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Machine learning recently has been used to identify the governing equationsfor dynamics in physical systems. The promising results from applications onsystems such as fluid dynamics and chemical kinetics inspire furtherinvestigation of these methods on complex engineered systems. Dynamics of thesesystems play a crucial role in design and operations. Hence, it would beadvantageous to learn about the mechanisms that may be driving the complexdynamics of systems. In this work, our research question was aimed ataddressing this open question about applicability and usefulness of novelmachine learning approach in identifying the governing dynamical equations forengineered systems. We focused on distillation column which is an ubiquitousunit operation in chemical engineering and demonstrates complex dynamics i.e.it's dynamics is a combination of heuristics and fundamental physical laws. Wetested the method of Sparse Identification of Non-Linear Dynamics (SINDy)because of it's ability to produce white-box models with terms that can be usedfor physical interpretation of dynamics. Time series data for dynamics wasgenerated from simulation of distillation column using ASPEN Dynamics. Onepromising result was reduction of number of equations for dynamic simulationfrom 1000s in ASPEN to only 13 - one for each state variable. Predictionaccuracy was high on the test data from system within the perturbation range,however outside perturbation range equations did not perform well. In terms ofphysical law extraction, some terms were interpretable as related to Fick's lawof diffusion (with concentration terms) and Henry's law (with ratio ofconcentration and pressure terms). While some terms were interpretable, weconclude that more research is needed on combining engineering systems withmachine learning approach to improve understanding of governing laws forunknown dynamics.

Can Machine Learning Identify Governing Laws For Dynamics in Complex Engineered Systems ? : A Study in Chemical Engineering