Project goals

The aim of the project is to create an intelligent algorithm for predicting the quality and quantity of wastewater flowing into the wastewater treatment plant. Prediction of these data in the order of hours to days will allow proposing operational measures to reduce the risk of pollution emissions, including simulation scenarios and risk analysis at WWTPs, or at relief chambers. The algorithm will use cloud-computing and machine learning. Based on an in-depth analysis of available wastewater data (qualitative and quantitative data, recurring events, ie days of the week, seasons, etc. together with precipitation forecasting), a neural network capable of predicting the quality of inflowing wastewater will be created. This knowledge will make it possible to create a database of scenarios of operational measures at WWTPs, to simulate the effect of their implementation and thus eliminate the risk of non-compliance with emission limits to aquatic ecosystems.