چكيده به لاتين
Water and wastewater treatment is a process that improves the quality to make it more suitable for a specific purpose. Today, one of the main applications of membranes is water and wastewater treatment. Micropollutants are pollutants found in small concentrations in water that are persistent and bioactive, meaning they are not completely dissociable and cannot be removed by conventional water treatment methods. For this reason, their detection and elimination has challenged the scientific community. These micropollutants have caused great concern because their presence in water supply systems endangers the health of humans and animals. To develop efficient techniques for their removal, it is necessary to understand their physical and chemical properties and to know all the processes capable of removing them. Membrane separation processes are used as a suitable method to remove micropollutants. Today, artificial intelligence is an advanced technology with the ability to combine human behavior and intelligence in machines or systems. Therefore, modeling based on artificial intelligence is the key to building automatic and intelligent systems according to today's needs. To solve real-world problems, different types of AI such as analytical, functional, interactive, textual, and visual AI can be used to enhance the intelligence and capabilities of an application. The removal of pollutants by nanofiltration and reverse osmosis membranes is a multidimensional process that includes the selection of membrane materials and the optimization of experimental conditions. It is difficult to discover the contribution of factors affecting the removal rate by trial and error experiments. However, the advanced machine learning method is a powerful tool to simulate this complex process, which includes 4 traditional learning algorithms (regression, support vector machines, artificial neural network, K nearest neighbor) and 4 group learning algorithms (random forest, decision tree, and gradient amplification) is used to predict the pollutant removal efficiency. The results have shown that group learning models make significantly better predictions than traditional models.
