Artificial Neural Network as a Virtual Sensor of Nitrate Nitrogen (V) Concentration in an Activated Sludge Reactor

Abstract

The paper discusses the use of an artificial neural network to control the operation of wastewater treatment plants with activated sludge. The task of the neural network in this case is to calculate (predict) the readings of the probe measuring the concentration of nitrate nitrogen (V) in one of the biological reactor tanks. Neural networks are known for their ability to universal approximation of virtually any relationship, including the function of many variables, but the process of “training” the network requires the presentation of many sets of input data and corresponding expected results. This is a difficulty in the case of wastewater treatment plants, because some key process parameters are usually not measured online (samples are taken and measurements are taken in the laboratory), and even if they are, the time intervals are large. Bearing in mind the aforementioned difficulty, this work uses a set of input data consisting only of information that can be measured with measuring probes. As a result of the conducted experiments a high compliance of the probe’s prediction with the expected values was obtained. The paper also presents data preparation and the network “training” process.

Review of Latest Advances in Nature-Inspired Algorithms for Optimization of Activated Sludge Processes

“…There are several layers: the input layer, where inputs are given as weights to input neurons; the output layer, where output neurons do processing based on the input using an activation function and generate output; and single/multiple hidden layers, where intermediary neurons process the weighted sums of the inputs. Sometimes, output neurons can also be connected to each other and not just to the previous inputs, but this is complex and uncommon [20,23]. The neuron weights are determined using multiple data sets' training and validation processes.…”

“…These training data sets will also help identify the number of hidden neurons; more data points used means more hidden neurons are required. Validation or verification using separate datasets should be conducted at the end of the training process to ensure it was achieved correctly [23]. A few examples of ANN used in WWTP modelling are given below.…”

“…Plonka [23] used a layered ANN to create a virtual sensor that measures nitratenitrogen in the activated sludge reactor tank. The predicted readings from the ANN were then compared to the actual probe in the reactor.…”

“…There has not been an exact method of calculating the size of an ANN, so this type of training is beneficial. The training was conducted using 'FannTool' graphical interface [23]. ANNs require large samples of inputs and outputs to train the network, in this case, the measuring probes.…”