CBE Seminar: Joel Paulson
Advanced Control Methods for Complex Chemical and Biological Systems
Strong trends in chemical engineering have led to increased complexity in plant design and operation, which has driven the demand for improved control techniques and methodologies. Furthermore, improved process control directly leads to smaller usage of resources, increased productivity, improved safety, and reduced pollution. Model predictive control (MPC) is the most advanced control technology widely practiced in industry. However, limitations in industrial MPC spurred significant research over the past few decades in the search for increased capability. For these advancements to be widely implemented by industry on complex (bio)chemical systems, they must adequately address all of the challenges associated with control design including: (1) large-scale nonlinear & multiscale physical phenomena, (2) tight product specifications & safety-critical constraints, (3) model uncertainties & disturbances, and (4) component faults & failures. In this talk, I discuss a unified framework for addressing these challenges. Specifically, I present efficient extensions of state-of-the-art MPC to handle very general probabilistic uncertainty descriptions, which are capable of accounting for both the range of uncertainty values and their likelihood of occurrence. Then, I develop a tractable active fault diagnosis framework that is able to quickly and safely detect non-obvious catastrophic system failures, in the presence of uncertainty, by injecting optimally-designed “auxiliary” inputs that minimize the probability of misdiagnosis subject to constraints. To demonstrate the efficacy of the developed methods, I present both experimental and realistic simulation results on many industrially-relevant problems including continuous pharmaceutical manufacturing and atmospheric plasma jets for biomedical applications.
Joel Paulson is a postdoctoral researcher at the University of California, Berkeley. He received his Ph.D. in Chemical Engineering from the Massachusetts Institute of Technology (MIT) in 2016. Prior to this, he received his M.S. in Chemical Engineering from MIT (2013) and his B.S. in Chemical Engineering from the University of Texas at Austin (2011). His research focuses on the development of systems engineering theory & methods for complex (bio)chemical systems, which includes advances in computer modeling, multi-scale simulation, optimization, and control. Modeling and control applications include carbon nanotube solar cells, continuous manufacturing of pharmaceuticals & biologic drugs, and plasma processes for semiconductor fabrication & biomedicine.