Global sensitivity analysis of the BSM2 dynamic influent disturbance scenario generator

Abstract

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.

Sensitivity Analysis and Anaerobic Digestion Modeling: A Scoping Review

“…A summary of the models and frequencies found in the 30 selected studies is presented in Table 1. Economic models of AD plants [56,57] BSM [58] Experimental-based model [59] Simplified AD bioprocess [42] Structured model for high-solids AD [60] Single-component feedstock digestion model with substrate inhibition [45] Simplified AD model (ADM1)…”

“…A GSA was conducted by Flores-Alsina et al [58] on a phenomenological model based on BSM2 that generates dynamic scenarios of an influent disturbance at wastewater treatment plants. Combined Monte Carlo simulations and SRC were employed for the GSA.…”

CSO Generator—A Parsimonious Wastewater Quality Model for Combined Sewer Overflows

“…This is achieved by integrating flow from a hydrodynamic model with a parsimonious wastewater quality model that has been calibrated using long-term water quality monitoring data. Conceptually, this approach is similar to the use of influent generators in the field of wastewater treatment plant modeling [16,18].…”

Including snowmelt in influent generation for cold climate WRRFs: comparison of data-driven and phenomenological approaches

“…The model has been applied to different case studies. 5 Other phenomenological IGs were applied for the integrated modelling of a variety of pollutants. 6 Regarding the data-driven approach, weather-based IGs have been studied based on data mining methodologies.…”

“…The model has been applied to different case studies. 5 Other phenomenological IGs were applied for the integrated modelling of a variety of pollutants. 6…”