US 11,656,606 B2
Site-wide operations management optimization for manufacturing and processing control
Dung Tien Phan, Pleasantville, NY (US); Lam Nguyen, Ossining, NY (US); Pavankumar Murali, Ardsley, NY (US); and Hongsheng Liu, Chapel Hill, NC (US)
Assigned to International Business Machines Corporation, Armonk, NY (US)
Filed by International Business Machines Corporation, Armonk, NY (US)
Filed on Aug. 20, 2020, as Appl. No. 16/998,642.
Prior Publication US 2022/0057786 A1, Feb. 24, 2022
Int. Cl. G05B 15/00 (2006.01); G05B 19/418 (2006.01); G06N 5/046 (2023.01); G06N 3/08 (2023.01); G06N 7/01 (2023.01)
CPC G05B 19/41835 (2013.01) [G06N 3/08 (2013.01); G06N 5/046 (2013.01); G06N 7/01 (2023.01)] 14 Claims
OG exemplary drawing
 
1. A computer-implemented method comprising:
applying, by a processor, an optimization algorithm to a control system of a processing plant based on whether the control system is guided by a linear-based predictive model or a non-linear-based predictive model in which a gradient is available, wherein applying the optimization algorithm comprises:
responsive to the control system being guided by a linear-based predictive model, applying a mixed-integer linear program to the linear-based predictive model, wherein the mixed-integer linear program comprises one or more individual decision trees, wherein a topology of each decision tree is fixed such that, for each feature vector x in the respective decision tree, there is a unique path leading to a leaf node of the tree from the root of the tree; and
responsive to the control system being guided by a non-linear-based predictive model, applying a two-level augmented Lagrangian method to the non-linear-based predictive model, wherein an augmented Lagrangian method is applied to non-linear coupling constraints in an outer level of the two-level augmented Lagrangian method and a multi-block alternating direction method of multipliers (ADMM) is applied to linear coupling constraints in an inner level of the two-level augmented Lagrangian method;
calculating, by the processor, a set variable using the optimization algorithm;
predicting, by the processor, an output based on the calculated set variable;
comparing, by the processor, an actual output at the processing plant to the predicted output; and
suspending, by the processor, a physical process at the processing plant in response to the actual output being a threshold value apart from the predicted output.