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 (S NH,e ) and the total nitrogen (S Ntot,e ) which are predicted considering influent parameters such as the ammonium concentration at the entrance of the WWTP reactor tanks (S NH,po ), the reactors' input flow rate (Q po ), the WWTP recirculation rate (Q a ) and the environmental temperature (T as ). Moreover, this work presents a new multi-objective control scenario which consists in a unique control structure performing the reduction of S NH,e and S Ntot,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 S NH,e and S Ntot,e , respectively.
Highlights
The pollutant reduction process of urban residual waters is performed at Wastewater Treatment Plants (WWTPs) which are facilities devoted to treating the incoming watersThe associate editor coordinating the review of this manuscript and approving it for publication was Gilberto Pastorello .and return them to its natural cycle
These pollutant reduction processes are carried out by means of highly complex and non-linear biological and biochemical processes which are described by the Activated Sludge Models (ASMs): the Activated Sludge Models No.1 (ASM1), No.2 (ASM2), No.2d (ASM2d) and No.3 (ASM3), all of them developed by the International Water Association (IWA)
It is observed that Overall Cost Index (OCI) is reduced a 37.2% since the control strategy is only applied when a violation is detected whereas Default Scenario (DS) and Hierarchical Scenario (HS) strategies are applied all the time
Summary
The pollutant reduction process of urban residual waters is performed at Wastewater Treatment Plants (WWTPs) which are facilities devoted to treating the incoming watersThe associate editor coordinating the review of this manuscript and approving it for publication was Gilberto Pastorello .and return them to its natural cycle. I. Pisa et al.: LSTM-Based Wastewater Treatment Plants Operation Strategies for Effluent Quality Improvement assure that pollutant parameters are maintained below certain limits. Pisa et al.: LSTM-Based Wastewater Treatment Plants Operation Strategies for Effluent Quality Improvement assure that pollutant parameters are maintained below certain limits These pollutant reduction processes are carried out by means of highly complex and non-linear biological and biochemical processes which are described by the Activated Sludge Models (ASMs): the Activated Sludge Models No. (ASM1), No. (ASM2), No.2d (ASM2d) and No. (ASM3), all of them developed by the International Water Association (IWA). ASM1 consists in a mathematical model showing the reduction of nitrogen derived pollutants [2] It has been extended with the development of Activated Sludge Models No. and 2d (ASM2 & ASM2d), where the former models the phosphorus pollutant reduction and the latter improves the denitrification process. ASM1 has been extended with Activated Sludge Model No. (ASM3), a new tool of the generation of Activated Sludge Models [3]
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