Abstract
Time series analysis models are very useful in modelling dynamic systems in science and engineering applications. This class of models is in fact able to represent the dynamic features of physical systems that are subject to often uncontrollable inputs with random components. Wastewater treatment plants are examples of dynamic systems, with inputs (flow, organic loads, etc.) that vary stochastically within more or less wide ranges. The use of stochastic models allows a more detailed representation of the dynamic nature of these systems, while retaining the degree of information contained in most deterministic models.
In this paper, time series analysis applications to wastewater treatment plant modelling are presented and discussed. Both univariate and multivariate stochastic processes are applied to sewage treatment plant data, and the results thus obtained are further analyzed and compared with those from βconventionalβ deterministic models.
Specifically, the above mentioned models are analyzed with respect to possible application in the daily operation of sewage treatment plants, by virtue of their predictive capacities and relative ease of determination. Furthermore, these models present other attractive features, such as adaptiveness and the possibility of extracting useful information about the system from the analysis of their structure. Adaptiveness refers to the possibility of continuously improving the model's performance as new information about the system is collected by manual or automatic monitoring. Performance of the models herein identified, and possible applications of these models in real systems are discussed.