System and method for operation optimization of an equipment

A system for operation optimization of an equipment by a neural network based optimizer, wherein the system comprising:

• a memory storing instructions;
• one or more communication interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
• receive, information associated with at least one equipment instance (j) as an input at a predefined sequence of timestamps, wherein the information associated with the at least one equipment instance (j) corresponds to at least one of (i) a plurality of control parameters (it), (ii) external input parameters (et), (iii) output parameters (ot), and (iv) historical operational data;
• train, a plurality of simulation models for each equipment instance using the information associated with the at least one equipment instance (j) to obtain a function (fj), wherein the function (fj) corresponds to a set of simulation models of the equipment instance (j);
• generate, at least one sequence of the external input parameters (et);
• process, the external input parameters (et) to obtain a fixed-dimensional vector that is passed as an input to a neural network to obtain an output vector (it);
• generate, a modified (it) from the output vector (it) based on a domain constraint value;
• compute, a reward (rt) based on (i) the function (fj), (ii) the modified (it), (iii) the external input parameters (et), and (iv) a reward function (Rj);
• and
• iteratively perform the steps of process, generate, and compute reward (rt) for a series of subsequent equipment instances after expiry of the predefined sequence of timestamps associated with a first equipment instance.

One or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes:

• receiving, information associated with at least one equipment instance (j) as an input at a predefined sequence of timestamps, wherein the information associated with the at least one equipment instance (j) corresponds to at least one of (i) a plurality of control parameters (it), (ii) external input parameters (et), (iii) output parameters (ot), and (iv) historical operational data;
• training, a plurality of simulation models for each equipment instance using the information associated with the at least one equipment instance (j) to obtain a function (fj), wherein the function (fj) corresponds to a set of simulation models of the equipment instance (j);
• generating, at least one sequence of the external input parameters (et);
• processing, the external input parameters (et) to obtain a fixed-dimensional vector that is passed as an input to a neural network to obtain an vector (it);
• generating, a modified (it) from the output vector (it) based on a domain constraint value;
• computing, a reward (rt) based on (i) the function (fj), (ii) the modified (it), (iii) the external input parameters (et), and (iv) a reward function (Rj);
• and
• iteratively performing the steps of processing, generating, and computing reward (rt) for a series of subsequent equipment instances after expiry of the predefined sequence of timestamps associated with a first equipment instance.