CONTROL LOOP PERFORMANCE MONITORING AND OPTIMIZATION BASED ON PLANT DATA

Abstract

The control of process variables is of great importance to a production plant. The failure of control loops may lead to off-spec production or cause the deterioration of equipment, which translate into economic losses. A method of control loop performance monitoring based on Stochastic Process Control has been studied and demonstrated on an Open Rack Vaporizer (ORV) system. The minimum variance controller (MVC) can be used as a benchmark. In the case of a servo problem, data can be used to identify the plant and disturbance models and the controller can then be tuned accordingly. A heat exchanger system involving boil-off gas (BOG) and high pressure liquified natural gas (HPLNG) has been studied. The problem is currently solved in a convoluted way due to the constraints involved. A validated model has been constructed based on plant data, and a simplified control strategy has been proposed based on the model.