An investigation on data mining and operating optimization for wet flue gas desulfurization systems
•The optimal target database of operating conditions for WFGD was investigated.•An improved fuzzy clustering (IFC) algorithm was proposed for data clustering.•L-G ratio, pH and slurry density were obtained to minimize unit SO2 removal cost.•An overall continuous optimal operation database was establ...
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| Veröffentlicht in: | Fuel (Guildford) Jg. 258; S. 116178 |
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15.12.2019
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| ISSN: | 0016-2361, 1873-7153 |
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| Abstract | •The optimal target database of operating conditions for WFGD was investigated.•An improved fuzzy clustering (IFC) algorithm was proposed for data clustering.•L-G ratio, pH and slurry density were obtained to minimize unit SO2 removal cost.•An overall continuous optimal operation database was established.
Finding the best conditions for current operation from historical data is of great meaning for power plants. In order to obtain the optimal conditions, a comprehensive evaluation criterion, using the minimum cost as an objective function, was established for a wet flue gas desulfurization (WFGD) system in this paper. A basic procedure was presented to set up the database of the system operating target conditions. To improve the accuracy of mining target data, an improved fuzzy clustering (IFC) algorithm was proposed. This algorithm used K-means results as initial conditions and fuzzy C-means algorithm as analytical method. Besides, the information entropy was also utilized in the IFC algorithm as an evaluation index. Results indicated that the proposed algorithm was more accurate than typical K-means and fuzzy C-means in data clustering. Additionally, the operating data for the WFGD system of a 600 MW unit were selected as the modeling samples. In this model, the unit SO2 removal cost was selected as the criterion for the assessment of operating conditions. The operating condition data were divided into different operating clusters based on the unit load and inlet SO2 concentration of the WFGD system. Using pH, liquid-gas ratio, and slurry density as an initial condition, optimal steady-state operating data were obtained. Finally, an overall operation database of this system was established, which could successfully obtain the continuous optimal operating conditions and provide operating guidance. |
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| AbstractList | •The optimal target database of operating conditions for WFGD was investigated.•An improved fuzzy clustering (IFC) algorithm was proposed for data clustering.•L-G ratio, pH and slurry density were obtained to minimize unit SO2 removal cost.•An overall continuous optimal operation database was established.
Finding the best conditions for current operation from historical data is of great meaning for power plants. In order to obtain the optimal conditions, a comprehensive evaluation criterion, using the minimum cost as an objective function, was established for a wet flue gas desulfurization (WFGD) system in this paper. A basic procedure was presented to set up the database of the system operating target conditions. To improve the accuracy of mining target data, an improved fuzzy clustering (IFC) algorithm was proposed. This algorithm used K-means results as initial conditions and fuzzy C-means algorithm as analytical method. Besides, the information entropy was also utilized in the IFC algorithm as an evaluation index. Results indicated that the proposed algorithm was more accurate than typical K-means and fuzzy C-means in data clustering. Additionally, the operating data for the WFGD system of a 600 MW unit were selected as the modeling samples. In this model, the unit SO2 removal cost was selected as the criterion for the assessment of operating conditions. The operating condition data were divided into different operating clusters based on the unit load and inlet SO2 concentration of the WFGD system. Using pH, liquid-gas ratio, and slurry density as an initial condition, optimal steady-state operating data were obtained. Finally, an overall operation database of this system was established, which could successfully obtain the continuous optimal operating conditions and provide operating guidance. Finding the best conditions for current operation from historical data is of great meaning for power plants. In order to obtain the optimal conditions, a comprehensive evaluation criterion, using the minimum cost as an objective function, was established for a wet flue gas desulfurization (WFGD) system in this paper. A basic procedure was presented to set up the database of the system operating target conditions. To improve the accuracy of mining target data, an improved fuzzy clustering (IFC) algorithm was proposed. This algorithm used K-means results as initial conditions and fuzzy C-means algorithm as analytical method. Besides, the information entropy was also utilized in the IFC algorithm as an evaluation index. Results indicated that the proposed algorithm was more accurate than typical K-means and fuzzy C-means in data clustering. Additionally, the operating data for the WFGD system of a 600 MW unit were selected as the modeling samples. In this model, the unit SO2 removal cost was selected as the criterion for the assessment of operating conditions. The operating condition data were divided into different operating clusters based on the unit load and inlet SO2 concentration of the WFGD system. Using pH, liquid-gas ratio, and slurry density as an initial condition, optimal steady-state operating data were obtained. Finally, an overall operation database of this system was established, which could successfully obtain the continuous optimal operating conditions and provide operating guidance. |
| ArticleNumber | 116178 |
| Author | Tang, Youfei Wang, Xingchao Qiao, Zongliang Gu, Hui Si, Fengqi Romero, Carlos E. Yao, XueZhong |
| Author_xml | – sequence: 1 givenname: Zongliang surname: Qiao fullname: Qiao, Zongliang email: qiaozongliang@seu.edu.cn organization: Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, China – sequence: 2 givenname: Xingchao orcidid: 0000-0002-6136-7717 surname: Wang fullname: Wang, Xingchao organization: Energy Research Center, Lehigh University, Bethlehem, PA 18015, USA – sequence: 3 givenname: Hui surname: Gu fullname: Gu, Hui organization: School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China – sequence: 4 givenname: Youfei surname: Tang fullname: Tang, Youfei organization: Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, China – sequence: 5 givenname: Fengqi surname: Si fullname: Si, Fengqi organization: Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, China – sequence: 6 givenname: Carlos E. surname: Romero fullname: Romero, Carlos E. organization: Energy Research Center, Lehigh University, Bethlehem, PA 18015, USA – sequence: 7 givenname: XueZhong surname: Yao fullname: Yao, XueZhong organization: Datang Environment Industry Group Co., Ltd., Nanjing 211106, China |
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| Cites_doi | 10.1016/j.fuproc.2017.03.024 10.1016/j.energy.2018.01.175 10.1016/j.fuproc.2014.08.009 10.1016/j.apenergy.2014.05.006 10.1016/j.apcata.2015.10.008 10.1016/0098-3004(84)90020-7 10.1016/j.fuproc.2008.04.004 10.1016/j.patrec.2019.02.017 10.1016/j.fuproc.2010.07.020 10.1515/eletel-2017-0046 10.1016/j.patcog.2017.06.023 10.1016/j.dsp.2019.04.004 10.1016/j.fss.2015.06.024 10.1016/j.engappai.2016.08.009 10.1016/j.knosys.2018.09.007 10.1016/j.applthermaleng.2019.02.032 10.1016/j.ces.2016.12.062 10.1016/j.jprocont.2016.01.002 10.1016/j.eswa.2015.08.036 10.1021/es304090e 10.1016/j.joei.2014.07.003 10.1016/j.jmva.2018.12.008 10.3390/e16073732 10.1016/j.bdr.2018.05.002 10.1016/j.joei.2014.09.002 10.1016/j.fuproc.2016.01.033 10.1016/j.cej.2016.01.020 10.1016/j.patcog.2019.04.014 10.1016/j.ins.2015.03.062 10.1016/j.eswa.2017.09.005 10.1016/j.engfracmech.2018.07.005 10.1016/j.mineng.2015.11.011 |
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| References | Chen, Wang, Zhuo (b0055) 2017; 162 Subashini, Sahoo, Sunil (b0125) 2016; 43 1990. Blasio, Carletti, Lundell (b0025) 2016; 86 Falniowski (b0145) 2014; 16 Jin, Weng (b0130) 2019; 90 Kallinikos, Farsari, Spartinos (b0045) 2010; 91 Zahra, Ghazanfar, Khalid (b0175) 2015; 320 Wang, Zhu, Zhang (b0050) 2015; 88 Viattchenin, Yaroma (b0155) 2017; 63 Gu, Cui, Zhu (b0080) 2018; 148 Wang, Wang, Xu (b0060) 2015; 508 Zheng, Xu, Zhang (b0005) 2014; 129 Tripathi, Sharma, Bala (b0095) 2018; 14 Klawonn, Kruse, Winkler (b0170) 2015; 281 Bao, Yang, Yan (b0030) 2010; 108 Fränti, Sieranoja (b0085) 2019; 93 Wang, Zhuang, Dai (b0065) 2017; 162 Huang, Ran, Hao (b0020) 2016; 289 Tunckaya, Koklukaya (b0070) 2015; 88 Meilă (b0100) 2019; 173 Islam, Estivill-Castro, Rahman (b0090) 2018; 91 Dotto, Farcomeni, Garca-Escudero (b0160) 2016; 11 Bai, Cheng, Liang (b0105) 2017; 71 Zhong, Gao, Huo (b0040) 2008; 89 Behnia, Chai, GhasemiGol (b0135) 2019; 210 Raquel, Mercedes, Rosa (b0015) 2013; 47 Al-Maliki, Alobaid, Kez (b0075) 2016; 39 Thong, Le (b0165) 2016; 56 Bezdek, Ehrlich, Full (b0180) 1984; 10 Altun (b0035) 2014; 128 Zhao, Liang, Dang (b0115) 2019; 163 Verma, Gupta, Kumar (b0110) 2019; 122 Xu, Gu, Ma (b0120) 2019; 151 Wu, Yang, Yan (b0010) 2016; 145 UCI machine learning repository: Lenses data set Wang (10.1016/j.fuel.2019.116178_b0065) 2017; 162 Wu (10.1016/j.fuel.2019.116178_b0010) 2016; 145 Huang (10.1016/j.fuel.2019.116178_b0020) 2016; 289 Viattchenin (10.1016/j.fuel.2019.116178_b0155) 2017; 63 Zhao (10.1016/j.fuel.2019.116178_b0115) 2019; 163 Wang (10.1016/j.fuel.2019.116178_b0050) 2015; 88 Klawonn (10.1016/j.fuel.2019.116178_b0170) 2015; 281 Tripathi (10.1016/j.fuel.2019.116178_b0095) 2018; 14 Tunckaya (10.1016/j.fuel.2019.116178_b0070) 2015; 88 Subashini (10.1016/j.fuel.2019.116178_b0125) 2016; 43 Jin (10.1016/j.fuel.2019.116178_b0130) 2019; 90 Thong (10.1016/j.fuel.2019.116178_b0165) 2016; 56 Dotto (10.1016/j.fuel.2019.116178_b0160) 2016; 11 Al-Maliki (10.1016/j.fuel.2019.116178_b0075) 2016; 39 Blasio (10.1016/j.fuel.2019.116178_b0025) 2016; 86 Bao (10.1016/j.fuel.2019.116178_b0030) 2010; 108 Bai (10.1016/j.fuel.2019.116178_b0105) 2017; 71 Verma (10.1016/j.fuel.2019.116178_b0110) 2019; 122 Bezdek (10.1016/j.fuel.2019.116178_b0180) 1984; 10 Behnia (10.1016/j.fuel.2019.116178_b0135) 2019; 210 Raquel (10.1016/j.fuel.2019.116178_b0015) 2013; 47 Gu (10.1016/j.fuel.2019.116178_b0080) 2018; 148 Xu (10.1016/j.fuel.2019.116178_b0120) 2019; 151 Altun (10.1016/j.fuel.2019.116178_b0035) 2014; 128 Zhong (10.1016/j.fuel.2019.116178_b0040) 2008; 89 Meilă (10.1016/j.fuel.2019.116178_b0100) 2019; 173 Islam (10.1016/j.fuel.2019.116178_b0090) 2018; 91 Zahra (10.1016/j.fuel.2019.116178_b0175) 2015; 320 Wang (10.1016/j.fuel.2019.116178_b0060) 2015; 508 Zheng (10.1016/j.fuel.2019.116178_b0005) 2014; 129 Kallinikos (10.1016/j.fuel.2019.116178_b0045) 2010; 91 Falniowski (10.1016/j.fuel.2019.116178_b0145) 2014; 16 10.1016/j.fuel.2019.116178_b0185 Fränti (10.1016/j.fuel.2019.116178_b0085) 2019; 93 Chen (10.1016/j.fuel.2019.116178_b0055) 2017; 162 |
| References_xml | – volume: 88 start-page: 118 year: 2015 end-page: 125 ident: b0070 article-title: Comparative analysis and prediction study for effluent gas emissions in a coal-fired thermal power plant using artificial intelligence and statistical tools publication-title: J Energy Inst – volume: 91 start-page: 402 year: 2018 end-page: 417 ident: b0090 article-title: Combining K-Means and a genetic algorithm through a novel arrangement of genetic operators for high quality clustering publication-title: Expert Syst Appl – reference: UCI machine learning repository: Lenses data set, – volume: 71 start-page: 375 year: 2017 end-page: 386 ident: b0105 article-title: Fast density clustering strategies based on the k-means algorithm publication-title: Pattern Recogn – volume: 89 start-page: 1025 year: 2008 end-page: 1032 ident: b0040 article-title: A model for performance optimization of wet flue gas desulfurization systems of power plants publication-title: Fuel Process Technol – volume: 128 start-page: 461 year: 2014 end-page: 470 ident: b0035 article-title: Assessment of marble waste utilization as an alternative sorbent to limestone for so 2 control publication-title: Fuel Process Technol – volume: 148 start-page: 789 year: 2018 end-page: 801 ident: b0080 article-title: A new approach for clustering in desulfurization system based on modified framework for gypsum slurry quality monitoring publication-title: Energy – volume: 210 start-page: 212 year: 2019 end-page: 227 ident: b0135 article-title: Advanced damage detection technique by integration of unsupervised clustering into acoustic emission publication-title: Eng Fract Mech – volume: 122 start-page: 45 year: 2019 end-page: 52 ident: b0110 article-title: A modified intuitionistic fuzzy c-means algorithm incorporating hesitation degree publication-title: Pattern Recogn Lett – volume: 108 start-page: 73 year: 2010 end-page: 79 ident: b0030 article-title: Experimental study of fine particles removal in the desulfurized scrubbing flue gas publication-title: Fuel – volume: 56 start-page: 121 year: 2016 end-page: 130 ident: b0165 article-title: Picture fuzzy clustering for complex data publication-title: Eng Appl Artif Intell – volume: 16 start-page: 3732 year: 2014 end-page: 3753 ident: b0145 article-title: On the connections of generalized entropies with shannon and kolmogorov-sinai publication-title: Entropy – reference: , 1990. – volume: 162 start-page: 1 year: 2017 end-page: 12 ident: b0055 article-title: Experimental and numerical study on effects of defectors on flow field distribution and desulfurization efficiency in spray towers publication-title: Fuel Process Technol – volume: 162 start-page: 227 year: 2017 end-page: 244 ident: b0065 article-title: Synergistic effect of droplet self-adjustment and rod bank internal on fluid distribution in a wfgd spray column publication-title: Chem Eng Sci – volume: 163 start-page: 416 year: 2019 end-page: 428 ident: b0115 article-title: A stratified sampling based clustering algorithm for large-scale data publication-title: Knowl-Based Syst – volume: 63 start-page: 341 year: 2017 end-page: 346 ident: b0155 article-title: A method for estimating the least number of objects in fuzzy clusters publication-title: Int. J. Electron Telecommun – volume: 508 start-page: 52 year: 2015 end-page: 60 ident: b0060 article-title: Selectivity of transition metal catalysts in promoting the oxidation of solid sulfites in flue gas desulfurization publication-title: Appl Catal A – volume: 145 start-page: 116 year: 2016 end-page: 122 ident: b0010 article-title: Improving the removal of ne particles by heterogeneous condensation during wfgd processes publication-title: Fuel Process Technol – volume: 93 start-page: 95 year: 2019 end-page: 112 ident: b0085 article-title: How much can k-means be improved by using better initialization and repeats? publication-title: Pattern Recogn – volume: 289 start-page: 537 year: 2016 end-page: 543 ident: b0020 article-title: Investigation on the removal of so3 in ammonia-based wfgd system publication-title: Chem Eng J – volume: 129 start-page: 187 year: 2014 end-page: 194 ident: b0005 article-title: Nitrogen oxide absorption and nitrite/nitrate formation in limestone slurry for wfgd system publication-title: Appl Energy – volume: 90 start-page: 100 year: 2019 end-page: 109 ident: b0130 article-title: A robust active contour model driven by fuzzy c-means energy for fast image segmentation publication-title: Digital Signal Process – volume: 43 start-page: 186 year: 2016 end-page: 196 ident: b0125 article-title: A non-invasive methodology for the grade identification of astrocytoma using image processing and artificial intelligence techniques publication-title: Expert Syst Appl – volume: 91 start-page: 1794 year: 2010 end-page: 1802 ident: b0045 article-title: Simulation of the operation of an industrial wet flue gas desulfurization system publication-title: Fuel Process Technol – volume: 88 start-page: 284 year: 2015 end-page: 291 ident: b0050 article-title: Numerical simulation research of flow field in ammonia-based wet flue gas desulfurization tower publication-title: J Energy Inst – volume: 151 start-page: 344 year: 2019 end-page: 353 ident: b0120 article-title: Data based online operational performance optimization with varying work conditions for steam-turbine system publication-title: Appl Therm Eng – volume: 11 start-page: 1 year: 2016 end-page: 20 ident: b0160 article-title: A fuzzy approach to robust regression clustering publication-title: Adv Data Anal Classif – volume: 320 start-page: 156 year: 2015 end-page: 189 ident: b0175 article-title: Novel centroid selection approaches for k means-clustering based recommender systems publication-title: Inf Sci Int J – volume: 10 start-page: 191 year: 1984 end-page: 203 ident: b0180 article-title: Fcm: The fuzzy c -means clustering algorithm publication-title: Comput Geosci – volume: 86 start-page: 43 year: 2016 end-page: 58 ident: b0025 article-title: Employing a step-wise titration method under semi-slow reaction regime for evaluating the reactivity of limestone and dolomite in acidic environment publication-title: Miner Eng – volume: 173 start-page: 1 year: 2019 end-page: 17 ident: b0100 article-title: Good (K-means) clusterings are unique (up to small perturbations) publication-title: J. Multivariate Anal – volume: 47 start-page: 2974 year: 2013 end-page: 2981 ident: b0015 article-title: Influence of limestone characteristics on mercury reemission in wfgd systems publication-title: Environ Sci Technol – volume: 14 start-page: 93 year: 2018 end-page: 100 ident: b0095 article-title: A novel clustering method using enhanced grey wolf optimizer and mapreduce publication-title: Big Data Res – volume: 281 start-page: 272 year: 2015 end-page: 279 ident: b0170 article-title: Fuzzy clustering: more than just fuzzification publication-title: Fuzzy Sets Syst – volume: 39 start-page: 123 year: 2016 end-page: 138 ident: b0075 article-title: Modelling and dynamic simulation of a parabolic trough power plant publication-title: J Process Control – volume: 162 start-page: 1 year: 2017 ident: 10.1016/j.fuel.2019.116178_b0055 article-title: Experimental and numerical study on effects of defectors on flow field distribution and desulfurization efficiency in spray towers publication-title: Fuel Process Technol doi: 10.1016/j.fuproc.2017.03.024 – volume: 148 start-page: 789 year: 2018 ident: 10.1016/j.fuel.2019.116178_b0080 article-title: A new approach for clustering in desulfurization system based on modified framework for gypsum slurry quality monitoring publication-title: Energy doi: 10.1016/j.energy.2018.01.175 – volume: 128 start-page: 461 year: 2014 ident: 10.1016/j.fuel.2019.116178_b0035 article-title: Assessment of marble waste utilization as an alternative sorbent to limestone for so 2 control publication-title: Fuel Process Technol doi: 10.1016/j.fuproc.2014.08.009 – volume: 129 start-page: 187 year: 2014 ident: 10.1016/j.fuel.2019.116178_b0005 article-title: Nitrogen oxide absorption and nitrite/nitrate formation in limestone slurry for wfgd system publication-title: Appl Energy doi: 10.1016/j.apenergy.2014.05.006 – ident: 10.1016/j.fuel.2019.116178_b0185 – volume: 508 start-page: 52 year: 2015 ident: 10.1016/j.fuel.2019.116178_b0060 article-title: Selectivity of transition metal catalysts in promoting the oxidation of solid sulfites in flue gas desulfurization publication-title: Appl Catal A doi: 10.1016/j.apcata.2015.10.008 – volume: 10 start-page: 191 year: 1984 ident: 10.1016/j.fuel.2019.116178_b0180 article-title: Fcm: The fuzzy c -means clustering algorithm publication-title: Comput Geosci doi: 10.1016/0098-3004(84)90020-7 – volume: 89 start-page: 1025 year: 2008 ident: 10.1016/j.fuel.2019.116178_b0040 article-title: A model for performance optimization of wet flue gas desulfurization systems of power plants publication-title: Fuel Process Technol doi: 10.1016/j.fuproc.2008.04.004 – volume: 122 start-page: 45 year: 2019 ident: 10.1016/j.fuel.2019.116178_b0110 article-title: A modified intuitionistic fuzzy c-means algorithm incorporating hesitation degree publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2019.02.017 – volume: 91 start-page: 1794 year: 2010 ident: 10.1016/j.fuel.2019.116178_b0045 article-title: Simulation of the operation of an industrial wet flue gas desulfurization system publication-title: Fuel Process Technol doi: 10.1016/j.fuproc.2010.07.020 – volume: 11 start-page: 1 year: 2016 ident: 10.1016/j.fuel.2019.116178_b0160 article-title: A fuzzy approach to robust regression clustering publication-title: Adv Data Anal Classif – volume: 63 start-page: 341 year: 2017 ident: 10.1016/j.fuel.2019.116178_b0155 article-title: A method for estimating the least number of objects in fuzzy clusters publication-title: Int. J. Electron Telecommun doi: 10.1515/eletel-2017-0046 – volume: 71 start-page: 375 year: 2017 ident: 10.1016/j.fuel.2019.116178_b0105 article-title: Fast density clustering strategies based on the k-means algorithm publication-title: Pattern Recogn doi: 10.1016/j.patcog.2017.06.023 – volume: 90 start-page: 100 year: 2019 ident: 10.1016/j.fuel.2019.116178_b0130 article-title: A robust active contour model driven by fuzzy c-means energy for fast image segmentation publication-title: Digital Signal Process doi: 10.1016/j.dsp.2019.04.004 – volume: 281 start-page: 272 year: 2015 ident: 10.1016/j.fuel.2019.116178_b0170 article-title: Fuzzy clustering: more than just fuzzification publication-title: Fuzzy Sets Syst doi: 10.1016/j.fss.2015.06.024 – volume: 56 start-page: 121 year: 2016 ident: 10.1016/j.fuel.2019.116178_b0165 article-title: Picture fuzzy clustering for complex data publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2016.08.009 – volume: 163 start-page: 416 year: 2019 ident: 10.1016/j.fuel.2019.116178_b0115 article-title: A stratified sampling based clustering algorithm for large-scale data publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2018.09.007 – volume: 151 start-page: 344 year: 2019 ident: 10.1016/j.fuel.2019.116178_b0120 article-title: Data based online operational performance optimization with varying work conditions for steam-turbine system publication-title: Appl Therm Eng doi: 10.1016/j.applthermaleng.2019.02.032 – volume: 162 start-page: 227 year: 2017 ident: 10.1016/j.fuel.2019.116178_b0065 article-title: Synergistic effect of droplet self-adjustment and rod bank internal on fluid distribution in a wfgd spray column publication-title: Chem Eng Sci doi: 10.1016/j.ces.2016.12.062 – volume: 39 start-page: 123 year: 2016 ident: 10.1016/j.fuel.2019.116178_b0075 article-title: Modelling and dynamic simulation of a parabolic trough power plant publication-title: J Process Control doi: 10.1016/j.jprocont.2016.01.002 – volume: 43 start-page: 186 year: 2016 ident: 10.1016/j.fuel.2019.116178_b0125 article-title: A non-invasive methodology for the grade identification of astrocytoma using image processing and artificial intelligence techniques publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2015.08.036 – volume: 47 start-page: 2974 year: 2013 ident: 10.1016/j.fuel.2019.116178_b0015 article-title: Influence of limestone characteristics on mercury reemission in wfgd systems publication-title: Environ Sci Technol doi: 10.1021/es304090e – volume: 108 start-page: 73 year: 2010 ident: 10.1016/j.fuel.2019.116178_b0030 article-title: Experimental study of fine particles removal in the desulfurized scrubbing flue gas publication-title: Fuel – volume: 88 start-page: 118 year: 2015 ident: 10.1016/j.fuel.2019.116178_b0070 article-title: Comparative analysis and prediction study for effluent gas emissions in a coal-fired thermal power plant using artificial intelligence and statistical tools publication-title: J Energy Inst doi: 10.1016/j.joei.2014.07.003 – volume: 173 start-page: 1 year: 2019 ident: 10.1016/j.fuel.2019.116178_b0100 article-title: Good (K-means) clusterings are unique (up to small perturbations) publication-title: J. Multivariate Anal doi: 10.1016/j.jmva.2018.12.008 – volume: 16 start-page: 3732 year: 2014 ident: 10.1016/j.fuel.2019.116178_b0145 article-title: On the connections of generalized entropies with shannon and kolmogorov-sinai publication-title: Entropy doi: 10.3390/e16073732 – volume: 14 start-page: 93 year: 2018 ident: 10.1016/j.fuel.2019.116178_b0095 article-title: A novel clustering method using enhanced grey wolf optimizer and mapreduce publication-title: Big Data Res doi: 10.1016/j.bdr.2018.05.002 – volume: 88 start-page: 284 year: 2015 ident: 10.1016/j.fuel.2019.116178_b0050 article-title: Numerical simulation research of flow field in ammonia-based wet flue gas desulfurization tower publication-title: J Energy Inst doi: 10.1016/j.joei.2014.09.002 – volume: 145 start-page: 116 year: 2016 ident: 10.1016/j.fuel.2019.116178_b0010 article-title: Improving the removal of ne particles by heterogeneous condensation during wfgd processes publication-title: Fuel Process Technol doi: 10.1016/j.fuproc.2016.01.033 – volume: 289 start-page: 537 year: 2016 ident: 10.1016/j.fuel.2019.116178_b0020 article-title: Investigation on the removal of so3 in ammonia-based wfgd system publication-title: Chem Eng J doi: 10.1016/j.cej.2016.01.020 – volume: 93 start-page: 95 year: 2019 ident: 10.1016/j.fuel.2019.116178_b0085 article-title: How much can k-means be improved by using better initialization and repeats? publication-title: Pattern Recogn doi: 10.1016/j.patcog.2019.04.014 – volume: 320 start-page: 156 year: 2015 ident: 10.1016/j.fuel.2019.116178_b0175 article-title: Novel centroid selection approaches for k means-clustering based recommender systems publication-title: Inf Sci Int J doi: 10.1016/j.ins.2015.03.062 – volume: 91 start-page: 402 year: 2018 ident: 10.1016/j.fuel.2019.116178_b0090 article-title: Combining K-Means and a genetic algorithm through a novel arrangement of genetic operators for high quality clustering publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2017.09.005 – volume: 210 start-page: 212 year: 2019 ident: 10.1016/j.fuel.2019.116178_b0135 article-title: Advanced damage detection technique by integration of unsupervised clustering into acoustic emission publication-title: Eng Fract Mech doi: 10.1016/j.engfracmech.2018.07.005 – volume: 86 start-page: 43 year: 2016 ident: 10.1016/j.fuel.2019.116178_b0025 article-title: Employing a step-wise titration method under semi-slow reaction regime for evaluating the reactivity of limestone and dolomite in acidic environment publication-title: Miner Eng doi: 10.1016/j.mineng.2015.11.011 |
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| Snippet | •The optimal target database of operating conditions for WFGD was investigated.•An improved fuzzy clustering (IFC) algorithm was proposed for data... Finding the best conditions for current operation from historical data is of great meaning for power plants. In order to obtain the optimal conditions, a... |
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| SubjectTerms | Air pollution control Algorithms Clustering Criteria Data mining Desulfurization Desulfurizing Electric power generation Entropy (Information theory) Evaluation Flue gas Flue gas desulfurization Improved fuzzy clustering algorithm Information processing Initial conditions Minimum cost Objective function Online Optimal target operating condition database Optimization Pollution control equipment Power plants Slurries Sulfur dioxide Unit loads |
| Title | An investigation on data mining and operating optimization for wet flue gas desulfurization systems |
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