A Methodology for Global Sensitivity Analysis of Activated Sludge Models: Case Study with Activated Sludge Model No. 3 (ASM3). (6th May 2019)

• Record Type: Journal Article
• Authors: Fortela, Dhan Lord B.; Farmer, Kyle; Zappi, Alex; Sharp, Wayne W.; Revellame, Emmanuel; Gang, Daniel; Zappi, Mark
• Is Part Of: Water environment research. Volume 91:Number 9(2019)
• Page Start: 865
• Page End: 876
• Publication Date: 2019-05-06
• Subjects: computational modeling -- functional principal component analysis -- Morris screening technique -- wastewater treatment; Water quality management -- Periodicals; Water -- Purification -- Periodicals; Water -- Pollution -- Periodicals; Water -- Pollution; Water -- Purification; Water quality management; Sewage; Water Pollution; Periodicals; Electronic journals; Periodicals; 628.16
• DOI: 10.1002/wer.1127
• Languages: English
• ISSNs: 1061-4303

Abstract

Abstract: The main objective of this study was to demonstrate a computational approach of global sensitivity analysis (GSA) integrated with functional principal component analysis (fPCA) for activated sludge models through aggregation of time‐dependent model response patterns into time‐independent coefficients of functional principal components (PCs). This proposed approach addresses the main issue of time‐varying character of GSA indices when calculated solely on the time‐dependent model outputs. The GSA‐fPCA methodology was implemented using the rigorous model Activated Sludge Model No. 3 (ASM3) as case study. The approach transforms the time‐dependent model outputs into functional PCs prior to calculation of GSA indices to remove the time‐varying character of the calculated GSA indices. This work focused on the evaluation of the following key computational factors that may significantly influence the performance of the GSA‐fPCA methodology: (a) model parameter sampling range, (b) model simulation period, (c) basis functions system, and (d) state of the system being modeled—batch or continuous activated sludge process. Results show that first few functional PCs capture up to 100% of the curve patterns in the time‐dependent model outputs. The sensitivity indices calculated from the PC scores via Morris' GSA technique elucidated parameter sensitivity patterns inherent to the complex mathematical structure of ASM3. Practitioner points: Functional principal components‐mediated GSA technique to remove time‐varying character of sensitivity indices derived from time‐dependent dynamical models. Technique amenable to improving efficiency of capturing response patterns into few functional principal components through various basis functions. Identifying priority parameters for ASM3 model calibration requires specification of target model outputs to which parameter sensitivities are calculated. GSA‐fPCA offers a comprehensive numerical approach to manipulating models depending on the intended applications: simple fast‐responding models to complex models. Abstract : The transformation of time‐series data via principal component basis functions results to aggregate scores of model response fluctuations. The time‐independent scores can then be used to calculate global sensitivity indices for the importance of model parameters.