US 12,217,198 B2
Method and system for process schedule reconciliation using machine learning and algebraic model optimization
Nihar Sahay, Houston, TX (US); and Dimitrios Varvarezos, Houston, TX (US)
Assigned to AspenTech Corporation, Bedford, MA (US)
Filed by AspenTech Corporation, Bedford, MA (US)
Filed on Dec. 3, 2020, as Appl. No. 17/111,273.
Prior Publication US 2022/0180295 A1, Jun. 9, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 10/0631 (2023.01); G05B 13/04 (2006.01); G05B 23/02 (2006.01); G06F 30/20 (2020.01); G06Q 10/04 (2023.01); G06Q 10/0633 (2023.01)
CPC G06Q 10/063116 (2013.01) [G05B 13/04 (2013.01); G05B 13/042 (2013.01); G05B 23/0245 (2013.01); G06F 30/20 (2020.01); G06Q 10/04 (2013.01); G06Q 10/0633 (2013.01)] 15 Claims
OG exemplary drawing
 
1. A computer-implemented method of process control and schedule reconciliation, comprising:
receiving by at least one digital processor a scheduling model formed of an initial schedule for reconciliation, the initial schedule having projected plant data for a first period of time regarding at least one chemical process of a subject plant;
importing, into a memory area accessible by the at least one digital processor, measured plant data for the first period of time regarding the at least one chemical process;
identifying dynamic optimization data representing trends in plant-wide process production data at time-varied values for key process and operation parameters;
processing the imported measured plant data and the projected plant data using mathematical modeling techniques and identifying event boundaries, and stream flowrates of materials associated with tanks and process units of the subject plant during the first period of time;
building an optimization model that mathematically represents a schedule reconciliation problem of processes of the subject plant during the first period of time, the optimization model applying penalties for deviating from: (i) the identified event boundaries during the first period of time, (ii) the identified stream flowrates of materials during the first period of time, (iii) the identified dynamic optimization data, (iv) pre-determined constraints, and (v) the initial schedule for the first period of time, along a period of time that includes priority slots such that solving the optimization model results in a reconciled schedule for the first period of time regarding the at least one chemical process that aligns the projected plant data for the first period of time regarding the at least one chemical process with the measured plant data for the first period of time regarding the at least one chemical process;
solving the optimization model to develop the reconciled schedule for the first period of time regarding the at least one chemical process, the reconciled schedule having reconciled plant data for the first period of time regarding the at least one chemical process consistent with the identified event boundaries and the identified stream flowrates of materials identified from the processing of the measured plant data and the projected plant data including minimizing deviation from the projected plant data, and providing a current event status, said importing, identifying, processing, building, and solving being automatically performed by the at least one digital processor;
automatically building a future schedule for a second period of time succeeding the first period of time in computer memory, using the reconciled plant data; and
controlling the subject plant during the second period of time using the built future schedule by automatically adjusting start time, stop time, and flow rates of tank to tank transfers and of operating modes of the process units.