Numerical an‎d Experimental Modeling of Copper Ion Removal from Copper Sulfate Solution Using Coated San‎d Nanofilters

3/21/2026 12:00:00 AM

Industrial development has led to the continuous pollution of the environment by heavy metals, causing numerous ecological crises. Copper, one of the most toxic heavy metals, is widely used in various industries, significantly increasing environmental contamination. Adsorption using cost-effective an‎d efficient adsorbents is a viable method for copper removal. In recent years, graphene san‎d nano filters have gained attention due to their use of inexpensive an‎d readily available materials, such as san‎d an‎d sucrose, along with a relatively simple synthesis process. However, the impact of san‎d type, sieving, an‎d composite material on adsorbent performance has not been thoroughly explored. Furthermore, the efficiency of the mentioned adsorbents can be improved by synthesizing 3D san‎d-graphene composite adsorbents through slight modifications to the manufacturing process. This research focuses on optimizing san‎d-based adsorbents for the removal of sodium chloride an‎d magnesium sulfate salts, addressing aqueous solution challenges. The study comprises three experimental stages an‎d two numerical simulation stages. In the first stage, various san‎d adsorbents an‎d graphene san‎d nano filters, derived from sucrose an‎d date syrup with different san‎d types an‎d grain sizes, were eva‎luated for their ability to remove sodium chloride an‎d magnesium sulfate salts. Silicate san‎d adsorbents showed good cation removal, while graphene nano filters made from date syrup were most effective in anion removal. In second stage, the adsorbents’ efficiency in removing copper ions from a copper sulfate solution was analyzed. Two nano filter adsorbents—one made of carbonate san‎d with graphene at a san‎d-to-sucrose ratio of 80:15 an‎d another at 80:20—achieved the highest copper removal rates of 99% an‎d 97%, respectively. In third stage, the most effective adsorbent, based on copper removal efficiency an‎d outlet flow rate, was selec‎ted for further characterization through additional experiments. The selec‎ted adsorbent underwent batch tests to determine its equilibrium properties, including an equilibrium time of 10 hours, an adsorbent content of 35 g/L, an‎d its most suitable kinetic an‎d isotherm models—Elovich an‎d Langmuir, respectively. Subsequently, fixed-bed experiments were conducted using the selec‎ted adsorbent with varying copper input concentrations. Adsorption breakthrough curves were plotted an‎d compared against various mathematical models. Among them, the Log-Gompertz model demonstrated the best fit (R² = 0.97–0.99) when comparing analytical models with experimental results across different copper concentrations. Next, numerical simulations were performed using COMSOL software to model the passage of a copper sulfate solution through the adsorbent bed. The simulation results were validated against experimental data, showing strong agreement, particularly in the initial an‎d middle stages of the adsorption breakthrough curve. Following this, a module was developed in OpenFOAM software to simulate the same process based on COMSOL’s equations, an‎d its results were also compared with experimental findings. This research confirms that the graphene-coated carbonate san‎d nano filter, synthesized from porous carbonate san‎d an‎d sucrose, is an innovative an‎d highly effective adsorbent for copper ion removal. Its characteristics have been comprehensively analyzed through both extensive experimental an‎d numerical simulations under batch an‎d fixed-bed conditions. The findings presented in this thesis contribute to the development of cost-effective an‎d readily available raw materials for producing efficient adsorbents, offering a practical solution for the removal of heavy metal contaminants.

جاذب ماسه‌اي , نانوفيلترهاي ماسه‌اي اندود شده , نرم‌افزار COMSOL , مدل LDF , نرم‌افزار OpenFOAM

Sand adsorbent , graphene sand composite adsorbent , COMSOL software , LDF model , OpenFOAM software

Hamed Omdehghiasi

Dr. Abbas Yeganeh-Bakhtiary