Dense wastewater discharges into marine environments can severely impact water bodies. This study addresses the disposal of hypersaline brines from desalination plants through multiport diffusers into seas and oceans. Accurate prediction of the mixing of discharges with the receiving water bodies is crucial for the optimal design of outfall systems. Designers can enhance mixing and increase dilution by modifying outfall properties. However, the interaction of discharges from multiport diffusers poses a significant challenge, impairing the mixing process. The main aim of this study is to improve multiport diffuser designs by limiting the negative effects of jet interaction on mixing. This research applies a three-dimensional numerical model, the Launder, Reece, and Rodi (LRR) turbulence model, to evaluate the predictive capabilities of the Reynolds Stress Models (RSM) for multiple dense jets and to explore the mixing characteristics and merging process of multiple jets. To validate the model, its predictions are compared with available experimental data. The LRR model showed good agreement with the experimental measurements, and the model outperformed the standard and re-normalization group (RNG) 𝑘−𝜀 turbulence models, making it a promising tool for studying the mixing behavior of multiport diffusers. This study proposes multiport diffusers with non-uniform port orientation as a means for mitigating the negative effect of jet mering on the mixing process and increasing dilution. Using the validated numerical model and the laser-induced fluorescence (LIF) technique, the effect of non-uniform port orientation on the mixing process is explored. The numerical results indicated that the orientation of adjacent jets significantly affected the behavior of individual jets. An individual jet exhibited a longer trajectory and higher dilution when its neighboring jets were disposed of with a different angle, compared to that of uniform discharges. Laboratory experiments on uniform and non-uniform diffusers, with varying port angles in the range of highest reported dilution rates for single discharges (40o-70o), are reported, and the major flow properties and merging processes are compared. Investigations revealed that non-uniform diffusers achieved overall higher mean dilutions due to different mixing behavior in the interaction zones. Non-uniform port orientation provided more space between the jets to expand before interacting with their neighbors, resulting in higher dilutions. This study challenges the application of formulae obtained from single discharge experiments for multiport diffuser designs and emphasizes the importance of considering source characteristics specific to multiport diffusers, such as angle difference, for efficient desalination outfall. The new data and analysis provided in this study can benefit the design of desalination discharge systems with considerable potential cost savings, especially for tunneled outfalls, due to shorter diffusers with non-uniform port orientations and environmental risk reductions.

Artificial intelligence techniques have been widely used for prediction in various areas of sciences and engineering. In the thesis, applications of AI techniques are studied to predict the dilution of industrial outfall discharges. The discharge of industrial effluents from the outfall systems is broadly divided into two categories on the basis of density. The effluent with density higher than the water receiving will sink and called as negatively buoyant jet. The effluent with density lower than the receiving water will rise and called as positively buoyant jet. The effluent discharge in the water body creates major environmental threats. In this work, negatively buoyant jet is considered. For the study, ANFIS model is taken into consideration and incorporated with algorithms such as GA, PSO and FFA to determine the suitable model for the discharge prediction. The training and test dataset for the ANFIS-type models are obtained by simulating the jet using the realizable k-ε turbulence model over a wide range of Froude numbers i.e. from 5 to 60 and discharge angles from 20 to 72.5 degrees employing OpenFOAM platform. Froude number and angles are taken as input parameters for the ANFIS-type models. The output parameters were peak salinity (Sm), return salinity (Sr), return point in x direction (xr) and peak salinity coordinates in x and y directions (xm and ym). Multivariate regression analysis has also been done to verify the linearity of the data using the same input and output parameters. To evaluate the performance of ANFIS, ANFIS-GA, ANFIS-PSO, ANFIS-FFA and multivariate regression model, some statistical parameters such as coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE) and average absolute deviation in percentage are determined. It has been observed that ANFIS-PSO is better in predicting the discharge characteristics.

Discharge of brine wastewater produced from industrial plants into adjacent coastal water bodies is considered as a preferable and common method currently used in many offshore industrial plants. Therefore, it is important to carefully study the behavior of jets and their environmental impacts on water bodies close to the discharge points, especially when the density is different between the jets and the receiving water. The main goal of this study is to improve the understanding of the mixing behaviour of jet trajectories for positively (offset) and negatively (inclined) buoyant jets when density is considered a significant factor, and also to examine the accuracy of some RANS turbulence models and one type of artificial neural network in predicting jet trajectory behaviours. In the first part of this study, experiments using a PIV system for offset buoyant jets were conducted in order to study the effect of the density differences (due to salinity [nonthermal] or temperature [thermal]) between the discharge and the receiving water body on the jet behavior, and the results showed that the nonthermal jets behaved differently as compared to the thermal jets, even though the densimetric Froude numbers (Frd) and density differences (∆ρ) were similar. In addition, a Reynolds-averaged Navier-Stokes (RANS) numerical model was performed using open-source CFD code (OpenFOAM) with a developed solver (modified form of the pisoFoam solver). The realizable k-ε model showed the best prediction among the models. Secondly, an extensive experimental study of an inclined dense jet for two angles (15°and 52°) was conducted to study the effect of these angles on the jets’ geometrical characteristics in the presence of a wide range of densimetric Froude numbers as well as with different discharge densities. More experimental data were obtained for these angles to be added to the previous data for the purpose of calibrating, validating, and comparing the various numerical models for future studies. The results of these experiments are used to evaluate the performance of a type of artificial neural network method called the group method of data handling (GMDH), and the GMDH results are then compared with existing analytical solutions in order to prove the accuracy of the GMDH method in simulating mixing behaviors in water bodies. Thirdly, a comprehensive study on predicting the geometrical characteristics of inclined negatively-buoyant jests using GMDH approach was conducted. The superiority of this model was demonstrated statistically by comparing to several previous analytical models. The results obtained from this study confirm that the GMDH model was highly accurate and was the best among others for predicting the geometrical characteristics of inclined negatively-buoyant jests.