| CPC G05B 19/4155 (2013.01) [G05B 19/4183 (2013.01); G05B 23/0254 (2013.01)] | 20 Claims |
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1. A method of optimizing input parameters for an industrial process, the method comprising:
executing a computation model of an industrial process using seed input parameters to generate a first output, the seed input parameters and first output forming a first data set;
applying a first Bayesian acquisition function to the first data set to generate a second input parameter and then executing the computation model using the second input parameter to generate a second data set;
applying a second Bayesian acquisition function to the first data set to generate a third input parameter and then executing the computation model using the third input parameter to generate a third data set, wherein the first and second Bayesian acquisition functions are different types of Bayesian acquisition functions from one another;
using the first and second Bayesian acquisition functions to analyze the first, second, and third data sets;
running the first Bayesian acquisition function based on data from the third data set generated by the second Bayesian acquisition function to generate a fourth input parameter and then executing the computation model using the fourth input parameter to generate a fourth data set;
running the second Bayesian acquisition function based on data from the second or third data set to generate a fifth input parameter and then executing the computational model using the fifth input parameter to generate a fifth data set; and
selecting a best input parameter from among an output from the first and second types of Bayesian acquisition functions, wherein the output comprises the first, second, third, fourth, or fifth input parameter; and
setting the best input parameter on a physical component that performs the industrial process.
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