Data scarcity in modelling and simulation of a large-scale WWTP: Stop sign or a challenge

Datascarcitycanbeconsideredasthemainlimitationforamorewidespreadutilizationofmathematicalmodelsinthedesign, optimization and control of biological nutrient removal activated sludge systems (BNRAS). High cost anddemandingworkloadrelatedtoexperimentaldataandsufficientsamplingcampaignsmakethedatacollectionprocessanunpleasantnecessityformanagingstakeholdersinmodellingprojects.Complicateduseofonline-sensorsleadingtofrequent erroneous readings and dynamic nature of wastewater treatment processes can intensify the data scarcityproblems. This paper investigates the influence of data scarcity on the development and calibration of wastewatertreatment plant (WWTP) models. A straightforward methodology is proposed to address the challenges associatedwith data qualityand quantity problems inmodelling ofa BNRASin thelargestItalian WWTP located in Castiglione,Italy. The plant operational modes, weather condition and sensor performance during the sampling campaigns werethe main sources of the data scarcity. Influent, biokinetic, aeration, hydraulic and transport, clarifier, energy con-sumptionandeffluentsub-modelswerecalibratedbyuseoftheproposedextensivestep-wisecalibrationprocess.TheMonte Carlo analysis was performed to quantify the uncertainty of the modelling results. The proposed methodologycould be implemented in engineering practice to develop and calibrate the WWTP models while it increases theawareness about modelling robustness and its characterized uncertainty to avoid bad modelling practice.