## 4 Conclusions  
In this paper, a successful implementation of a single-layer economic oriented model predictive control approach for the optimization of the operation of wastewater treatment plants (WWTPs) in a simulation environment is presented. Even though this study used empirical models based on data gathering and the adequate software and communication systems, the technique could be implemented in real plants.  
An _OCI_ reduction of 13% with respect to the standard NMPC focused on set-point tracking is achieved, with the pure EMPC focused on the optimization of the energy costs (EMPC-OCI), and ammonium concentration in the effluent reduces by 21% when using the EMPC focused on the minimization of the off-specification ammonium concentration in the effluent. Those improvements are achieved by performing a dynamic optimization of the operation, taking advantage of the variations in the load to find more favourable operating conditions on a given operating window.  
The results demonstrate that the implementation of novel advanced control techniques as single-layer EMPCs can produce significant energy savings and improvements in WWTP performance. The single-layer strategy has the advantage of being simpler than other strategies, e.g., [9], which obtain further reductions in costs but with a more complex hierarchical framework for nitrate and oxygen control. On the other hand, in the pioneering implementation of EMPCs to the BSM1 model presented in [16] the full model is used for predictions, therefore, a novel feature presented in this work is the implementation of techniques to face modeling errors. It is also encouraging the potential use of a reduced model with partial information with prediction purposes. Alternative formulations oriented towards the increase of model quality should be investigated, as they will have a direct repercussion on the final quality of the obtained control.  
Future work is oriented to exploit these characteristics in different scenarios, and introduce additional optimization objectives in the economic-oriented controller formulation. It is encouraging to promote continuing research in the application of these advanced control strategies to improve WWTP operation.  
**Acknowledgments:** The authors wish to thank the support of the Spanish Government through the Ministerio de Economia y Competitividad (MINECO) projects DPI2015-67341-C2-1-R, DPI2016-77271-R also with FEDER funding. The Samuel Sodirzano Foundation through project FS/21-2015 and the IWA Task Group from the Department of Industrial Electrical Engineering and Automation (IEA), Lund University, Sweden ([PERSON], [PERSON]) for the BSM1 models.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments and [PERSON] and [PERSON] analyzed the results; all the authors have contributed to write the paper.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Controller** & **\% AE Variation** & **\% PE Variation** & **\% OCI Variation** \\ \hline EMPC-OCI & \(-12.55\) & \(-23.13\) & \(-3.13\) \\ EMPC-OCI + NH & \(1.47\) & \(-37.61\) & \(-0.24\) \\ EMPC-NH & \(13.00\) & \(-38.06\) & \(2.24\) \\ [PERSON] and [PERSON] EMPC-NH & \(3.73\) & \(3.30\) & \(-9.78\) \\ [PERSON] and LiuEMPC-OCI + NH & \(-3.40\) & \(6.75\) & \(-11.18\) \\ \hline \end{tabular}
\end{table}
Table 4: Performance indices variation respect to the BSM1 PI default strategy.  
**Conflicts of Interest:** The authors declare no conflict of interest.