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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. [ABSTRACT FROM AUTHOR]

Three consulting teams conducted independent modelling projects for three different wastewater treatment plants ranging in size from approximately 113,800 m(3)/d (30 mgd) to 530,000 m(3)/d (140 mgd), in different parts of the world (USA and Finland). The plants have different treatment objectives ranging from nitrification and partial denitrification (nitrate plus nitrite <8.7 mg/L) to enhanced nutrient removal (total nitrogen <3 mg/L, total phosphorus <0.3 mg/L). Commonly-used models were applied in the case studies, including ASM3 (using the GPS-X simulator), New General (using GPS-X), Dold (using BioWin), and a variation of the Dold model methanol degradation capabilities (NGmeth within GPS-X). The authors compare and contrast the modelling approaches taken, including calibration and validation approaches, sensitivity analyses, and the application of results to full-scale studies, designs and operations. Despite several differences between the approaches, there are many similarities which are discussed in light of the IWA draft uniform protocol for activated sludge modelling. The authors also discuss current modelling limitations and offer suggestions to improve the state of the art.