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| Optimization of reverse osmosis desalination process of brackish water based on response surface method |
| WANG Xin-tong1, GAO Feng2, WU You-bing3, YANG Yu2, SONG Ri-quan4, SUN XIN1, LU Jin-suo1 |
1. School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China;
2. Inner Mongolia Water Resources Research Institute, Hohhot 010011, China;
3. Ma’anshan Urban & Rural Planning & Design Institute Co. Ltd., Ma'anshan 243011, China;
4. Inner Mongolia Water Resources and Hydropower Survey and Design Institute Co. Ltd., Hohhot 010020, China |
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Abstract In order to cope with the shortage of fresh water resources and improve the performance of reverse osmosis (RO) process in brackish waters, a single-stage multi-channel low-pressure reverse osmosis pilot device was designed, and a simulation and optimization model for RO desalination process of brackish water was established using Box-Behnken response surface analysis method (RSM). Under various conditions of feed concentration, influent flow rate and the ratio of concentrated water to fresh water, the salt rejection rate and membrane permeation flux of the system were investigated, and the performance index and specific energy consumption (SEC) of the system were analysed and calculated. The results showed that feed concentration and the ratio of concentrated water to fresh water were significant factors affecting the rejection rate. Higher influent flow at low feed concentration could not significantly increase the salt rejectionrate, but increase the specific energy consumption of the equipment. Under the condition of 5000mg/L feed concentration, when the influent flow rate was controlled to be 258.02L/h and the ratio of concentrated water to fresh water was 1.39, the best desalination performance could be achieved, the performance index was 10.58L/(m2·h), and the relative lowest specific energy consumption was 5.15(kW·h)/m3. The constructed RSM model was able to simulate the process of reverse osmosis desalination of brackish water, and the SEC was well predicted with R2 value of 0.9925, and the rejection rate was predicted with RMSE value only of 0.36. The RSM model could optimize the process conditions under different feed concentrations, and guide the long-term efficient operation of RO desalination process.
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Received: 09 November 2023
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[1] Igobo O N, Davies P A. Isothermal Organic Rankine Cycle (ORC) driving Reverse Osmosis (RO) desalination:Experimental investigation and case study using R245 fa working fluid[J]. Applied Thermal Engineering, 2018,136:740-746.
[2] Miyakawa H, Shaiae M, Green T, et al. Reliable sea water Ro operation with high water recovery and no-chlorine/no-sbs dosing in Arabian Gulf, Saudi Arabia[J]. Membranes, 2021,11:141.
[3] Li M. A unified model-based analysis and optimization of specific energy consumption in BWRO and SWRO[J]. Industrial&Engineering Chemistry Research, 2013,52(48):17241-17248.
[4] Kim J, Park K, Hong S. Optimization of two-stage seawater reverse osmosis membrane processes with practical design aspects for improving energy efficiency[J]. Journal of Membrane Science, 2020, 601:117889.
[5] 王辛铜,孙昕,卢金锁,等.微咸湖泊反渗透脱盐无机结垢风险评价及静态阻垢技术研究[J].广东水利水电, 2023,(9):31-36. Wang X T, Sun X, Lu J S, et al. Reverse osmosis desalting brackish lakes inorganic scaling risk assessment and static anti-scaling technology research[J]. Guangdong Water Resources and Hydropower, 2023,(9):31-36.
[6] Sanna A, Buchspies B, Ernst M, et al. Decentralized brackish water reverse osmosis desalination plant based on PV and pumped storage-Technical analysis[J]. Desalination, 2021,516:115232.
[7] Cabrera P, Carta J A, González J, et al. Artificial neural networks applied to manage the variable operation of a simple seawater reverse osmosis plant[J]. Desalination, 2017,416:140-156.
[8] Igomu E M, Ige E O, Adesina O A. Coupled modeling and process optimization in a genetic-algorithm paradigm for reverse osmosis dialysate production plant[J]. South African Journal of Chemical Engineering, 2022,42:337-350.
[9] Elsheikh A H, Sharshir S W, Abd Elaziz M, et al. Modeling of solar energy systems using artificial neural network:A comprehensive review[J]. Solar Energy, 2019,180:622-639.
[10] 魏福强,琚佳琪,胡超,等.响应面法优化电解印染反渗透浓水研究[J].水处理技术, 2021,47(11):71-76. Wei F Q, Ju J Q, Hu C, et al. Response surface optimization in electrolysis of the printing and dyeing reverse osmosis concentrate[J]. Technology of Water Treatment, 2021,47(11):71-76.
[11] Srivastava A, K A, Nair A, et al. Response surface methodology and artificial neural network modelling for the performance evaluation of pilot-scale hybrid nanofiltration (NF)&reverse osmosis (RO) membrane system for the treatment of brackish ground water[J]. Journal of Environmental Management, 2021,278:111497.
[12] Yusefi F, Zahedi M M, Ziyaadini M. Evaluation for the optimization of two conceptual 200,000m3/day capacity RO desalination plant with different intake seawater of Oman Sea and Caspian Sea[J]. Applied Water Science, 2021,11.
[13] Elazhar F, Elazhar M, El-Ghzizel S, et al. Nanofiltration-reverse osmosis hybrid process for hardness removal in brackish water with higher recovery rate and minimization of brine discharges[J]. Process Safety and Environmental Protection, 2021,153:376-383.
[14] 赵相山,杜春良,王晶晶,等.膜法淡化苦咸水的现状分析[J].山东化工, 2021,50(10):81-83. Zhang X S, Du C L, Wang J J, et al. Status analysis of desalting brackish water by membrane method[J]. Shandong Chemical Industry, 2021,50(10):81-83.
[15] 俄有浩,严平,李文赞,等.中国内陆干旱、半干旱区苦咸水分布特征[J].中国沙漠, 2014,34(2):565-573. E Y H, Yan P, Li W Z, et al. Characteristics and distribution of brackish water in arid and semi-arid interior of China[J]. Journal of Desert Research, 2014,34(2):565-573.
[16] Bezerra M A, Santelli R E, Oliveira E P, et al. Response surface methodology (RSM) as a tool for optimization in analytical chemistry[J]. Talanta, 2008,76(5):965-977.
[17] Nam S, Kim S, Her N, et al. Performance assessment and optimization of forward osmosis-low pressure ultrafiltration hybrid system using machine learning for rhodamine B removal[J]. Desalination, 2022, 543:116102.
[18] Schunke A, Hernandez G, Padhye L, et al. Energy recovery in SWRO desalination:Current status and new possibilities[J]. Frontiers in Sustainable Cities, 2020,2.
[19] Abdulsalam Ebrahim M, Karan S, Livingston A G. On the influence of salt concentration on the transport properties of reverse osmosis membranes in high pressure and high recovery desalination[J]. Journal of Membrane Science, 2020,594:117339.
[20] Im S J, Kim M, Jeong G, et al. Corrigendum to "Possibility assessment of ultrafiltration membrane pre-treatment efficiency for brackish water reverse osmosis-based wastewater reuse:Lab and demonstration" [J]. Chemosphere, 2022,306:135643.
[21] Du J, Zhang X, Feng X, et al. Desalination of high salinity brackish water by an NF-RO hybrid system[J]. Desalination. 2020,491:114445.
[22] Al-Obaidi M A, Alsarayreh A A, Al-Hroub A M, et al. Performance analysis of a medium-sized industrial reverse osmosis brackish water desalination plant[J]. Desalination, 2018,443:272-284.
[23] 曹伟新.基于能耗和膜耗的反渗透海水淡化系统的成本分析[J].给水排水, 2020,56(8):73-79. Cao W X. Cost evaluation based on energy and membrane consumption for sea water reverse osmosis system[J]. Water and Wastewater Engineering, 2020,56(8):73-79.
[24] 王敬威,刘研萍,琚宜文,等.高盐、高硬度、高浊度煤系气田产出水预处理工艺参数优化[J].中国科学院大学学报, 2022,39(1):74-82. Wang J W, Liu Y P, Ju Y W, et al. Optimization of pretreatment parameters of produced water in coal series gas field with high salt, high hardness, and high turbidity[J]. Journal of University of Chinese Academy of Sciences, 2022,39(1):74-82.
[25] Dolar D, Košutić K, Strmecky T. Hybrid processes for treatment of landfill leachate:Coagulation/UF/NF-RO and adsorption/UF/NF-RO[J]. Separation and Purification Technology, 2016,168:39-46.
[26] Dadari S, Rahimi M, Zinadini S. Crude oil desalter effluent treatment using high flux synthetic nanocomposite NF membrane-optimization by response surface methodology[J]. Desalination, 2016,377:34-46.
[27] Singh R, Bhunia P, Dash R R. Optimization of bioclogging in vermifilters:A statistical approach[J]. Journal of Environmental Management, 2019,233:576-585.
[28] Singh R, Bhunia P, Dash R R. Optimization of organics removal and understanding the impact of HRT on vermifiltration of brewery wastewater[J]. Science of The Total Environment, 2019,651:1283-1293.
[29] El-Emam R S, Dincer I. Thermodynamic and thermoeconomic analyses of seawater reverse osmosis desalination plant with energy recovery[J]. Energy, 2014,64:154-163.
[30] Ibrar I, Yadav S, Altaee A, et al. A machine learning approach for prediction of reverse solute flux in forward osmosis[J]. Journal of Water Process Engineering, 2023,54:103956.
[31] Aladwani S H, Al-Obaidi M A, Mujtaba I M. Performance of reverse osmosis based desalination process using spiral wound membrane:Sensitivity study of operating parameters under variable seawater conditions[J]. Cleaner Engineering and Technology, 2021,5:100284. |
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