Date-Driven Soft-Sensor Design for Biological Wastewater Treatment Using Deep Neural Networks and Genetic Algorithms

抄録

In wastewater treatment plants (WWTPs), some variables such as BOD₅ and COD that are related to effluent quality, are difficult to measure directly online due to technical or economic limitations. To deal with this problem, a soft sensor that is based on a deep neural network with a named stacked autoencoder (SAE) is developed for WWTPs. Neural networks with deep structure are superior to shallow ones when facing complex problems in modern applications, which makes them suitable for wastewater treatment processes. However, deep structures are difficult to train when using traditional learning algorithms, and there are no general guidelines for identifying the proper network structure for a specific application. In the present work, a deep learning technique called the greedy layer-wise training algorithm is employed to train a deep neural network, and a genetic-algorithm strategy is developed for identifying the number of neurons in each hidden layer. In order to demonstrate its usefulness, the proposed soft sensor is tested through the test-bed Benchmark Simulation Model No. 1 (BSM1) with different weather conditions. The results indicate that the proposed soft sensor based on a deep-structure neural network can achieve better prediction and generalization performance in comparison with commonly used methodologies.