Concentrated Brine and Heat Dispersion into Shallow Coastal Waters of the Arabian Gulf

  • Sami Al-SaneaEmail author
  • Jamel Orfi
Conference paper
Part of the Environmental Science and Engineering book series (ESE)


The main goal of this work is to assess the possible impacts of an existing desalination plant on the marine environment under various discharge conditions. Assessment is made through the determination, by using mathematical modeling, of the excess salinity and temperature distributions over the nominal seawater values as caused by the desalination plant effluent discharge. This chapter presents first a review of brine discharge models and studies followed by a rigorous numerical analysis study of a typical discharge problem into the Arabian Gulf. The mathematical formulation centers on the concept of shallow water equations in which the 3-D problem is reduced to an equivalent 2-D one by integrating the governing equations over the depth of flow. Appropriate boundary conditions, seabed friction, wind stress, and heat transfer correlations for thermal exchange at water-air interface are used. After validating the numerical model, it is applied to determine the salinity and temperature distributions in shallow coastal waters resulting from effluent discharge from an existing desalination plant situated on the Arabian Gulf. Parametric studies of the effects of a number of influential conditions are carried out by using the actual seabed topography and plant discharge and intake port locations. Effects of sea current magnitude and direction and plant discharge flow rate are in particular presented and analyzed. Possible plant discharge-intake port interactions were predicted with varying degrees of influence. The results presented indicated such interactions and quantified values of salinity and temperature at the plant intake port.


Mass Flow Rate Desalination Plant Intake Port Shallow Coastal Water Desalination Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This project was supported by NSTIP strategic technologies program number (08-ENV405-2) in the Kingdom of Saudi Arabia.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Mechanical Engineering DepartmentKing Saud UniversityRiyadhSaudi Arabia

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