Online optimization of boiler operation based on information integration and case-based reasoning

References

• Aamodt, A., and E. Plaza. 1994. Case-based reasoning: Foundational issues, methodological variations, and system approaches[J]. Ai Communications 7 (1):39–59. doi:10.3233/AIC-1994-7104.
   Web of Science ®Google Scholar
• Alam, M. S., N. Sultana, and S. Hossain. 2021. Bayesian optimization algorithm based support vector regression analysis for estimation of shear capacity of FRP reinforced concrete members[J]. Applied Soft Computing 105 (2004):107281. doi:10.1016/j.asoc.2021.107281.
   Google Scholar
• Arat, H., , et al . 2021. A comprehensive numerical investigation of unsteady-state two-phase flow in gravity assisted heat pipe enclosure[J]. Thermal Science and Engineering Progress 25:100993 . doi:10.1016/j.tsep.2021.100993.
   Google Scholar
• Arat, H., , et al . 2021b. Experimental study on heat transfer characteristics of closed thermosyphon at different volumes and inclination angles for variable vacuum pressures[J]. Case Studies in Thermal Engineering 26:101117. doi:10.1016/j.csite.2021.101117.
   Web of Science ®Google Scholar
• Arslan, O., and O. Erbas. 2021. Investigation on the improvement of the combustion process through hybrid dewatering and air pre-heating process: A case study for a 150 MW coal-fired boiler[J]. Journal of the Taiwan Institute of Chemical Engineers 121:229–40. doi:10.1016/j.jtice.2021.04.012.
   Web of Science ®Google Scholar
• Babita, P., and R. B. Mishra . 2010. Data mining and CBR integrated methods in medicine: A review[J]. International Journal of Medical Engineering and Informatics. 2(2):205–18. doi:10.1504/IJMEI.2010.031521.
   Google Scholar
• Bermúdez, C. A., , , et al . 2020. Three-dimensional CFD simulation of a large-scale grate-fired biomass furnace[J]. Fuel Processing Technology 198:106219. doi:10.1016/j.fuproc.2019.106219.
   Web of Science ®Google Scholar
• Bo, L., and A. Lyngfelt. 2002. Optimization of emissions from fluidized bed combustion of coal, biofuel and waste[J]. International Journal of Energy Research 26 (13):1191–202. doi:10.1002/er.844.
   Web of Science ®Google Scholar
• Chang, K. Y., C. Y. Chang, W. J. Wang, and C. Y. Chen. 2012. Modeling polarization of aDMFC system via neural network with immune-based particle swarm optimization[J]. International Journal of Green Energy 9 (1–4):309–21. doi:10.1080/15435075.2011.621481.
   Web of Science ®Google Scholar
• Chu, J., , , , et al . 2021. Short-term prediction of urban PM2.5 based on a hybrid modified variational mode decomposition and support vector regression model[J]. Environmental Science and Pollution Research. 28(1):56–72. doi:10.1007/s11356-020-11065-8.
   PubMed Web of Science ®Google Scholar
• Dal Secco, S., O. Juan, and M. Louis-Louisy. 2015. Using a genetic algorithm and CFD to identify low NOx configurations in an industrial boiler[J]. Fuel 158 (oct.15):672–83.
   Google Scholar
• Echi, S., , et al . 2019. CFD simulation and optimization of industrial boiler[J]. Energy 169:105–14. doi:10.1016/j.energy.2018.12.006.
   Web of Science ®Google Scholar
• Gu, Y., W. Zhao, and Z. Wu. 2011. Online adaptive least squares support vector machine and its application in utility boiler combustion optimization systems[J]. Journal of Process Control 21 (7):1040–48. doi:10.1016/j.jprocont.2011.06.001.
   Web of Science ®Google Scholar
• Guo, G., et al, . KNN model-based approach in classification[C]. OTM Confederated International Conferences” On the Move to Meaningful Internet Systems”. Springer, Berlin, Heidelberg, 2003: 986–96.
   Google Scholar
• Gupta, D., and N. Natarajan. 2021. Prediction of uniaxial compressive strength of rock samples using density weighted least squares twin support vector regression[J]. Neural Computing and Applications 1–8. doi:10.1007/s00521-021-06171-8.
   Web of Science ®Google Scholar
• Jafarizadeh, S. 2020. Fastest mixing reversible markov chain: Clique lifted graphs & subgraphs[J]. IEEE Transactions on Signal and Information Processing over Networks 6:88–104. doi:10.1109/TSIPN.2020.2964211.
   Web of Science ®Google Scholar
• Jiang, A., H. Yuan, and D. Li. 2021. Energy management for a community-level integrated energy system with photovoltaic prosumers based on bargaining theory[J]. Energy 225 (3):120272. doi:10.1016/j.energy.2021.120272.
   Google Scholar
• Karr, A. F. 2006. Exploratory data mining and data cleaning[J]. Journal of the American Statistical Association 101 (473):399–399. doi:10.1198/jasa.2006.s81.
   Google Scholar
• Kim, J., , , et al . 2021. A review of the numerical modeling of pulverized coal combustion for High-Efficiency, Low-Emissions (HELE) power generation[J]. Energy & Fuels. 35(9):7434–66. doi:10.1021/acs.energyfuels.1c00343.
   Web of Science ®Google Scholar
• Kolodner, J. L. 1992. An introduction to case-based reasoning[J]. Artificial Intelligence Review 6 (1):3–34. doi:10.1007/BF00155578.
   Web of Science ®Google Scholar
• Laubscher, R., and P. Rousseau. 2020. Numerical investigation on the impact of variable particle radiation properties on the heat transfer in high ash pulverized coal boiler through co-simulation[J]. Energy 195:117006. doi:10.1016/j.energy.2020.117006.
   Web of Science ®Google Scholar
• Li, X., P. Niu, and J. Liu. 2018. Combustion optimization of a boiler based on the chaos and lévy flight vortex search algorithm[J]. Applied Mathematical Modelling. 58: 3–18.
   Web of Science ®Google Scholar
• Liu, X., and R. C. Bansal. 2014. Integrating multi-objective optimization with computational fluid dynamics to optimize boiler combustion process of a coal fired power plant[J]. Applied Energy 130 (10):658–69. doi:10.1016/j.apenergy.2014.02.069.
   Google Scholar
• Majhi, B., and P. K. Sa. 2007. FLANN-based adaptive threshold selection for detection of impulsive noise in images[J]. AEU-International Journal of Electronics and Communications 61 (7):478–84. doi:10.1016/j.aeue.2006.08.007.
   Web of Science ®Google Scholar
• Mateo, R. G., et al, . 2013. Modeling species distributions from heterogeneous data for the biogeographic regionalization of the european bryophyte flora[J]. PLoS ONE. 8(2):e55648. doi:10.1371/journal.pone.0055648.
   PubMed Web of Science ®Google Scholar
• Mo, R., and Z. Yin. 2020. Exploring software bug-proneness based on evolutionary clique modeling and analysis[J]. Information and Software Technology 128 (6):106380. doi:10.1016/j.infsof.2020.106380.
   Google Scholar
• Niu, Y., , et al . 2020. Case-based reasoning based on grey-relational theory for the optimization of boiler combustion systems[J]. ISA transactions 103:166–76. doi:10.1016/j.isatra.2020.03.024.
   PubMed Web of Science ®Google Scholar
• Osman, H. M. 2017. Optimizing model base predictive control for combustion boiler processat high model uncertainty[J]. Chemical and Biochemical Engineering Quarterly 31:313–24. doi:10.15255/CABEQ.2016.867.
   Web of Science ®Google Scholar
• Priya, S. E. 2016. Design of an adaptive constrained based neuro-fuzzy controller for fault detection of a power plant system [J]. Indian Journal of Computer Science and Engineering 7:208–18.
   Google Scholar
• Sankar, G., D. S. Kumar, and K. R. Balasubramanian. 2019. Computational modeling of pulverized coal fired boilers–A review on the current position[J]. Fuel 236:643–65. doi:10.1016/j.fuel.2018.08.154.
   Web of Science ®Google Scholar
• Saravanakumar, R., and D. Jena. 2016. Nonlinear control of awind turbine based on nonlinear estimation techniques for maximum power extraction[J]. International Journal of Green Energy 13 (1–5):309–19. doi:10.1080/15435075.2014.952424.
   Web of Science ®Google Scholar
• Secco, S. D., , et al . 2015a. Using a genetic algorithm and CFD to identify low NOx configurations in an industrial boiler[J]. Fuel 158:672–83.
   Web of Science ®Google Scholar
• Secco, S. D., et al . 2015b. Using a genetic algorithm and CFD to identify low NOx configurations in an industrial boiler[J]. Fuel 158:672–83. doi:10.1016/j.fuel.2015.06.021.
   Web of Science ®Google Scholar
• Shi, Y., et al . 2019. Combustion optimization of ultra supercritical boiler based on artificial intelligence[J]. Energy. 170(MAR.1):804–17. doi:10.1016/j.energy.2018.12.172.
   Google Scholar
• Shields, A., S. Tihonova, R. Stott, L. A. Saputelli, Z. Haris, and A. Verde. 2016. An integrated production modelling workflow for CSG production forecasting and optimisation[J]. Journal of Natural Gas Science and Engineering 34:733–50. doi:10.1016/j.jngse.2016.06.065.
   Web of Science ®Google Scholar
• Tang, Z., and Z. Zhang. 2019. The multi-objective optimization of combustion system operations based on deep data-driven models[J]. Energy 182 (SEP.1):37–47. doi:10.1016/j.energy.2019.06.051.
   Google Scholar
• Vikhansky, A., E. Bar-Ziv, B. Chudnovsky, A. Talanker, E. Eddings, and A. Sarofim. 2004. Measurements and numerical simulations for optimization of the combustion process in a utility boiler[J]. International Journal of Energy Research 28 (5):391–401. doi:10.1002/er.971.
   Web of Science ®Google Scholar
• Walter, A. S., and S. S. Wilks. 2003. Exploratory data mining and data cleaning[M]. John Wiley & Sons, Inc.
   Google Scholar
• Wang, C., et al, . 2018. Optimizing combustion of coal fired boilers for reducing NOx emission using Gaussian Process[J]. Energy. 153(JUN.15):149–58. doi:10.1016/j.energy.2018.01.003.
   Google Scholar
• Wang, P., , , et al . 2019. Experiments and simulation on co-combustion of semicoke and coal in a full-scale tangentially fired utility boiler[J]. Energy & Fuels. 33(APR):3012–27. doi:10.1021/acs.energyfuels.8b04482.
   Google Scholar
• Wang, Y., G. Chen, and B. Yan . 2021. A coupling energy system of 10 clean-energy heating systems: A case study in Shandong province in China[J]. International Journal of Green Energy. 18 (1):1–16.
   Google Scholar
• Watson, I. 1999. Case-based reasoning is a methodology not a technology[M], 213–23. London: Research and Development in Expert Systems XV. Springer.
   Google Scholar
• Xia, J., , et al . 2014. An online case-based reasoning system for coal blends combustion optimization of thermal power plant[J]. International Journal of Electrical Power & Energy Systems 62:299–311. doi:10.1016/j.ijepes.2014.04.036.
   Web of Science ®Google Scholar
• Xia, J., et al . 2017. Visual subspace clustering based on dimension relevance[J]. Journal of Visual Languages & Computing. 41(aug):79–88. doi:10.1016/j.jvlc.2017.05.003.
   Google Scholar
• Xiong, W., and H. Pan. 2021. Interaction screening for high-dimensional heterogeneous data via robust hybrid metrics[J]. Statistics in Medicine 40 (29):6651–73. doi:10.1002/sim.9204.
   PubMed Web of Science ®Google Scholar
• Yang, R., , , , et al . 2021. Study on NOx emission during corn straw/sewage sludge co-combustion: Experiments and modelling[J]. Fuel 285:119208. doi:10.1016/j.fuel.2020.119208.
   Web of Science ®Google Scholar
• Ye, T., et al . 2021. Modeling and optimization of the NOX generation characteristics of the coal-fired boiler based on interpretable machine learning algorithm. International Journal of Green Energy 1–15. doi:10.1080/15435075.2021.1947827.
   Web of Science ®Google Scholar
• Zadravec, T., , , et al . 2020. The impacts of different profiles of the grate inlet conditions on freeboard CFD in a waste wood-fired grate boiler[J]. Applied Energy 268:115055. doi:10.1016/j.apenergy.2020.115055.
   Web of Science ®Google Scholar
• Zheng, J., et al . 2020. A dynamic emergency decision-making method based on group decision making with uncertainty information[J]. International Journal of Disaster Risk Science. 11(5):667–79. doi:10.1007/s13753-020-00308-4.
   Web of Science ®Google Scholar
• Zheng, W., et al . 2019. Multi-objective combustion optimization based on data-driven hybrid strategy[J]. Energy 191:116478. doi:10.1016/j.energy.2019.116478.
   Web of Science ®Google Scholar
• Zheng, Z., M. Yao, and W. Wu. 2010. Numerical simulation on combustion and emission processes of premixed/direct-injected fuel stratification combustion[J]. International Journal of Green Energy 7 (5):498–515. doi:10.1080/15435075.2010.515192.
   Web of Science ®Google Scholar
• Zhou, H., M. Zhou, Z. Liu, M. Cheng, and J. Chen. 2016. Modeling NOx emission of coke combustion in iron ore sintering process and its experimental validation[J]. Fuel 179 (9):322–31. doi:10.1016/j.fuel.2016.03.098.
   Google Scholar