Delta-operator-based adaptive model predictive control and online optimization of a natural gas liquefaction process

- A novel energy optimizing control system for a natural gas liquefaction process.
- Combined optimizer, model predictive controller, and model parameter estimator.
- Performance validation in a numerical 100 ton-per-day LNG plant.

The production of liquefied natural gas (LNG) is an energy-intensive process. The required temperature is approximately −160 °C at atmospheric pressure. As a result, energy efficiency is the major concern in the process operation. Addressing this issue, we propose a new energy optimizing control system for the LNG process. It consists of an online steady-state optimizer, a model predictive controller (MPC), and a model parameter estimator. The optimizer computes optimum compression ratios and warm-end delta-temperature, while the MPC steers the process toward the target operating conditions. Particularly, the MPC was developed in a delta-form for better numerical stability during continuous operation of a multiple-input multiple-output system with widely distributed time constants. To minimize process perturbation by identification experiments, the model for controller design was derived from a rigorous LNG simulator. To cope with the model error from the true system, a small number of tunable parameters were introduced so that they can be corrected online by model parameter estimator during the process operation. The performance of the developed operation system was demonstrated in a numerical 100 ton-per-day LNG plant, which was precisely constructed to replicate an actual plant in Incheon, Korea.