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

Abstract

Abstract Protecting the environment and ensuring the availability of potable water requires efficient wastewater treatment. This paper investigates the need for an advanced process optimization model to enhance the efficacy of wastewater treatment processes. Existing optimization models for wastewater treatment are frequently incapable of effectively analyzing historical data, optimizing dosages, or predicting optimal process parameters. To circumvent these restrictions, 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 is proposed. Extensive experimentation with multiple datasets and samples, including the Melbourne Wastewater Treatment Dataset, Urban Wastewater Treatment, and Global Wastewater Treatment, demonstrates significant improvements in our proposed model. Compared to recently proposed models, our method results in an average improvement of 8.5% in treatment quality, a 4.9% reduction in delay, and a 9.5% increase in water purity. Beyond the datasets mentioned, our model's applications and use cases provide a valuable framework for optimizing wastewater treatment in a variety of settings. The adaptability and effectiveness of the proposed model make it suitable for both small and large treatment plants.