The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 06, 2023

Filed:

Oct. 24, 2019
Applicant:

Exxonmobil Technology and Engineering Company, Annandale, NJ (US);

Inventors:

Stijn De Waele, Flemington, NJ (US);

Myun-Seok Cheon, Whitehouse Station, NJ (US);

Kuang-Hung Liu, Basking Ridge, NJ (US);

Shivakumar Kameswaran, Katy, TX (US);

Francisco Trespalacios, Spring, TX (US);

Dimitri J. Papageorgiou, Stewartsville, NJ (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G05B 17/02 (2006.01); G06F 30/20 (2020.01); G06F 17/18 (2006.01); G06N 3/08 (2023.01); G06F 18/214 (2023.01);
U.S. Cl.
CPC ...
G05B 17/02 (2013.01); G06F 17/18 (2013.01); G06F 18/214 (2023.01); G06F 30/20 (2020.01); G06N 3/08 (2013.01);
Abstract

Aspects of the technology described herein comprise a surrogate model for a chemical production process. A surrogate model is a machine learned model that uses a collection of inputs and outputs from a simulation of the chemical production process and/or actual production data as training data. Once trained, the surrogate model can estimate an output of a chemical production process given an input to the process. Surrogate models are not directly constrained by physical conditions in a plant. This can cause them to suggest optimized outputs that the not possible to produce in the real world. It is a significant challenge to train a surrogate model to only produce outputs that are possible. The technology described herein improves upon previous surrogate models by constraining the output of the surrogate model to outputs that are possible in the real world.


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