Document Detail

Global sensitivity analysis of the BSM2 dynamic influent disturbance scenario generator.
MedLine Citation:
PMID:  22592459     Owner:  NLM     Status:  In-Data-Review    
This paper presents the results of a global sensitivity analysis (GSA) of a phenomenological model that generates dynamic wastewater treatment plant (WWTP) influent disturbance scenarios. This influent model is part of the Benchmark Simulation Model (BSM) family and creates realistic dry/wet weather files describing diurnal, weekend and seasonal variations through the combination of different generic model blocks, i.e. households, industry, rainfall and infiltration. The GSA is carried out by combining Monte Carlo simulations and standardized regression coefficients (SRC). Cluster analysis is then applied, classifying the influence of the model parameters into strong, medium and weak. The results show that the method is able to decompose the variance of the model predictions (R(2)> 0.9) satisfactorily, thus identifying the model parameters with strongest impact on several flow rate descriptors calculated at different time resolutions. Catchment size (PE) and the production of wastewater per person equivalent (QperPE) are two parameters that strongly influence the yearly average dry weather flow rate and its variability. Wet weather conditions are mainly affected by three parameters: (1) the probability of occurrence of a rain event (Llrain); (2) the catchment size, incorporated in the model as a parameter representing the conversion from mm rain · day(-1) to m(3) · day(-1) (Qpermm); and, (3) the quantity of rain falling on permeable areas (aH). The case study also shows that in both dry and wet weather conditions the SRC ranking changes when the time scale of the analysis is modified, thus demonstrating the potential to identify the effect of the model parameters on the fast/medium/slow dynamics of the flow rate. The paper ends with a discussion on the interpretation of GSA results and of the advantages of using synthetic dynamic flow rate data for WWTP influent scenario generation. This section also includes general suggestions on how to use the proposed methodology to any influent generator to adapt the created time series to a modeller's demands.
Xavier Flores-Alsina; Krist V Gernaey; Ulf Jeppsson
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Water science and technology : a journal of the International Association on Water Pollution Research     Volume:  65     ISSN:  0273-1223     ISO Abbreviation:  Water Sci. Technol.     Publication Date:  2012  
Date Detail:
Created Date:  2012-05-17     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9879497     Medline TA:  Water Sci Technol     Country:  England    
Other Details:
Languages:  eng     Pagination:  1912-22     Citation Subset:  IM    
Division of Industrial Electrical Engineering and Automation (IEA), Department of Measurement Technology and Industrial Electrical Engineering (MIE), Lund University, Box 118, SE-221 00 Lund, Sweden E-mail:
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