Artificial Neural Network for nitrogen and ammonia effluent limit violations risk detection in Wastewater Treatment Plants

One of the major concerns in Wastewater Treatment Plant (WWTP) operation is that of satisfying the legal requirements that impose maximum allowable concentration levels for effluent pollutants. Not meeting these requirements may generate economic punishment in terms of fines in addition, of course, to the environmental consequences. The effluent limit violations is usually measured as a side performance measure to existing WWTP control and operation approaches. However no explicit way of tackling this issue is found. In this paper a first step towards this direction is proposed in terms of a prognostication of the situations of risk. This is to say when the effluent is close to generate a limit violation for some of the limiting components. This is accomplished by means of effluent pollutants concentration prediction by using Artificial Neural Networks (ANN). The prediction is applied to a controlled plant and it is shown how a logical signal (therefore amenable for monitoring and decision) can be generated at the instants where such a risk is detected.