This research utilizes modeling, simulation and control tools for improving the operation of a gas dehydration plant. In particular, realistic process conditions are utilized in conjunction with the process simulator ASPENPlus in both steady-state as well as dynamic mode to determine conditions that lead to minimization of product loss under upset conditions.
Dr. Srinivas Palanki, Professor, Dan F. Smith Department of Chemical Engineering
In a natural gas processing plant, unit operations for gas processing depend on the gas composition, the type of facility, and the product specifications. Among these operations, dehydration is a common technique to remove water from natural gas. Low-temperature separation with monoethylene glycol (MEG) injection process is a common dehydration technique for the natural gas processing. However, the MEG-based dehydration system frequently suffers a significant glycol loss and poor natural gas liquid (NGL) recovery during plant upset conditions, causing both economic loss as well as increased air emissions.
This research focuses on an industrial-scale natural gas treatment process to identify the root causes of glycol losses and proposes control strategies for minimizing MEG loss and maximizing NGL recovery. Both steady-state as well as dynamic models are developed using the Aspen simulation framework. Steady-state simulations are utilized to pinpoint the areas where most of the glycol is lost. Then, a dynamic model is developed to study plant upset conditions, minimize the glycol loss, improve product gas specifications and reduce plant operating costs.
Two new control strategies are developed to minimize MEG loss. The first control strategy utilizes the traditional framework of several PID control loops and includes: (1) a new ratio control for the stripping gas with stripper column feed, (2) a temperature control at stripper column overhead by manipulating rich MEG stream flow from LTS, and (3) a composition controller for stripper column bottom flow (lean MEG) cascaded with reboiler temperature. It is shown that this control strategy stabilizes the stripper column operation and reduces MEG losses by 37%.
A second control strategy that utilizes a hierarchical control system comprised of Dynamic Matrix Control (DMC) and basic regulatory control loops is constructed to further optimize the plant operation in terms eliminating MEG losses and minimizing the operating costs with desired product quality under various process upsets. Both standard DMC and adaptive DMC controller models are developed based on the subspace identification model and simulated on Aspen manufacturing platform. It is shown that the developed hierarchical control system outperforms the regulatory PID controller strategy in minimizing MEG losses under process constraints.
This project was partially funded by the National Institutes of Standards and Technology.