Abstract : The purpose of this article is to use ANN models to forecast how well industries dispose of their waste water (IDWW). It's useful for anything from evaluating potential design and modelling errors to managing day-to-day operations at a IDWW. This work utilizes data on chemical oxygen demand (COD), biochemical oxygen demand (BOD), total suspended solid (TSS), pH, temperature (T), and other factors to develop a predictive model of the system's performance. Techniques like artificial neural networks (ANNs) may be used to evaluate environmental balance stability and cut down on operational costs. The model's viability as a soft sensor for control and management systems for IDWWs was determined by comparing observed and forecasted output variables using statistical analysis measures such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R). When it comes to evaluating data, neural network analysis often use the PYTHON programming language, however ANN models provide an alternative, simpler method.

Keyword : Artificial Neural Network (ANN), Industry dispose Waste Water, Modeling, Python*, Statistical Analysis