Henrique Oyama is a Ph.D. candidate in the Dept. of Chemical Engineering and Materials Science at Wayne State University, where he served as a graduate research and teaching assistant. He received several awards and recognitions at Wayne State University, including the Master of Science Highest Distinction and the 2022 Summer Dissertation Award, which was awarded in the Graduate School’s annual competition for advanced Ph.D. students, which aided in funding his research. His core research area is in chemical process control and process systems engineering. He received his undergraduate degree in Chemical Engineering from the Federal University of Uberlândia, Brazil. As an undergraduate, Henrique Oyama received many awards and honors, including a grant proposal award in the process control field, a fellowship of the Young Talents Program for Science, and a fellowship of the Tutorial Education Program of Chemical Engineering for excellence in academics, teaching, research, and service activities.
Next-generation manufacturing technologies offer increasing production autonomy for chemical process industries. However, they also demand unique requirements for real-time process monitoring and safe design. In particular, the design of control strategies to prevent process losses while at the same time being resilient against external attacks is critical to ensuring safe and profitable operations. Motivated by this, we have developed novel control designs and theories for cyberattack-handling strategies for nonlinear process systems under model-based control designs that use nonlinear control theory and optimization to compute optimal control actions that would cause hard process requirements to be satisfied. Specifically, the proposed control/detection procedure and the mathematical conditions for detection and safe process operation, including when multiple attack events happen, were based on: a) randomized online modifications to a model-based control formulation with safety constraints to potentially detect cyberattacks; b) the use of multiple state estimators to flag deviations from “normal” process behavior.