Data analytics in control and operation of municipal wastewater treatment plants: qualitative analysis of needs and barriers.

This study aims to identify barriers and needs for the application of data analytics in municipal wastewater treatment. The study was conducted through a series of interviews with stakeholders involved in instrumentation, control, and automation of wastewater treatment plants. Opportunities and limitations observed by different stakeholders were assessed with a thematic analysis. Thematic analysis enabled a broader consideration of social and organizational aspects related to process control, operation, and maintenance. Identified key barriers for applying data analytics included laborious instrumentation maintenance, unstable control loops, and deficient customization of digital tools for users at wastewater treatment plants. Development needs include easier data processing tools, quality assurance of instrumentation, and controller tuning. Results indicate that the perceived potential of data analytics is highly dependent on the performance of underlying physical and digital systems, as well as the control strategies and operating environment of the plant. Despite the barriers, users and developers see many potential applications for data analytics and expect them to have a central role in the control and operation of wastewater treatment plants in the future.

Nitrate estimation in the denitrifying post-filtration unit of a municipal wastewater treatment plant: the Viikinmäki case.

In this work we present and discuss the design of an array of soft-sensors to estimate the nitrate concentration in the denitrifying post-filtration unit at the Viikinmäki wastewater treatment plant in Helsinki (Finland). The developed sensors aim at supporting the existing hardware analyzers by providing a reliable back-up system in case of malfunction of the instruments. In the attempt to design easy to implement and interpretable sensors, computationally light linear models have been considered. However, due to the intrinsic nonlinearity of the process, also nonlinear but still computationally affordable models have been considered for comparison. The experimental results demonstrate the potential of the developed soft-sensors and the possibility for an on-line implementation in the plant's control system as alternative monitoring devices.