Forecasting influent flow rate and composition with occasional data for supervisory management system by time series model

The information on the incoming load to wastewater treatment plants is not often available to apply modelling for evaluating the effect of control actions on a full-scale plant. In this paper, a time series model was developed to forecast flow rate, COD, NH4(+)-N and PO4(3-)-P in influent by using 2...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:Water science and technology : a journal of the International Association on Water Pollution Research. - 1986. - 53(2006), 4-5 vom: 05., Seite 185-92
1. Verfasser: Kim, J R (VerfasserIn)
Weitere Verfasser: Ko, J H, Im, J H, Lee, S H, Kim, S H, Kim, C W, Park, T J
Format: Aufsatz
Sprache:English
Veröffentlicht: 2006
Zugriff auf das übergeordnete Werk:Water science and technology : a journal of the International Association on Water Pollution Research
Schlagworte:Journal Article Research Support, Non-U.S. Gov't Phosphates Quaternary Ammonium Compounds
Beschreibung
Zusammenfassung:The information on the incoming load to wastewater treatment plants is not often available to apply modelling for evaluating the effect of control actions on a full-scale plant. In this paper, a time series model was developed to forecast flow rate, COD, NH4(+)-N and PO4(3-)-P in influent by using 250 days data of field plant operation data. The data for 150 days and 100 days were used for model development and model validation, respectively. The missing data were interpolated by the spline method and the time series model. Three different methods were proposed for model development: one model and one-step to seven-step ahead forecasting (Method 1); seven models and one-step-ahead forecasting (Method 2); and one model and one-step-ahead forecasting (Method 3). Method 3 featured only one-step-ahead forecasting that could avoid the accumulated error and give simple estimation of coefficients. Therefore, Method 3 was the reliable approach to developing the time series model for the purpose of this research
Beschreibung:Date Completed 07.11.2006
Date Revised 17.09.2019
published: Print
Citation Status MEDLINE
ISSN:0273-1223