A Comparative Study of Multi Objective Optimization Algorithms for a Cellular Automata Model

Kar, Samarjit; Majumder, S.; Constales, Denis; Pal, Tandra; Dutta, Abhishek

doi:10.24275/rmiq/sim395

Cited by 5 publications

(2 citation statements)

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“…The separation efficiency was reported for each simulation, so a multiple linear regression was applied to the results to correlate the separation efficiency as a function of the main variables and the significant double interactions between variables. This correlation was employed to perform an optimization effort using a formal optimization method called genetic algorithm NSGA-II [29], programmed in MATLAB ® to obtain the optimum conditions for the desalting that minimizes the water and salt content from the oil. The model is only valid under the range of selected variables.…”

Section: Name Equationmentioning

confidence: 99%

Numerical Study of Electrostatic Desalting: A Detailed Parametric Study

et al. 2022

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A systematic process analysis was conducted to study the effect of the main variables in an industrial electrostatic desalter, such as electric field intensity, wash water content, droplet size, and oil viscosity, on the efficiency of the separation of water from oil. The analysis was assessed through an already published and validated CFD multiphase numerical model that considers the expression of the frequency of collisions as a function of the mentioned process variables. Additionally, the study allowed the formal optimization exercise of the operation to maximize the separation efficiency. The most significant variables were the initial water content and the electric field intensity, while the temperature (oil viscosity) had an effect to a lower extent. An increase in the electric field and temperature and a decrease in the water content improved the water separation from oil. Optimum values suggested from the factorial experimental design and the optimization implemented in this work indicated the use of an electric field of 3 kV/cm, water content of 3%, and an oil viscosity of 0.017 kg/ms. At the same time, the droplet size showed no significant effect under the conditions explored in this work.

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Section: Name Equationmentioning

confidence: 99%

Numerical Study of Electrostatic Desalting: A Detailed Parametric Study

et al. 2022

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“…In recent years, there has been a growing interest in computational evolution, which has led to the development of many optimization algorithms (Sexton et al, 2011;Fernandez et al, 2019;Penghui et al, 2020;Kar et al, 2020;García-Muñoz et al, 2021). They can complete the prediction of systems by improving the speed of convergence and reducing the possibility of being trapped in local minima (Seifi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%