Incremental fast relevance vector regression model based multi-pollutant emission prediction of biomass cogeneration systems
Exact and trusty prediction of pollutant emissions is pivotal for optimal combustion control in biomass cogeneration systems, which possess multiple variables, high-volume data streams, and dynamic characteristics. Aiming at the multivariate dynamic systems, this paper extends a classical fast relev...
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| Published in: | Control engineering practice Vol. 149; p. 105986 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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01.08.2024
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| ISSN: | 0967-0661, 1873-6939 |
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| Abstract | Exact and trusty prediction of pollutant emissions is pivotal for optimal combustion control in biomass cogeneration systems, which possess multiple variables, high-volume data streams, and dynamic characteristics. Aiming at the multivariate dynamic systems, this paper extends a classical fast relevance vector regression (FRVR) algorithm into a multivariate form to accomplish synchronous multi-pollutant prediction. Meanwhile, a flexible and effective online training strategy is proposed to solve the problems of low accuracy of multi-step prediction and lack of dynamic updating capability. First, the given dataset is divided utilizing the k-means clustering method to enhance the clustering of similar features and expedite the prediction process. Then, the classical FRVR algorithm is extended into a multiple-output form, enabling the simultaneous prediction of multiple pollutant emissions. Moreover, the incremental learning method is introduced into the proposed multivariate FRVR model to improve its dynamic performance and online learning ability. Finally, the proposed method’s effectiveness is verified through a biomass cogeneration systems case. Experimental findings fully illustrate that the proposed method provides the lower RMSE and MAE while runtime decreases by 50% and R2 reaches 96%. The proposed method significantly outperforms others, showing excellent potential in the pollutant prediction field.
•An IMFRVR algorithm is proposed to predict multiple pollutant concentrations.•The k-means method cluster the data to extract useful information from the original data.•The MFRVR model is built by setting a Gaussian distribution to the FRVR weight matrix.•The incremental learning algorithm is employed to update prediction model dynamically. |
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| AbstractList | Exact and trusty prediction of pollutant emissions is pivotal for optimal combustion control in biomass cogeneration systems, which possess multiple variables, high-volume data streams, and dynamic characteristics. Aiming at the multivariate dynamic systems, this paper extends a classical fast relevance vector regression (FRVR) algorithm into a multivariate form to accomplish synchronous multi-pollutant prediction. Meanwhile, a flexible and effective online training strategy is proposed to solve the problems of low accuracy of multi-step prediction and lack of dynamic updating capability. First, the given dataset is divided utilizing the k-means clustering method to enhance the clustering of similar features and expedite the prediction process. Then, the classical FRVR algorithm is extended into a multiple-output form, enabling the simultaneous prediction of multiple pollutant emissions. Moreover, the incremental learning method is introduced into the proposed multivariate FRVR model to improve its dynamic performance and online learning ability. Finally, the proposed method’s effectiveness is verified through a biomass cogeneration systems case. Experimental findings fully illustrate that the proposed method provides the lower RMSE and MAE while runtime decreases by 50% and R2 reaches 96%. The proposed method significantly outperforms others, showing excellent potential in the pollutant prediction field.
•An IMFRVR algorithm is proposed to predict multiple pollutant concentrations.•The k-means method cluster the data to extract useful information from the original data.•The MFRVR model is built by setting a Gaussian distribution to the FRVR weight matrix.•The incremental learning algorithm is employed to update prediction model dynamically. |
| ArticleNumber | 105986 |
| Author | Zhao, Lianna He, Defeng Wang, Xiuli Sun, Zhifei Wu, Shaomin |
| Author_xml | – sequence: 1 givenname: Xiuli surname: Wang fullname: Wang, Xiuli organization: College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China – sequence: 2 givenname: Zhifei surname: Sun fullname: Sun, Zhifei organization: College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China – sequence: 3 givenname: Defeng surname: He fullname: He, Defeng email: hdfzj@zjut.edu.cn organization: College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China – sequence: 4 givenname: Shaomin surname: Wu fullname: Wu, Shaomin organization: Kent Business School, University of Kent, Canterbury, Kent CT2 7FS, United Kingdom – sequence: 5 givenname: Lianna surname: Zhao fullname: Zhao, Lianna organization: Information School, The University of Sheffield, Sheffield, South Yorkshire S10 2AH, United Kingdom |
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| CitedBy_id | crossref_primary_10_1016_j_apenergy_2024_124179 crossref_primary_10_1016_j_energy_2025_135049 crossref_primary_10_1016_j_energy_2025_138442 crossref_primary_10_1016_j_eswa_2025_127969 crossref_primary_10_1016_j_applthermaleng_2025_126709 crossref_primary_10_1002_qre_70075 |
| Cites_doi | 10.1016/j.isatra.2018.11.024 10.1109/TNNLS.2023.3290974 10.1016/j.applthermaleng.2017.06.104 10.1016/j.energy.2010.03.035 10.1016/j.fuel.2021.121975 10.1016/j.egyr.2019.09.012 10.1016/j.renene.2012.06.045 10.1016/j.energy.2015.11.020 10.1016/j.energy.2013.02.062 10.1109/TCST.2023.3287758 10.1109/TNNLS.2022.3201511 10.1016/j.rser.2014.07.181 10.1016/j.energy.2022.123339 10.1016/j.measurement.2014.11.031 10.1016/j.fuel.2020.117811 10.1016/j.automatica.2021.109930 10.1016/j.jmsy.2021.03.019 10.1016/j.engappai.2011.08.005 10.1109/TIM.2015.2411999 10.1016/j.patrec.2008.02.004 10.1016/j.fuel.2017.08.065 10.1016/j.energy.2019.116482 10.1109/TIE.2021.3114724 10.1016/j.energy.2019.116597 10.1109/TCYB.2021.3105453 |
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| Keywords | Extended fast relevance vector regression algorithm Pollutant emission prediction Incremental learning method k-means clustering method |
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| References | Feng, Wang, Wang, Li (b5) 2022; 135 Park, Park, Ryu, Baek, Kim, Park (b19) 2017; 124 Thayananthan, Navaratnam, Stenger, Torr, Cipolla (b23) 2008; 29 Nazari, Shahhoseini, Sohrabi-Kashani, Davari, Paydar, Delavar-Moghadam (b17) 2010; 35 Chen, Luo, Huang, Jiang, Kaynak (b4) 2023; 35 Li, Lu, Li, Yan (b12) 2015; 64 Tan, Xia, Zhang, Fang, Chen (b21) 2016; 94 Liu, Zhou, Pan, Peng, Peng (b15) 2015; 63 Xie, Gao, Zhang, Niu, Wang (b29) 2020; 190 Tipping, Faul (b25) 2003 Li, Xu, Chen, Jia, Ma (b14) 2022 Yang, Ma, Lv, Bai (b31) 2020; 274 Tipping (b24) 2001; 1 Lv, Liu, Yang, Zeng (b16) 2013; 55 Kang, Jeong, Farid, Hwang (b10) 2017; 210 Qiu (b20) 2013; 50 Chen, Liu, Alippi, Huang, Liu (b3) 2024; 35 Li, Fei, Jia, Lu, Kaynak, Zio (b11) 2024 Jia, Ma, Guo, Li (b8) 2021; 61 Nunes, Matias, Catalão (b18) 2014; 40 Bishop (b1) 2006; 2 Han, Li, Hossain, Qi, Zhang, Xu (b7) 2022; 308 Li, Wang, Lu, Jiang (b13) 2021; 52 Thayananthan, Navaratnam, Stenger, Torr, Cipolla (b22) 2006 Wang, Jiang, Lu (b27) 2019; 87 Chen, Huang (b2) 2023; 31 Xu, Chen, Liu, Wang, Chai, Zheng, Chen (b30) 2023; 72 Jin, Scherer, Sutanudjaja, Tukker, Behrens (b9) 2022; 245 Wang, Jiang, Wu, Lu, Ding (b28) 2021; 69 Yang, Wang, Li (b32) 2020; 192 Valente, Tarelho, Costa (b26) 2020; 6 Zhou, Zhao, Zheng, Wang, Cen (b33) 2012; 25 Guo, He (b6) 2022; 71 Tipping (10.1016/j.conengprac.2024.105986_b25) 2003 Yang (10.1016/j.conengprac.2024.105986_b32) 2020; 192 Feng (10.1016/j.conengprac.2024.105986_b5) 2022; 135 Liu (10.1016/j.conengprac.2024.105986_b15) 2015; 63 Chen (10.1016/j.conengprac.2024.105986_b4) 2023; 35 Tipping (10.1016/j.conengprac.2024.105986_b24) 2001; 1 Guo (10.1016/j.conengprac.2024.105986_b6) 2022; 71 Thayananthan (10.1016/j.conengprac.2024.105986_b23) 2008; 29 Chen (10.1016/j.conengprac.2024.105986_b2) 2023; 31 Nazari (10.1016/j.conengprac.2024.105986_b17) 2010; 35 Wang (10.1016/j.conengprac.2024.105986_b27) 2019; 87 Valente (10.1016/j.conengprac.2024.105986_b26) 2020; 6 Lv (10.1016/j.conengprac.2024.105986_b16) 2013; 55 Thayananthan (10.1016/j.conengprac.2024.105986_b22) 2006 Han (10.1016/j.conengprac.2024.105986_b7) 2022; 308 Li (10.1016/j.conengprac.2024.105986_b13) 2021; 52 Wang (10.1016/j.conengprac.2024.105986_b28) 2021; 69 Li (10.1016/j.conengprac.2024.105986_b14) 2022 Nunes (10.1016/j.conengprac.2024.105986_b18) 2014; 40 Jin (10.1016/j.conengprac.2024.105986_b9) 2022; 245 Xie (10.1016/j.conengprac.2024.105986_b29) 2020; 190 Kang (10.1016/j.conengprac.2024.105986_b10) 2017; 210 Jia (10.1016/j.conengprac.2024.105986_b8) 2021; 61 Qiu (10.1016/j.conengprac.2024.105986_b20) 2013; 50 Xu (10.1016/j.conengprac.2024.105986_b30) 2023; 72 Chen (10.1016/j.conengprac.2024.105986_b3) 2024; 35 Li (10.1016/j.conengprac.2024.105986_b11) 2024 Tan (10.1016/j.conengprac.2024.105986_b21) 2016; 94 Bishop (10.1016/j.conengprac.2024.105986_b1) 2006; 2 Zhou (10.1016/j.conengprac.2024.105986_b33) 2012; 25 Li (10.1016/j.conengprac.2024.105986_b12) 2015; 64 Park (10.1016/j.conengprac.2024.105986_b19) 2017; 124 Yang (10.1016/j.conengprac.2024.105986_b31) 2020; 274 |
| References_xml | – volume: 64 start-page: 2409 year: 2015 end-page: 2416 ident: b12 article-title: Prediction of pollutant emissions of biomass flames through digital imaging, contourlet transform, and support vector regression modeling publication-title: IEEE Transactions on Instrumentation and Measurement – volume: 63 start-page: 143 year: 2015 end-page: 151 ident: b15 article-title: Lithium-ion battery remaining useful life estimation with an optimized relevance vector machine algorithm with incremental learning publication-title: Measurement – volume: 192 year: 2020 ident: b32 article-title: Prediction of the NOx emissions from thermal power plant using long-short term memory neural network publication-title: Energy – volume: 190 year: 2020 ident: b29 article-title: Dynamic modeling for NOx emission sequence prediction of SCR system outlet based on sequence to sequence long short-term memory network publication-title: Energy – volume: 55 start-page: 319 year: 2013 end-page: 329 ident: b16 article-title: A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler publication-title: Energy – volume: 1 start-page: 211 year: 2001 end-page: 244 ident: b24 article-title: Sparse Bayesian learning and the relevance vector machine publication-title: Journal of Machine Learning Research – volume: 35 start-page: 6166 year: 2024 end-page: 6179 ident: b3 article-title: Explainable intelligent fault diagnosis for nonlinear dynamic systems: From unsupervised to supervised learning publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 308 year: 2022 ident: b7 article-title: An ensemble deep learning model for exhaust emissions prediction of heavy oil-fired boiler combustion publication-title: Fuel – volume: 87 start-page: 217 year: 2019 end-page: 224 ident: b27 article-title: Multivariate relevance vector regression based degradation modeling and remaining useful life prediction publication-title: ISA Transcations – volume: 35 start-page: 2969 year: 2023 end-page: 2983 ident: b4 article-title: Transfer learning-motivated intelligent fault diagnosis designs: A survey, insights, and perspectives publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 245 year: 2022 ident: b9 article-title: Climate change and CCS increase the water vulnerability of China’s thermoelectric power fleet publication-title: Energy – volume: 124 start-page: 1247 year: 2017 end-page: 1256 ident: b19 article-title: CFD analysis on bioliquid co-firing with heavy fuel oil in a 400 MWe power plant with a wall-firing boiler publication-title: Applied Thermal Engineering – start-page: 1 year: 2024 end-page: 11 ident: b11 article-title: Novel outlier-robust accelerated degradation testing model and lifetime analysis method considering time-stress-dependent factors publication-title: IEEE Transactions on Industrial Informatics – start-page: 124 year: 2006 end-page: 138 ident: b22 article-title: Multivariate relevance vector machines for tracking publication-title: Computer vision–ECCV 2006: 9th European conference on computer vision, Graz, Austria, May 7–13, 2006, proceedings, part III 9 – volume: 274 year: 2020 ident: b31 article-title: Real-time dynamic prediction model of NOx emission of coal-fired boilers under variable load conditions publication-title: Fuel – volume: 71 start-page: 1 year: 2022 end-page: 14 ident: b6 article-title: An integrated method for bearing state change identification and prognostics based on improved relevance vector machine and degradation model publication-title: IEEE Transactions on Instrumentation and Measurement – volume: 29 start-page: 1302 year: 2008 end-page: 1310 ident: b23 article-title: Pose estimation and tracking using multivariate regression publication-title: Pattern Recognition Letters – volume: 69 start-page: 9514 year: 2021 end-page: 9523 ident: b28 article-title: Multivariate relevance vector regression based degradation modeling and remaining useful life prediction publication-title: IEEE Transactions on Industrial Electronics – volume: 31 start-page: 2805 year: 2023 end-page: 2818 ident: b2 article-title: Fault-tolerant soft sensors for dynamic systems publication-title: IEEE Transactions on Control Systems Technology – volume: 61 start-page: 773 year: 2021 end-page: 781 ident: b8 article-title: A sample entropy based prognostics method for lithium-ion batteries using relevance vector machine publication-title: Journal of Manufacturing Systems – start-page: 276 year: 2003 end-page: 283 ident: b25 article-title: Fast marginal likelihood maximisation for sparse Bayesian models publication-title: International workshop on artificial intelligence and statistics – volume: 210 start-page: 282 year: 2017 end-page: 289 ident: b10 article-title: Effect of staged combustion on low NOx emission from an industrial-scale fuel oil combustor in South Korea publication-title: Fuel – volume: 2 start-page: 1122 year: 2006 end-page: 1128 ident: b1 article-title: Pattern recognition and machine learning publication-title: Springer Google Schola – volume: 135 year: 2022 ident: b5 article-title: Backstepping-based distributed abnormality localization for linear parabolic distributed parameter systems publication-title: Automatica – volume: 6 start-page: 483 year: 2020 end-page: 489 ident: b26 article-title: Emissions mitigation by control of biomass feeding in an industrial biomass boiler publication-title: Energy Reports – volume: 52 start-page: 13168 year: 2021 end-page: 13180 ident: b13 article-title: Conditional joint distribution-based test selection for fault detection and isolation publication-title: IEEE Transactions on Cybernetics – volume: 40 start-page: 153 year: 2014 end-page: 160 ident: b18 article-title: A review on torrefied biomass pellets as a sustainable alternative to coal in power generation publication-title: Renewable and Sustainable Energy Reviews – start-page: 7742 year: 2022 end-page: 7750 ident: b14 article-title: A general degradation process of useful life analysis under unreliable signals for accelerated degradation testing publication-title: IEEE Transactions on Industrial Informatics – volume: 50 start-page: 94 year: 2013 end-page: 102 ident: b20 article-title: Testing of flue gas emissions of a biomass pellet boiler and abatement of particle emissions publication-title: Renewable Energy – volume: 72 start-page: 1 year: 2023 end-page: 15 ident: b30 article-title: A novel adaptive SMO-based simultaneous diagnosis method for igbt open-circuit faults and current sensor incipient faults of inverters in pmsm drives for electric vehicles publication-title: IEEE Transactions on Instrumentation and Measurement – volume: 94 start-page: 672 year: 2016 end-page: 679 ident: b21 article-title: Modeling and reduction of NOX emissions for a 700 MW coal-fired boiler with the advanced machine learning method publication-title: Energy – volume: 35 start-page: 2992 year: 2010 end-page: 2998 ident: b17 article-title: Experimental determination and analysis of CO2, SO2 and NOx emission factors in Iran’s thermal power plants publication-title: Energy – volume: 25 start-page: 147 year: 2012 end-page: 158 ident: b33 article-title: Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization publication-title: Engineering Applications of Artificial Intelligence – volume: 87 start-page: 217 year: 2019 ident: 10.1016/j.conengprac.2024.105986_b27 article-title: Multivariate relevance vector regression based degradation modeling and remaining useful life prediction publication-title: ISA Transcations doi: 10.1016/j.isatra.2018.11.024 – volume: 35 start-page: 2969 issue: 3 year: 2023 ident: 10.1016/j.conengprac.2024.105986_b4 article-title: Transfer learning-motivated intelligent fault diagnosis designs: A survey, insights, and perspectives publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2023.3290974 – volume: 124 start-page: 1247 year: 2017 ident: 10.1016/j.conengprac.2024.105986_b19 article-title: CFD analysis on bioliquid co-firing with heavy fuel oil in a 400 MWe power plant with a wall-firing boiler publication-title: Applied Thermal Engineering doi: 10.1016/j.applthermaleng.2017.06.104 – volume: 35 start-page: 2992 issue: 7 year: 2010 ident: 10.1016/j.conengprac.2024.105986_b17 article-title: Experimental determination and analysis of CO2, SO2 and NOx emission factors in Iran’s thermal power plants publication-title: Energy doi: 10.1016/j.energy.2010.03.035 – volume: 1 start-page: 211 issue: Jun year: 2001 ident: 10.1016/j.conengprac.2024.105986_b24 article-title: Sparse Bayesian learning and the relevance vector machine publication-title: Journal of Machine Learning Research – volume: 308 year: 2022 ident: 10.1016/j.conengprac.2024.105986_b7 article-title: An ensemble deep learning model for exhaust emissions prediction of heavy oil-fired boiler combustion publication-title: Fuel doi: 10.1016/j.fuel.2021.121975 – start-page: 1 year: 2024 ident: 10.1016/j.conengprac.2024.105986_b11 article-title: Novel outlier-robust accelerated degradation testing model and lifetime analysis method considering time-stress-dependent factors publication-title: IEEE Transactions on Industrial Informatics – start-page: 276 year: 2003 ident: 10.1016/j.conengprac.2024.105986_b25 article-title: Fast marginal likelihood maximisation for sparse Bayesian models – volume: 6 start-page: 483 year: 2020 ident: 10.1016/j.conengprac.2024.105986_b26 article-title: Emissions mitigation by control of biomass feeding in an industrial biomass boiler publication-title: Energy Reports doi: 10.1016/j.egyr.2019.09.012 – volume: 50 start-page: 94 year: 2013 ident: 10.1016/j.conengprac.2024.105986_b20 article-title: Testing of flue gas emissions of a biomass pellet boiler and abatement of particle emissions publication-title: Renewable Energy doi: 10.1016/j.renene.2012.06.045 – volume: 94 start-page: 672 year: 2016 ident: 10.1016/j.conengprac.2024.105986_b21 article-title: Modeling and reduction of NOX emissions for a 700 MW coal-fired boiler with the advanced machine learning method publication-title: Energy doi: 10.1016/j.energy.2015.11.020 – volume: 55 start-page: 319 year: 2013 ident: 10.1016/j.conengprac.2024.105986_b16 article-title: A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler publication-title: Energy doi: 10.1016/j.energy.2013.02.062 – volume: 31 start-page: 2805 issue: 6 year: 2023 ident: 10.1016/j.conengprac.2024.105986_b2 article-title: Fault-tolerant soft sensors for dynamic systems publication-title: IEEE Transactions on Control Systems Technology doi: 10.1109/TCST.2023.3287758 – volume: 35 start-page: 6166 issue: 5 year: 2024 ident: 10.1016/j.conengprac.2024.105986_b3 article-title: Explainable intelligent fault diagnosis for nonlinear dynamic systems: From unsupervised to supervised learning publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2022.3201511 – volume: 40 start-page: 153 year: 2014 ident: 10.1016/j.conengprac.2024.105986_b18 article-title: A review on torrefied biomass pellets as a sustainable alternative to coal in power generation publication-title: Renewable and Sustainable Energy Reviews doi: 10.1016/j.rser.2014.07.181 – volume: 2 start-page: 1122 year: 2006 ident: 10.1016/j.conengprac.2024.105986_b1 article-title: Pattern recognition and machine learning publication-title: Springer Google Schola – volume: 245 year: 2022 ident: 10.1016/j.conengprac.2024.105986_b9 article-title: Climate change and CCS increase the water vulnerability of China’s thermoelectric power fleet publication-title: Energy doi: 10.1016/j.energy.2022.123339 – volume: 71 start-page: 1 year: 2022 ident: 10.1016/j.conengprac.2024.105986_b6 article-title: An integrated method for bearing state change identification and prognostics based on improved relevance vector machine and degradation model publication-title: IEEE Transactions on Instrumentation and Measurement – volume: 63 start-page: 143 year: 2015 ident: 10.1016/j.conengprac.2024.105986_b15 article-title: Lithium-ion battery remaining useful life estimation with an optimized relevance vector machine algorithm with incremental learning publication-title: Measurement doi: 10.1016/j.measurement.2014.11.031 – volume: 274 year: 2020 ident: 10.1016/j.conengprac.2024.105986_b31 article-title: Real-time dynamic prediction model of NOx emission of coal-fired boilers under variable load conditions publication-title: Fuel doi: 10.1016/j.fuel.2020.117811 – volume: 72 start-page: 1 year: 2023 ident: 10.1016/j.conengprac.2024.105986_b30 article-title: A novel adaptive SMO-based simultaneous diagnosis method for igbt open-circuit faults and current sensor incipient faults of inverters in pmsm drives for electric vehicles publication-title: IEEE Transactions on Instrumentation and Measurement – start-page: 124 year: 2006 ident: 10.1016/j.conengprac.2024.105986_b22 article-title: Multivariate relevance vector machines for tracking – volume: 135 year: 2022 ident: 10.1016/j.conengprac.2024.105986_b5 article-title: Backstepping-based distributed abnormality localization for linear parabolic distributed parameter systems publication-title: Automatica doi: 10.1016/j.automatica.2021.109930 – volume: 61 start-page: 773 year: 2021 ident: 10.1016/j.conengprac.2024.105986_b8 article-title: A sample entropy based prognostics method for lithium-ion batteries using relevance vector machine publication-title: Journal of Manufacturing Systems doi: 10.1016/j.jmsy.2021.03.019 – volume: 25 start-page: 147 issue: 1 year: 2012 ident: 10.1016/j.conengprac.2024.105986_b33 article-title: Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2011.08.005 – volume: 64 start-page: 2409 issue: 9 year: 2015 ident: 10.1016/j.conengprac.2024.105986_b12 article-title: Prediction of pollutant emissions of biomass flames through digital imaging, contourlet transform, and support vector regression modeling publication-title: IEEE Transactions on Instrumentation and Measurement doi: 10.1109/TIM.2015.2411999 – volume: 29 start-page: 1302 issue: 9 year: 2008 ident: 10.1016/j.conengprac.2024.105986_b23 article-title: Pose estimation and tracking using multivariate regression publication-title: Pattern Recognition Letters doi: 10.1016/j.patrec.2008.02.004 – volume: 210 start-page: 282 year: 2017 ident: 10.1016/j.conengprac.2024.105986_b10 article-title: Effect of staged combustion on low NOx emission from an industrial-scale fuel oil combustor in South Korea publication-title: Fuel doi: 10.1016/j.fuel.2017.08.065 – volume: 190 year: 2020 ident: 10.1016/j.conengprac.2024.105986_b29 article-title: Dynamic modeling for NOx emission sequence prediction of SCR system outlet based on sequence to sequence long short-term memory network publication-title: Energy doi: 10.1016/j.energy.2019.116482 – start-page: 7742 year: 2022 ident: 10.1016/j.conengprac.2024.105986_b14 article-title: A general degradation process of useful life analysis under unreliable signals for accelerated degradation testing publication-title: IEEE Transactions on Industrial Informatics – volume: 69 start-page: 9514 issue: 9 year: 2021 ident: 10.1016/j.conengprac.2024.105986_b28 article-title: Multivariate relevance vector regression based degradation modeling and remaining useful life prediction publication-title: IEEE Transactions on Industrial Electronics doi: 10.1109/TIE.2021.3114724 – volume: 192 year: 2020 ident: 10.1016/j.conengprac.2024.105986_b32 article-title: Prediction of the NOx emissions from thermal power plant using long-short term memory neural network publication-title: Energy doi: 10.1016/j.energy.2019.116597 – volume: 52 start-page: 13168 issue: 12 year: 2021 ident: 10.1016/j.conengprac.2024.105986_b13 article-title: Conditional joint distribution-based test selection for fault detection and isolation publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2021.3105453 |
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| SubjectTerms | Extended fast relevance vector regression algorithm Incremental learning method k-means clustering method Pollutant emission prediction |
| Title | Incremental fast relevance vector regression model based multi-pollutant emission prediction of biomass cogeneration systems |
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