The objective is to develop and validate a novel, low-cost (<$75-100/BHP, depending on the IRC’s size), field-installable (installation time <3 hours), remotely controlled, retrofit kit with integrated sensors for Integral Reciprocating Compressors (IRCs) used in the production, gathering, transmission, and processing sections of the natural gas industry. The proposed technology aims to reduce emissions and improve operating efficiencies, combustion stability, and operational envelope of IRCs, while saving cost. The development of the proposed retrofit kit will utilize various computational, data analytics, and machine learning models to correlate sensor and emissions data at all operational points with the performance parameters of the compressor. Validation and optimization will be performed via comprehensive lab and field tests.

University of Oklahoma – Norman, OK 73019
 

The simplicity, fuel flexibility, and lower operational cost of large IRCs have rendered them as ideal candidates for various applications, including gas gathering, gas transmission, and gas processing. However, the operation of IRCs over a variety of applications and power ranges is limited due to the challenge of meeting regulatory emission standards. Exploration and production companies or compressor rental fleet providers typically purchase high horsepower IRCs for the early stage of well production. As production declines or gas prices fluctuate, companies need to operate the compression system under varying operating conditions in order to accommodate load and speed. However, the majority of IRCs are designed to work optimally only at their rated load and speed due to their simple two-stroke working principle. In partial load scenarios (e.g., 0-80% rated power), the unburned hydrocarbon emissions, including methane, increase dramatically. The development of this technology will provide improved, low-cost retrofit kits for reducing emissions and improving the performance of IRCs.

This project demonstrates the application of smart devices that can be integrated with old and new assets to provide a low-cost, field-installable retrofit alternative that will reduce emissions and enhance the performance of reciprocating compressors. The proposed approach is expected to be desirable for a variety of large industrial integral compressors used in the natural gas industry and provide improved efficiency. The system will allow continuous operation to prevent production losses due to down time of the compressor and will provide a significant reduction in the operation and maintenance costs mainly by eliminating the need for expensive catalysts with a relatively short lifetime.

• Conducted comprehensive testing of engine and compressor performance under various operating conditions, including different fuel mixtures, ambient temperatures, and loads, resulting in a detailed data set.
• Utilized advanced emission analyzers such as MKS, ECM, and Testo to monitor and record emission results accurately.
• Developed and validated a mathematical model for NOx and O2 sensors, enabling accurate sensor performance evaluation.
• Fabricated and implemented a testing and calibration setup for NOx and O2 sensors, providing a reliable evaluation of sensor performance and degradation under various operating conditions.
• Designed and deployed an automation system comprising various sensors to capture different engine parameters accurately.
• Designed and manufactured an air management unit capable of controlling the air-to-fuel ratio, resulting in a significant reduction in emissions under partial load operation.
• Successfully reduced methane and volatile organic compound emissions by up to 70% at different loads and speeds, meeting the EPA standards.
• Developed a programmable logic controller platform to control the technology with WAGO Automation support, providing a user-friendly interface to operate the system.
• Successfully deployed the developed system to the field, enabling data collection from operating engines to develop control algorithms for emissions management.
• Collaborated with industrial partners to develop a monitoring platform using the data collected from lab and field units, enabling comprehensive analysis and evaluation of the technology.
• Presented the research findings at three conferences and two peer-reviewed journal articles, highlighting the significant contributions to the field.
• Conducted simulations of the field environment to accelerate the commercialization of the technology.
• Organized several training sessions for graduate and undergraduate students working on the project, enabling them to gain hands-on experience in the field.
• Shared and discussed the project's progress and results with different companies, enabling collaboration and further development of the technology.

The academic and industry teams have collaborated to deploy an automated air management system (AMS) in the field, which has been installed on an operational engine. They are actively gathering data and working together to develop a remote monitoring platform that can be utilized as a predictive and preventative maintenance tool for industrial machines.

 $1,488,391.00

$394,751.00
 

NETL – Joseph Renk (Joseph.Renk@netl.doe.gov or 412-386-6406)
University of Oklahoma – Dr. Pejman Kazempoor (pkazempoor@ou.edu or 405-325-7885