This document discusses using digital twin, low-code programming, and machine learning techniques to remotely monitor natural gas-fired reciprocating engines. It aims to predict failures, reduce emissions, and identify the most effective machine learning models. The methods create a digital twin instance using NodeRed programming. Twenty machine learning algorithms in an AI API were tested. Results show isolation forest and ridge regression best for predictive maintenance and load prediction, with thermodynamic vs ML load error below 1.10%. The algorithms anticipated sensor failures from real-time data trends. Limitations and improving reliability are also discussed.
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Predicting NGFRE Failures and Load with ML
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• Outdated centralized monitoring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Outdated centralized monitoring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
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)
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