Enhanced Control Scheme with Predictive Temperature Control Features for Optimized C2 Recovery
Authors: Chishti Khurram S. 
Published: February 2025

Complex and convoluted flow-based control scheme posed serious challenges in maintaining licensor’s guaranteed C2 recovery of 97% in a newly installed Deep Ethane Recovery facility. Due to overall unit flow fluctuation, de-methanizer column pressure and temperature profile remained cyclic causing C2 giveaway and off-spec C2+ NGL production. This paper discusses an Enhanced Control Scheme that was developed to resolve these challenges and to achieve licensor’s guaranteed C2 recovery of 97%. The proposed control scheme uses a simplified and innovative technique to control the complex Recycle Split Vapor (RSV) process that uses auto-refrigeration for C2+ extraction. The newly developed control scheme has exceeded its objective by extracting 99% C2 recovery on consistent basis. This is achieved by segregating the high-pressure and low-pressure sides of the plant, and eliminating the race conditions between the two most critical controllers to precisely control the de-methanizer overhead and turboexpander separator temperatures. The distinction between the high-pressure and low-pressure is achieved at the turboexpander separator equipment which feeds to turboexpanders, de-methanizer column, and downstream equipment. This segregation provides a precise control to maintain the differential pressure across turboexpanders to maximize C2 recovery. In addition, this enhanced control scheme helps minimize overall unit flow oscillations by eliminating the flow-based control from the control scheme.

The Dynamic Realtime Optimizer (DRTO) algorithm acts on the control valves around heat exchangers bypass paths to define an optimum temperature profile for the De-methanizer overhead temperature to avoid heat integration spin, prevent excessive condensation in the separator, control the de-methanizer liquid loading, and prevent column flooding. All variables such as feed gas composition, feed gas temperature, feed gas blending with various different sources, and optimum plant loading will add constraints to achieve ultimate objective which is to achieve C2 recovery. Variations in any one of these dependent variables and/or combination of variables will adversely affect the overall C2 recovery. DCS operators are not optimization engineers, neither would they be able to track changes in these variables on continuous basis hence they would not be able to extract the maximum throughput from plant and ultimately leading to C2+ giveaway in sales gas and less C2+ liquid extraction. In order to achieve maximum plant throughput, the DRTO algorithm would monitor and measure these variables on real time basis, calculate the impact of these constraints, and successfully predict changes that would derive the plant in a direction of optimum recovery. The initial regression-based model of DRTO algorithm precisely predicts these changes, calculates the set points of critical parameters accordingly, and adjusts the critical ratios to maintain C2 recovery at 99%. Although, initial response of the regression model of DRTO is quite encouraging however, more work is required in terms of finding its dependencies to improve its accuracy. In addition, more data is required on feed gas composition and feed gas temperature variations to test and train the improved model.