This research introduces a groundbreaking approach to real-time remote monitoring of natural gas-fired reciprocating engines (NGFREs) by leveraging advanced digital twin technology, low-code programming solutions, and machine learning applications for data analytics. Our research presents a comprehensive methodology for integrating these technologies to develop a robust remote predictive monitoring system for NGFREs. The method involves acquiring data from the NGFREs, extracting relevant features, and applying machine learning algorithms to detect faults, diagnose issues, predict future problems, and estimate loading. We demonstrate our proposed approach's practical application and benefits through a case study involving an AJAX C-42 engine installed at a commercial gas well site in Cement, Oklahoma, and a test unit at the University of Oklahoma Energy Sustainability and Carbon Management Research Center in Norman, Oklahoma. We propose using a solar-powered IIoT (Industrial Internet of Things) edge device for data collection, which reads real-time suction and discharge pressures, speed, exhaust temperature, NOx, and O2 levels from the AJAX C-42 engine. Secure MQTT messages communicate via cellular modem signal with a cloud-based, low-code IIoT application development platform that hosts the AJAX C-42 digital twin. The digital twin interacts with Artificial Intelligence (AI) based Machine Learning (ML) Application Programming Interface (API) using Node-RED, a low-code programming language. This allows the ML API to utilize the digital twin's real-time data for predicting maintenance needs (PdM) and the loading of NGFRE. Accurate PdM prediction can help improve machine health and uptime, promoting savings and performance improvement. And accurate load predictions can help identify ways to mitigate emissions effectively. In conclusion, the innovative approach presented in this research has the potential to revolutionize the real-time remote monitoring, maintenance strategies, and emissions reduction of NGFREs. By integrating digital twin technology, low-code programming solutions, and machine learning, we can enhance the efficiency and effectiveness of monitoring, reduce downtime, and optimize performance, ultimately leading to significant improvements in the operation and maintenance of natural gas-fired reciprocating engines at gas well sites.

1. REVOLUTIONIZING PREDICTIVE REAL-TIME REMOTE MONITORING OF
NATURAL GAS-FIRED RECIPROCATING ENGINES: DIGITAL TWIN, LOW-CODE
PROGRAMMING, AND MACHINE LEARNING TECHNIQUES
Carlos D. Pena, Mohammed A. Moinuddin Ansari, Jamie D. Lynch, Jeff Kimmel*, Pablo Acosta**, Pejman Kazempoor
*Elipsa AI - CEO, **Prescient Devices - VP Engineering

2. Overview
1. Background
• NGFRE definition & applications
• Maintenance strategies
• US gas demand: 26-30 Tcf by 2035
• Outdated centralized monitoring
2. Research Questions
• Question 1: Can ML models detect NGFRE Failures?
• Question 2: What are the best-fit ML Models?
• Question 3: What are the ML Models Limitations?
• Question 4: Can load prediction help reduce emissions in
NGFREs?
3. Methods
• Digital Twin Instance, DPI
• NodeRed Low-code Programming
• Twenty Machine Learning Algorithms AI REST API
• Real-time Remote Dashboard REST API
4. Results and Analysis
• Comparing thermodynamics and machine learning
methods, it yield a load prediction error below 1.10%
• Predictive Algorithm anticipated NOx/O2 sensor failure
Overview 1. Background 3. Method 4. Results and Analysis Closing Remarks
Overview 3. Method 4. Results and Analysis Closing Remarks
2. Research Questions

3. 1. Background
US gas demand: 26-30 Tcf by 2035 (EIA, 2011)
Overview 1. Background 2. Research Questions 3. Method 4. Results and Analysis Closing Remarks
Main Maintenance Strategies
Natural Gas-Fired Reciprocating Engine
Outdated centralized monitoring
(Ciaburro, 2022)

4. 2. Research Questions
 RQ1: What abnormal operational conditions contribute to the failure of natural gas-fired reciprocating engines, and how
can machine-learning algorithms use real-time process data trends and well-known prefixed threshold values to predict
these failures?
 RQ2: How can a machine-learning algorithm be developed and trained to accurately predict natural gas-fired
reciprocating engine failures and concurrently reduce emissions, and what are the most effective algorithms for these tasks?
 RQ3: What are the limitations of using real-time machine-learning algorithms for predicting natural gas-fired
reciprocating engine failures, and how can these limitations be addressed to improve its reliability, probability, and accuracy?
 RQ4: How can machine-learning algorithms model the impacts of different emissions reduction strategies and identify
the most reliable and cost-effective approaches?
Overview 1. Background 2. Research Questions 3. Method 4. Results and Analysis Closing Remarks

5. 3. Method (1/4)
(Hassan, 2023)
Overview 1. Background 2. Formulation 3. Method 4. Results and Analysis Closing Remarks

6. 3. Method (2/4)
Overview 1. Background 2. Formulation 3. Method 4. Results and Analysis Closing Remarks
1. AJAX C-42 Engine/Compressor Package - NGFRE
2. IIoT edge-to-cloud programmable logic controller by Wago Corporation
3. Cellular uplink service for remote internet access
4. Digital Twin: NodeRed Low-code programming by Prescient Devices
5. Cloud-based database by Influx DB 2.0 Cloud
6. Real-time remote dashboard by Wago Corporation Cloud REST API
7. Machine Learning Models REST API by Elipsa AI

7. 3. Method (3/4)
Overview 1. Background 2. Formulation 3. Method 4. Results and Analysis Closing Remarks
Predictive Maintenance:
The Isolation Forest Algorithm is well-suited for
Predicting the faults and identifying the key
factors influencing prediction outcomes (called
drivers)
Machine Performance:
After testing 20 different ML models, the Ridge
Regression Algorithm emerged as the optimal
choice for predicting engine load
Schematic Diagram for the Experimental Setup (Ansari, 2023)

8. 3. Method (4/4)
Overview 1. Background 2. Formulation 3. Method 4. Results and Analysis Closing Remarks

9. 4. Results and Analysis (1/3)
Overview 1. Background 2. Formulation 3. Method 4. Results and Analysis Closing Remarks

10. 4. Results and Analysis (2/3)
Thermodynamics vs Machine Learning Error
Overview 1. Background 2. Formulation 3. Method 4. Results and Analysis Closing Remarks
NOx/O2 Sensor Failure
Cross Correlations

11. 4. Results and Analysis (3/3)
Overview 1. Background 2. Formulation 3. Method 4. Results and Analysis Closing Remarks
Real-time Remote Dashboard REST API

12. Mentor
Professor Dr. Pejman Kazempoor
Carlos D. Pena
PhD Student
Aerospace and Mechanical Engineering
University of Oklahoma
Norman, Oklahoma
Carlos.Pena@ou.edu
References
Ciaburro, G., Machine fault detection methods based on machine learning algorithms: A review. Mathematical biosciences and
engineering : MBE, 2022. 19(11): p. 11453-11490
EIA, “Annual Energy Outlook 2011 with Projections to 2035,” U.S. DOE, 2011.
http://www.eia.gov/forecasts/aeo, accessed on July 24, 2011
Hafiz Ahmad Hassan, M.H., Mohammed A. Moinuddin Ansari, Carlos D. Pena, James D. Lynch, Pejman Kazempoor, Ramkumar N.
Parthasarathy, Integrated system to reduce emissions from natural gas-fired reciprocating engines. Journal of Cleaner Production, 2023.
396
Mohammed A. Moinuddin Ansari, C.D.P., Pejman Kazempoor, Machine Learning and Data Analysis Model to Predict Engine Performance
and Reduce Emissions from Natural Gas-Fired Reciprocating Engines. 2023, American Institute of Aeronautics and Astronautics &
American Society of Mechanical Engineers
Special thanks to Jamie D. Lynch, Jeff Kimmel,
and Dr. Pablo Acosta-Serafini
Thank you for your time