Enabling High-Performance Safety-Critical Offshore and Subsea Automatic Control Systems using Embedded Optimization

Model predictive control (MPC) benytter numeriske metoder for automatisk å optimalisere ytelsen til prosessanlegg. Dette fører til bedre regularitet og ytelse som produksjonsrate, produktkvalitet, vedlikeholdskostnader og andre ytelsesindikatorer. Gjennom utvikle av nye programvareverktøy og tilhørende teoretisk analyse har prosjektet muliggjort å implementere MPC i ultra-pålitelige industrielle datamaskiner for utfordrende anvendelser i petroleumsindustrien. Metodene er testet i industrielt verifiserte simuleringsstudier for automatisk styring av undervannsseparasjon, hiv-kompensert boring, og nedihullspumper (ESP).

Todays technology for optimization-based control and MPC are essentially limited to slow processes (update rates in minutes or seconds) that have a dedicated lower-level control system (such as a decentralized control system) and a dedicated safety-system . Today?s MPC technology is therefore based on server-type or PC-like computers and software solutions that does not meet the oil and gas industrys standard for safety and reliability in stand-along operations. In new applications such as subsea processin g and automated intelligent drilling the existing MPC technology is not suited, and must be enhanced for computational efficiency and software reliability. This projects answer to this challenge in to enable MPC on ultra-reliable industrial computer sys tem hardware such as microcontrollers and PLCs, and thereby providing the petroleum industry with automatic control implementation technology that will enable more advanced functionality to be more easily built into such control systems. There is a clear trend towards increased levels of automation, autonomy, built-in intelligence and integrated software-based functionality in control and monitoring systems that will be enabled by this project since embedded numeric optimization methods offer the most pot ent technology to make real-time choices and automated decisions with no or little human intervention. The research is centered around two PhD students that will be supervised and co-supervised in a highly interdisciplinary setup that combines the partn ers expertize in embedded optimization, automatic control, subsea technology, drilling technology and electric drives. The project is divided into work packages such that each PhD student will contribute both with research on new embedded optimization me thods, but mostly within application case studies in close collaboration with Statoil.