Prediction and Control of Coke Plant Wastewater Quality using Machine Learning Techniques

The present study focuses on examining the fate of coal constituents—carbon, sulphur, nitrogen and chlorine from coal blend to coke oven wastewater. Further, the impact of coal constituents and coke making process on wastewater quality has studied and analyzed the effects with plant data. Understanding helps in development of coke oven wastewater quality prediction model using machine learning techniques. A reliable model helps in minimize the operation costs and stable operation of treatment plant. The developed model is implemented and validated using plant scale data obtained for coke plant at Tata Steel Jamshedpur. The model provided accurate predictions of the effluent stream of by product plant, in terms of chemical oxygen demand (COD) and total dissolved solids (TDS) when using coal constituents and coke making process parameters as an input. Implementation strategy of model helps to control the wastewater quality within environmental limit with ease.