title: State estimation for large-scale wastewater treatment plants.
abstract: Many relevant process states in wastewater treatment are not measurable, or their measurements are subject to considerable uncertainty. This poses a serious problem for process monitoring and control. Model-based state estimation can provide estimates of the unknown states and increase the reliability of measurements. In this paper, an integrated approach is presented for the optimization-based sensor network design and the estimation problem. Using the ASM1 model in the reference scenario BSM1, a cost-optimal sensor network is designed and the prominent estimators EKF and MHE are evaluated. Very good estimation results for the system comprising 78 states are found requiring sensor networks of only moderate complexity.
authors.affiliation: AVT Process Systems Engineering, RWTH Aachen University, Germany.
authors.forename: Jan
authors.initials: J
authors.lastname: Busch
authors.forename: David
authors.initials: D
authors.lastname: Elixmann
authors.forename: Peter
authors.initials: P
authors.lastname: Kühl
authors.forename: Carine
authors.initials: C
authors.lastname: Gerkens
authors.forename: Johannes P
authors.initials: JP
authors.lastname: Schlöder
authors.forename: Hans G
authors.initials: HG
authors.lastname: Bock
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Marquardt
mesh_terms: D000465:Algorithms; D000641:Ammonia / Q000032:analysis; D003198:Computer Simulation; D003365:Costs and Cost Analysis; D004867:Equipment Design; D013997:Time Factors; D062065:Wastewater; D018508:Water Purification / Q000191:economics / Q000295:instrumentation
chemical_list: D000641:Ammonia; D062065:Wastewater
keywords: ASM; BSM1; EKF; Moving horizon estimation; State estimation; Wastewater treatment