The Dow Chemical Company

John M Wassick

John J McDonough

Patrick S McCroskey

David K Steckler

Chun S Wang

Vicki Rothhaar

Kalyanmoy Deb

Yashesh Deepakkumer Dhebar

Erik David Goodman

An embodiment involves a hybrid machine learning classifier that uses a random forest of decision tree classifiers to predict a tariff code prefix, and uses a plurality of expert trees to predict a tariff code suffix from properties related to chemical components associated with the respective tariff code prefixes. The embodiment also involves: determining a proportion of a dominant chemical component in comparison to other chemical components in a new set of chemical components; calculating similarity scores for the new set of chemical components and words associated with the tariff code prefixes; generating a feature vector from the proportion and the similarity scores; and obtaining a predicted tariff code including a predicted tariff code prefix determined by applying the random forest to the feature vector, and a predicted tariff code suffix determined by traversing a particular expert tree in accordance with properties related to the new set of chemical components.

Hybrid machine learning model for code classification

A model predictive controller for a process control system which includes a real-time executive sequencer and an interactive modeler. The interactive modeler includes both a process model and an independent disturbance model. The process model represents the dynamic behavior of the physical process, while the disturbance model represents current and future deviations from the process model. The interactive modeler estimates current process states from the process model and input data received from the executive sequencer. The executive sequencer then projects a set of future process parameter values, which are sought to be controlled, over a predetermined control horizon. The interactive modeler then solves a set of equations as to how the physical process will react to control changes in order to determine an optimized set of control changes. As a result, the process control system will be able to accurately track a predetermined set-point profile in the most effective and cost efficient manner.

Model predictive controller

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Midland, MI, United States of America

John M Wassick