Estimation of effluent nutrients in municipal MBBR process


  • Tiina Komulainen
  • Abdul Malik Baqeri
  • Einar Nermo
  • Arvind Keprate
  • Torgeir Saltnes
  • Katrine Marsten Jansen
  • Olga Korostynska



wastewater treatment, effluent prediction, time-series models, transfer functions, state-space models


The recently updated European Union’s Urban Waste Water Treatment Directive proposal, European Green Deal, Biodiversity Strategy for 2030, and EU’s Energy System Integration highlight a pressing need for innovative biological nutrient removal processes and energy-efficient control methods to reduce pollution and minimize the carbon footprint at water resource recovery facilities. The aim of the PACBAL research project is to develop estimation methods for nutrient profile in a novel industrial Moving Bed Biofilm Reactor (MBBR) process. This study devises and assesses a range of data-driven methods to estimate effluent phosphorus concentration by utilizing a combination of real sensors with software models. The resulting virtual sensor could facilitate the design of energy-efficient control strategies. The case study data are collected from the MBBR process at Hias water resource recovery facility in Norway. Data sets from December 2022 to March 2023 include varying weather conditions, such as rain, dry, and snow. The Hias Process consists of three anaerobic and seven aerobic zones, where biomass carriers removes over 90 percent of the phosphorus from the wastewater in simultaneous biological processes. The industrial online measurements include wastewater flowrate, aeration rates, dissolved oxygen and nutrients COD and NO2/ NO3 at inlet and total suspended solids at outlet. Dynamic data-driven models indluding transfer functions, state-space models and ARX models, were developed and compared to estimate the outlet phosphorus concentration. Model fitness to validation data was around 7% with ARX models, and up to 18% with tranfer function models and state-space models. The first and second order models gave similar results. The state-space models will be developed further and implemented to into virtual sensors that will enable energy-efficient control strategy development.