Decision making occurs throughout all aspects of chemical engineering, with common examples including choosing which reactors and separators to include in the design of a new facility, considering how to adjust a flowrate to control a reactor to maximize production rate in a safe manner, or even deciding which parameters to change to get as much useful information as possible from an experiment. Research in the field of process systems engineering aims to develop new theory, methods, and algorithms for making optimal decisions in a computationally efficient manner.
AA’s Process Systems Research Team focuses on emergent paradigm shifts in chemical manufacturing essential to achieve sustainability improvements that current and future generations demand. This includes creating computational tools which enable chemical production performed at small-scale facilities in a time-varying fashion, mirroring the shift in energy production from large, base-load fossil fuel plants to intermittent, distributed renewable facilities. This also includes considering the interaction of many new objectives beyond economics, including GHG emissions, water usage, public health, and environmental justice. Unfortunately, these problems can be significantly more challenging to solve than their traditional counterparts. However, our team brings unique expertise in identifying and exploiting structure to make solving these problems easier.
While motivated by problems in sustainability, the optimization tools that our team develops can be generally applied to a wide range of fields, including (but not limited to) supply chain management, systems biology, machine learning, public policy, game theory, and optimal control. We are always interested in expanding upon our domains of application and are happy to discuss any and all potential new collaborations.