P 2 O: AI-Driven Framework for Managing and Securing Wastewater Treatment Plants

Abstract

Wastewater treatment plants (WWTPs) are critical infrastructures responsible for processing wastewater before discharging effluent to rivers and other potential uses. WWTPs use large, connected deep tunnels for storing sanitary and wet-weather flows for treatment. However, wastewater in those systems cannot exceed safe tunnel levels in order to prevent overflows of untreated wastewater into the environment. Further, WWTPs are among the 16 national lifeline infrastructure sectors in which the utilization of sensor technology has increased, making the sectors vulnerable to all forms of cyber threats. Considering these challenges, the work presented in this manuscript uncovers the role of AI at WWTPs by focusing on two problems: tunnel water-level prediction and detection of security threats. This is done by proposing an AI framework: P2O (prediction, protection, and optimization). The prediction module forecasts the tunnel water level using deep-learning models based on the current wastewater flow in the tunnel and other inputs from the sensors and gauges. The protection module focuses on classifying the intentionality of an anomaly, i.e., whether an attack is adversarial in nature or merely an outlier, using recurrent neural network models. Last, the optimization module aims to provide actionable recommendations to pump operators using a genetic algorithm. The experimental results of P2O indicate that the prediction module can predict the tunnel water level with 85% accuracy, and the protection module can detect about 97% of intentional attacks on WWTPs. AI models within P2O are evaluated; the experimental results are presented and discussed.Practical ApplicationsThis manuscript presents P2O, which is a novel AI framework that can predict about 85% of wastewater overflow incidences and about 95% of intentional cyberattacks on a WWTP, as indicated in the experiments. The deployment of P2O at a WWTP is essential, especially considering the adverse effects of overflowing wastewater on the environment (i.e., rivers and other water bodies). Moreover, cyberattacks on WWTPs can be subtle, making them challenging to detect; on average, most of them are noticed within one week to one month after the attack. This makes national infrastructure vulnerable to external and internal threats, influencing the well-being of water bodies and overall national security. P2O provides a real-time monitoring interface and can recommend optimal actions in different scenarios (i.e., outliers) for pump operators and process engineers at WWTPs.