Date of Degree
MS (Master of Science)
First Committee Member
Second Committee Member
Third Committee Member
Wastewater treatment plants (WWTP) involve several complex physical, biological and chemical processes. Often these processes exhibit non-linear behavior that is difficult to describe by classical mathematical models. Safer operation and control of a WWTP can be achieved by developing a modeling tool for predicting the plant performance. In the last decade, many studies were realized in wastewater treatment based on intelligent methods which are related to modeling WWTP. These studies are about predictions of WWTP parameters, process control of WWTP, estimating WWTP output parameters characteristics. In many studies, neural network models were used to model chemical and physical attributes in the flow rate. In this Thesis, a data-driven approach for analyzing water quality is introduced. Improvements in the data collection of information system allow collection of large volumes of data. Although improvements in data collection systems have given researchers sufficient information about various systems, they must be used in conjunction with novel data-mining algorithms to build models and recognize patterns in large data sets. Since the mid 1990's, data mining has been successfully used for model extraction and describing various phenomena of interest.
Data mining, DTs and MLPs, Predictive model, Wastewater treatment plant
x, 89 pages
Includes bibliographical references (pages 84-89).
Copyright 2011 Rahilsadat Hosseini
Hosseini, Rahilsadat. "Wastewater's total influent estimation and performance modeling: a data driven approach." MS (Master of Science) thesis, University of Iowa, 2011.