Improving the efficiency of a wastewater treatment plant by fuzzy control and neural networks

Abstract

One of the main problems in operating a wastewater treatment plant is the purification of the excess water from dewatering and pressing of sludge. Because of a high load of organic material and of nitrogen it has to be buffered and treated together with the inflowing wastewater. Different control strategies are discussed. A combination of neural network for predicting outflow values one hour in advance and fuzzy controller for dosing the sludge water are presented. This design allows the construction of a highly non-linear predictive controller adapted to the behaviour of the controlled system with a relatively simple and easy to optimise fuzzy controller. Measurement results of its operation on a municipal wastewater treatment plant of 60,000 inhabitant equivalents are presented and discussed. In several months of operation the system has proved very reliable and robust tool for improving the system's efficiency.

Shall we use hardware sensor measurements or soft-sensor estimates? Case study in a full-scale WWTP

“…As for the municipal WWTPs, the most typical methods used for designing soft-sensors based on operation data are multivariate statistical (e.g., Rosen and Olsson, 1998;Rosen and Lennox, 2001;Moon et al, 2009) and artificial neural network (e.g., Bongards, 2001;Hong et al, 2003;Lee et al, 2008) techniques. The majority of the data derived soft-sensors that have been proposed for the biological wastewater treatment concern estimation of variables associated with the content of organic matter or nitrogen compounds .…”

Artificial Neural Network Modeling for Biological Removal of Organic Carbon and Nitrogen from Slaughterhouse Wastewater in a Sequencing Batch Reactor

“…Moreover, the neural network model possesses a distinctive ability of learning nonlinear functional relationship without requiring a structural knowledge of the process to be modeled. Several examples of successful development of software sensor mechanism based on the neural network approach are described in the literature [11][12][13][14]. The application of ANN modeling in environmental engineering field is, however, still being undergone.…”

Monitoring of Anoxic/Oxic Process for Nitrogen and Chemical Oxygen Demand Removal Using Fuzzy Neural Networks

“…On the other hand, the fuzzy control is very difficult to design and adjust automatically. Thus, it is necessary to design the fuzzy neural network (FNN) model, which includes the advantages of a fuzzy control and neural network (Bongards, 2001). The FNN combines fuzzy logic control with an ANN and realizes fuzzy logic by the FNN.…”