The primary objective is to qualify and enhance Embedded Optimization algorithms for use in model predictive control (MPC) for automatic control in subsea processing and automated intelligent drilling.
The main secondary objectives are
- Research leading to new embedded optimization algorithms, MPC design methods, implementation and verification tools for embedded MPC suited for safety-critical applications offshore and subsea;
- Qualify and demonstrate embedded optimization for automatic control in subsea processing, managed pressure drilling and production optimization case studies in collaboration with Statoil;
Todayís 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/or a dedicated safety-system. Todayís MPC technology is therefore based on server-type or PC-like computers and software solutions that does not meet the oil and gas industryís standard for safety and reliability in stand-alone operations. In new applications such as subsea processing and automated intelligent drilling the existing MPC technology has some limitations, and should be enhanced for computational efficiency and software reliability.
This projectís answer to this challenge in to enable MPC on ultra-reliable industrial computer system 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 potent technology to make real-time choices and automated decisions with no or little human intervention.