System and method for building and deploying a sustainable dynamic reduced-order model (SDROM) for an industrial process

A computer-based system for creating a model for operating a real-world industrial process, 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:
• train a reduced order model (ROM) of a subject industrial process using simulation data generated from one or more simulations of the subject industrial process performed using a first-principles model (FPM) of the subject industrial process;
• construct a sustainable dynamic reduced-order model (SDROM) of the subject industrial process by:
• (i) dividing the trained ROM into a plurality of multiple-input single-output (MISO) sub-models;
• (ii) partitioning each MISO sub-model into multiple component terms;
• and
• (iii) inserting a gain factor into each partitioned component term to form a gain vector for each MISO sub-model;
• and
• deploy the SDROM online to operate the subject industrial process,
• wherein deploying the SDROM online includes:
• receiving a stream of real-time data of the subject industrial process;
• and
• determining, using the received stream of real-time data, optimal operation settings of the subject industrial process based on a predicted output and estimated local gains from the plurality of MISO sub-models.

A computer program product for creating a model for operating a real-world industrial process, the computer program product comprising:

• a non-transitory computer-readable medium having computer-readable program instructions stored thereon, the instructions, when executed by a processor, causing the processor to:
• train a reduced order model (ROM) of a subject industrial process using simulation data generated from one or more simulations of the subject industrial process performed using a first-principles model (FPM) of the subject industrial process;
• construct a sustainable dynamic reduced-order model (SDROM) of the subject industrial process by:
• (i) dividing the trained ROM into a plurality of multiple-input single-output (MISO) sub-models;
• (ii) partitioning each MISO sub-model into multiple component terms;
• and
• (iii) inserting a gain factor into each partitioned component term to form a gain vector for each MISO sub-model;
• and
• deploy the SDROM online to operate the subject industrial process, wherein deploying the SDROM online includes:
• receiving a stream of real-time data of the subject industrial process;
• and
• determining, using the received stream of real-time data, optimal operation settings of the subject industrial process based on a predicted output and estimated local gains determining optimal operation settings of the subject industrial process based on a predicted output and estimated local gains from the plurality of MISO sub-models.