To improve drinking water quality while reducing operating costs, many drinking water utilities are investing in advanced process control and automation technologies. The use of artificial intelligence technologies, specifically artificial neural networks, is increasing in the drinking water treatment industry as they allow for the development of robust nonlinear models of complex unit processes. This paper highlights the utility of artificial neural networks in water quality modelling as well as drinking water treatment process modelling and control through the presentation of several case studies at two large-scale water treatment plants in Edmonton, Alberta.
The drinking water treatment industry has seen a recent increase in the use of artificial neural networks (ANNs) for process modelling and offline process control tools and applications. While conceptual frameworks for integrating the ANN technology into the real-time control of complex treatment processes have been proposed, actual working systems have yet to be developed. This paper presents development and application of an ANN model-based advanced process control system for the coagulation process at a pilot-scale water treatment facility in Edmonton, Alberta, Canada. The system was successfully used to maintain a user-defined set point for effluent quality, by automatically varying operating conditions in response to changes in influent water quality. This new technology has the potential to realize significant operational cost saving for utilities when applied in full-scale applications.
Virtually all water utilities are looking at improving the operation of their plants to keep control of costs and to meet stringent water quality regulations. Better process control and automation of the plants can help achieve these goals. However, traditional control techniques such as proportional-integral-derivative (PID) can be inadequate when automating certain water treatment processes such as turbidity, organics, or hardness removal in a clarification process. Advanced process control techniques are alternatives to mitigate this impediment. At the cornerstone of many advanced process control techniques is a model of the process being controlled, which can be developed using artificial neural networks (ANNs). This paper describes various advanced process control techniques, the potentially large role of ANN models in implementing these techniques, and issues and solutions when using ANN in a real-time control system.Key words: artificial neural networks, model-based control, proportional-integral-derivative control, forward and inverse models, direct and indirect control.Résumé : Presque tous les services publics d'eau cherchent à améliorer le fonctionnement de leurs stations pour contrôler leurs coûts et rencontrer les exigences réglementaires sévères de la qualité de l'eau. Une amélioration du contrôle des procédés et de l'automatisation des stations peut permettre d'atteindre ce but. Cependant, les techniques de commande traditionnelles, telles que la commande proportionnelle, intégrale et dérivée (PID), peuvent être inadéquates lors de l'automatisation de certains procédés de traitement de l'eau tels que l'élimination de la turbidité, des matières organiques ou de la dureté dans un procédé de clarification. Des techniques avancées de contrôle des procédés sont un autre choix pour atténuer cet obstacle. La base de plusieurs techniques avancées de contrôle des procédés est un modèle du procédé qui est contrôlé, ce qui peut être développé en utilisant des réseaux neuronaux artificiels (RNA). Cet article décrit diverses techniques avancées de contrôle des procédés, le rôle potentiellement important des modèles RNA dans l'implantation de telles techniques et les questions et solutions qui surviennent lors de l'utilisation des RNA dans un système de contrôle en temps réel. Mots clés : réseaux neuronaux artificiels, commande basée sur un modèle, commande proportionnelle, intégrale et dérivée, modèles directs et indirects, commandes directes et indirectes. [Traduit par la Rédaction]
This paper reports on the application of artificial neural network (ANN) techniques for predicting the concentration of trihalomethanes (THMs) in finished water at the E.L. Smith Water Treatment Plant (WTP) in Edmonton, Alberta, Canada. The formation of THMs in finished water involves many complex chemical reactions and interactions that are difficult to model using conventional methods. The formation of THMs has been found to be correlated to raw and treated water quality characteristics such as colour, pH, and temperature and chemical addition such as chlorine, alum, and powder activated carbon (PAC). Three models were derived using raw water, post clarification water, and a combination of raw and post clarification water parameter inputs. The model that most successfully predicted the concentration of THMs in finished water is the model that uses clarifier effluent parameter inputs. This model can be used at the E.L. Smith WTP for early detection of potentially high THM concentrations in finished water and gives plant operators enough advanced warning to reduce THM precursors. With an adequate understanding of water treatment plant processes and THM formation potential it will be fairly easy for any water treatment facility, which has a few years of historical plant data, to develop its own ANN model for predicting the formation of THM in finished water. Key words: artificial neural networks, water treatment process, water treatment modeling, trihalomethane formation.
The objective of this research was to examine the accumulation and adsorption capacity of powdered activated carbon (PAC) in a solids contact slurry recirculating clarifier (SCSRC). If high concentrations of PAC can be achieved in a sludge blanket, then PAC could be an efficient alternative to granular activated carbon for plants requiring only intermittent organics removal. A method to determine the PAC concentration in the clarifier slurry was developed based on a gravimetric analysis approach. It was found that very high concentrations of PAC (>4,000 mg/L) could be accumulated in an SCSRC. Isotherm tests showed that the adsorptive capacity of the PAC‐containing slurry for chloroform appeared to decrease somewhat when the residence time of the PAC in the clarifier was longer than 100 h.
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