Baxter C W, Shariff R, Stanley S J, Smith D W, Zhang Q, Saumer E D
Department of Civil Engineering, University of British Columbia, Vancouver, Canada.
Water Sci Technol. 2002;45(4-5):9-17.
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.
饮用水处理行业最近在将人工神经网络（ANN）用于过程建模以及离线过程控制工具和应用方面有所增加。虽然已经提出了将ANN技术集成到复杂处理过程实时控制中的概念框架，但实际的工作系统尚未开发出来。本文介绍了一种基于ANN模型的先进过程控制系统的开发与应用，该系统用于加拿大艾伯塔省埃德蒙顿市一个中试规模水处理设施的混凝过程。该系统通过根据进水水质变化自动改变运行条件，成功地将出水水质维持在用户定义的设定点。这项新技术在全规模应用时有可能为公用事业公司节省大量运营成本。
