System and methods for automated model development from plant historical data for advanced process control
US11754998B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 19, 2020 |
| Grant date | Sep 12, 2023 |
| Priority date | — |
| Expiry date | Aug 18, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG05B2219/42033
- WIPO fieldControl
- WIPO sectorInstruments
Abstract
Systems and methods provide a new paradigm of Advanced Process Control that includes building and deploying APC seed models. Embodiments provide automated data cleansing and selection in model identification and adaption in multivariable process control (MPC) techniques. Rather than plant pre-testing onsite for building APC seed models, the embodiments help APC engineers to build APC seed models from existing plant historical data with self-learning automation and pattern recognition, AI techniques. Embodiments further provide “growing” and “calibrating” the APC seed models online with non-invasive closed loop step testing techniques. PID loops and associated SP, PV, and OPs are searched and identified. Only “informative moves” data is screened, identified, and selected among a long history of process variables for seed model development and MPC application. The seed models are efficiently developed while skipping the costly traditional pre-testing steps and minimizing the interferences to the subject production process.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.