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Three topics of research on energy from salinity gradient of oceans.


Power generation from Salinity Gradient is a new topic of research that is steadily attracting interest from the researchers of renewable energy. The scarcity and rising demand for conventional fuels have also aggravated the importance of alternative sources of energy where power from salinity gradient or Blue Energy can be a useful solution.

As the topic is new and mostly unexplored, doing research in this aspect, have a very high potential. 

Research ideas like :

1) Cost Minimization of Power Extraction from Saline Water Gradient-based Power Plants by Nature Based Optimization Techniques

The first objective of the work will be to identify parameters/s, which contribute towards the total expenditure for the installation of Salinity Gradient-based power plants. After all the settings were defined, and the significance of the weights of the parameters is determined, the next step will be to formulate the indicator in such a manner that it becomes directly proportional to the expenditure for operation and maintenance of salinity gradient based power plants. The second objective will be to implement nature-based optimization algorithms like Ant Colony, Artificial Bee Hive, etc., to minimize the cost function. The significance of the parameters can be estimated by the application of Multi-Criteria Decision Making methods(MCDM), and the nature-based optimization techniques can be used for minimization of the function. The result of the optimization will yield the ratio at which the significant parameters must behave to produce minimum expenditure to run a saline water-based power plant.

2)Estimation of Power Potential from Saline Water Gradient in European/Asian/American Coastal regions: A Polynomial Neural Network Approach

Polynomial Neural Networks is an advanced form of neural network which can self-detect the number of inputs and hidden layers, required for prediction of the most accurate output. The Group Method of Data Handling(GMDH) is a technique by which polynomial neural networks(PNN) are trained where it analyses hundreds of different training algorithms and identifies the best algorithm automatically with the help of a fitness function. This kind of technology can be implemented to estimate the power potential from the saline water gradient in various locations of different continents. An isohyetal map can be produced from the predictions so that the place with the highest potential can be easily delineated.

3)Location selection for the installation of Pressure Retarded Osmosis (PRO)/Reverse Electrodialysis (RED)/Hydrocratic Ocean Energy-based Salinity Gradient Power plants with the help of MCDM-GMDH or MCDM or GMDH.

MCDMs are an objective procedure to identify the better option among the many available alternatives.GMDH is a technique for training PNNs.These two technologies can be applied to identify locations with higher feasibility among multiple locations selected for the installation of Saline Water-based power plants. At first, the parameters which can choose the plausible sites can be identified, and then with the help of a function, the areas with the highest potentiality can be selected. The function will be a product function of the chosen parameters and its significance in detecting suitable regions. For different kinds of saline water-based power generation units(such as Pressure Retarded Osmosis (PRO)/Reverse Electrodialysis (RED)/Hydrocratic Ocean Energy), various features will be selected and the significance of the features will also change. The calculation of relevance will be executed with the help of MCDM methods, and function will be predicted with the help of GMDH based PNNs.

...can attract project funds as well as may help to publish research articles in reputed journals. As a pioneer, citations for such research will be high compared to those for other conventional topics of renewable energy.

Thanking you,

Founding and Honorary Editor

Innovate For Sustainability

The Water and Energy Journal Publisher

The Water Energy and Informatics


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