International Journal of Engineering and Modern Technology (IJEMT )
E-ISSN 2504-8848
P-ISSN 2695-2149
VOL. 10 NO. 11 2024
DOI: 10.56201/ijemt.v10.no11.2024.pg55.67
Adeloye Olalekan Michael and Igbagara Princewill Wonyibrakemi
The models for the description of the performance of the reverse osmosis process for the behaviour of the boiler feed water were presented. The models developed were to predict the feed solution concentration and transmembrane pressure during the treatment process as the separation would involve the diffusion of solvent from the solution with more solvent to the solution with low solvent. The diffusion takes place as the impurities in the boiler feed water are diffused through the membrane employed in reverse osmosis process for treatment of boiler feed water. The operational parameters were obtained from the plant and serve as input data for the computer program developed using MatLab software. The model results showed agreement with previous studies with minimum deviation. The simulations showed optimum separation when the transmembrane pressure was about 180Pa with the reverse osmosis process adjusted to real rejection fraction of 0.4%. Also, the impurities as at this transmembrane pressure of 180Pa had been diffused completely to clean and clear solution through the membrane. Hence, total flux been adjusted in the reverse osmosis process for boiler feed water treatment at optimum of 178Pa would be the transmembrane pressure. This means that total flux is a better sensitive parameter to be used in the purification of boiler feed water for the diffusion of impurities from the boiler feed water since the transmembrane pressure was lower when total flux was used as adjustable variable.
Trans-membrane Pressure. Total Flux; Membrane; Diffusion; MatLab Software
Ahmad, A. L., Chong, M. F. & Bhatia, S. (2007). Mathematical Modeling of Multiple Solutes
System for Reverse Osmosis Process in Palm Oil Mill Effluent (POME) Treatment.
Chemical Engineering Journal, 132(1), 183–193.
Al-Bastaki, N. M. & Abbas, (2019). Modeling an Industrial Reverse Osmosis Unit. Desalination,
126, 33–39
Al-Obaidi, M. A., Alsarayreh, A. A. & Mujtaba, I. M. (2020). Scope and Limitations of
Modelling, Simulation, and Optimization of a Spiral Wound Reverse Osmosis Process-
Based Water Desalination. Processes, 8(5), 573-585. https://doi.org/10.3390/pr8050573
Asadi, N., Soleimanimehr, H. & Alinia-ziazi, A. (2021). An Investigation on Boiler Feed Water
Treatment using Reverse Osmosis and Ion Exchange by WAVE Software. Journal of
Applied Research in Water and Wastewater, 8(2), 124-128.
Bagheri, G. & Alizadeh, M. (2021). Optimization and Performance Simulation of Reverse
Osmosis
Desalination
Plants.
Renewable
Energy,
178,
118–126.
https://doi.org/10.1016/j.renene.2021.05.061.
Bashir, M., Qamar, M. & Sher, F. (2020). Modelling the Effect of Operational Conditions on the
Performance of Reverse Osmosis Desalination Plants. Journal of Water Process
Engineering, 37, 101537. https://doi.org/10.1016/j.jwpe.2020.101537.
Cai, Q., Lee, B. C. Y., Ong, S. L. & Hu, J. (2021). Application of a Multi-objective Artificial
Neural Network (ANN) in Industrial Reverse Osmosis Concentrate Treatment with a
Fluidized Bed Fenton Process: Performance Prediction and Process Optimization. ACS
ES&T Water, 1, 847–858
Cath, T., Childress, A. & Elimelech, M. (2006) Forward Osmosis: Principles, Applications,
and Recent Developments. Journal of Membrane Science 281, 70–87.
Choi, J., Hong, S. & Moon, S. (2021). Design and Simulation of Reverse Osmosis Process in a
Hybrid Forward Osmosis System. Journal of Water Process Engineering, 4(1), 101865.
https://doi.org/10.1016/j.jwpe.2020.101865.
Cornelissen, E. R., Harmsen, D. J. H., Blankert, B., Wessels, L. P. & Van der Meer, W. G. J.
(2021). Effect of Minimal Pre-Treatment on Reverse Osmosis Using Surface Water as
a Source. Desalination, 1, 115056.
El-Nakla, S. M. & Emam, E. A. (2022). Optimization of Reverse Osmosis Membranes for
Desalination and Boiler Feed Water Treatment. Desalination, 527, 115-128.
https://doi.org/10.1016/j.desal.2022.115558
Fahmy, S. & Ahmed, M. (2021). Numerical Simulation of Reverse Osmosis Membranes for
Water Desalination. Computational Water, Energy, and Environmental Engineering,
10(3), 65–75. https://doi.org/10.4236/cweee.2021.103006
Garcia-Pérez, A., Fernández-García, F. & Rico-Jiménez, J. J. (2022). Modelling and Simulation
of Reverse Osmosis Desalination Plants for Optimal Operation. Journal of Water Process
Engineering, 45, 101-113. https://doi.org/10.1016/j.jwpe.2021.101243.
Henthorne, L. (2019). The Current State of Desalination. In IDA World Congress, Dubai.
UAE.
Khawaji, A. D., Kutubkhanah, I. K. & Wie, J. M. (2018) Advances in Seawater Desalination
Technologies. Desalination 221, 47–69.
Kim, D. Y., Gu, B. & Yang, D. R. (2013). An Explicit Solution of the Mathematical Model for
Osmotic Desalination Process. Korean J. Chem. Eng., 30, 1691–1699.
Ligaraya, M., Park, S., Park, J. S., Park, J., Kim, Y. & Cho, K. H. (2020) Energy Projection of
the Seawater Battery Desalination System using the Reverse Osmosis System Analysis
Model. Chemical Engineering Journal, 395(125082), 1–10.
Monjezi, A. A., Chen, Y., Vepa, R., Kashy-out, A. B., Hassan, G., Fath, H. E., Kassem, A. Y.
& Shaheed, M. H. (2020). Development of an Off-grid Solar Energy Powered Reverse
Osmosis Desalination System for Continuous Production of Freshwater with Integrated
Photovoltaic Thermal (PVT) Cooling. Desalination 495(1), 114679.
Prabhakaran, R. & Raman, S. (2022). Optimizing Reverse Osmosis Desalination Systems for
Energy and Cost Savings. Renewable and Sustainable Energy Reviews, 159, 112-130.
https://doi.org/10.1016/j.rser.2022.112130.
Rashid, T., Ahmed, S. & Qureshi, K. (2024). Numerical Simulations of Energy Recovery in
Reverse Osmosis Desalination Processes using Pressure Exchange Mechanisms.
Desalination, 545, 116-208. https://doi.org/10.1016/j.desal.2023.116208.