[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117026-en":3,"doc-seo-117026-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},117026,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Enhancing Performance and Reducing Emissions in Natural Gas Aspirated Engines Through Machine Learning Algorithm - A Thesis","The thesis tackles efficiency and sustainability challenges in the natural gas sector by improving Natural Gas Fired Reciprocating Engines (NGFRE), which deliver performance while producing significant emissions. Using an experimental setup with the AJAX DPC-81 engine compressor across 40%–75% operational loads, the study collects real-time sensor data on performance, emissions, and vibration. The Air Management System is varied via air/fuel ratio and analyzed alongside bypass-valve settings. Machine learning models including linear regression, artificial neural networks, and support vector machines are integrated with a PLC for prediction and adaptive control. Results show major emission reductions, such as methane decreasing by 64%, NOx by 52%, and VOC by 50% at specific operating conditions.","UNIVERSITY OF OKLAHOMA  \nGRADUATE COLLEGE  \nENHANCING PERFORMANCE AND REDUCING EMISSIONS IN NATURAL GAS  \nASPIRATED ENGINES THROUGH MACHINE LEARNING ALGORITHM  \nA THESIS  \nSUBMITTED TO THE GRADUATE FACULTY  \nin partial fulfillment of the requirements for the  \nDegree of  \nMASTER OF SCIENCE  \nBy  \nMOHAMMED AMEER MOINUDDIN ANSARI  \nNorman, Oklahoma  \nENHANCING PERFORMANCE AND REDUCING EMISSIONS IN NATURAL GAS ASPIRATED ENGINES THROUGH MACHINE LEARNING ALGORITHM  \nA THESIS APPROVED FOR THE  \nSCHOOL OF AEROSPACE AND MECHANICAL ENGINEERING  \nBY THE COMMITTEE CONSISTING OF  \nDr. Pejman Kazempoor, Chair  \nDr. Wilson E. Merchan-Merchan  \nDr. Iman Ghamarian  \n©Copyright by Mohammed Ameer Moinuddin Ansari 2023 All Rights Reserved.  \nAcknowledgments  \nFirstly, I am deeply grateful for the guidance and support provided by my advisor, Dr. Pejman Kazempoor, whose expertise and mentorship have been invaluable throughout my research journey. His encouragement and wisdom have not only shaped this work but have also been instrumental in my personal and professional growth.  \nMy sincere appreciation extends to the committee members, Dr. Wilson E. Merchan-Merchan and Dr. Iman Ghamarian, for their insightful feedback and the valuable time they dedicated to enriching my thesis.  \nI owe a profound debt of gratitude to my parents, whose unwavering support and belief in my abilities have been the cornerstone of my career. Their love and encouragement have been my guiding light, providing strength and inspiration at every step.  \nA special acknowledgment goes to my sister, who has been nothing short of a backbone throughout this journey. Her steadfast support, unwavering faith, and invaluable encouragement have been my constant source of motivation. In her, I found not just a sister but a mentor and a friend. \"She is not just a sister, but a star that brightens my world.\"  \nI extend my thanks to all my colleagues at the Sustainable Energy and Carbon Management Center (SECM) at the University of Oklahoma. I am particularly grateful to team members Hafiz Ahmad Hassan, Carlos de Castro Pena, Matthew D. Hartless, Tu Nguyen, Raelin B. Lane, and our Lab manager James D. Lynch, for their collaboration, camaraderie, and shared commitment to our collective goals.  \nEach of you has played a pivotal role in this journey, and for that, I am eternally grateful.  \nAbstract  \nIn an era where the global energy landscape is increasingly defined by the dual imperatives of efficiency and sustainability, the natural gas sector stands at a crucial juncture. The engines powering this sector, especially Natural Gas Fired Reciprocating Engines (NGFRE), are well known for their performance as well as considerable emissions, posing a stark challenge to environmental sustainability goals. This thesis addresses this pivotal issue, presenting a machine learning-based solution to optimize NGFRE performance while substantially reducing their environmental footprint.  \nThe research is anchored in an experimental framework involving the AJAX DPC-81 engine compressor, evaluated across a spectrum of operational loads from 40% to 75% . The study leverages an extensive array of sensors to collect detailed real-time data on engine performance, emissions, and vibration parameters. Central to the methodology is the strategic adjustment of the Air Management System (AMS), varying air/fuel ratio to explore their impact on engine dynamicsand emissions. The study also incorporates a comprehensive vibration analysis, providing critical insights into the engine's operational stability under different load conditions. Machine Learning (ML) techniques, including Linear Regression, Artificial Neural Networks (ANN), and Support Vector Machines (SVM), are integrated with a Programmable Logic Controller (PLC) . This integration not only facilitates a nuanced analysis of the collected data but also enables the accurate prediction of engine performance, paving the way for real-time adaptive control system","cbCais8yV973aldu","https://ap.wps.com/l/cbCais8yV973aldu","pdf",10576023,1,144,"English","en",105,"# Acknowledgments\n# Abstract\n# Table of Contents\n# Abbreviations\n# List of Figures\n# List of Tables\n# Chapter","[{\"question\":\"What problem does the thesis address in natural gas engines?\",\"answer\":\"It addresses the challenge of improving Natural Gas Fired Reciprocating Engines (NGFRE) performance while substantially reducing their emissions to support sustainability goals.\"},{\"question\":\"What experimental setup and operating range are used?\",\"answer\":\"The research evaluates the AJAX DPC-81 engine compressor across operational loads from 40% to 75%, collecting real-time sensor data for performance, emissions, and vibration.\"},{\"question\":\"How does the machine learning approach reduce emissions and enable control?\",\"answer\":\"Machine learning techniques (linear regression, ANN, SVM) are integrated with a programmable logic controller to analyze collected data and accurately predict engine performance, supporting real-time adaptive control. The study reports large reductions in methane, NOx, and VOC under selected operating conditions.\"}]",1785673135,363,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"enhancing-performance-and-reducing-emissions-in-natural-gas-aspirated-engines-through-machine-learning-algorithm-a-thesis","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/enhancing-performance-and-reducing-emissions-in-natural-gas-aspirated-engines-through-machine-learning-algorithm-a-thesis/117026/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the thesis address in natural gas engines?","Question",{"text":74,"@type":75},"It addresses the challenge of improving Natural Gas Fired Reciprocating Engines (NGFRE) performance while substantially reducing their emissions to support sustainability goals.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What experimental setup and operating range are used?",{"text":79,"@type":75},"The research evaluates the AJAX DPC-81 engine compressor across operational loads from 40% to 75%, collecting real-time sensor data for performance, emissions, and vibration.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the machine learning approach reduce emissions and enable control?",{"text":83,"@type":75},"Machine learning techniques (linear regression, ANN, SVM) are integrated with a programmable logic controller to analyze collected data and accurately predict engine performance, supporting real-time adaptive control. 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