This paper initially reviews existing empirical models which predict head or pressure increase of two-phase petroleum fluids in electrical submersible pumps (ESPs), then, proposes an alternative model, a fully connected cascade (FCC in short) artificial neural network to serve the same purpose. Empirical models of ESP are extensively in use; while analytical models are yet to be vastly employed in practice due to their complexity, reliance on over-simplified assumptions or lack of accuracy. The proposed FCC is trained and cross-validated with the same data used in developing a number of empirical models; however, the developed model presents higher accuracy than the aforementioned empirical models. The mean of absolute prediction error of the FCC for the experimental data not used in its training, is 68% less than the most accurate existing empirical model.
This paper proposes a fuzzy model to estimate the head of gaseous petroleum fluids (GPFs) driven by electrical submersible pumps (ESPs). The proposed fuzzy model is an alternative to widely used empirical models. Numerical and analytical models have been also proposed to estimate heads of GPFs in ESPs, which have failed to reliably serve the function. The developed fuzzy model evidently outperforms comparable empirical models in terms of accuracy and presents a mean absolute estimation error of 52.4% less than the most accurate existing empirical model.
Energy is the main concern in the design and implementation of desalination plants; this concern is even more significant in remote areas with limited or no access to the main power grid. This paper presents an off‐grid hybrid renewable energy assessment for a reverse osmosis desalination plant. The plant with a 10 m3 desalination capacity is located in al‐Batinah region in the north of Oman. For better assessment, the meteoritic data of the site such as temperature, wind velocity, solar radiation, and clearness index are extracted. The simulation has been done using Homer software, and all possible combinations of photovoltaic panel/wind turbine/generator/battery are calculated and compared with one another. At last, two different approaches, a complete renewable hybrid energy with and without generator backup, are compared and the results are provided. The results of the experiment show that the most economic off‐grid hybrid energy system is a combination of a 12.5 kW PV panel with 335 W energy production for each panel, a battery bank consisting of 12 batteries with 205 Ah capacity, and a generator as a backup. The cost of energy for one kWhr is $0.28; on the other hand, a hybrid renewable energy system with the combination of PV/wind turbine/battery costs $0.37 per kWhr.
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