System and method for building and deploying prescriptive analytics to predict and control end product quality in batch production monitoring and optimization

A computer system for creating a machine learning predictive model for real-world batch production industrial process monitoring and optimization, the system comprising:

• a processor;
• and
• a memory with computer code instructions stored thereon, the processor and the memory, with the computer code instructions, being configured to cause the system to:
• obtain historical operating data from a plurality of batch production runs of an industrial process;
• standardize the obtained historical operating data for each run of the plurality of batch production runs;
• for each batch production run of the plurality:
• (i) partition standardized operating data corresponding to the batch production run into one or more stages and (ii) determine one or more signature for each of the one or more stages using the partitioned standardized operating data corresponding to the one or more stages;
• associate each determined signature with a class label based upon output of a batch production run of the plurality corresponding to the determined signature conforming with operational standards or not conforming with the operational standards;
• train a machine learning predictive pattern model with at least a subset of the determined signatures as inputs and associated class labels as outputs, wherein the training configures the model to predict, based on operating data from a real-world batch production process, whether output of the real-world batch production process will conform or not conform with the operational standards;
• automatically receive sensor data from an ongoing batch production run of the industrial process;
• process the received sensor data with the trained machine learning predictive pattern model to determine a prediction of output of the ongoing batch production run as conforming or non-conforming with the operational standards;
• wherein the determined prediction includes an indication of statistical probability in the determined prediction;
• at a current batch progress stage of the ongoing batch production run, examine the indication of statistical probability in the determined prediction;
• and
• perform an online root-cause analysis if:
• (i) the determined prediction indicates the output is non-conforming and (ii) the indication of statistical probability in the determined prediction is above a threshold.

A computer program product for creating a machine learning predictive model for real-world batch production industrial process monitoring and optimization, the computer program product comprising:

• one or more non-transitory computer-readable storage devices and program instructions stored on at least one of the one or more storage devices, the program instructions, when loaded and executed by a processor, cause an apparatus associated with the processor to:
• obtain historical operating data from a plurality of batch production runs of an industrial process;
• standardize the obtained historical operating data for each run of the plurality of batch production runs;
• for each batch production run of the plurality:
• (i) partition standardized operating data corresponding to the batch production run into one or more stages and (ii) determine one or more signature for each of the one or more stages using the partitioned standardized operating data corresponding to the one or more stages;
• associate each determined signature with a class label based upon output of a batch production run of the plurality corresponding to the determined signature conforming with operational standards or not conforming with the operational standards;
• train a machine learning predictive pattern model with at least a subset of the determined signatures as inputs and associated class labels as outputs, wherein the training configures the model to predict, based on operating data from a real-world batch production process, whether output of the real-world batch production process will conform or not conform with the operational standards;
• automatically receive sensor data from an ongoing batch production run of the industrial process;
• process the received sensor data with the trained machine learning predictive pattern model to determine a prediction of output of the ongoing batch production run as conforming or non-conforming with the operational standards;
• wherein the determined prediction includes an indication of statistical probability in the determined prediction;
• at a current batch progress stage of the ongoing batch production run, examine the indication of statistical probability in the determined prediction;
• and
• perform an online root-cause analysis if:
• (i) the determined prediction indicates the output is non-conforming and (ii) the indication of statistical probability in the determined prediction is above a threshold.