A novel metabolic-ASM model for full-scale biological nutrient removal systems. (15th March 2020)

• Record Type: Journal Article
• Authors: Santos, Jorge M.M.; Rieger, Leiv; Lanham, Ana B.; Carvalheira, Mónica; Reis, Maria A.M.; Oehmen, Adrian
• Is Part Of: Water research. Volume 171(2020)
• Publication Date: 2020-03-15
• Subjects: Activated sludge model (ASM) -- Biological nutrient removal (BNR) -- Enhanced biological phosphorous removal (EBPR) -- Metabolic modelling -- Polyphosphate accumulating organism (PAO) -- Glycogen accumulating organism (GAO); Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2019.115373
• Languages: English
• ISSNs: 0043-1354

Abstract

Abstract: This study demonstrates that META-ASM, a new integrated metabolic activated sludge model, provides an overall platform to describe the activity of the key organisms and processes relevant to biological nutrient removal (BNR) systems with a robust single-set of default parameters. This model overcomes various shortcomings of existing enhanced biological phosphorous removal (EBPR) models studied over the last twenty years. The model has been tested against 34 data sets from enriched lab polyphosphate accumulating organism (PAO)-glycogen accumulating organism (GAO) cultures and experiments with full-scale sludge from five water resource recovery facilities (WRRFs) with two different process configurations: three stage Phoredox (A2/O) and adapted Biodenitro™ combined with a return sludge sidestream hydrolysis tank (RSS). Special attention is given to the operational conditions affecting the competition between PAOs and GAOs, capability of PAOs and GAOs to denitrify, metabolic shifts as a function of storage polymer concentrations, as well as the role of these polymers in endogenous processes and fermentation. The overall good correlations obtained between the predicted versus measured EBPR profiles from different data sets support that this new model, which is based on in-depth understanding of EBPR, reduces calibration efforts. On the other hand, the performance comparison between META-ASM and literature models demonstrates that existing literature models require extensive parameter changes and have limited predictive power, especially in the prediction of long-term EBPR performance. The development of such a model able to describe in detail the microbial and chemical transformations of BNR systems with minimal adjustment to parameters suggests that the META-ASM model is a powerful tool to predict and mitigate EBPR upsets, optimise EBPR performance and to evaluate new process designs. Graphical abstract: Image 1 Highlights: Current EBPR models are lacking detailed and dynamic process understanding. META-ASM model overcomes various shortcomings of existing EBPR models. Model was tested against 34 different data sets from enriched and full-scale sludge. This novel model reduces calibration efforts needed for EBPR processes. META-ASM model predicts EBPR performance as a function of operational conditions.