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:
Apr. 09, 2024

Filed:

Nov. 05, 2021
Applicant:

The Boston Consulting Group, Inc., Boston, MA (US);

Inventors:

John Thomas Clark, Jr., North Salt Lake, UT (US);

Joakim Kalvenes, Glencoe, IL (US);

Jason Thomas Stewart, Venice, IL (US);

Rohin Wood, Perth, AU;

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G05B 13/02 (2006.01); G05B 13/04 (2006.01);
U.S. Cl.
CPC ...
G05B 13/0265 (2013.01); G05B 13/042 (2013.01); G05B 13/048 (2013.01);
Abstract

Methods, systems, and computer storage media for providing an optimal control configuration for a material processing system are provided. In operation, a material processing engine accesses causal graph input data. Causal graph input data includes input data of a continuous flow process. Based on the causal graph and the input data, a causal graph that aligns with do-calculus manipulations—associated with determining identifiable causal relationships corresponding to input materials of the continuous flow process—is generated. The causal graph is parsed based on the do-calculus manipulations to determine valid conditioning sets associated with estimating a causal impact on an optimization target. Based on the valid conditioning sets, an optimal control configuration comprising optimal control variable values is generated. Generating the optimal control configuration comprising the optimal control variable values associated with the continuous flow process is based on solving a deterministic convex optimization problem and a corresponding stochastic optimization problem.


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