Calibration and validation of a phenomenological influent pollutant disturbance scenario generator using full-scale data
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- 作者：Xavier Flores-Alsina ; Ramesh Saagi ; Erik Lindblom ; Carsten Thirsing ; Dines Thornberg ; Krist V. Gernaey ; Ulf Jeppsson
- 关键词：Catchment modelling ; Flow prediction ; Influent modelling ; Load prediction ; Scenario analysis
- 刊名：Water Research
- 出版年：15 March, 2014
- 年：2014
- 卷：51
- 期：Complete
- 页码：172-185
- 全文大小：970 K
文摘
The objective of this paper is to demonstrate the full-scale feasibility of the phenomenological dynamic influent pollutant disturbance scenario generator (DIPDSG) that was originally used to create the influent data of the International Water Association (IWA) Benchmark Simulation Model No. 2 (BSM2). In this study, the influent characteristics of two large Scandinavian treatment facilities are studied for a period of two years. A step-wise procedure based on adjusting the most sensitive parameters at different time scales is followed to calibrate/validate the DIPDSG model blocks for: 1) flow rate; 2) pollutants (carbon, nitrogen); 3) temperature; and, 4) transport. Simulation results show that the model successfully describes daily/weekly and seasonal variations and the effect of rainfall and snow melting on the influent flow rate, pollutant concentrations and temperature profiles. Furthermore, additional phenomena such as size and accumulation/flush of particulates of/in the upstream catchment and sewer system are incorporated in the simulated time series. Finally, this study is complemented with: 1) the generation of additional future scenarios showing the effects of different rainfall patterns (climate change) or influent biodegradability (process uncertainty) on the generated time series; 2) a demonstration of how to reduce the cost/workload of measuring campaigns by filling the gaps due to missing data in the influent profiles; and, 3) a critical discussion of the presented results balancing model structure/calibration procedure complexity and prediction capabilities.