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Artículos de revistas sobre el tema "Reverse osmosis networks"

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Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 6 de febrero de 2022

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1

Maskan, Fazilet, Dianne E. Wiley, Lloyd P. M. Johnston y David J. Clements. "Optimal design of reverse osmosis module networks". AIChE Journal 46, n.º 5 (mayo de 2000): 946–54. http://dx.doi.org/10.1002/aic.690460509.

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2

El-Halwagi, Mahmoud M. "Synthesis of reverse-osmosis networks for waste reduction". AIChE Journal 38, n.º 8 (agosto de 1992): 1185–98. http://dx.doi.org/10.1002/aic.690380806.

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3

Voros, N., Z. B. Maroulis y D. Marinos-Kouris. "Optimization of reverse osmosis networks for seawater desalination". Computers & Chemical Engineering 20 (enero de 1996): S345—S350. http://dx.doi.org/10.1016/0098-1354(96)00068-3.

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4

Zhu, M. "Optimal design and scheduling of flexible reverse osmosis networks". Journal of Membrane Science 129, n.º 2 (9 de julio de 1997): 161–74. http://dx.doi.org/10.1016/s0376-7388(96)00310-9.

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5

Jiang, Aipeng, Lorenz T. Biegler, Jian Wang, Wen Cheng, Qiang Ding y Shu Jiangzhou. "Optimal operations for large-scale seawater reverse osmosis networks". Journal of Membrane Science 476 (febrero de 2015): 508–24. http://dx.doi.org/10.1016/j.memsci.2014.12.005.

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6

Du, Yawei, Lixin Xie, Yuxin Wang, Yingjun Xu y Shichang Wang. "Optimization of Reverse Osmosis Networks with Spiral-Wound Modules". Industrial & Engineering Chemistry Research 51, n.º 36 (29 de agosto de 2012): 11764–77. http://dx.doi.org/10.1021/ie300650b.

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7

Lu, Yanyue, Anping Liao y Yangdong Hu. "Design of reverse osmosis networks for multiple freshwater production". Korean Journal of Chemical Engineering 30, n.º 5 (27 de febrero de 2013): 988–96. http://dx.doi.org/10.1007/s11814-013-0009-8.

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8

Saif, Y., A. Elkamel y M. Pritzker. "Optimal design of reverse-osmosis networks for wastewater treatment". Chemical Engineering and Processing: Process Intensification 47, n.º 12 (noviembre de 2008): 2163–74. http://dx.doi.org/10.1016/j.cep.2007.11.007.

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9

See, H. J., D. I. Wilson, V. S. Vassiliadis y G. T. Parks. "Design of reverse osmosis (RO) water treatment networks subject to fouling". Water Science and Technology 49, n.º 2 (1 de enero de 2004): 263–70. http://dx.doi.org/10.2166/wst.2004.0139.

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Identifying the optimal design for an RO membrane network is not straightforward when significant fouling occurs. The most robust optimal network design will feature the minimal capital and operating costs over the anticipated lifetime of the plant, and is therefore strongly dependent on the fouling behaviour and associated mitigation. This study considers the case where the likely fouling behaviour is known and investigates how to incorporate this knowledge into the design and operation of a network. The optimisation task is complicated by the highly non-linear nature of the problem owing to membrane behaviour, fouling behaviour, network interactions and operating parameter constraints (pressures and flows). In this work, fouling is modelled as an exponential decay in membrane permeability. Three optimisation approaches are used to evaluate candidate networks: (i) laborious comparison of pre-selected individual network designs; (ii) deterministic gradient search methods, and (iii) a simulated-annealing-based hybrid stochastic-deterministic method. All of the approaches consider various configurations of a two-stage network with a maximum of three membrane units in each stage, represented in a superstructure model. The results from the approaches are compared and the most effective method for network design is discussed.
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10

Du, Yawei, Lixin Xie, Yan Liu, Shaofeng Zhang y Yingjun Xu. "Optimization of reverse osmosis networks with split partial second pass design". Desalination 365 (junio de 2015): 365–80. http://dx.doi.org/10.1016/j.desal.2015.03.019.

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11

See, H. J., V. S. Vassiliadis y D. I. Wilson. "Optimisation of membrane regeneration scheduling in reverse osmosis networks for seawater desalination". Desalination 125, n.º 1-3 (noviembre de 1999): 37–54. http://dx.doi.org/10.1016/s0011-9164(99)00122-8.

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12

Marcovecchio, Marian G., Pío A. Aguirre y Nicolás J. Scenna. "Global optimal design of reverse osmosis networks for seawater desalination: modeling and algorithm". Desalination 184, n.º 1-3 (noviembre de 2005): 259–71. http://dx.doi.org/10.1016/j.desal.2005.03.056.

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13

Du, Yawei, Xuefei Liang, Yan Liu, Lixin Xie y Shaofeng Zhang. "Exergo-economic analysis and multi-objective optimization of seawater reverse osmosis desalination networks". Desalination 466 (septiembre de 2019): 1–15. http://dx.doi.org/10.1016/j.desal.2019.04.030.

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14

Liu, Chao, Chao Wang, Yongqiang Guo, Jiaming Zhang, Yingying Cao, Hongpeng Liu, Zhen Hu y Chunhua Zhang. "High-performance polyamide membrane with tailored water channel prepared via bionic neural networks for textile wastewater treatment". Journal of Materials Chemistry A 7, n.º 12 (2019): 6695–707. http://dx.doi.org/10.1039/c9ta00031c.

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A novel aromatic polyamide (PA) reverse osmosis membrane with nanoscale water channels and hydrophilic molecular skin surface, similar to those of biomimetic neural networks, was realized for textile wastewater treatment via the facile strategy of interfacial polymerization with the addition of free radicals.
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15

Du, Yawei, Yan Liu, Shaofeng Zhang y Yingjun Xu. "Optimization of Seawater Reverse Osmosis Desalination Networks with Permeate Split Design Considering Boron Removal". Industrial & Engineering Chemistry Research 55, n.º 50 (6 de diciembre de 2016): 12860–79. http://dx.doi.org/10.1021/acs.iecr.6b02225.

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16

Gu, Jianghang, Jiu Luo, Mingheng Li, Chunyan Huang y Yi Heng. "Modeling of pressure drop in reverse osmosis feed channels using multilayer artificial neural networks". Chemical Engineering Research and Design 159 (julio de 2020): 146–56. http://dx.doi.org/10.1016/j.cherd.2020.04.019.

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17

Linke, Patrick. "A Novel Approach for Targeting and Optimal Design of Efficient Seawater Reverse Osmosis Desalination Networks". Qatar Foundation Annual Research Forum Proceedings, n.º 2011 (noviembre de 2011): EVP15. http://dx.doi.org/10.5339/qfarf.2011.evp15.

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18

Du, Yawei, Lixin Xie, Jie Liu, Yuxin Wang, Yingjun Xu y Shichang Wang. "Multi-objective optimization of reverse osmosis networks by lexicographic optimization and augmented epsilon constraint method". Desalination 333, n.º 1 (enero de 2014): 66–81. http://dx.doi.org/10.1016/j.desal.2013.10.028.

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19

Cabrera, Pedro, José A. Carta, Jaime González y Gustavo Melián. "Artificial neural networks applied to manage the variable operation of a simple seawater reverse osmosis plant". Desalination 416 (agosto de 2017): 140–56. http://dx.doi.org/10.1016/j.desal.2017.04.032.

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20

Wang, Jichao, Shanshan Gao, Jiayu Tian, Fuyi Cui y Wenxin Shi. "Recent Developments and Future Challenges of Hydrogels as Draw Solutes in Forward Osmosis Process". Water 12, n.º 3 (3 de marzo de 2020): 692. http://dx.doi.org/10.3390/w12030692.

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Forward osmosis (FO) has been recently regarded as a promising water treatment technology due to its lower energy consumption and lower membrane fouling propensity compared to the reverse osmosis (RO). The absence of suitable draw solute constraints the wide-range application of the FO. Hydrogels are three-dimensional hydrophilic polymer networks that can absorb a huge amount of water. Particularly, stimuli-responsive polymer hydrogels can undergo a reversible volume change or solution-gel phase transition in response to external environmental stimuli, including temperature, light, pressure, solvent composition, and pH. These intrinsic properties indicate the lowest regeneration cost of draw solutes compared to the thermal method and other membrane processes. This review aims to introduce the research progress on hydrogels as draw solutes, clarify the existing problems and point out the further research direction.
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21

Buabeng-Baidoo, Esther y Thokozani Majozi. "Effective Synthesis and Optimization Framework for Integrated Water and Membrane Networks: A Focus on Reverse Osmosis Membranes". Industrial & Engineering Chemistry Research 54, n.º 38 (15 de septiembre de 2015): 9394–406. http://dx.doi.org/10.1021/acs.iecr.5b01803.

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22

Khaouane, Latifa, Yamina Ammi y Salah Hanini. "Modeling the Retention of Organic Compounds by Nanofiltration and Reverse Osmosis Membranes Using Bootstrap Aggregated Neural Networks". Arabian Journal for Science and Engineering 42, n.º 4 (28 de septiembre de 2016): 1443–53. http://dx.doi.org/10.1007/s13369-016-2320-2.

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23

Huliienko, S. V., Y. M. Korniienko y K. O. Gatilov. "Modern trends in the mathematical simulation of pressure-driven membrane processes". Journal of Engineering Sciences 7, n.º 1 (2020): F1—F21. http://dx.doi.org/10.21272/jes.2020.7(1).f1.

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The presented article is an attempt to evaluate the progress in the development of the mathematical simulation of the pressure-driven membrane processes. It was considered more than 170 articles devoted to the simulation of reverse osmosis, nanofiltration, ultrafiltration, and microfiltration and the others published between 2000 and 2010 years. Besides the conventional approaches, which include the irreversible thermodynamics, diffusion and pore flow (and models which consider the membrane surface charge for nanofiltration process), the application of the methods the computational fluid dynamics, artificial neural networks, optimization, and economic analysis have been considered. The main trends in this field have been pointed out, and the areas of using approaches under consideration have been determined. The technological problems which have been solved using the mentioned approaches have also been considered. Although the question of the concentration polarization has not been considered separately, it was defined that, in many cases, the sufficiently accurate model cannot be designed without considering this phenomenon. The findings allow evaluating more thoroughly the development of the simulation of pressure-driven membrane processes. Moreover, the review allows choosing the strategy of the simulation of the considered processes. Keywords: membrane, simulation, model, reverse osmosis nanofiltration, ultrafiltration, microfiltration.
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24

Antas, Francisco P. de S., Jair J. R. de Freitas, André M. de Oliveira, Nildo da S. Dias, Alexandre de O. Lima y Osvaldo N. de Sousa Neto. "A proposed index to assess quality of waters from desalination plants". Revista Brasileira de Engenharia Agrícola e Ambiental 22, n.º 10 (octubre de 2018): 667–72. http://dx.doi.org/10.1590/1807-1929/agriambi.v22n10p667-672.

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ABSTRACT Reverse osmosis is the technology commonly used to produce fresh water from brackish groundwater. Due to the reject brine generated in desalination plants by reverse osmosis, vulnerability assessment to define critical areas to monitor waters from desalination has been an important tool for delineating the monitoring networks required for surveillance of potential salinization sites. The objective of this study was to assess the quality of waters sampled in desalination plants by a quality index, which provides a relative assessment of water vulnerability to potential salinization. The present study proposes an index to assess the quality of waters from desalination plants initially using the chemical parameters electrical conductivity, sodium adsorption ratio, Mg2+/Ca2+ ratio, and the ions sodium, chloride and bicarbonate. The index to assess quality of waters from desalination plants showed good performance and can include additional parameters referring to the soil and crop exploited. High values of the relative index of quality of waters from desalination plants are considered as possible indicators of risk of soil salinization and groundwater contamination. The highest level of land use impact on the quality of waters from desalination plants was found in reject brine samples followed by well water samples.
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25

Ammi, Yamina, Latifa Khaouane y Salah Hanini. "Prediction of the rejection of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes using neural networks". Korean Journal of Chemical Engineering 32, n.º 11 (17 de septiembre de 2015): 2300–2310. http://dx.doi.org/10.1007/s11814-015-0086-y.

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26

Powar, Vishwas y Rajendra Singh. "Stand-Alone Direct Current Power Network Based on Photovoltaics and Lithium-Ion Batteries for Reverse Osmosis Desalination Plant". Energies 14, n.º 10 (12 de mayo de 2021): 2772. http://dx.doi.org/10.3390/en14102772.

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Plummeting reserves and increasing demand of freshwater resources have culminated into a global water crisis. Desalination is a potential solution to mitigate the freshwater shortage. However, the process of desalination is expensive and energy-intensive. Due to the water-energy-climate nexus, there is an urgent need to provide sustainable low-cost electrical power for desalination that has the lowest impact on climate and related ecosystem challenges. For a large-scale reverse osmosis desalination plant, we have proposed the design and analysis of a photovoltaics and battery-based stand-alone direct current power network. The design methodology focusses on appropriate sizing, optimum tilt and temperature compensation techniques based on 10 years of irradiation data for the Carlsbad Desalination Plant in California, USA. A decision-tree approach is employed for ensuring hourly load-generation balance. The power flow analysis evaluates self-sufficient generation even during cloud cover contingencies. The primary goal of the proposed system is to maximize the utilization of generated photovoltaic power and battery energy storage with minimal conversions and transmission losses. The direct current based topology includes high-voltage transmission, on-the-spot local inversion, situational awareness and cyber security features. Lastly, economic feasibility of the proposed system is carried out for a plant lifetime of 30 years. The variable effect of utility-scale battery storage costs for 16–18 h of operation is studied. Our results show that the proposed design will provide low electricity costs ranging from 3.79 to 6.43 ¢/kWh depending on the debt rate. Without employing the concept of baseload electric power, photovoltaics and battery-based direct current power networks for large-scale desalination plants can achieve tremendous energy savings and cost reduction with negligible carbon footprint, thereby providing affordable water for all.
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27

Ammi, Yamina, Latifa Khaouane y Salah Hanini. "A Model Based on Bootstrapped Neural Networks for Modeling the Removal of Organic Compounds by Nanofiltration and Reverse Osmosis Membranes". Arabian Journal for Science and Engineering 43, n.º 11 (2 de agosto de 2018): 6271–84. http://dx.doi.org/10.1007/s13369-018-3484-8.

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28

Li, Qian, Jorge Loy-Benitez, KiJeon Nam, Soonho Hwangbo, Jouan Rashidi y ChangKyoo Yoo. "Sustainable and reliable design of reverse osmosis desalination with hybrid renewable energy systems through supply chain forecasting using recurrent neural networks". Energy 178 (julio de 2019): 277–92. http://dx.doi.org/10.1016/j.energy.2019.04.114.

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29

Abdul Ghani, Latifah, Ilyanni Syazira Nazaran, Nora’aini Ali y Marlia Mohd Hanafiah. "Improving Prediction Accuracy of Socio-Human Relationships in a Small-Scale Desalination Plant". Sustainability 12, n.º 17 (26 de agosto de 2020): 6949. http://dx.doi.org/10.3390/su12176949.

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This study examines who are the social actors in coordinating the environmental hot spots along the process of desalination. The integrated model design of life cycle modeling and Social Network Analysis is evaluated holistically by the inventory of life cycle and actor engagement ratings. Instances of the first small-scale reverse osmosis desalination plant project in Kelantan, Malaysia were used to meet the demands of this study. Environmental performance is measured through the Eco-Indicator 99 method in the Life Cycle Assessment Principles. Meanwhile, the network analysis of the actors’ networks involves stakeholders visualized through the UCINET software. The results show three hotspot points of membrane and brine disposal, the use of electrical energy, and the use of chemicals. The results acknowledged that 87 percent of the actors’ involvement from the dominant stakeholder group has been in control of the management and of the aforementioned hotspot. Undoubtedly, the results of this study can provide a better understanding of the potential market of actors to work with a more accurate and polycentric information flow for the development of more established desalination systems. This intriguing research will require further exploration in future studies.
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30

Asghari, Morteza, Amir Dashti, Mashallah Rezakazemi, Ebrahim Jokar y Hadi Halakoei. "Application of neural networks in membrane separation". Reviews in Chemical Engineering 36, n.º 2 (28 de enero de 2020): 265–310. http://dx.doi.org/10.1515/revce-2018-0011.

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AbstractArtificial neural networks (ANNs) as a powerful technique for solving complicated problems in membrane separation processes have been employed in a wide range of chemical engineering applications. ANNs can be used in the modeling of different processes more easily than other modeling methods. Besides that, the computing time in the design of a membrane separation plant is shorter compared to many mass transfer models. The membrane separation field requires an alternative model that can work alone or in parallel with theoretical or numerical types, which can be quicker and, many a time, much more reliable. They are helpful in cases when scientists do not thoroughly know the physical and chemical rules that govern systems. In ANN modeling, there is no requirement for a deep knowledge of the processes and mathematical equations that govern them. Neural networks are commonly used for the estimation of membrane performance characteristics such as the permeate flux and rejection over the entire range of the process variables, such as pressure, solute concentration, temperature, superficial flow velocity, etc. This review investigates the important aspects of ANNs such as methods of development and training, and modeling strategies in correlation with different types of applications [microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), electrodialysis (ED), etc.]. It also deals with particular types of ANNs that have been confirmed to be effective in practical applications and points out the advantages and disadvantages of using them. The combination of ANN with accurate model predictions and a mechanistic model with less accurate predictions that render physical and chemical laws can provide a thorough understanding of a process.
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31

Franco, Edian F., Pratip Rana, Ana Lidia Queiroz Cavalcante, Artur Luiz da Silva, Anne Cybelle Pinto Gomide, Adriana R. Carneiro Folador, Vasco Azevedo, Preetam Ghosh y Rommel T. J. Ramos. "Co-Expression Networks for Causal Gene Identification Based on RNA-Seq Data of Corynebacterium pseudotuberculosis". Genes 11, n.º 7 (14 de julio de 2020): 794. http://dx.doi.org/10.3390/genes11070794.

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Corynebacterium pseudotuberculosis is a Gram-positive bacterium that causes caseous lymphadenitis, a disease that predominantly affects sheep, goat, cattle, buffalo, and horses, but has also been recognized in other animals. This bacterium generates a severe economic impact on countries producing meat. Gene expression studies using RNA-Seq are one of the most commonly used techniques to perform transcriptional experiments. Computational analysis of such data through reverse-engineering algorithms leads to a better understanding of the genome-wide complexity of gene interactomes, enabling the identification of genes having the most significant functions inferred by the activated stress response pathways. In this study, we identified the influential or causal genes from four RNA-Seq datasets from different stress conditions (high iron, low iron, acid, osmosis, and PH) in C. pseudotuberculosis, using a consensus-based network inference algorithm called miRsigand next identified the causal genes in the network using the miRinfluence tool, which is based on the influence diffusion model. We found that over 50% of the genes identified as influential had some essential cellular functions in the genomes. In the strains analyzed, most of the causal genes had crucial roles or participated in processes associated with the response to extracellular stresses, pathogenicity, membrane components, and essential genes. This research brings new insight into the understanding of virulence and infection by C. pseudotuberculosis.
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32

Al-Obaidi, M. A., C. Kara-Zaïtri y I. M. Mujtaba. "Performance evaluation of multi-stage and multi-pass reverse osmosis networks for the removal of N-nitrosodimethylamine -D6 (NDMA) from wastewater using model-based techniques". Journal of Environmental Chemical Engineering 6, n.º 4 (agosto de 2018): 4797–808. http://dx.doi.org/10.1016/j.jece.2018.06.014.

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33

Ammi, Yamina, Salah Hanini y Latifa Khaouane. "An artificial intelligence approach for modeling the rejection of anti-inflammatory drugs by nanofiltration and reverse osmosis membranes using kernel support vector machine and neural networks". Comptes Rendus. Chimie 24, n.º 2 (21 de mayo de 2021): 243–54. http://dx.doi.org/10.5802/crchim.76.

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34

Farahbakhsh, Javad, Mohammad Delnavaz y Vahid Vatanpour. "Simulation and characterization of novel reverse osmosis membrane prepared by blending polypyrrole coated multiwalled carbon nanotubes for brackish water desalination and antifouling properties using artificial neural networks". Journal of Membrane Science 581 (julio de 2019): 123–38. http://dx.doi.org/10.1016/j.memsci.2019.03.050.

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35

Abbas, Thabit, Qays M. Ammouri, Thikra Shihab y Ola Jabur. "Decreasing the Tendency of Water to Form Scale and Corrosion in Cooling Towers – South Refineries Company (SRC) – Iraq". Journal of Petroleum Research and Studies 11, n.º 2 (29 de junio de 2021): 15–29. http://dx.doi.org/10.52716/jprs.v11i2.512.

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Scale formation and corrosion phenomena are major technical problems at Basra Refinery/ South Refineries Company (SRC). These technical problems are concentrated in cooling towers which are used to supply cooling water to the processing units. The Scales and corrosion products precipitate inside cooling units, heat exchangers and pipeline networks affecting negatively efficiency of refinery’s equipment. In this work, a real plant data was collected from four cooling towers which is supplied the coolant to the crude distillation units of the refinery and also from the raw water supplier. The collected data was fitted by Langelier model to predict the tendency of cooling water for scale forming and activation of corrosion. The obtained result shows that the cooling water has a tendency to form scale (CaCO3 Precipitation) at cooling tower units. Also the feed water has the same tendency for scale forming.After analyzing the LSI (Langelier Saturation Index) results, the research team recommends the particular company to overcome this problem by installing a Reverse Osmosis (RO) plant for treating raw water to decrease the concentrations of total dissolved solids (TDS) which result scale and corrosion in the parts of cooling tower as an option to solve the problem.
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36

Abbas, Thabit, Qays Muthna, Thikra Shihab y Ola Jabur. "Decreasing the Tendency of Water to Form Scale and Corrosion in Cooling Towers – South Refineries Company (SRC) – Iraq". Journal of Petroleum Research and Studies 11, n.º 2 (20 de junio de 2021): 15–29. http://dx.doi.org/10.52716/jprs.v11i2.495.

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Scale formation and corrosion phenomena are major technical problems at Basra Refinery/ South Refineries Company (SRC). These technical problems are concentrated in cooling towers which are used to supply cooling water to the processing units. The Scales and corrosion products precipitate inside cooling units, heat exchangers and pipeline networks affecting negatively efficiency of refinery’s equipment. In this work, a real plant data was collected from four cooling towers which is supplied the coolant to the crude distillation units of the refinery and also from the raw water supplier. The collected data was fitted by Langelier model to predict the tendency of cooling water for scale forming and activation of corrosion. The obtained result shows that the cooling water has a tendency to form scale (CaCO3 Precipitation) at cooling tower units. Also the feed water has the same tendency for scale forming.After analyzing the LSI (Langelier Saturation Index) results, the research team recommends the particular company to overcome this problem by installing a Reverse Osmosis (RO) plant for treating raw water to decrease the concentrations of total dissolved solids (TDS) which result scale and corrosion in the parts of cooling tower as an option to solve the problem.
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37

Parra, Abdon, Mario Noriega, Lidia Yokohama y Miguel Bagajewicz. "Reverse Osmosis Network Rigorous Design Optimization". Industrial & Engineering Chemistry Research 58, n.º 8 (17 de enero de 2019): 3060–71. http://dx.doi.org/10.1021/acs.iecr.8b02639.

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38

Kamal, Athar, Sami G. Al-Ghamdi y Muammer Koç. "Assessing the Impact of Water Efficiency Policies on Qatar’s Electricity and Water Sectors". Energies 14, n.º 14 (19 de julio de 2021): 4348. http://dx.doi.org/10.3390/en14144348.

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Water and electricity have a unique relationship in the modern world as one requires the other in a complex system of networks to supply the utility to the customers. This energy–water interaction is especially peculiar in the Gulf Cooperation Council, where there are limited water resources, but extremely high use rates. Qatar provides a unique case in terms of extreme water scarcity and excessive water use. To understand the intricate network, this paper establishes an updated and comprehensive qualitative model of the water system in the country with the help of a water balance and system dynamics (causal loop diagram) methodology. Regression estimates are then used to estimate future water and energy consumption in addition to carbon dioxide emissions until the year 2050. Finally, system dynamics (stock and flow diagram) is used to determine the supply impacts of efficiency policies including limiting of groundwater abstraction to only 50 million m3, reduction of water consumption in the household, commercial and industrial sector by 10%, and gradual increase in the share of reverse osmosis (RO)-produced desalinated water to 50% in order to assess the supply volume, electricity consumption and CO2 emissions. The efficient use of water in different sectors of the economy results in a combined saving of 1222 GWh (8.1%) or 594,000 tons CO2. Furthermore, by moving to membrane-based desalination technology energy consumption and carbon dioxide emissions can be reduced by 3672 GWh (24.3%) and 1.8 million tons CO2, respectively. Further results suggest that while replacing groundwater with desalinated water can increase the energy consumption significantly, reuse of treated wastewater has almost the same footprint as groundwater, but can increase the resilience of the system considerably as groundwater abstraction levels are lowered to their renewal rates.
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39

Ammi, Yamina, Latifa Khaouane y Salah Hanini. "A Comparison of “Neural Networks and Multiple Linear Regressions” Models to Describe the Rejection of Micropollutants by Membranes". Kemija u industriji 69, n.º 3-4 (2020): 111–27. http://dx.doi.org/10.15255/kui.2019.024.

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A rejection process of organic compounds by nanofiltration and reverse osmosis membranes was modelled using the artificial neural networks. Three feed-forward neural networks based on quantitative structure-activity relationship (QSAR-NN models) characterised by a similar structure (twelve neurons for QSAR-NN<sub>1</sub>, QSAR-NN<sub>2</sub>, and QSAR-NN<sub>3</sub> in the input layer, one hidden layer and one neuron in the output layer), were constructed with the aim of predicting the rejection of organic compounds. A set of 1394 data points for QSAR-NN<sub>1</sub>, 980 data points for QSAR-NN<sub>2</sub>, and 436 data points for QSAR-NN<sub>3</sub> were used to construct the neural networks. Good agreements between the predicted and experimental rejections were obtained by QSAR-NN models (the correlation coefficient for the total dataset were 0.9191 for QSAR-NN<sub>1</sub>, 0.9338 for QSAR-NN<sub>2</sub>, and 0.9709 for QSAR-NN<sub>3</sub>). Comparison between the feed-forward neural networks and multiple linear regressions based on quantitative structure-activity relationship “QSAR-MLR” revealed the superiority of the QSAR-NN models (the root mean squared errors for the total dataset for the QSAR-NN models were 10.6517 % for QSAR-NN<sub>1</sub>, 9.1991 % for QSAR-NN<sub>2</sub>, and 5.8869 % for QSAR-NN<sub>3</sub>, and for QSAR-MLR models they were 20.1865 % for QSAR-MLR<sub>1</sub>, 19.3815 % for QSAR-MLR<sub>2</sub>, and 16.2062 % for QSAR-MLR<sub>3</sub>).
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40

Safder, Usman, Pouya Ifaei, KiJeon Nam, Jouan Rashidi y ChangKyoo Yoo. "Availability and reliability analysis of integrated reverse osmosis - forward osmosis desalination network". DESALINATION AND WATER TREATMENT 109 (2018): 1–7. http://dx.doi.org/10.5004/dwt.2018.22147.

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41

Saif, Y. y A. Almansoori. "Synthesis of reverse osmosis desalination network under boron specifications". Desalination 371 (septiembre de 2015): 26–36. http://dx.doi.org/10.1016/j.desal.2015.05.012.

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42

Schiek, William A. y Emerson M. Babb. "Impact of Reverse Osmosis on Southeast Milk Markets". Journal of Agricultural and Applied Economics 21, n.º 2 (diciembre de 1989): 63–75. http://dx.doi.org/10.1017/s0081305200001187.

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AbstractThe Southeast is a net importer of milk and milk products. Milk must be imported from other regions at certain times of the year. Reverse osmosis (RO) is a new processing technology which could significantly reduce milk transportation costs between regions by removing half the water from raw milk prior to shipment. A network flow algorithm, which incorporates federal milk orders and solves for the least cost procurement pattern, was used to assess the impact of RO on southeast milk marketing orders under alternative raw product pricing scenarios.
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43

Belila, A., J. El-Chakhtoura, P. E. Saikaly, M. C. M. van Loosdrecht y J. S. Vrouwenvelder. "Eukaryotic community diversity and spatial variation during drinking water production (by seawater desalination) and distribution in a full-scale network". Environmental Science: Water Research & Technology 3, n.º 1 (2017): 92–105. http://dx.doi.org/10.1039/c6ew00265j.

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44

Virapan, S., R. Saravanane y V. Murugaiyan. "Reverse Osmosis Desalination Performance Using Artificial Neural Network Approach with Optimization". Asian Journal of Water, Environment and Pollution 13, n.º 3 (18 de julio de 2016): 95–102. http://dx.doi.org/10.3233/ajw-160031.

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45

Saif, Yousef, Ali Elkamel y Mark Pritzker. "Global Optimization of Reverse Osmosis Network for Wastewater Treatment and Minimization". Industrial & Engineering Chemistry Research 47, n.º 9 (mayo de 2008): 3060–70. http://dx.doi.org/10.1021/ie071316j.

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46

Alnouri, Sabla Y. y Patrick Linke. "Optimal seawater reverse osmosis network design considering product water boron specifications". Desalination 345 (julio de 2014): 112–27. http://dx.doi.org/10.1016/j.desal.2014.04.030.

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47

Saif, Yousef, Muhammad Rizwan, Ali Almansoori y Ali Elkamel. "MINLP Model for Reverse Osmosis Network Design under Time-Variant Operation Constraints". Industrial & Engineering Chemistry Research 58, n.º 49 (22 de noviembre de 2019): 22315–23. http://dx.doi.org/10.1021/acs.iecr.9b05450.

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48

Libotean, Dan, Jaume Giralt, Francesc Giralt, Robert Rallo, Tom Wolfe y Yoram Cohen. "Neural network approach for modeling the performance of reverse osmosis membrane desalting". Journal of Membrane Science 326, n.º 2 (enero de 2009): 408–19. http://dx.doi.org/10.1016/j.memsci.2008.10.028.

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49

Al-Obaidi, M. A., C. Kara-Zaïtri y I. M. Mujtaba. "Optimal Reverse Osmosis Network Configuration for the Rejection of Dimethylphenol from Wastewater". Journal of Environmental Engineering 144, n.º 1 (enero de 2018): 04017080. http://dx.doi.org/10.1061/(asce)ee.1943-7870.0001284.

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50

Cruz-Silva, Rodolfo, Shigeki Inukai, Takumi Araki, Aaron Morelos-Gomez, Josue Ortiz-Medina, Kenji Takeuchi, Takuya Hayashi et al. "High Performance and Chlorine Resistant Carbon Nanotube/Aromatic Polyamide Reverse Osmosis Nanocomposite Membrane". MRS Advances 1, n.º 20 (2016): 1469–76. http://dx.doi.org/10.1557/adv.2016.232.

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ABSTRACTEfficient water desalination constitutes a major challenge for the next years and reverse osmosis membranes will play a key role to achieve this target. In this work, a high-performance reverse osmosis nanocomposite membrane was prepared by interfacial polymerization in presence of multiwalled carbon nanotubes. The effect of carbon nanotubes on the chlorine resistance, antifouling and desalination performance of the nanocomposite membranes was studied. We found that the addition of carbon nanotubes not only improved the membrane performance in terms of flow and antifouling, but also inhibited the chlorine degradation of these membranes. Several reports have acknowledged the benefits of adding carbon nanotubes to aromatic PA nanocomposite membranes, but little attention has been paid to the mechanisms related to the improvement of flow rate, selectivity and chlorine tolerance. We carried out a comprehensive study of the chemical and physical effects of carbon nanotubes on the fully crosslinked polyamide network. The chemical structure, chlorine resistance and membrane degradation was studied by several analytical techniques, permeation and fouling studies, whereas the microstructure of the nanocomposite was studied by small and wide angle X-ray scattering, high resolution transmission electron microscopy, and molecular dynamics. We found that the addition of the nanotube affects the interfacial polymerization, resulting in a polymer network with smaller pore size and higher sodium and chlorine rejection. We simulated the hydration of the membrane in seawater and found that the radial distribution function of water confined in the pores of the nanocomposite membrane exhibited smaller clusters of water molecules, thus suggesting a dense membrane structure. We analysed the network mobility and found that the nanotube provides mechanical stability to the polymer matrix. This study presents solid evidence towards more efficient and robust reverse osmosis membranes using carbon nanotubes as mechanical reinforcing and chlorine protection additive.
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