Model-based methodology for the design of optimal control strategies in MBR plants.

This paper proposes a model-based methodology that allows synthesising the most appropriate strategies for optimising the operation of wastewater treatment plants (WWTPs). The methodology is applied with the aim of maximising the nitrogen removal in membrane bioreactors (MBRs). The proposed procedure is based on a systematic approach composed of four steps. First, a sensitivity analysis of the input variables is carried out in order to obtain a first assessment of the potential for operational improvements. Then, the optimum input variable values are calculated by a model-based optimisation algorithm that minimises a cost function associated with the effluent total nitrogen at different temperatures. Then, the optimum operational strategies are identified. Finally, these operational strategies form the conceptual knowledge base for designing automatic control laws. The obtained optimal control strategies have shown a significant improvement in performance in comparison with fixed operation for the studied case, reducing the total nitrogen by 40%.

Advanced data management for optimising the operation of a full-scale WWTP.

The lack of appropriate data management tools is presently a limiting factor for a broader implementation and a more efficient use of sensors and analysers, monitoring systems and process controllers in wastewater treatment plants (WWTPs). This paper presents a technical solution for advanced data management of a full-scale WWTP. The solution is based on an efficient and intelligent use of the plant data by a standard centralisation of the heterogeneous data acquired from different sources, effective data processing to extract adequate information, and a straightforward connection to other emerging tools focused on the operational optimisation of the plant such as advanced monitoring and control or dynamic simulators. A pilot study of the advanced data manager tool was designed and implemented in the Galindo-Bilbao WWTP. The results of the pilot study showed its potential for agile and intelligent plant data management by generating new enriched information combining data from different plant sources, facilitating the connection of operational support systems, and developing automatic plots and trends of simulated results and actual data for plant performance and diagnosis.