GPA '18: Data match simulation aids in NGL plant retrofit design
AUSTIN—On Day 1 of GPA Midstream's 2018 Annual Meeting, Ortloff Engineers shared an afternoon presentation on how to evaluate an existing NGL recovery plant before finalizing a process retrofit design.
Speaker Daryl Jensen explained how retrofits can be necessary due to changes in operating conditions, such as flows or pressures; changes in economic conditions; or the availability of more advanced technologies.
Starting a retrofit project. Preparing for a retrofit is more complicated than preparing to build a new plant, because existing equipment must be adapted to a new design, versus installing new equipment based on a new design. With an increase in the recovery of NGL, more liquid products will result, which must be accounted for in the retrofit design, Jensen said.
The first step in a retrofit design is to determine the areas of the plant that will be affected. For example, heat in columns must be carefully calculated. The cryogenic unit is another complicated process that requires much data gathering during the design process, Jensen said.
Design simulation for data match. The last step in a retrofit design is to use an advanced simulation tool to verify the original design performance, recovery and horsepower (hp), and to ensure that the retrofit design will yield the intended results. Simulation goals include targeting the correct feed composition, separator temperature, column pressure, and other parameters.
After the simulation is performed, plant data must be gathered so that a data match simulation can be constructed and potential equipment margins can be identified. Critical field data needed for the evaluation include feed and product compositions from online analyzers; lab samples of feed gas, liquid products and residue gas; pressure, temperature and flow at the inlet and outlet of each piece of equipment; refrigeration and compression systems temperatures, pressures, flows and compositions; stream conditions for all column feeds; and ambient conditions for air cooler modeling.
Jensen recommended starting with 24 hr of data at 5-min. intervals at a relatively smooth operation to identify the most stable window of operation. From these data, engineers can identify the ideal window near the laboratory sample time that is as close to the design capacity of the plant as possible.
After all data is gathered, a data match simulation is run to identify shortcomings in plant performance and explain why they exist, and also to ensure that existing equipment performance is reflected in the retrofit simulation and performance estimate. Key areas for data match include overall material balance and recovery and hp results.
Using these data, several critical items can be adjusted to match the reported temperatures and pressures:
• Side reboiler duties
• Inlet gas flow splits to the side reboiler
• Pressure drop from the inlet to the expander
• Pressure drop from the column overhead to the booster compressor
• Pressure drop from the booster discharge to the residue section
• Pressure drop from the residue compressor discharge to the delivery point
• Refrigeration temperatures at chillers and coolers.
Temperatures should match within 2°F, and calculated pressures should match within 2%, Jensen explained.
Comparing the data match to the original design simulation helps identify problem areas and/or performance limitations. Once identified, these issues can be fixed or noted as the retrofit design is developed. The data match simulation also provides good information on constraints for existing equipment.
Finalizing the retrofit design. After the data match simulation is performed, a retrofit design simulation can be carried out by adding the retrofit design equipment to the original design simulation. The retrofit simulation should be adjusted until all constraints are met. Once a good match is achieved, then the retrofit design and/or the strategy for compression and equipment replacement can be refined.
"The final result is that you have a retrofit design simulation that reflects the reality of the existing equipment or field," Jensen said. The simulation can also be used to pinpoint which items must be specially adjusted or corrected from the original design.
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Incorporating economic data into process modeling is key to optimizing operations and maximizing profits at gas processing plants. However, maintaining optimal operations are often challenging due to changing market dynamics, contract structures and increasing process flexibility. Today, gas processors are leveraging Predictive Control and First Principles models to accurately determine and control the optimal operating targets in real time based on the most current plant conditions and profitability, optimizing recovery of natural gas liquids. Learn how real-time analytics, combined with decision support tools, empower companies to:
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