Systematic development of an artificial neural network model for real-time prediction of ground-level ozone in Edmonton, Alberta, Canada.

Ground-level ozone is a secondary pollutant that has recently gained notoriety for its detrimental effects on human and vegetation health. In this paper, a systematic approach is applied to develop artificial neural network (ANN) models for ground-level ozone (O3) prediction in Edmonton, Alberta, Canada, using ambient monitoring data for input. The intent of these models is to provide regulatory agencies with a tool for addressing data gaps in ambient monitoring information and predicting O3 events.

A combined transfer-function noise model to predict the dynamic behavior of a full-scale primary sedimentation tank.

Studying how and to what extent effluent TSS and COD are related to influent TSS, COD, and flow in a primary sedimentation process is the objective of this paper. The analysis is based on data collected hourly over two periods of sampling, each lasted 1 week at an Edmonton, Alberta sewage treatment plant. In order to establish a dynamic model for the system, the methodology of Box and Jenkins (Time series Analysis: Forecasting and Control, Holden-Day, Oakland, CA, 1976) was utilized.

A neural network model to predict the wastewater inflow incorporating rainfall events.

Under steady-state conditions, a wastewater treatment plant usually has a satisfactory performance because these conditions are similar to design conditions. However, load variations constitute a large portion of the operating life of a treatment facility and most of the observed problems in complying with permit requirements occur during these load transients. During storm events upsets to the different physical and biological processes may take place in a wastewater treatment plant, and therefore, the ability to predict the hydraulic load to a treatment facility during such events is very beneficial for the optimization of the treatment process.