Paper Info
Title
Application of evolutionary optimisers in data-based calibration of Activated Sludge Models
Abstract
Modelling activated sludge systems has become an accepted practice in Wastewater Treatment Plant (WWTP) design, teaching and research, and Activated Sludge Models (ASM) are by far the most widely used models for activated sludge systems. In most ASM applications, calibration is based on more or less ad-hoc and trial and error approaches. Calibration of the ASMs remains the weakest link in the overall process of modelling biological wastewater treatment. In this paper, a calibration approach is proposed where the need for expert knowledge and modeller effort is significantly reduced. The calibration approach combines identifiability analysis and evolutionary optimisers to automate the ASM calibration. Identifiability analysis is used to deal with poor identifiability of the model structures and evolutionary optimisers are used to identify the model parameters. The applied evolutionary optimisers are Genetic Algorithms and Differential Evolution. Performance of the evolutionary optimisers is compared with a previously proposed calibration approach based on Monte Carlo simulations. All methods were capable of calibrating the model when given enough computation time. However, some of the evolutionary optimisation methods had an advantage in terms of computation time against the Monte Carlo method.
Year
DOI
Venue
2012
10.1016/j.eswa.2011.12.041
Expert Syst. Appl.
Keywords
Field
DocType
error approach,asm calibration,activated sludge models,evolutionary optimisation method,evolutionary optimisers,computation time,calibration approach,model parameter,identifiability analysis,asm application,data-based calibration,activated sludge system,differential evolution,genetic algorithms
Monte Carlo method,Trial and error,Activated sludge,Computer science,Identifiability,Differential evolution,Artificial intelligence,Calibration,Genetic algorithm,Machine learning,Computation
Journal
Volume
Issue
ISSN
39
7
0957-4174
Citations 
PageRank 
References 
2
0.38
4
Authors
2
Name
Order
Citations
PageRank
Jukka Keskitalo120.72
Kauko Leiviskä2386.66