Optimizing wastewater treatment sampling strategies through Markov Chain Monte Carlo Bayesian inference in Activated Sludge Model No. 3
type: CreativeWork
description: Activated sludge models provide mechanistic frameworks for understanding biological wastewater treatment processes, but their practical implementation remains constrained by challenges in parameter estimation and uncertainty quantification under realistic monitoring conditions. This thesis develops a comprehensive Bayesian inference framework for Activated Sludge Model No. 3 that integrates sensitivity screening, identifiability diagnostics, and Markov chain Monte Carlo estimation to deliver robust parameter estimates and predictive uncertainty bounds across diverse monitoring designs. The methodology combines normalised sensitivity analysis to identify negligible parameters, profile likelihood analysis to distinguish estimable from non-estimable parameters, and principal component analysis to represent correlation structures among weakly identifiable parameters through latent variables. The No-U-Turn sampler implements full Bayesian inference, integrating literature-informed priors with synthetic observational data to characterise posterior distributions for 36 kinetic and stoichiometric parameters. Five monitoring regimes spanning intensive 14-day campaigns with four daily measurements to sparse year-long programmes with weekly sampling were systematically evaluated, controlling measurement noise and system configuration. Three inference configurations were compared across all monitoring regimes: structured workflow employing sensitivity screening, identifiability classification, and dimensionality reduction; progressively simplified configurations removing screening steps; and direct estimation of all parameters from literature-informed priors without preprocessing. Diagnostic statistics including convergence measures, effective sample sizes, and divergence rates quantified sampling performance, while posterior predictive checks assessed forecast accuracy for effluent compounds including chemical oxygen demand, nitrogen species, alkalinity, and suspended solids. …
abstract: Activated sludge models provide mechanistic frameworks for understanding biological wastewater treatment processes, but their practical implementation remains constrained by challenges in parameter estimation and uncertainty quantification under realistic monitoring conditions. This thesis develops a comprehensive Bayesian inference framework for Activated Sludge Model No. 3 that integrates sensitivity screening, identifiability diagnostics, and Markov chain Monte Carlo estimation to deliver robust parameter estimates and predictive uncertainty bounds across diverse monitoring designs. The methodology combines normalised sensitivity analysis to identify negligible parameters, profile likelihood analysis to distinguish estimable from non-estimable parameters, and principal component analysis to represent correlation structures among weakly identifiable parameters through latent variables. The No-U-Turn sampler implements full Bayesian inference, integrating literature-informed priors with synthetic observational data to characterise posterior distributions for 36 kinetic and stoichiometric parameters. Five monitoring regimes spanning intensive 14-day campaigns with four daily measurements to sparse year-long programmes with weekly sampling were systematically evaluated, controlling measurement noise and system configuration. Three inference configurations were compared across all monitoring regimes: structured workflow employing sensitivity screening, identifiability classification, and dimensionality reduction; progressively simplified configurations removing screening steps; and direct estimation of all parameters from literature-informed priors without preprocessing. Diagnostic statistics including convergence measures, effective sample sizes, and divergence rates quantified sampling performance, while posterior predictive checks assessed forecast accuracy for effluent compounds including chemical oxygen demand, nitrogen species, alkalinity, and suspended solids. …
author: Van den Berg, Ruan
language: en
date published: 2026-03
date modified: 2026-07-24T04:40:12Z
provider: Stellenbosch University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Stellenbosch : Stellenbosch University