Cyberattack awareness and resiliency of integrated moving horizon estimation and model predictive control of complex process networks

This paper explores data-assisted cyber-attack awareness and resiliency of optimization-based estimation and control of integrated process systems to malicious attacks on measurement sensors. The proposed estimation and control architecture consists of an integrated nonlinear moving horizon estimation (MHE) and model predictive control (MPC), where the MHE estimates the unmeasured state variables of the system required by MPC design. In addition, a measured output data-driven cyberattack detection framework is developed by frequently employing a feedforward neural network during the closed-loop process, identifying the cyberattacks from the interactive network-level dynamics. Finally, the integrated process of benzene alkylation with ethylene to produce ethylbenzene is considered a benchmark to demonstrate the application of cyberattack awareness and resiliency of the proposed control architecture in the presence of different types of adversarial cyberattacks interfering with the temperature measurement sensors.