Modeling brine discharge dispersion from two adjacent desalination outfalls in coastal waters

Purnama, Anton; Shao, Dongdong

doi:10.1016/j.desal.2015.02.012

Cited by 10 publications

(3 citation statements)

References 18 publications

(38 reference statements)

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“…Due to the increase in population growth and groundwater depletion, the demand for fresh and potable water has led to rising growth in desalination plants, especially in arid and semi-arid regions such as the Persian Gulf, Red Sea, and the Gulf of Oman [1]. It has also been estimated that the percentage of water shortage will increase by 60% by the year 2025 [2].…”

Section: Introductionmentioning

confidence: 99%

“…Hence, since about 97.5% of the total volume of the hydrosphere is contained in seas and oceans [3], desalination plants are the most viable solution for today's drinking water problems, however, these plants cause many negative impacts. The effluent from desalination plants, called 'brine', is discharged into the seawater and contains concentrated salt, which is almost double the salinity of the receiving water and ends up adding this salinity to the seawater [1]. Along with this, if a desalination plant is using a multistage flash (MSF) technique, then the brine could also raise turbidity and temperature (Bleninger and Jirka, 2008) [4].…”

Section: Introductionmentioning

confidence: 99%

“…To meet the existing regulations, a diffuser can be placed at the end of the outfall system to dilute the concentrated brine-since in the absence of dilution-brine plume extends its vicinity and will be harmful to the ecosystem [1]. It has also been reported that the discharge of brine using inclined dense jets has been in use since the 1970s, in which dilution and geometry are the major parameters to be considered [5].…”

Section: Introductionmentioning

confidence: 99%

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Applications of ANFIS-Type Methods in Simulation of Systems in Marine Environments

Jain

Toussi

Mohammadian

et al. 2022

MCA

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ANFIS-type algorithms have been used in various modeling and simulation problems. With the help of algorithms with more accuracy and adaptability, it is possible to obtain better real-life emulating models. A critical environmental problem is the discharge of saline industrial effluents in the form of buoyant jets into water bodies. Given the potentially harmful effects of the discharge effluents from desalination plants on the marine environment and the coastal ecosystem, minimizing such an effect is crucial. Hence, it is important to design the outfall system properly to reduce these impacts. To the best of the authors’ knowledge, a study that formulates the effluent discharge to find an optimum numerical model under the conditions considered here using AI methods has not been completed before. In this study, submerged discharges, specifically, negatively buoyant jets are modeled. The objective of this study is to compare various artificial intelligence algorithms along with multivariate regression models to find the best fit model emulating effluent discharge and determine the model with less computational time. This is achieved by training and testing the Adaptive Neuro-Fuzzy Inference System (ANFIS), ANFIS-Genetic Algorithm (GA), ANFIS-Particle Swarm Optimization (PSO) and ANFIS-Firefly Algorithm (FFA) models with input parameters, which are obtained by using the realizable k-ε turbulence model, and simulated parameters, which are obtained after modeling the turbulent jet using the OpenFOAM simulation platform. A comparison of the realizable k-ε turbulence model outputs and AI algorithms’ outputs is conducted in this study. Statistical parameters such as least error, coefficient of determination (R2), Mean Absolute Error (MAE), and Average Absolute Deviation (AED) are measured to evaluate the performance of the models. In this work, it is found that ANFIS-PSO performs better compared to the other four models and the multivariate regression model. It is shown that this model provides better R2, MAE, and AED, however, the non-hybrid ANFIS model provides reasonably acceptable results with lower computational costs. The results of the study demonstrate an error of 6.908% as the best-case scenario in the AI models.

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Section: Introductionmentioning

confidence: 99%