System and method for just in time characterization of raw materials

A system for data characterization of materials based on plant data, comprising:

• one or more hardware processors;
• a communication interface;
• and
• a memory storing a plurality of instructions, wherein the plurality of instructions when executed, cause the one or more hardware processors to:
• receive the plant data from an industrial plant as input;
• determine, by processing the plant data, change in one or more raw materials used in the industrial plant, wherein the change in the one or more raw materials is detected at least at a plant level or an equipment level;
• determine at least one class that matches each of the one or more raw materials, using at least one material class identification model, wherein the determined at least one class is a newly defined class or is from a set of pre-defined classes;
• predict material characteristics for each of the one or more raw materials;
• select at least one of a plurality of predictive models associated with at least one of the predicted material characteristics and the determined at least one class of the one or more raw materials;
• predict performance of the industrial plant, using the selected at least one predictive model;
• and
• generate at least one recommendation to optimize performance of the industrial plant based on the predicted performance, if a measured actual performance of the industrial plant is below a threshold of performance.

A non-transitory computer readable medium for characterization of materials based on plant data, the non-transitory computer readable medium performs the characterization of the materials by:

• receiving the plant data from an industrial plant as input, via one or more hardware processors;
• determining, by processing the plant data via the one or more hardware processors, change in one or more raw materials used in the industrial plant, wherein the change in the one or more raw materials is detected at least at a plant level or an equipment level;
• determining at least one class that matches each of the one or more raw materials, using at least one material class identification model, via the one or more hardware processors, wherein the determined at least one class is a newly defined class or is from a set of pre-defined classes;
• predicting material characteristics for each of the one or more raw materials, via the one or more hardware processors;
• selecting at least one of a plurality of predictive models associated with at least one of the predicted material characteristics and the determined at least one class of the one or more raw materials, via the one or more hardware processors;
• predicting performance of the industrial plant, using the selected at least one predictive model, via the one or more hardware processors;
• and
• generating at least one recommendation to optimize performance of the industrial plant based on the predicted performance, if an obtained actual performance of the industrial plant is below a threshold of performance, via the one or more hardware processors.