LSTM-Based Wastewater Treatment Plants Operation Strategies for Effluent Quality Improvement

Abstract

Wastewater Treatment Plants (WWTPs) are facilities devoted to managing and reducing the pollutant concentrations present in the urban residual waters. Some of them consist in nitrogen and phosphorus derived products which are harmful for the environment. Consequently, certain constraints are applied to pollutant concentrations in order to make sure that treated waters comply with the established regulations. In that sense, efforts have been applied to the development of control strategies that help in the pollutant reduction tasks. Furthermore, the appearance of Artificial Neural Networks (ANNs) has encouraged the adoption of predictive control strategies. In such a fashion, this work is mainly focused on the adoption and development of them to actuate over the pollutant concentrations only when predictions of effluents determine that violations will be produced. In that manner, the overall WWTP's operational costs can be reduced. Predictions are generated by means of an ANN-based Soft-Sensor which adopts Long-Short Term Memory cells to predict effluent pollutant levels. These are the ammonium (SNH,e) and the total nitrogen (SNtot,e) which are predicted considering influent parameters such as the ammonium concentration at the entrance of the WWTP reactor tanks (SNH,po), the reactors' input flow rate (Qpo), the WWTP recirculation rate (Qa) and the environmental temperature (Tas). Moreover, this work presents a new multi-objective control scenario which consists in a unique control structure performing the reduction of SNH,e and SNtot,e concentrations simultaneously. Performance of this new control approach is contrasted with other strategies to determine the improvement provided by the ANN-based Soft-Sensor as well as by the fact of being controlling two pollutants at the same time. Results show that some brief and small violations are still produced. Nevertheless, an improvement in the WWTPs performance w.r.t. the most common control strategies around 96.58% and 98.31% is achieved for SNH,e and SNtot,e, respectively.

Keywords

• Artificial neural networks
• BSM2 framework
• industrial control
• violation reduction
• water pollution