[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117492-en":3,"doc-seo-117492-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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":27,"seo_description":14,"update_tm":28,"read_time":29},117492,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Machine Learning-Enhanced Laser Absorption Spectroscopy for Harsh-Environment Combustion Diagnosis","Laser absorption spectroscopy (LAS) is widely used for monitoring reactive flow-field parameters in industrial combustion, but harsh operating conditions introduce noise and interference that distort measured absorption spectra and increase uncertainty in combustion analysis. This work presents a machine learning-enhanced LAS method integrating a Denoising Autoencoder (DAE) with an LSTM network to suppress noise-induced distortions and recover the underlying spectral sequence. Using in situ experimental data, the approach supports portable industrial diagnostics across hard-to-model multi-source noise. Validation on an APU gas turbine aero-engine focuses on exhaust temperature measurements and shows high-fidelity spectrum recovery with temperature measurement deviation below 7.9°C, enabling more convenient, accurate, stable diagnostic estimates.","Machine Learning-Enhanced Laser Absorption Spectroscopy for Harsh-Environment Combustion Diagnosis  \nYuan Chen, Jiangnan Xia, Rui Zhang, Yikai Xia, Minqiu Zhou, Yalei Fu, Ihab Ahmed, Ian Armstrong, Abhishek Upadhyay, Michael Lengden, Walter Johnstone, Paul Wright, Krikor Ozanyan, Mohamed Pourkashanian, Hugh McCann, Chang Liu*  \nAbstract—Laser absorption spectroscopy (LAS) has been widely adopted as a diagnostic tool for reactive flow-field monitoring in industrial combustion applications. In spite of various advancements in LAS signal processing schemes, these harsh environments inevitably impose noise and interference on the LAS measurement data, thus increasing inaccuracy and uncertainty in combustion analysis. This paper proposes a machine learning-enhanced LAS methodology that significantly mitigates noise-induced distortions in absorption spectra, yielding a more accurate representation of the original spectral sequence through continuous measurements. The proposed method is a novel architecture that integrates a Denoising Autoencoder (DAE) with a Long Short-Term Memory (LSTM) network for enhanced LAS signal analysis. Developed entirely using in situ experimental data, this approach ensures strong portability for industrial combustion diagnostics, where multi-source measurement noise is difficult to model or quantify. To validate the proposed method, we conducted a combustion experiment on an Auxiliary Power Unit (APU), a full-scale commercial gas turbine aero-engine, focusing on exhaust temperature measurements using our advanced LAS technique. The experimental results demonstrate the efficacy of the proposed method in recovering high-fidelity absorption spectra from the noise-contaminated data, enabling more convenient, accurate and stable APU exhaust temperature measurements with a standard deviation below 7.9 􀁱 C. This indicates its significant potential in industrial combustion diagnostics, offering a reliable tool for precise analysis and assessment in harsh environments.  \nIndex Terms— Laser absorption spectroscopy; Combustion diagnostics; Denoise Autoencoder; Long Short-Term Memory; Gas turbine.  \nI. INTRODUCTION  \nMeasurement  \nfor various  \nof reactive flow-field parameters is important industrial combustion processes, especially  \nfor diagnosis and performance evaluation of gas turbines and combustion engines. Laser absorption spectroscopy (LAS) [1- 3], benefiting from its high sensitivity, accuracy and speed, has been used as an effective diagnostic tool to provide real-world  \nThe authors would like to acknowledge the financial support from Engineering and Physical Sciences Research Council Programme Grant (EP/T012595/1), Platform Grant (EP/P001661/1), Impact Acceleration Account (PV120) and EU H2020 Cleansky2 (JTI-CS2-2017-CFP06-ENG-03- 16) . (Corresponding author: Chang Liu.)  \nY. Chen, J. Xia, R. Zhang, Y. Xia, Q. Zhou, Y. Fu, A. Upadhyay, H. McCann, C. Liu are with the School of Engineering, University of Edinburgh, Edinburgh EH9 3JL, U.K. ([e-mail: ](e-mail: C.Liu@ed.ac.uk)[C.Liu@ed.ac.uk](e-mail: C.Liu@ed.ac.uk)).  \ncombustion process parameters such as temperature [4-6], species concentrations [6-10] and velocity [11], which are critical to evaluate fuel efficiency, carbon and pollutant emissions, and combustion instability. It also facilitates understanding of underlying physics and chemical reactions [12], leading to development and optimization of new industrial combustors with better efficiency and stability when burning carbon-free and/or sustainable fuels.  \nTo improve the accuracy and robustness of LAS measurement, signal processing schemes, such as wavelength modulation [13] and cepstral analysis [14], have been rapidly developed in recent years. These improvements are aimed at extracting useful spectral information from noise-contaminated raw measurements, thus enabling better signal-to-noise ratio (SNR) of LAS measurement. Despite these efforts, the harsh industrial environments expose LAS im","cbCaisI9jkvTxK5Q","https://ap.wps.com/l/cbCaisI9jkvTxK5Q","pdf",1225881,1,9,"English","en",105,"# Abstract\n# Index Terms\n# Introduction\n## Background and motivation\n## Existing LAS noise-reduction approaches\n## Motivation for machine learning methods","[{\"question\":\"Why does laser absorption spectroscopy (LAS) face challenges in harsh combustion environments?\",\"answer\":\"Harsh conditions introduce noise and interference from sources that are difficult to model or quantify, which distorts the measured absorption spectra and increases uncertainty in gas-parameter estimation.\"},{\"question\":\"What machine learning architecture is proposed to enhance LAS signal analysis?\",\"answer\":\"The method combines a Denoising Autoencoder (DAE) with a Long Short-Term Memory (LSTM) network to reduce noise-induced distortions and better recover the original spectral sequence.\"},{\"question\":\"How was the proposed approach validated and what was the outcome?\",\"answer\":\"A combustion experiment was conducted on an Auxiliary Power Unit (APU) gas turbine aero-engine, targeting exhaust temperature measurements. Results demonstrate recovered high-fidelity absorption spectra from noise-contaminated data, supporting stable APU exhaust temperature measurements with deviation below 7.9°C.\"}]","Machine Learning-Enhanced Laser Absorption Spectroscopy for Harsh-Environment Combustion Diagnosis | PDF",1785676213,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-enhanced-laser-absorption-spectroscopy-for-harsh-environment-combustion-diagnosis","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-enhanced-laser-absorption-spectroscopy-for-harsh-environment-combustion-diagnosis/117492/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why does laser absorption spectroscopy (LAS) face challenges in harsh combustion environments?","Question",{"text":76,"@type":77},"Harsh conditions introduce noise and interference from sources that are difficult to model or quantify, which distorts the measured absorption spectra and increases uncertainty in gas-parameter estimation.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What machine learning architecture is proposed to enhance LAS signal analysis?",{"text":81,"@type":77},"The method combines a Denoising Autoencoder (DAE) with a Long Short-Term Memory (LSTM) network to reduce noise-induced distortions and better recover the original spectral sequence.",{"name":83,"@type":74,"acceptedAnswer":84},"How was the proposed approach validated and what was the outcome?",{"text":85,"@type":77},"A combustion experiment was conducted on an Auxiliary Power Unit (APU) gas turbine aero-engine, targeting exhaust temperature measurements. Results demonstrate recovered high-fidelity absorption spectra from noise-contaminated data, supporting stable APU exhaust temperature measurements with deviation below 7.9°C.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]