Optimizing water purification processes and reducing environmental pollution with the help of artificial intelligence

Maryam Motie¹

1) Ph.D. Student, Department of Environmental Engineering, Islamic Azad University, Ahvaz Branch, Ahvaz, Iran

IOI Code : ABZE-ABGGDC
Publication : 5th International Congress on Civil Engineering, Architecture, Building Materials and Environment(caucong.com)
Abstract :
    Water treatment is considered one of the most important environmental challenges of the present era. Due to increasing pollution of water resources and the need to supply safe water, improving and optimizing this process is essential. Traditional water treatment methods such as filtration, chlorination, and activated sludge, although effective, have high energy and chemical consumption. Artificial Intelligence (AI), with its capability to process large and complex datasets, can assist in optimizing water treatment processes. This study aims to investigate and analyze the application of artificial intelligence algorithms, including Artificial Neural Networks (ANN), Genetic Algorithm (GA), Reinforcement Learning (RL), and Support Vector Machine (SVM), in optimizing water treatment processes and reducing environmental pollution. The data used included parameters such as pH, turbidity, temperature, and chlorine concentration, collected from real and simulated water treatment plants. Evaluation criteria included prediction accuracy, mean squared error, and reduction in energy and chemical consumption. The results showed that the Artificial Neural Network achieved the best performance in predicting effluent water quality with an accuracy of 95.3%. The Genetic Algorithm (GA) reduced chlorine consumption by 22%. Reinforcement Learning (RL) succeeded in reducing pump energy consumption by up to 18%. The SVM model, with an accuracy of 88.7% and shorter execution time, was more suitable for real-time systems. The findings indicated that artificial intelligence can optimize water treatment processes and lead to reduced energy and chemical consumption. However, challenges such as the need for complete datasets, implementation costs, and computational complexity require special attention. The results of this study demonstrate that artificial intelligence has high potential for improving water treatment processes. ANN, GA, and RL models were effective in optimizing and predicting effluent water quality. The use of these technologies can contribute to reducing environmental pollution and ensuring the sustainability of water resources. It is recommended that future research explore the use of hybrid models and larger datasets.

Keywords : water treatment artificial intelligence artificial neural network genetic algorithm reinforcement learning support vector machine optimization environmental pollution