Continuous production process optimization using machine learning

A system for optimization of a continuous production process, comprising:

• at least one processor;
• and
• a processor-readable memory device storing instructions that when executed by the at least one processor causes the at least one processor to perform operations including:
• receiving input data comprising a plurality of datasets each including one or more variables relating to a production equipment involved in the continuous production process;
• generating different prediction models based on the input data, wherein each of the different prediction models is configured to output a target prediction relating to the production equipment;
• generating an objective optimization model based on each target prediction output from each of the different prediction models, wherein the objective optimization model comprises a deep neural network;
• generating a loss function corresponding to the objective optimization model;
• and
• optimizing a plurality of weights for a plurality of parameters of the different prediction models using backpropagation of the deep neural network and the loss function, resulting in a plurality of optimized weights for the parameters of the different prediction models.

A computer program product for optimization of a continuous production process, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• receive input data comprising a plurality of datasets each including one or more variables relating to a production equipment involved in the continuous production process;
• generate different prediction models based on the input data, wherein each of the different prediction models is configured to output a target prediction relating to the production equipment;
• generate an objective optimization model based on each target prediction output from each of the different prediction models, wherein the objective optimization model comprises a deep neural network;
• generate a loss function corresponding to the objective optimization model;
• and
• optimize a plurality of weights for a plurality of parameters of the different prediction models using backpropagation of the deep neural network and the loss function, resulting in a plurality of optimized weights for the parameters of the different prediction models.