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As a key part in the water environmental protection chain, the safe and stable operation of wastewater treatment plant is the prerequisite to ensure the quality of effluent. A major requirement for achieving this goal relies on the availability of real-time monitoring of key or primary process indicators. These indicators reflect the important information of wastewater treatment processes, but most of them are hard-to-measure or not easy to measure. Their real-time availability is often associated with expensive capital and maintenance costs, as well as being characterized by time-delayed responses that are often unsuitable for real-time monitoring. In order to deal with the online real-time acquisition of these variables, the soft-sensing techniques provide a feasible and effective solution. This dissertation surveys and discusses the modeling of data-driven soft-sensing techniques in municipal wastewater treatment plants. The main contributions are described as follows. A deep neural network soft-sensor model for wastewater treatment is proposed. Supervised neural network methods are one of the most popular data-driven soft-sensor modeling methods in wastewater treatment. However, most of them are shallow architecture, which are incapable of performing tasks effectively when got stuck in extremely complex situations, such as severe weather conditions. One of the potential solutions is resort to the neural networks with multi-layer structure, i.e., the deep neural networks. However, deep architecture neural networks suffered the difficulty of training, poor generalization ability, and so on, and thus be few successful application cases in early period. Furthermore, wastewater treatment has its unique practical matters, leading to the difficulty of collecting adequate data. Therefore, few literatures devoted deep neural networks to the soft sensor modeling for wastewater treatment. In response to this situation, a deep neural network soft-sensor model for wastewater treatment is constructed based on a deep learning model: stacked auto-encoder. At last, the main research work and corresponding results of this dissertation are briefly concluded, and the future research are discussed.
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