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Abstract Recently, genetic algorithms have gained attention for improving wastewater treatment processes. This study investigated the impact of reverse osmosis membrane technology on removing chrome particles from wastewater. A new method to compute water and chrome permeation coefficients was introduced. The study employed a quasi-experimental design, utilizing laboratory data. A chrome solution was passed through a reverse osmosis membrane pilot. Feed chromium concentration and driving pressure were independent variables, while permeate chromium concentration and flux rate were dependent variables. The Elitist Non-dominated Sorting Genetic Algorithm (E-NSGA) optimized the removal process. Equations were developed for computing flux rate, chromium concentration, and treatment cost rate. Points representing optimal solutions were obtained, and a 16.3-bar pressure proved optimal. Results were compared to experimental data, with minor discrepancies due to errors in laboratory experimentation and computation of permeation coefficients. E-NSGA demonstrated effectiveness for optimizing membrane systems. Overall, this research contributes to advancing wastewater treatment processes.
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Nourozi et al. (2024) studied this question.
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