Biokinetic soft-sensing using Thiothrix and Ca. Microthrix bacteria to calibrate secondary settling, aeration and NO emission digital twins.

Climate resilience in water resource recovery facilities (WRRFs) necessitates improved adaptation to shock-loading conditions and mitigating greenhouse gas emission. Data-driven learning methods are widely utilised in soft-sensors for decision support and process optimization due to their simplicity and high predictive accuracy. However, unlike for mechanistic models, transferring machine-learning-based insights across systems is largely infeasible, which limits communication and knowledge sharing. To harness the benefits of both approaches, this study introduces a mechanistic online soft-sensor (MOSS) developed to calibrate digital twins of secondary settling tanks (hydraulic shock), aeration systems and nitrous oxide (NO) greenhouse gas emission. MOSS integrates biokinetic models of filamentous microbial predictors to calibrate digital twins through meta-models (data-driven part), updated using offline settling column tests and amplicon sequencing data for microbial analysis. For the first time, this approach employs multi-filamentous-community predictors for dynamic calibration, i.e., Thiothrix and Ca. Microthrix. The calibration and early-warning capabilities of MOSS are demonstrated using experimental data from a laboratory-scale WRRF.