Industrial Wastewater Treatment Plant Forecasting Using Neural Networks

The efficiency of the Wastewater Treatment Plant (WWTP) strongly depends on the inlet flow and the component concentrations of the wastewater. The forecasting of WWTP load gives promising opportunities to accomplish relevant operational actions. A predictive control strategy has been implemented. Taking advantage of the recognized properties of universal approximation of neural networks (NN), a description of the industrial plant behavior has been obtained. Multi NN have been used to forecast the total pollution and the quantity of the wastewater. A structural analysis of the main pollution’s sources in complex chemical plants is done to achieve this purpose. Classifying the pollution’s sources in accordance with their type and temporally, a set of clusters has been differentiated. Separate NN describe these clusters. To illustrate the applicability of the developed approach an example of a water purification control system in the wastewater treatment installation of a crude oil production plant has been presented. The obtained results show that the using of the pollution degree forecasting makes easier the processes in the wastewater treatment plants since it permits better model based control to be realized.