Enhanced Oxygen Demand Prediction in Effluent Re-actors with ANN Modeling

The amount of oxygen present in water, known as Dissolved Oxygen (DO), is impacted by a range of physical, chemical, and biological factors. This measurement is pivotal for assessing the condition of water, as it directly reflects the ability of aquatic ecosystems to sustain marine organisms. Evaluating water quality frequently involves the use of Chemical Oxygen Demand (COD). In the context of facilities treating wastewater, a combination of biological, physical, and chemical techniques is employed to manage industrial waste and remove contaminants before they are discharged into water bodies. Discharging untreated industrial waste into natural water sources is a major cause of water contamination. Standards mandate that the concentration of DO in waste should exceed 3 mg per liter. However, industries aim to maintain low DO levels to minimize the risk of pipe corrosion. Due to the time-consuming nature of manual COD measurement, industries often neglect to check DO levels before disposing of waste. The proposed study seeks to forecast the COD of treated waste from a wastewater treatment plant by utilizing crucial data gathered by sensors from the initial waste. This approach ensures that industries undertake suitable waste treatment before disposal, safeguarding marine life and enhancing the quality of water accessible for daily use.