For flow rates and reactor volumes, data points that exhibited a rapid decline to zerowhich generally did not occur -were removed and short-term, minor operational fluctuations in sensor signals were smoothed out (Figures ). The true minimum and maximum ranges of the flow rates and volumes were determined by on-site operators and communicated to the research team weekly. Separately, peak smoothing was minimized for temperature and light intensity readings to retain the true diel variation (Figure ). For orthophosphate and microalgae biomass concentrations, online monitoring data was complemented by on-site analyses of grab or composite samples (Table ). When comparing two measurements of the same constituent, discrepancies were (at times) identified between the online sensor data and the on-site sampling and analysis data (Figure ). For instance, the effluent orthophosphate sensor overestimated concentration in early April (Figure ), and the microalgae biomass sensor underestimated solids concentration until mid-April (at which time the sensor baseline was shifted; Figure ). In response, the sensor signals for orthophosphate and biomass were first smoothed to eliminate outliers, and then further corrected to match the magnitude of the daily on-site laboratory measurement data. At the same time, however, we endeavored not to lose the trend information provided by the online sensors to capitalize on the advantages of both laboratory measurement data (higher accuracy) and online monitoring data (higher quantity). The pre-processed, long-term, continuous online monitoring data were used to define system features (e.g., reactor volumes) or model inputs, with the detailed conditions and results of data reconciliation for each parameter summarized in Table . In the system set-up, the flow rates and reactor volumes were fixed to the average values of the reconciled data to reduce the computational intensity required for ODE calculation and integration. Due to the increase in mix tank volume (15.4%) from late March to early April (Figure ), the standard deviation of the single pass time for the mix tank was relatively high (0.04 d), but negligible variabilities (≤0.003 d)