Abstract Blockages are a major issue for wastewater utilities around the world, causing loss of service, environmental pollution, and significant clean-up costs. Increasing telemetry in combined sewer overflows (CSOs) provides the opportunity for near real-time data-driven modelling of wastewater networks. This paper presents a novel methodology, designed to detect blockages and other unusual events in the proximity of CSO chambers in near real-time. The methodology utilises an evolutionary artificial neural network (EANN) model for short-term CSO level predictions and statistical process control (SPC) techniques to analyse unusual level behaviour. The methodology was evaluated on historic blockage events from several CSOs in the UK and was demonstrated to detect blockage events quickly and reliably, with a low number of false alarms.

中文翻译：

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使用进化人工神经网络和统计过程控制近实时检测联合下水道溢流附近的堵塞

摘要 堵塞是世界各地污水处理设施的主要问题，会导致服务损失、环境污染和高昂的清理成本。增加联合下水道溢流 (CSO) 中的遥测技术为废水网络的近实时数据驱动建模提供了机会。本文提出了一种新颖的方法，旨在近乎实时地检测 CSO 室附近的堵塞和其他异常事件。该方法利用进化人工神经网络 (EANN) 模型进行短期 CSO 级别预测和统计过程控制 (SPC) 技术来分析异常级别行为。该方法对来自英国几个 CSO 的历史阻塞事件进行了评估，并被证明可以快速可靠地检测阻塞事件，并且误报率很低。