King Abdullah University of Science and Technology (KAUST), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Thuwal 23955-6900, Saudi Arabia.
Computer Science Department, University of Oran, 1 Ahmed Ben Bella, Algeria Street El senia el mnouer bp, 31000 Oran, Algeria.
J Environ Manage. 2018 Oct 1;223:807-814. doi: 10.1016/j.jenvman.2018.06.087. Epub 2018 Jul 5.
The efficient operation of wastewater treatment plants (WWTPs) is key to ensuring a sustainable and friendly green environment. Monitoring wastewater processes is helpful not only for evaluating the process operating conditions but also for inspecting product quality. This paper presents a flexible and efficient fault detection approach based on unsupervised deep learning to monitor the operating conditions of WWTPs. Specifically, this approach integrates a deep belief networks (DBN) model and a one-class support vector machine (OCSVM) to separate normal from abnormal features by simultaneously taking advantage of the feature-extraction capability of DBNs and the superior predicting capacity of OCSVM. Here, the DBN model, which is a powerful tool with greedy learning features, accounts for the nonlinear aspects of WWTPs, while OCSVM is used to reliably detect the faults. The developed DBN-OCSVM approach is tested through a practical application on data from a decentralized WWTP in Golden, CO, USA. The results from the DBN-OCSVM are compared with two other detectors: DBN-based K-nearest neighbor and K-means algorithms. The results show the capability of the developed strategy to monitor the WWTP, suggesting that it can raise an early alert to the abnormal conditions.
污水处理厂（WWTP）的高效运行是确保可持续和友好绿色环境的关键。监测废水处理过程不仅有助于评估工艺运行条件，还有助于检查产品质量。本文提出了一种基于无监督深度学习的灵活高效的故障检测方法，用于监测 WWTP 的运行状况。具体来说，该方法结合了深度置信网络（DBN）模型和单类支持向量机（OCSVM），通过同时利用 DBN 的特征提取能力和 OCSVM 的卓越预测能力，将正常和异常特征分开。这里，具有贪婪学习功能的强大工具 DBN 模型考虑了 WWTP 的非线性方面，而 OCSVM 则用于可靠地检测故障。通过在美国科罗拉多州戈尔登的分散式 WWTP 的实际应用对所开发的 DBN-OCSVM 方法进行了测试。将 DBN-OCSVM 的结果与另外两个检测器进行了比较：基于 DBN 的 K-最近邻和 K-均值算法。结果表明，所开发的策略具有监测 WWTP 的能力，表明它可以对异常情况发出早期警报。
