The robot has a special container for collecting water samples. This container has special sensors that determine the condition of the sample and transmit information to the comparison unit. The comparison unit, based on the knowledge base, determines the conformity of the water sample to the standards. If the amount of pollutants is exceeded, the information goes to the units for determining the amount and type of pollutants. In the results processing unit, decisions are made regarding the method of purification and the amount of purification preparation. In the future, this scheme will be modified and synthesized. More attention should be paid to developing a database and knowledge that is part of an intelligent decision support system. The application of this scheme to the analysis of oil pollution is possible not only for the determination of oil pollution in water resources.

Keywords

oil pollutions, identification, decision-making, knowledge data base

Full Text:

PDF

References

1. Holyk, O.P., Zhesan, R.V. & Ismail, Mohammad. (2019). Obgruntuvannia avtomatyzatsii kompiuterno-intehrovanoi tekhnolohii identyfikatsii ta monitorynhu naftovykh sabrudnen [Rationale for the Development of Automated Computer-integrated Technology for the Identification and Monitoring of Oil Pollution], Central Ukrainian Scientific Bulletin. Technical Sciences, No 1(32), 220-227. DOI: 10.32515/2664-262X.2019.1(32).220-227. [in Ukraine].

2. Holyk, O.P., Zhesan, R.V. & Ismail, Mohammad. (2019). Modeliuvannia imovirnosti nadkhodzhennia soniachnoi radiatsii dlia system ochyshchennia vid naftovykh sabrudnen [Modeling the probability of solar radiation for oil pollution treatment systems]. Conference proceedings from Intellectual system for decision making and problems of computational intelligence: Mizhnarodna naukova konferentsiia (21-25 travnia 2019 roku). (pp. 42-43). Zaliznyi Port: FOP Vyshemyrskii V.S. [in Ukraine].

3. Holyk, O.P., Zhesan, R.V., Miroshnichenko, M.S. & Ismail, Mohammad. (2019). Poshuk optymalnykh rishen shchodo vyboru metodiv ochyshchennia vodnykh resursiv vid naftovykh zabrudnen [The searching to the optimal decision for the metod selection for the water treatment from oil pollution]. Scientific notes of Taurida National V.I. Vernadsky University". Series: Technical Sciences, Vol. 30 (69), № 5, Part І, 75-80. DOI: https://doi.org/10.32838/2663-5941/2019.5-1/12 [in Ukraine].

4. Plehov, V.H., Dyachenko, V.V., Dyachenko, I.L. (2012). Avtomatizatsiya protsessov biologicheskoy ochistki stochnyih vod predpriyatiy neftyanoy promyishlennosti [Automation of processes of biological wastewater treatment of oil industry enterprises]. Vestnik PNIPU. Himicheskaya tehnologiya i biotehnologiya - Bulletin PNRPU. Chemical technology and biotechnology, №14, 22-33. Retrieved from https://cyberleninka.ru/article/n/avtomatizatsiya-protsessov-biologicheskoy-ochistki-stochnyh-vod-predpriyatiy-neftyanoy-promyshlennosti [in Russian].

5. Maherramov, A.M., Azizov, A.A., Alosmanov, R.M., Buniat-zade, I.A. & Patrusheva, O.V. (2011). Udalenie tonkikh neftianykh plenok s vodnoy poverkhnosti [Removing thin oil films from a water surface]. Molodoy uchenyiy - Young scientist, №7, Vol.133, 65-56. Retrieved from https://moluch.ru/archive/30/3451/ [in Russian].

6. Marchenko, L.A., Beloholov, E.A., Marchenko, A.A., Buhaets, O.N. & Bokovikova, T.N. (2012). Issledovanie vozmozhnosti sorbtsionnoy ochistki pri likvidatsii neftianykh zahriazneniy [Study of the possibility of sorption purification in the elimination of oil pollution]. Scientific Journal of KubSAU, №84. Retrieved from https://cyberleninka.ru/article/n/issledovanie-vozmozhnosti-sorbtsionnoy-ochistki-pri-likvidatsii-neftyanyh-zagryazneniy [in Russian].

7. Dolgopolova, V.L., & Patrusheva, O.V. (2016). Sposobyi ochistki morskih akvatoriy ot neftyanyih zagryazneniy [Methods for cleaning offshore areas from oil pollution]. Molodoy uchenyiy - Young scientist, №29(133), 229-234. Retrieved from https://moluch.ru/archive/133/37456/ [in Russian].

8. Liu, Bo & Chen, Bing & Zhang, Baiyu. (2017). Oily Wastewater Treatment by Nano-TiO2-Induced Photocatalysis. IEEE Nanotechnology Magazine, Vol. PP. 1-1. DOI: 10.1109/MNANO.2017.2708818

9. Yeber, María & Paul, Elvira & Soto, Carolina. (2012). Chemical and biological treatments to clean oily wastewater: Optimization of the photocatalytic process using experimental design. Desalination and Water Treatment, Vol. 47, 295-299. DOI: 10.1080/19443994.2012.696413

10. Kardena, Edwan & Hidayat, Syarif & Nora, Silvia & Helmy, Qomarudin. (2017). Biological Treatment of Synthetic Oilfield-Produced Water in Activated Sludge Using a Consortium of Endogenous Bacteria Isolated from A Tropical Area. Journal of Petroleum & Environmental Biotechnology, Vol. 08. DOI: 10.4172/2157-7463.1000331

11. Lusinier, Nicolas et al. (2019). Biological Treatments of Oilfield Produced Water: A Comprehensive Review. SPE Journal. DOI: 10.2118/195677-PA [in English].

12. Ochistka vody ot nefti. Vse o vode. veb-sajt. Retrieved from: http://www.o8ode.ru/ article/answer/clean/o4ictka_vody_ot_nefti.htm [[in Russian].]

13. Santos, Henrique, Carmo, Flávia, Paes, Jorge, Rosado, Alexandre & Peixoto, Raquel. (2011). Bioremediation of Mangroves Impacted by Petroleum. Water, Air, & Soil Pollution, Vol. 216, 329-350. DOI: 10.1007/s11270-010-0536-4

14. Li, Yao, Wang, Lizhe,Chen, Lajiao, Ma, Yan, Zhu, Xiaomin & Chu, Bin. (2013). Application of DDDAS in marine oil spill management: A new framework combining multiple source remote sensing monitoring and simulation as a symbiotic feedback control system. 2013. International Geoscience and Remote Sensing Symposium (IGARSS). 4526-4529. DOI: 10.1109/IGARSS.2013.6723842

GOST Style Citations

Пристатейна бібліографія ГОСТ

  1. Голик О.П., Жесан Р.В., Ісмаіл Мухаммед Обґрунтування автоматизації комп’ютерно-інтегрованої технології ідентифікації та моніторингу нафтових забруднень. Центральноукраїнський науковий вісник. Технічні науки. 2019. Вип. 1 (32). 220-227. DOI: https://doi.org/10.32515/2664-262X.2019.1(32).220-227
  2. Голик О. П., Жесан Р. В., Ісмаіл Мухаммед Моделювання імовірності надходження сонячної радіації для систем очищення від нафтових забруднень. Інтелектуальні системи прийняття рішень і проблеми обчислювального інтелекту: зб. матеріалів конф., 21-25 травня 2019 р. Херсон: Видавництво ФОП Вишемирський В.С., 2019. С. 42-43.
  3. Голик О.П., Жесан Р.В., Мірошніченко М.С., Ісмаіл Мухаммед Пошук оптимальних рішень щодо вибору методів очищення водних ресурсів від нафтових забруднень. Вчені записки Таврійського національного університету імені В.І. Вернадського. Серія Технічні науки. 2019. Т. 30 (69). № 5. Частина І. 75-80. DOI: https://doi.org/10.32838/2663-5941/2019.5-1/12
  4. Плехов В. Г., Дьяченко В. В., Дьяченко И. Л. Автоматизация процессов биологической очистки сточных вод предприятий нефтяной промышленности. Вестник ПНИПУ. Химическая технология и биотехнология. 2012. №14. С. 22-33. URL: https://cyberleninka.ru/article/n/avtomatizatsiya-protsessov-biologicheskoy-ochistki-stochnyh-vod-predpriyatiy-neftyanoy-promyshlennosti (звернення: 13.05.2019).
  5. Магеррамов А. М., Азизов А. А., Алосманов Р. М., Буният-заде И. А., Керимова Э. С. Удаление тонких нефтяных пленок с водной поверхности. Молодой ученый. 2011. №7. Т.1. С. 65-68. URL https://moluch.ru/archive/30/3451/ (дата звернення: 16.09.2019).
  6. Марченко Людмила Анатольевна, Белоголов Ефим Анатольевич, Марченко Артем Андреевич, Бугаец Ольга Николаевна, Боковикова Татьяна Николаевна. Исследование возможности сорбционной очистки при ликвидации нефтяных загрязнений. Научный журнал КубГАУ - Scientific Journal of KubSAU. 2012. №84. URL: https://cyberleninka.ru/article/n/issledovanie-vozmozhnosti-sorbtsionnoy-ochistki-pri-likvidatsii-neftyanyh-zagryazneniy (дата звернення: 13.09.2019).
  7. Долгополова В. Л., Патрушева О. В. Способы очистки морских акваторий от нефтяных загрязнений. Молодой ученый. 2016. №29 (133). C. 229-234. URL: https://moluch.ru/archive/133/37456/ (дата звернення: 13.05.2019).
  8. Liu, Bo & Chen, Bing & Zhang, Baiyu. Oily Wastewater Treatment by Nano-TiO2-Induced Photocatalysis. IEEE Nanotechnology Magazine. 2017. Vol. PP. 1-1. DOI: 10.1109/MNANO.2017.2708818
  9. Yeber, María & Paul, Elvira & Soto, Carolina. Chemical and biological treatments to clean oily wastewater: Optimization of the photocatalytic process using experimental design. Desalination and Water Treatment. 2012. Vol. 47. 295-299. DOI: 10.1080/19443994.2012.696413
  10. Kardena, Edwan & Hidayat, Syarif & Nora, Silvia & Helmy, Qomarudin. Biological Treatment of Synthetic Oilfield-Produced Water in Activated Sludge Using a Consortium of Endogenous Bacteria Isolated from A Tropical Area. Journal of Petroleum & Environmental Biotechnology. 2017. Vol. 08. DOI: 10.4172/2157-7463.1000331
  11. Lusinier, Nicolas & Seyssiecq, Isabelle & Sambusiti, Cecilia & Jacob, Matthieu & Nicolas, Lesage & Roche, Nicolas. Biological Treatments of Oilfield Produced Water: A Comprehensive Review. SPE Journal. 2019. DOI: 10.2118/195677-PA
  12. Очистка воды от нефти. Все о воде. веб-сайт. URL: http://www.o8ode.ru/article/answer/clean/o4ictka_vody_ot_nefti.htm (дата звернення: 14.12.2019)
  13. Santos, Henrique & Carmo, Flávia & Paes, Jorge & Rosado, Alexandre & Peixoto, Raquel. Bioremediation of Mangroves Impacted by Petroleum. 2011. Water, Air, & Soil Pollution. Vol. 216. 329-350. DOI: 10.1007/s11270-010-0536-4
  14. Li, Yao & Wang, Lizhe & Chen, Lajiao & Ma, Yan & Zhu, Xiaomin & Chu, Bin. Application of DDDAS in marine oil spill management: A new framework combining multiple source remote sensing monitoring and simulation as a symbiotic feedback control system. 2013. International Geoscience and Remote Sensing Symposium (IGARSS). 4526-4529. DOI: 10.1109/IGARSS.2013.6723842
Copyright (c) 2019 Olena Holyk, Ihor Volkov, Mohammad Ismail