Influent generator for probabilistic modeling of nutrient removal wastewater treatment plants. (March 2016)

• Record Type: Journal Article
• Authors: Talebizadeh, Mansour; Belia, Evangelia; Vanrolleghem, Peter A.
• Is Part Of: Environmental modelling & software. Volume 77(2016:Mar.)
• Page Start: 32
• Page End: 49
• Publication Date: 2016-03
• Subjects: Bayesian estimation -- Probabilistic design -- Uncertainty analysis -- Urban hydrology -- Wastewater composition -- Weather generator; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.11.005
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The availability of influent wastewater time series is crucial when using models to assess the performance of a wastewater treatment plant (WWTP) under dynamic flow and loading conditions. Given the difficulty of collecting sufficient data, synthetic generation could be the only option. In this paper a hybrid of statistical (a Markov chain-gamma model for stochastic generation of rainfall and two different multivariate autoregressive models for stochastic generation of air temperature and influent time series in dry conditions) and conceptual modeling techniques is proposed for synthetic generation of influent time series. The time series of rainfall and influent in dry weather conditions are generated using two types of statistical models. These two time series serve as inputs to a conceptual sewer model for generation of influent time series. The application of the proposed influent generator to the Eindhoven WWTP shows that it is a powerful tool for realistic generation of influent time series and is well-suited for probabilistic design of WWTPs as it considers both the effect of input variability and total model uncertainty. Highlights: Influent generator for synthetic generation of realistic dynamic influent time series. Straight forward incorporation of climate and basic characteristics of catchment into the generator. Variability and uncertainty are explicitly considered into the generated influent time series. Application of the developed tool to a real case study.