Oqova suvlarni tozalash texnologiyalarini takomillashtirish, ularning energiya sarfini kamaytirish, suv tannarxini tushirish va sifatini oshirish kabi muammolarning bitta yechimi jarayonni to‘g‘ri boshqarish hisoblanadi. Ushbu ishda birinchi marta oqova suvlarni ion-almashinish smolalari yordamida tozalash texnologiyasi ishlab chiqilgan bo‘lib, qurilma Qo‘ng‘irot soda zavodidagi oqova suv aralashmasini tozalash jarayonida sinovdan o‘tkazilgan. Zavod hududida o‘tkazilgan sinov natijalariga ko‘ra, oqova suv tarkibidagi umumiy tuz miqdori 1885 mg/l dan 27,3 mg/l gacha tushirilgan. Umumiy qattiqlik esa 9,3 dan 0,27 gacha pasaytirilgan. pH miqdori 9,5 dan 7,5 gacha kamaytirilgan. Lekin 1 m3 oqova suvni tozalash uchun 1,8–2,5 kWh energiya sarflangan va buni kamaytirish talab etiladi. Mazkur maqolada oqova suvlarni ion-almashinish smolalari orqali tozalash jarayonini noqat’iy mantiq asosida boshqarish modeli ishlab chiqilgan. Suv tarkibidagi umumiy tuzlar miqdori, pH ko‘rsatkichi hamda suvning umumiy qattiqligi asosiy parametrlar hisoblanadi. Qurilmadan olingan tajriba natijalari bo‘yicha belgilangan miqdorda suv sarfini nazorat qilish orqali ushbu parametrlarni noqat’iy mantiq asosida boshqarishning Matlab dasturi yordamida imitatsion modeli qurildi. Natijalar shuni ko‘rsatdiki, ishlab chiqilgan modelni jarayonda qo‘llab, suvdagi umumiy tuzlar konsentratsiyasini 99 %gacha tozalikda boshqarish, rostlash vaqtini esa 20 sekundgacha kamaytirish mumkin. Rostlash vaqtini kamaytirish orqali energiya sarfini o‘rtacha 1,8–2,5 kWh dan 1,2–1,8 kWh gacha oraliqda minimallashtirishga erishilgan.
Oqova suvlarni tozalash texnologiyalarini takomillashtirish, ularning energiya sarfini kamaytirish, suv tannarxini tushirish va sifatini oshirish kabi muammolarning bitta yechimi jarayonni to‘g‘ri boshqarish hisoblanadi. Ushbu ishda birinchi marta oqova suvlarni ion-almashinish smolalari yordamida tozalash texnologiyasi ishlab chiqilgan bo‘lib, qurilma Qo‘ng‘irot soda zavodidagi oqova suv aralashmasini tozalash jarayonida sinovdan o‘tkazilgan. Zavod hududida o‘tkazilgan sinov natijalariga ko‘ra, oqova suv tarkibidagi umumiy tuz miqdori 1885 mg/l dan 27,3 mg/l gacha tushirilgan. Umumiy qattiqlik esa 9,3 dan 0,27 gacha pasaytirilgan. pH miqdori 9,5 dan 7,5 gacha kamaytirilgan. Lekin 1 m3 oqova suvni tozalash uchun 1,8–2,5 kWh energiya sarflangan va buni kamaytirish talab etiladi. Mazkur maqolada oqova suvlarni ion-almashinish smolalari orqali tozalash jarayonini noqat’iy mantiq asosida boshqarish modeli ishlab chiqilgan. Suv tarkibidagi umumiy tuzlar miqdori, pH ko‘rsatkichi hamda suvning umumiy qattiqligi asosiy parametrlar hisoblanadi. Qurilmadan olingan tajriba natijalari bo‘yicha belgilangan miqdorda suv sarfini nazorat qilish orqali ushbu parametrlarni noqat’iy mantiq asosida boshqarishning Matlab dasturi yordamida imitatsion modeli qurildi. Natijalar shuni ko‘rsatdiki, ishlab chiqilgan modelni jarayonda qo‘llab, suvdagi umumiy tuzlar konsentratsiyasini 99 %gacha tozalikda boshqarish, rostlash vaqtini esa 20 sekundgacha kamaytirish mumkin. Rostlash vaqtini kamaytirish orqali energiya sarfini o‘rtacha 1,8–2,5 kWh dan 1,2–1,8 kWh gacha oraliqda minimallashtirishga erishilgan.
Одним из решений таких проблем, как совершенствование технологий очистки сточных вод, снижение их энергоёмкости, снижение стоимости воды и повышение её качества, является правильное управление процессом. В данной работе впервые была разработана технология очистки сточных вод с использованием ионообменных смол, а также испытано устройство процесса очистки смеси сточных вод на Кунградском содовом заводе. По результатам проведённых на заводе испытаний общее количество солей в сточных водах снизилось с 1885 до 27,3 мг/л. Тотальный хард уменьшен с 9,3 до 0,27, pH снижена с 9,5 до 7,5. Но расход электроэнергии на очистку 1 м3 сточных вод составил 1,8–2,5 кВт·ч, поэтому необходимо его снизить. В данной статье разработана модель управления процессом очистки сточных вод с использованием ионообменных смол, основанная на недетерминированной логике. Основными параметрами являются количество общих солей в воде, показатель pH и общая жёсткость воды. На основе экспериментальных результатов, полученных на приборе, посредством программы Matlab была построена имитационная модель управления этими параметрами на основе нечёткой логики. Результаты показывают, что с помощью разработанной модели можно контролировать концентрацию общих солей в воде чистотой до 99 %, а время регулировки можно сократить до 20 секунд. Минимизация энергопотребления в среднем с 1,8–2,5 до 1,2–1,8 кВт·ч достигнута за счёт сокращения времени регулировки.
Proper process control practice can be regarded as one of effective solutions to such problems as improving wastewater treatment technologies, reducing energy consumption as well as lowering the cost of water and increasing its quality. As a result of this work, wastewater treatment technology using ionexchange resins has been developed, and the device - tested in the process of treating the wastewater mixture at the Kungrad soda factory. According to the results of the tests taken place at the factory, the total dissolved solids in the waste water decreased from 1885 mg/l to 27.3 mg/l. Overall hardness reduced from 9.3 to 0.27. The pH went down from 9.5 to 7.5. However, the energy consumption parameters went up to 2.5 from 1.8 kWh for purifying 1 m3 of wastewater, that needs to be reduced. This article reviews development of a model for controlling the process of wastewater treatment using ion-exchange resins based on fuzzy logic. The main parameters are the total dissolved solids in the water, the pH indicator, and the total hardness of the water. Based on the experimental findings retrieved from the device, a simulation model of control on these parameters based on fuzzy logic was built using the Matlab software. Findings show that the developed model enables control on concentrations of total salts in water with purity up to 99 %, as well as reduction of settling time to 20 seconds. Minimization of energy consumption from an average of 1.8-2.5 kWh to 1.2-1.8 kWh was achieved by reducing the adjustment time.
| № | Author name | position | Name of organisation |
|---|---|---|---|
| 1 | Eshbobayev J.A. | tayanch doktorant | Toshkent kimyo-texnologiya instituti Shahrisabz filiali |
| № | Name of reference |
|---|---|
| 1 | Al-Khuzaie, M. M., & Abdul Maulud, K. N. (2023). Optimization pollutants removals from wastewater treatment plant using artificial neural networks. IOP Conference Series: Earth and Environmental Science, 1167 (1), 012053. doi:10.1088/1755-1315/1167/1/012053 |
| 2 | Arias, M. F. C., Bru, L. V. I., Rico, D. P., & Galvañ, P. V. (2011). Comparison of ion exchange resins used in reduction of boron in desalinated water for human consumption. Desalination, 278(1–3), 244–249. doi:10.1016/j.desal.2011.05.030 |
| 3 | Ayaz, M., Namazi, M. A., Din, M. A. U., Ershath, M. I. M., Mansour, A., & Aggoune, el-H. M. (2022). Sustainable seawater desalination: Current status, environmental implications and future expectations. Desalination, 540, 116022. doi:10.1016/j.desal.2022.116022 |
| 4 | Chiroşcă, A., Dumitraşcu, G., Barbu, M., & Caraman, S. (2011). Fuzzy Control of a Wastewater Treatment Process. In J. Watada, G. Phillips-Wren, L. C. Jain, & R. J. Howlett (Eds.), Intelligent Decision Technologies (Vol. 10, pp. 155–163). Springer Berlin Heidelberg. doi:10.1007/978-3-642-22194-1_16 |
| 5 | Curto, D., Franzitta, V., & Guercio, A. (2021). A review of the water desalination technologies. Applied Sciences, 11 (2), 670. doi:org/10.3390/app11020670 |
| 6 | Darre, N. C., & Toor, G. S. (2018). Desalination of water: a review. Current Pollution Reports, 4 (2), 104–111. doi:10.1007/s40726-018-0085-9 |
| 7 | Dhakal, N., Salinas-Rodriguez, S. G., Hamdani, J., Abushaban, M., Sawalha, H., Schippers, J., & Kennedy, M. (2022). Is Desalination a Solution to Freshwater Scarcity in Developing Countries? Membranes, 12, 381. doi:10.3390/membranes12040381 |
| 8 | Elsaid, K., Kamil, M., Sayed, E. T., Abdelkareem, M. A., Wilberforce, T., & Olabi, A. (2020). Environmental impact of desalination technologies: A review. Science of The Total Environment, 748, 141528. doi:10.1016/j. scitotenv.2020.141528 |
| 9 | Eshbobaev, J. A., Khamidov, B. T., Norkobilov, A. T., & Kodirov, O. S. (2023). Development of the computer model of the waste water treatment process using ion-exchange resins in the Matlab program. Chemical Technology, Control and Management, 4, 63–69. doi:10.59048/2181-1105.1484 |
| 10 | Eshbobaev, J., Norkobilov, A., Turakulov, Z., Khamidov, B., & Kodirov, O. (2023). Field trial of solarpowered ion-exchange resin for the industrial wastewater treatment process. ECP 2023, 47. doi:10.3390/ ECP2023-14626 |
| 11 | Ghazi, Z. M., Rizvi, S. W. F., Shahid, W. M., Abdulhameed, A. M., Saleem, H., & Zaidi, S. J. (2022). An overview of water desalination systems integrated with renewable energy sources. Desalination, 542, 116063. doi:10.1016/j.desal.2022.116063 |
| 12 | Gleick, P. H., & Cooley, H. (2009). The World’s Water 2008-2009: The Biennial Report on Freshwater Resources. Island Press. |
| 13 | Ihsanullah, I., Atieh, M. A., Sajid, M., & Nazal, M. K. (2021). Desalination and environment: A critical analysis of impacts, mitigation strategies, and greener desalination technologies. Science of The Total Environment, 780, 146585. doi:10.1016/j.scitotenv.2021.146585 |
| 14 | Jones, E., Qadir, M., Van Vliet, M. T. H., Smakhtin, V., & Kang, S. (2019). The state of desalination and brine production: a global outlook. Science of The Total Environment, 657, 1343–1356. doi:10.1016/j. scitotenv.2018.12.076 |
| 15 | Kamolov, A., Turakulov, Z., Norkobilov, A., Variny, M., & Fallanza, M. (2023). Decarbonization challenges and opportunities of power sector in Uzbekistan: a simulation of turakurgan natural gas-�ired combined cycle power Plant with exhaust gas recirculation. Engineering Proceedings, 37 (1), Article 1. doi:10.3390/ECP2023- 14648 |
| 16 | Ming, C. C. (2017). Evaluation, modelling and control of ultrafiltration membrane water treatment systems. |
| 17 | Nour, M., Said, S. M., Ramadan, H., Ali, A., & Farkas, C. (2018). Control of Electric Vehicles Charging Without Communication Infrastructure. 2018 Twentieth International Middle East Power Systems Conference (MEPCON), 773–778. doi:10.1109/MEPCON.2018.8635277 |
| 18 | Qiao, J.-F., Hou, Y., Zhang, L., & Han, H.-G. (2018). Adaptive fuzzy neural network control of wastewater treatment process with multiobjective operation. Neurocomputing, 275, 383–393. doi:10.1016/j. neucom.2017.08.059 |
| 19 | Saavedra, A., Valdés, H., Mahn, A., & Acosta, O. (2021). Comparative analysis of conventional and emerging technologies for seawater desalination: northern chile as a case study. Membranes, 11 (3), 180. doi:10.3390/membranes11030180 |
| 20 | Santín, I., Pedret, C., & Vilanova, R. (2015). Fuzzy control and model predictive control configurations for effluent violations removal in wastewater treatment plants. Industrial & Engineering Chemistry Research, 54 (10), 2763–2775. doi:10.1021/ie504079q |
| 21 | Sharon, H., & Reddy, K. S. (2015). A review of solar energy driven desalination technologies. Renewable and Sustainable Energy Reviews, 41, 1080–1118. doi:10.1016/j.rser.2014.09.002 |
| 22 | Yalaletdinova, A., Malkova, M., Vozhdaeva, M., Serebryakov, P., Kantor, O., & Kantor, E. (2024). Prediction of optimal coagulant and flocculant dosage for water treatment at surface water intake. E3S Web of Conferences, 480, 02009. doi:10.1051/e3sconf/202448002009 |