Improve Gas Processing Economics Through Nitrogen Rejection Unit Optimization

Improve Gas Processing Economics Through Nitrogen Rejection Unit Optimization

May 10, 2016 | Gas Processing, Optimization

Cryogenic nitrogen rejection units (NRUs) play a critical role in gas processing operations. In addition to helping meet pipeline reid vapor pressure (RVP) specifications and reducing emissions, removal of inert nitrogen from the raw stream reduces transport volume and increases the calorific (i.e., heating) value of natural gas. As a result, improving the operation of NRUs can have significant impact on processing economics.

When nitrogen is separated from natural gas, a small percentage of methane typically comes with it. The overall objective of NRU optimization is to reduce that percentage, thus creating a purer stream of nitrogen to be vented or sold. The installation of advanced monitoring equipment, such as a gas chromatograph can be used to determine how an NRU is performing by measuring the amount of methane in the reject stream. A higher content of methane indicates that the NRU is in need of maintenance.

Unit-specific process models of an NRU can help to identify which variables/parameters can be adjusted to improve the separation process. This, coupled with routine cleaning and maintenance of heat exchangers can also increase process efficiency and reduce operating costs, thus producing a higher volume of saleable gas and increasing profits.

Continuous monitoring of the separation process is necessary to determine when maintenance activities should be scheduled. Although each operation will require its own degree of precision, the use of flow meters, remote transmitters, and gas chromatographs should be installed to monitor the composition of the rejected stream. The use of process analysis software can also be used to optimize NRU operation.

Because optimization of NRUs can be achieved at a relatively low capital cost, they often represent an area with a high return on investment (ROI). This is especially the case with large units, as the incremental costs of optimization through maintenance and/or process model development remains relatively static regardless of a unit’s capacity.