Using plant data to estimate biodegradable COD fractions – case study kwaMashu WWTP

• DOI: 10.2166/wst.2022.314
• Publication Date: 2022-10-07T14:31:51Z
• Authors: Barbara Brouckaert; Christopher Brouc...; Akash Singh; Kaverajen Pillay; Xavier Flores-Alsina; David Ikumi
• Keywords: Biological Oxygen Demand Analysis; Plant-wide steady state model; cod fractionation; Sewage; Parameter identifiability; plant-wide model; Wastewater; Environmental technology. Sanitary engineering; Waste Disposal, Fluid; 6. Clean water; Water Purification; 12. Responsible consumption; Data reconciliation; Plant-wide model; 13. Climate action; 11. Sustainability; COD fractionation; data reconciliation; parameter identifiability; TD1-1066

Abstract A modelling study is under way in preparation for a planned upgrade of the capacity of the kwaMashu WWTP in eThekwini, South Africa, from 50 to 80 ML/d. When the configuration of an existing plant is to be changed, the most critical part of the model calibration is the influent wastewater fractionation. However, the constantly varying characteristics of wastewater make experimental determination of an adequately representative set of components difficult, time-consuming and expensive, which constitutes significant barriers to the adoption of modelling by many municipalities. Compliance and process monitoring generate large sets of influent measurements of chemical oxygen demand (COD), free and saline ammonia (FSA), total suspended solids (TSS), etc., but these are insufficient for modelling purposes. In particular, biodegradability is not routinely measured. However, since influent fractionation is designed to predict the fate of material in the wastewater treatment process, it should be possible to infer the fractionation from a combination of influent and plant measurements. This case study demonstrates the application of a pair of modelling tools, a probabilistic influent fractionator and a simplified steady‑state plant‑wide model, to estimate the influent fractionation, together with certain unmeasured or unreliable operational parameters.