Date of Award
Spring 1986
Document Type
Thesis - Restricted
Degree Name
Master of Science (MS)
Department
Civil, Construction, and Environmental Engineering
First Advisor
Novotny, Vladimir
Second Advisor
Katz, William J.
Third Advisor
Zanoni, A. E.
Abstract
Box-Jenkins type Auto-Regressive Integrated Moving Average (ARIMA) modeling techniques have been successful in building, identifying, fitting and checking models for time series and dynamic systems. Researchers have taken discrete data collected from environmental systems, such as wastewater treatment plants, to build predictive models utilized to assist in system operation and control. With the assumption that future data is a reflection of historical trends, the researcher can forecast future values of a time series from current and past data. For this research, wastewater treatment plant operational data from Kenosha, Wisconsin was obtained for modeling purposes. Water supply, influent flow and influent BOD data were modeled and forecasts were obtained over a period during which actual data was available. The forecast values were compared to the available data to assess the accuracy of the models. This type of modeling can be utilized in order to implement automated (or manual) control schemes within a collection and treatment system. Since the data base is continuously updated, the forecasts reflect the present as well as prior state of the system. By comparing the forecast data to a target value or goal, the operator or automated control process can make the appropriate control decisions.
Recommended Citation
Lee, David M., "Application of ARIMA Models to Wastewater Treatment Plant Data" (1986). Master's Theses (1922-2009) Access restricted to Marquette Campus. 4961.
https://epublications.marquette.edu/theses/4961