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The paper presents robust design methods for the automatic control of a dam±river system, where the action variable is the upstream¯ow rate and the controlled variable the downstream¯ow rate. The system is modeled with a linear model derived analytically from simpli®ed partial derivative equations describing open-channel¯ow dynamics. Two control methods (pole placement and Smith predictor) are compared in terms of performance and robustness. The pole placement is done on the sampled model, whereas the Smith predictor is based on the continuous model. Robustness is estimated with the use of margins and also with the use of a bound on multiplicative uncertainty taking into account the model errors, due to the nonlinear dynamics of the system. Simulations are carried out on a nonlinear model of the river and performance and robustness of both controllers are compared to the ones of a continuous-time PID controller.

This paper investigates the problem of detection and isolation of attacks on a water distribution network comprised of cascaded canal pools. The proposed approach employs a bank of delay-differential observer systems. The observers are based on an analytically approximate model of canal hydrodynamics. Each observer is insensitive to one fault/attack mode and sensitive to other modes. The design of the observers is achieved by using a delay-dependent linear matrix inequality method. The performance of our model-based diagnostic scheme is tested on a class of adversarial scenarios based on a generalized fault/attack model. This model represents both classical sensor-actuator faults and communication network-induced deception attacks. Our particular focus is on stealthy deception attacks in which the attacker's goal is to pilfer water through canal offtakes. Our analysis reveals the benefits of accurate hydrodynamic models in detecting physical faults and cyber attacks to automated canal systems. We also comment on the criticality of sensor measurements for the purpose of detection. Finally, we discuss the knowledge and effort required for a successful deception attack.Index Terms-Delay systems, fault diagnosis, intrusion detection, supervisory control and data acquisition (SCADA) systems, supervisory control.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is shed in the feces of infected people. As a consequence, genomic RNA of the virus can be detected in wastewater. Although the presence of viral RNA does not inform on the infectivity of the virus, this presence of genetic material raised the question of the effectiveness of treatment processes in reducing the virus in wastewater and sludge. In this work, treatment lines of 16 wastewater treatment plants were monitored to evaluate the removal of SARS-CoV-2 RNA in raw, processed waters and sludge, from March to May 2020. Viral RNA copies were enumerated using reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) in 5 different laboratories. These laboratories participated in proficiency testing scheme and their results demonstrated the reliability and comparability of the results obtained for each one. SARS-CoV-2 RNA was found in 50.5% of the 101 influent wastewater samples characterized. Positive results were detected more frequently in those regions with a COVID-19 incidence higher than 100 cases per 100,000 inhabitants. Wastewater treatment plants (WWTPs) significantly reduced the occurrence of virus RNA along the water treatment lines. Secondary treatment effluents showed an occurrence of SARS-CoV-2 RNA in 23.3% of the samples and no positive results were found after MBR and chlorination. Non-treated sludge (from primary and secondary treatments) presented a higher occurrence of SARS-CoV-2 RNA than the corresponding water samples, demonstrating the affinity of virus particles for solids. Furthermore, SARS-CoV-2 RNA was detected in treated sludge after thickening and anaerobic digestion, whereas viral RNA was completely eliminated from sludge only when thermal hydrolysis was applied. Finally, co-analysis of SARS-CoV-2 and F-specific RNA bacteriophages was done in the same water and sludge samples in order to investigate the potential use of these bacteriophages as indicators of SARS-CoV-2 fate and reduction along the wastewater treatment.

The integrator delay (ID) model is a popular and simple way to model a canal for control purposes. In this paper, particular attention is given to accurately model the delay and the integrator gain in any flow configuration. Based on a previous work by the authors allowing to have a very accurate frequency domain representation of Saint-Venant equations, the paper proposes a new approximate model: the integrator delay zero (IDZ) model has an integrator and a delay in low frequencies, and models the high frequencies by a constant gain and a delay. Analytical formulas are derived to compute the model parameters for a canal pool possibly in backwater conditions. The IDZ model is compared with an accurate model on two example canals for different flow conditions. The comparisons done in the frequency domain and in the time domain show the accuracy of the model.