Fault Detection of Wastewater Treatment Plants Based on an Improved Kernel Extreme Learning Machine Method

Abstract

In order to ensure the stable operation, improve efficiency, and enhance sustainability of wastewater treatment systems, this paper investigates the fault detection problem in wastewater treatment process based on an improved kernel extreme learning machine method. Firstly, a kernel extreme learning machine (KELM) model optimized by an improved mutation bald eagle search (IMBES) optimizer is proposed to generate point predictions of effluent quality parameters. Then, based on the point prediction results, the confidence interval of effluent quality parameters is calculated using kernel density estimation (KDE) method. This interval represents the bounds of system uncertainty and unknown disturbance at normal conditions and can be treated as the threshold for fault diagnosis. Finally, the effectiveness of the proposed method is illustrated by two datasets obtained from the BSM1 wastewater simulation platform and an actual water platform. Experimental results show that compared with other methods such as CNN, LSTM, and IBES-LSSVM, this method has a significant improvement in prediction accuracy, and at the same confidence level, it ensures fault detection rate while generating smaller confidence intervals.

Research on Intrusion Detection Based on an Enhanced Random Forest Algorithm

“…Currently, with the development of new information technologies, such as the Internet of Things, cloud computing, and big data, the issue of information security is increasingly emphasized. In order to guarantee the security of data, a large number of models for classification and avoidance, facilitated by focusing on the behavior of different nodes, have been generated, such as the Enhanced Random Forest Classifier (ERF-KMC) with the K-mean clustering algorithm, which helps to accurately classify the attacks so that the system can more accurately block the attacking information and protect the security of the information [10], and the Improvement of Mutant Bald Eagle Search (IMBES) optimizer optimization of the Kernel Extreme Learning Machine (KELM) model, which predicts data information as points and analyzes parameter confidence intervals through point density metrics as system security intervals and reduces the interference caused by errors [11]; encrypting traditional standard protocols through elliptic curve encryption technology, which guarantees the network and data security while meeting the network time constraints, is also a way to solve the problem [12]. And the intrusion-detection mechanism as the second security protection mechanism, through the real-time monitoring of the network, can ensure the security of network resources at the same time but also effectively reduces the losses caused by network attacks.…”

Semi-Supervised Anomaly Detection of Dissolved Oxygen Sensor in Wastewater Treatment Plants

“…It can be used in many different fields where detecting abnormalities or outliers is essential for maintaining the system's health, sustaining security, or optimizing processes. There are various examples of problems involving fault detection, such as frauds [1], network intrusions [2], manufacturing defects [3], anomaly detection in time series data [4], cybersecurity [5,6], microfluidics [7][8][9][10], and most importantly, anomaly detection in wastewater treatment plants [11][12][13].…”