Performance comparison of GRG algorithm with evolutionary algorithms in an aqueous electrolyte system
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ORIGINAL ARTICLE
Performance comparison of GRG algorithm with evolutionary algorithms in an aqueous electrolyte system Seyed Hossein Hashemi1,2 · Seyed Ali Mousavi Dehghani3 · Seyed Ehsan Samimi3 · Mahmood Dinmohammad3 · Seyed Abdolrasoul Hashemi4 Received: 4 April 2020 / Accepted: 21 May 2020 © Springer Nature Switzerland AG 2020
Abstract Excel solver is a powerful tool for optimization of linear and nonlinear problems. With this unique tool, the user can achieve an optimal value for the desired objective function in Excel cell. This solver acts on a group of cells that are directly or indirectly associated with the function; thus, the user-defined values will be optimized. In the present work, 13 existing species in an electrolyte solution have been considered to predict the activity coefficient of inorganic ions in the electrolyte solution, which includes H2O, CO2(aq), H+, Na+, Ba2+, Ca2+, Sr2+, Mg2+, OH–, Cl−, SO4, CO3, HCO3. In this study, to predict the activity coefficient of species in the system, Extended UNIQUAC activity coefficient model was considered and its parameters optimized using Excel solver tool based on GRG algorithm. Total error for optimization of adjustable parameters of Extended UNIQUAC model for 13 desired ions at three temperatures 298.15 K, 323.15 K and 373.15 K with the Excel solver tool was 0.0087. The results of GRG algorithm were favorable than those of ICA, PSO and ABC algorithms. The results of this optimization are intended to predict mineral deposition. The number of adjustable variables (model parameters for optimization) is over 200, and the number of target functions is 39. Keywords Evolutionary algorithms · Optimization · Excel solver tool · Extended UNIQUAC model
Introduction Optimization is referred to the process of finding the necessary conditions to minimize the required attempts or maximize the desired efficiency. This procedure in optimization methods will be done by making changes on the idea and the initial assumption which depends on the case and survey methods, as well as the range of the changes. For better performance, each algorithm should produce new points in the search space of objective function by the use of operators * Seyed Hossein Hashemi [email protected] 1
Graduate Msc Chemical Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
2
Young Researchers and Elite Club, Shiraz Branch, Islamic Azad University, Shiraz, Iran
3
Research Institute of Petroleum Industry, NIOC, West Blvd. of Azadi Sports Complex, P.O. Box: 14757‑3311, Tehran, Iran
4
Graduate Msc Computer Engineering, Gachsaran Oil and Gas Exploitation Company, Kohgiluyeh and Boyer‑Ahmad, Iran
on initial points and gradually tends to the optimal location of this space. In many conventional search methods (gradient method), the governing decision rules act in the way that moves from one point to the point in which it may lead to mistake and error in optimization because they possibly converge to a local maximum point. This defect in optimization of
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