WIT Press

Developing A Wastewater Treatment Monitoring Tool

Price

Free (open access)

Paper DOI

10.2495/WM020741

Volume

56

Pages

Published

2002

Size

444 kb

Author(s)

C Capilla, S Barceló & J M Prats

Abstract

In this paper we discuss and illustrate with an example the application of statistical methods to develop a monitoring tool in a wastewater treatment process. The objective is to control and monitoring the system performance using the database information of process parameters and quality characteristics, which are recorded in form of time series. This type of database is traditionally used to summarize the short or long term variability of effluent quality parameters such as suspended solids or the five-day biological oxygen demand BODS. The summaries are then used to check whether the environmental quality standards are fulfilled. The temporal dependence between the quality variables however can be exploited using statistical models, not only to keep a check on the environmental standards but also to forecast critical quality characteristics, whose analytical determination implies a temporal delay in the measurement process, e.g. BOD5, and therefore to predict in advance process upsets and out-of-control situations. The application of time series models is considered in this work and the performance of statistical monitoring tools is studied by simulation of some special disturbances which may occur in the process. 1 Introduction Wastewater treatment is a complex and dynamic process that usually operates under varying conditions and is regularly subject to checking of compliance with environmental standards. Information of these processes comes in the form of multiple time series. Traditional approaches to analyze the effluent quality and its potential environmental impact, do not model the characteristic serial correlation and the dynamic relationships among them adequately. Most published reports only contain data summaries such as histograms and statistics as the average and dispersion parameters (Berthouex, Hunter and Pallensen [l]). They frequently fail to understand the dynamic mechanisms that govern

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