[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116937-en":3,"doc-seo-116937-105":30,"detail-sidebar-cat-0-en-105":91},{"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":27,"seo_description":14,"update_tm":28,"read_time":29},116937,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Tabular Machine Learning Methods for Predicting Gas Turbine Emissions","Gas turbine emissions monitoring is essential for controlling harmful pollutants such as nitrogen oxides (NOx) and carbon monoxide (CO) that affect both the environment and public health. This work evaluates machine learning approaches for emissions prediction by comparing an industry chemical kinetics baseline with two models built using SAINT and XGBoost. Experiments use a Siemens Energy gas turbine test-bed tabular dataset for training and validation. The study also examines how adding更多 features increases complexity while introducing more missing values.","arXiv :2307 .08386v 1 [ cs .LG] 17 Jul 2023  \nTabular Machine Learning Methods for Predicting Gas  \nTurbine Emissions  \nRebecca Pottsa , Rick Hackneyb and Georgios Leontidisca Department of Computing Science, University of Aberdeen, Aberdeen, AB24 3UE, UK  \n[r.potts.21@abdn.ac.uk](r.potts.21@abdn.ac.uk)  \nb Siemens Energy Industrial Turbomachinery Ltd. , Lincoln, LN6 3AD, UK  \n[richard.hackney@siemens-energy.com](richard.hackney@siemens-energy.com)  \nc Interdisciplinary Centre for Data and AI, University of Aberdeen, Aberdeen, AB24 3FX, UK  \n[georgios.leontidis@abdn.ac.uk](georgios.leontidis@abdn.ac.uk)  \nAbstract  \nPredicting emissions for gas turbines is critical for monitoring harmful pollutants being released into the atmosphere. In this study, we evaluate the performance of machine learning models for predicting emissions for gas turbines. We compare an existing predictive emissions model, a first principles-based Chemical Kinetics model [1], against two machine learning models we developed based on SAINT [2] and XGBoost [3], to demonstrate improved predictive performance of nitrogen oxides (NOx) and carbon monoxide (CO) using machine learning techniques. Our analysis utilises a Siemens Energy gas turbine test bed tabular dataset to train and validate the machine learning models. Additionally, we explore the trade-off between incorporating more features to enhance the model complexity, and the resulting presence of increased missing values in the dataset.  \nKeywords: gas turbines, machine learning, tabular data, transformers, PEMS, emissions  \n1. Introduction  \nGas turbines are widely employed in power generation and mechanical drive applications, but their use is associated with the production of harmful emissions, including nitrogen oxides (NOx) and carbon monoxide (CO), which pose environmental and health risks. Regulations have been implemented to limit emissions and require monitoring.  \nTo monitor emissions from gas turbines, a Continuous Emissions Monitoring System (CEMS) is commonly employed, which involves sampling gases and analysing their composition to quantify emissions. While CEMS can accurately measure emissions in real-time, it can lead to a high cost to the process owner, including requiring daily maintenance to avoid drift. As a result, CEMS may not always be properly maintained, leading to inaccurate or unreliable measurements.  \nPredictive emissions monitoring system (PEMS) models provide an alternative method of monitoring emissions that is cost-effective and requires minimal maintenance compared to CEMS, while not requiring the large physical space needed for CEMS gas analysis. PEMS is  \nUpdate Model  \nNew tree is added to the model. Weights of each tree are based on performance on training data  \nRepeat until performance stops  \nimproving, or predetermined  \nnumber of trees is reached  \nFigure 1: XGBoost initialisation, training, and prediction process.  \ntrained on historical data using process parameters such as temperatures and pressures, and uses real-time data to generate estimations for emissions.  \nTo develop a PEMS model, it is necessary to validate the model’s predictive accuracy using data with associated emissions values [4] . In our experiments, we used test bed tabular data consisting of tests conducted over a wide range of operating conditions to train our models. Gradient-boosted decision trees (GBDTs) such as XGBoost [3] and LightGBM [5] have demonstrated excellent performance in the tabular domain, and are widely regarded asthe standard solution for structured data problems.  \nPrevious studies comparing neural networks (NNs) and GBDTs for tabular regression have generally found that GBDTs match or outperform NN-based models, particularly when evaluated on datasets not documented in their original papers [6], while some NN-based methods are beginning to outperform GBDTs, such as SAINT [2] .  \nWe compare the predictive performance of an industry used Chemical Kinetics PEMS model [1], ","cbCainsFrRrn5bYs","https://ap.wps.com/l/cbCainsFrRrn5bYs","pdf",834808,1,23,"English","en",105,"# 1. Introduction\n## Continuous Emissions Monitoring Systems (CEMS) and PEMS\n## Motivation and validation for predictive monitoring\n# 2. Background\n## 2.1. Gradient-Boosted Decision Trees\n## 2.2. Attention and Transformers","[{\"question\":\"What models are compared for predicting gas turbine emissions?\",\"answer\":\"The study compares an industry chemical kinetics predictive emissions model as a baseline with two machine learning models developed using SAINT and XGBoost.\"},{\"question\":\"How are the machine learning models evaluated and trained?\",\"answer\":\"Models are trained and validated on a Siemens Energy gas turbine test-bed tabular dataset containing tests across a wide range of operating conditions with associated emissions values.\"},{\"question\":\"What trade-off does the study analyze regarding model features?\",\"answer\":\"The analysis explores the balance between using more features to increase model complexity and the resulting increase in missing values within the dataset.\"}]","Tabular Machine Learning Methods for Predicting Gas Turbine Emissions | PDF",1785672629,58,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"tabular-machine-learning-methods-for-predicting-gas-turbine-emissions","",{"@graph":36,"@context":85},[37,54,68],{"@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/tabular-machine-learning-methods-for-predicting-gas-turbine-emissions/116937/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What models are compared for predicting gas turbine emissions?","Question",{"text":75,"@type":76},"The study compares an industry chemical kinetics predictive emissions model as a baseline with two machine learning models developed using SAINT and XGBoost.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the machine learning models evaluated and trained?",{"text":80,"@type":76},"Models are trained and validated on a Siemens Energy gas turbine test-bed tabular dataset containing tests across a wide range of operating conditions with associated emissions values.",{"name":82,"@type":73,"acceptedAnswer":83},"What trade-off does the study analyze regarding model features?",{"text":84,"@type":76},"The analysis explores the balance between using more features to increase model complexity and the resulting increase in missing values within the dataset.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"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":106,"slug":138},19,"General","general"]