Design of an improved process optimization model for enhancing the efficiency of the wastewater treatment process

Efficient wastewater treatment is crucial for environmental protection and ensuring the availability of potable water. However, existing optimization models often fall short in effectively analyzing historical data, optimizing dosages, or predicting optimal process parameters. This paper aims to address these challenges by proposing an advanced process optimization model that enhances the efficacy of wastewater treatment processes. Our objectives include the development of a novel method for optimizing various aspects of the treatment procedure using Auto Encoders (AE), Genetic Algorithm (GA), and Vector Autoregressive Moving Average (VARMA) models. Extensive experimentation with multiple datasets and samples, including the Melbourne Wastewater Treatment Dataset, Urban Wastewater Treatment, and Global Wastewater Treatment, was conducted to validate our model. The results demonstrate significant improvements in our proposed model, with an average improvement of 8.5% in treatment quality, a 4.9% reduction in delay, and a 9.5% increase in water purity compared to recently proposed models. We conclude that our model provides a valuable framework for optimizing wastewater treatment in a variety of settings due to its adaptability and effectiveness. Future research could explore the integration of additional advanced techniques and real-time monitoring systems to further enhance the model's precision, efficiency, and robustness.