Modelling of high-rate activated sludge process: Assessment of model parameters by sensitivity and uncertainty analyses

Hazal Gulhan; Reza Faraji Dizaji; Muhammed Nimet Hamidi; Amr Mustafa Abdelrahman; Safak Basa; Seyma Cingoz; Ismail Koyuncu; Huseyin Guven; Hale Ozgun; Mustafa Evren Ersahin; Recep Kaan Dereli; Izzet Ozturk

doi:10.1016/j.scitotenv.2024.170102

Modelling of high-rate activated sludge process: Assessment of model parameters by sensitivity and uncertainty analyses

Sci Total Environ. 2024 Mar 10:915:170102. doi: 10.1016/j.scitotenv.2024.170102. Epub 2024 Jan 14.

Affiliations

¹ Istanbul Technical University, Civil Engineering Faculty, Environmental Engineering Department, Ayazaga Campus, Maslak 34469, Istanbul, Turkey. Electronic address: gulhan@itu.edu.tr.
² Istanbul Technical University, Civil Engineering Faculty, Environmental Engineering Department, Ayazaga Campus, Maslak 34469, Istanbul, Turkey.
³ ISKI, Istanbul Water and Sewerage Administration, Eyup 34060, Istanbul, Turkey.
⁴ Istanbul Technical University, Civil Engineering Faculty, Environmental Engineering Department, Ayazaga Campus, Maslak 34469, Istanbul, Turkey; National Research Center on Membrane Technologies, Istanbul Technical University, 34469 Maslak, Istanbul, Turkey.
⁵ University College Dublin, School of Chemical and Bioprocess Engineering, Belfield, Dublin 4, Ireland.

PMID: 38228239
DOI: 10.1016/j.scitotenv.2024.170102

Abstract

The objective of this study is to develop a mechanistic model to predict the long-term dynamic performance of High-Rate Activated Sludge (HRAS) process, including the removal of carbon (COD), nitrogen (N), and phosphorus (P). The model was formulated with inspiration from Activated Sludge Models No. 1 and 3 (ASM1 and ASM3) to incorporate essential mechanisms, such as adsorption and storage substrate, specific to HRAS systems. A stepwise protocol was followed for calibration with dynamic data from a pilot-scale HRAS plant. Sensitivity analysis identified influential model parameters, including maximum specific growth rate (μ), growth yield (Y_H), storage yield (Y_STO), storage rate (k_STO), decay rate (b), and half saturation of the readily biodegradable substrate for growth (K_S1). The calibrated model achieved prediction efficiencies above the normalized Mean Absolute Error (MAE) of 70 % for mixed liquor suspended solids (MLSS), total chemical oxygen demand (TCOD), soluble COD (SCOD), particulate COD (XCOD), total nitrogen (TN), ammonia nitrogen (SNH), total phosphorus (TP), soluble TP (STP), and particulate TP (XTP). Uncertainty analysis revealed that SCOD was underestimated. Based on the dynamic profiles of uncertainty bands and observed data, there is potential for improving the estimation of dynamic behavior in STP. The observed discrepancies may be attributed to variations in wastewater characteristics during the monitoring period, particularly concerning the phosphorus (P) fractions of the readily biodegradable substrate (S_S) and soluble inerts (S_I), which were not considered as dynamically changing parameters in the model.

Keywords: Adsorption; Calibration protocol; Dynamic modelling; Nutrients; Storage.