Utilization of collinearity in regression modeling of activated sludge processes

Identification of collinear groups among the variables of biological wastewater treatment processes is considered. The SROV (stepwise regression using orthogonalized variables) program was used to analyze the data that were collected in an experimental lane of a full-scale waste water treatment plant. The analysis has identified 10 collinear groups among the 22 measured variables of the process. It has been shown that a member of such a group (target variable) can be represented as a linear combination of the other members (predictive variables) and the associated correlation coefficient can be used for assessing the quality of the representation. The proposed technique can be beneficial in determining how many independent variables have to be measured in a process. It can be used for developing soft sensors and also for selecting the measurements that have to be used in determining parameters of mechanistic models in order to prevent numerical ill-conditioning.