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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. With this approach, stochastic and transfer-function components can be combined to form a dynamic model and the relative importance of these two components can be quantitatively assessed. The models were able to explain the data very well. Using the models as parts of a real-time control scheme was also discussed. [Copyright &y& Elsevier]

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. Most of the hydrologic and hydraulic models describing sewage collection systems are deterministic. Such models require detailed knowledge of the system and usually rely on a large number of parameters, some of which are uncertain or difficult to determine. Presented in this paper, an artificial neural network (ANN) model that is used to make short-term predictions of wastewater inflow rate that enters the Gold Bar Wastewater Treatment Plant (GBWWTP), the largest plant in the Edmonton area (Alberta, Canada). The neural model uses rainfall data, observed in the collection system discharging to the plant, as inputs. The building process of the model was conducted in a systematic way that allowed the identification of a parsimonious model that is able to learn (and not memorize) from past data and generalize very well to unseen data that was used to validate the model. The neural network model gave excellent results. The potential of using the model as part of a real-time process control system is also discussed. [Copyright &y& Elsevier]

In the past few years, artificial neural networks (ANNs) have been used in describing and modelling wastewater treatment processes. Artificial neural network models can be identified without a detailed knowledge of the kinetics of the system to be modelled. Also, ANN models can potentially contain a great deal of information about the system itself, including the same type of information contained in conventional deterministic models. The fact that these models can be continuously updated with minimal resource requirements makes them very attractive for application in a real-time control scenario. In the current paper, applications of ANNs in the field of wastewater treatment performance prediction are reviewed. In addition, this paper presents a case study that reports some comprehensive modelling work to develop nonlinear neural network prediction models for the Gold Bar Wastewater Treatment Plant (GBWWTP), the largest sewage treatment facility in Edmonton, Alberta. [ABSTRACT FROM AUTHOR]