Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter August 12, 2022

A review on the application of machine learning for combustion in power generation applications

  • Kasra Mohammadi , Jake Immonen , Landen D. Blackburn , Jacob F. Tuttle , Klas Andersson and Kody M. Powell EMAIL logo

Abstract

Although the world is shifting toward using more renewable energy resources, combustion systems will still play an important role in the immediate future of global energy. To follow a sustainable path to the future and reduce global warming impacts, it is important to improve the efficiency and performance of combustion processes and minimize their emissions. Machine learning techniques are a cost-effective solution for improving the sustainability of combustion systems through modeling, prediction, forecasting, optimization, fault detection, and control of processes. The objective of this study is to provide a review and discussion regarding the current state of research on the applications of machine learning techniques in different combustion processes related to power generation. Depending on the type of combustion process, the applications of machine learning techniques are categorized into three main groups: (1) coal and natural gas power plants, (2) biomass combustion, and (3) carbon capture systems. This study discusses the potential benefits and challenges of machine learning in the combustion area and provides some research directions for future studies. Overall, the conducted review demonstrates that machine learning techniques can play a substantial role to shift combustion systems towards lower emission processes with improved operational flexibility and reduced operating cost.


Corresponding author: Kody M. Powell, Department of Chemical Engineering, University of Utah, 50 S. Central Campus Dr., Room 3290 MEB, Salt Lake City, UT 84112-9203, USA; and Department of Mechanical Engineering, University of Utah, 1495 E 100 S., Room 1550 MEK, Salt Lake City, UT 84112, USA, E-mail:

Funding source: United States Department of Energy (DOE)

Award Identifier / Grant number: DE-FE0031754

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This work is funded by the United States Department of Energy (DOE) under the DE-FE0031754 grant, which is affiliated with the DOE’s Office of Fossil Energy.

  3. Conflict of interest statement: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

Abhilash, P.M. and Chakradhar, C.D. (2020). ANFIS modelling of mean gap voltage variation to predict wire breakages during wire EDM of Inconel 718. CIRP J. Manuf. Sci. Technol. 31: 153–164, https://doi.org/10.1016/j.cirpj.2020.10.007.Search in Google Scholar

Adams, D., Oh, D.H., Kim, D.W., Lee, C.H., and Oh, M. (2020). Prediction of SOx–NOx emission from a coal-fired CFB power plant with machine learning: plant data learned by deep neural network and least square support vector machine. J. Clean. Prod. 270: 1–16, https://doi.org/10.1016/j.jclepro.2020.122310.Search in Google Scholar

Adams, D., Oh, D.H., Kim, D.W., Lee, C.H., and Oh, M. (2021). Deep reinforcement learning optimization framework for a power generation plant considering performance and environmental issues. J. Clean. Prod. 291: 1–21, https://doi.org/10.1016/j.jclepro.2021.125915.Search in Google Scholar

Adedeji, P.A., Akinlabi, S., Madushele, N., and Olatunji, O.O. (2020). Wind turbine power output very short-term forecast: a comparative study of data clustering techniques in a PSO-ANFIS model. J. Clean. Prod. 254: 1–16, https://doi.org/10.1016/j.jclepro.2020.120135.Search in Google Scholar

Aghbashlo, M., Peng, W., Tabatabaei, M., Kalogirou, S.A., Soltanian, S., Hosseinzadeh-Bandbafha, H., Mahian, O., and Lam, S.S. (2021). Machine learning technology in biodiesel research: a review. Prog. Energy Combust. Sci. 85: 1–112, https://doi.org/10.1016/j.pecs.2021.100904.Search in Google Scholar

Aliramezani, M., Koch, C.R., and Shahbakhti, M. (2022). Modeling, diagnostics, optimization, and control of internal combustion engines via modern machine learning techniques: a review and future directions. Prog. Energy Combust. Sci. 88: 1–38, https://doi.org/10.1016/j.pecs.2021.100967.Search in Google Scholar

Alkabbani, H., Ahmadian, A., Zhu, Q., and Elkamel, A. (2021). Machine learning and metaheuristic methods for renewable power forecasting: a recent review. Front. Chem. Eng. 3: 1–21, https://doi.org/10.3389/fceng.2021.665415.Search in Google Scholar

Anowar, F., Sadaoui, S., and Selim, B. (2021). Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE). Comput. Sci. Rev. 40: 1–112, https://doi.org/10.1016/j.cosrev.2021.100378.Search in Google Scholar

Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M., and Inman, D.J. (2021). A review of vibration-based damage detection in civil structures: from traditional methods to Machine Learning and Deep Learning applications. Mech. Syst. Signal Process. 147: 1–45, https://doi.org/10.1016/j.ymssp.2020.107077.Search in Google Scholar

Ayoub, M. (2020). A review on machine learning algorithms to predict daylighting inside buildings. Sol. Energy 202: 249–275, https://doi.org/10.1016/j.solener.2020.03.104.Search in Google Scholar

Balachandar, G., Khanna, N., and Das, D. (2013). Biohydrogen production from organic wastes by dark fermentation. In: Biohydrogen. Elsevier, Amsterdam, pp. 103–144.10.1016/B978-0-444-59555-3.00006-4Search in Google Scholar

Bertram, A.M. (2019). Machine learning assisted optimization with applications to diesel engine optimization with the particle swarm optimization algorithm, Ph.D. thesis. Iowa State University, Ames.Search in Google Scholar

Bhander, G. and Jozewicz, W. (2017). Analysis of emission reduction strategies for power boilers in the US pulp and paper industry. Energy Emiss. Control Technol 5: 27–37, https://doi.org/10.2147/eect.s139648.Search in Google Scholar

Bhatt, A.N. and Shrivastava, N. (2022). Application of artificial neural network for internal combustion engines: a state of the art review. Arch. Comput. Methods Eng. 29: 897–919, https://doi.org/10.1007/s11831-021-09596-5.Search in Google Scholar PubMed PubMed Central

Binkhonain, M. and Zhao, L. (2019). A review of machine learning algorithms for identification and classification of non-functional requirements. Expert Syst. Appl. X 1: 1–13, https://doi.org/10.1016/j.eswax.2019.100001.Search in Google Scholar

Blackburn, L., Tuttle, J.F., Andersson, K., Fry, A., and Powell, K. (2022). Development of novel dynamic machine learning-based optimization of a coal-fired power plant. Comput. Chem. Eng. 163: 107848, https://doi.org/10.1016/j.compchemeng.2022.107848.Search in Google Scholar

Bratina, B., MuŜkinja, N., and Tovornik, B. (2009). Recurrent auto-associative artificial neural network model of biomass steam boiler system. In: IFAC Proc., Vol. 42, pp. 210–215.10.3182/20090210-3-CZ-4002.00043Search in Google Scholar

Breiman, L. (2001). Random forests. Mach. Learn. 45: 5–32, https://doi.org/10.1023/a:1010933404324.10.1023/A:1010933404324Search in Google Scholar

Carbot-Rojas, D.A., Escobar-Jiménez, R.F., Gómez-Aguilar, J.F., García-Morales, J., and Téllez-Anguiano, A.C. (2020). Modelling and control of the spark timing of an internal combustion engine based on an ANN. Combust. Theor. Model. 24: 510–529, https://doi.org/10.1080/13647830.2019.1704888.Search in Google Scholar

Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., and Lopez, A. (2020). A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing 408: 189–215, https://doi.org/10.1016/j.neucom.2019.10.118.Search in Google Scholar

Chan, V. and Chan, C. (2017). Learning from a carbon dioxide capture system dataset: application of the piecewise neural network algorithm. Petroleum 3: 56–67, https://doi.org/10.1016/j.petlm.2016.11.004.Search in Google Scholar

Cheng, Y., Xu, L., Li, X., and Chen, L. (2015). Online estimation of coal calorific value from combustion radiation for coal-fired boilers. Combust. Sci. Technol. 187: 1487–1503, https://doi.org/10.1080/00102202.2015.1019618.Search in Google Scholar

Cheng, Y., Huang, Y., Pang, B., and Zhang, W. (2018). ThermalNet: a deep reinforcement learning-based combustion optimization system for coal-fired boiler. Eng. Appl. Artif. Intell. 74: 303–311, https://doi.org/10.1016/j.engappai.2018.07.003.Search in Google Scholar

De, S., Kaiadi, M., Fast, M., and Assadi, M. (2007). Development of an artificial neural network model for the steam process of a coal biomass cofired combined heat and power (CHP) plant in Sweden. Energy 32: 2099–2109, https://doi.org/10.1016/j.energy.2007.04.008.Search in Google Scholar

Dike, H.U., Zhou, Y., Deveerasetty, K.K., and Wu, Q. (2018). Unsupervised learning based on artificial neural network: a review. In: 2018 IEEE Int. Conf. Cyborg Bionic Syst., pp. 322–327.10.1109/CBS.2018.8612259Search in Google Scholar

Dorigo, M. (1992). Optimization, learning and natural algorithms, Ph.D. thesis. Milan, Polytechnic University of Milan.Search in Google Scholar

Dridi, S. (2021). Unsupervised learning – a systematic literature review, Available at: https://www.researchgate.net/publication/357380639_Unsupervised_Learning_-_A_Systematic_Literature_Review.10.31219/osf.io/kpqr6Search in Google Scholar

Duku, M.H., Gu, S., and Ben Hagan, E. (2011). A comprehensive review of biomass resources and biofuels potential in Ghana. Renew. Sustain. Energy Rev. 15: 404–415, https://doi.org/10.1016/j.rser.2010.09.033.Search in Google Scholar

Emmert-Streib, F., Yang, Z., Feng, H., Tripathi, S., and Dehmer, M. (2020). An introductory review of deep learning for prediction models with big data. Front. Artif. Intell. 3: 1–23, https://doi.org/10.3389/frai.2020.00004.Search in Google Scholar PubMed PubMed Central

Fathi, S., Srinivasan, R., Fenner, A., and Fathi, S. (2020). Machine learning applications in urban building energy performance forecasting: a systematic review. Renew. Sustain. Energy Rev. 133: 1–13, https://doi.org/10.1016/j.rser.2020.110287.Search in Google Scholar

Fawagreh, K., Gaber, M.M., and Elyan, E. (2014). Random forests: from early developments to recent advancements. Syst. Sci. Control Eng. 2: 602–609, https://doi.org/10.1080/21642583.2014.956265.Search in Google Scholar

Geem, Z.W., Kim, J.H., and Loganathan, G.V. (2001). A new heuristic optimization algorithm: harmony search. Simulation 76: 60–68.10.1177/003754970107600201Search in Google Scholar

Ghiat, I. and Al-Ansari, T. (2021). A review of carbon capture and utilisation as a CO2 abatement opportunity within the EWF nexus. J. CO2 Util. 45: 1–14, https://doi.org/10.1016/j.jcou.2020.101432.Search in Google Scholar

de Gouw, J.A., Parrish, D.D., Frost, G.J., and Trainer, M. (2014). Reduced emissions of CO2, NOx, and SO2 from U.S. power plants owing to switch from coal to natural gas with combined cycle technology. Earth’s Future 2: 75–82.10.1002/2013EF000196Search in Google Scholar

Grekousis, G. (2019). Artificial neural networks and deep learning in urban geography: a systematic review and meta-analysis. Comput. Environ. Urban Syst. 74: 244–256, https://doi.org/10.1016/j.compenvurbsys.2018.10.008.Search in Google Scholar

Guo, H.N., Wu, S.B., Tian, Y.J., Zhang, J., and Liu, H.T. (2021). Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: a review. Bioresour. Technol. 319: 1–13, https://doi.org/10.1016/j.biortech.2020.124114.Search in Google Scholar PubMed

Güven, İ. and Şimşir, F. (2020). Demand forecasting with color parameter in retail apparel industry using artificial neural networks (ANN) and support vector machines (SVM) methods. Comput. Ind. Eng. 147: 1–9.10.1016/j.cie.2020.106678Search in Google Scholar

Han, Z., Li, J., Zhang, B., Hossain, M.M., and Xu, C. (2021). Prediction of combustion state through a semi-supervised learning model and flame imaging. Fuel 289: 1–15, https://doi.org/10.1016/j.fuel.2020.119745.Search in Google Scholar

Hinton, G.E., Osindero, S., and Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Comput. 18: 1527–1554, https://doi.org/10.1162/neco.2006.18.7.1527.Search in Google Scholar PubMed

Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Comput. 9: 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735.Search in Google Scholar PubMed

Holden, A.J., Robbins, D.J., Stewart, W.J., Smith, D.R., Schultz, S., Wegener, M., Linden, S., Hormann, C., Enkrich, C., Soukoulis, C.M., et al.. (2006). Reducing the dimensionality of data with neural networks. Science 313: 504–507.10.1126/science.1127647Search in Google Scholar PubMed

Holland, J. (1975). Adaptation in natural and artificial systems. MIT Press, Cambridge.Search in Google Scholar

Ibrahim, M.S., Dong, W., and Yang, Q. (2020). Machine learning driven smart electric power systems: current trends and new perspectives. Appl. Energy 272: 1–19, https://doi.org/10.1016/j.apenergy.2020.115237.Search in Google Scholar

Ippolito, M., Ferguson, J., and Jenson, F. (2021). Improving facies prediction by combining supervised and unsupervised learning methods. J. Pet. Sci. Eng. 200: 1–15, https://doi.org/10.1016/j.petrol.2020.108300.Search in Google Scholar

Jafari, S. and Nikolaidis, T. (2019). Meta-heuristic global optimization algorithms for aircraft engines modelling and controller design; a review, research challenges, and exploring the future. Prog. Aero. Sci. 104: 40–53, https://doi.org/10.1016/j.paerosci.2018.11.003.Search in Google Scholar

Jawad, J., Hawari, A.H., and Javaid Zaidi, S. (2021). Artificial neural network modeling of wastewater treatment and desalination using membrane processes: a review. Chem. Eng. J. 419: 1–21, https://doi.org/10.1016/j.cej.2021.129540.Search in Google Scholar

Kalogirou, S.A. (2003). Artificial intelligence for the modeling and control of combustion processes: a review. Prog. Energy Combust. Sci. 29: 515–566, https://doi.org/10.1016/s0360-1285(03)00058-3.Search in Google Scholar

Katoch, S., Chauhan, S.S., and Kumar, V. (2021). A review on genetic algorithm: past, present, and future. Multimed. Tool. Appl. 80: 8091–8126, https://doi.org/10.1007/s11042-020-10139-6.Search in Google Scholar PubMed PubMed Central

Kennedy, J., and Eberhart, R. (1995). Particle swarm optimization. In: Proc. ICNN’95 – Int. Conf. Neural Networks, Vol. 4, pp. 1942–1948.Search in Google Scholar

Kochenderfer, M.J. and Wheller, T.A. (2019). Algorithms for optimization. MIT Press, Cambridge.Search in Google Scholar

Korpela, T., Kumpulainen, P., Majanne, Y., Häyrinen, A., and Lautala, P. (2017). Indirect NOx emission monitoring in natural gas fired boilers. Control Eng. Pract. 65: 11–25, https://doi.org/10.1016/j.conengprac.2017.04.013.Search in Google Scholar

Krishnanand, K.N., and Ghose, D. (2005). Detection of multiple source locations using a glowworm metaphor with applications to collective robotics. In: Proc. – 2005 IEEE Swarm Intell. Symp. SIS 2005, pp. 84–91.Search in Google Scholar

Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks. In: Proc. 25th Int. Conf. Neural Inf. Process. Syst., pp. 1–9.Search in Google Scholar

Kumar, A., Kumar, N., Baredar, P., and Shukla, A. (2015). A review on biomass energy resources, potential, conversion and policy in India. Renew. Sustain. Energy Rev. 45: 530–539, https://doi.org/10.1016/j.rser.2015.02.007.Search in Google Scholar

Larrea, M., Porto, A., Irigoyen, E., Barragán, A.J., and Andújar, J.M. (2021). Extreme learning machine ensemble model for time series forecasting boosted by PSO: application to an electric consumption problem. Neurocomputing 452: 465–472, https://doi.org/10.1016/j.neucom.2019.12.140.Search in Google Scholar

Lecun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature 521: 436–444, https://doi.org/10.1038/nature14539.Search in Google Scholar PubMed

Leung, D.Y.C., Caramanna, G., and Maroto-Valer, M.M. (2014). An overview of current status of carbon dioxide capture and storage technologies. Renew. Sustain. Energy Rev. 39: 426–443, https://doi.org/10.1016/j.rser.2014.07.093.Search in Google Scholar

Li, F., Zhang, J., Oko, E., and Wang, M. (2017). Modelling of a post-combustion CO2 capture process using extreme learning machine. Int. J. Coal Sci. Technol. 4: 33–40, https://doi.org/10.1007/s40789-017-0158-1.Search in Google Scholar

Li, F., Zhang, J., Shang, C., Huang, D., Oko, E., and Wang, M. (2018). Modelling of a post-combustion CO2 capture process using deep belief network. Appl. Therm. Eng. 130: 997–1003, https://doi.org/10.1016/j.applthermaleng.2017.11.078.Search in Google Scholar

Li, G., Niu, P., Zhang, W., and Liu, Y. (2013). Model NOx emissions by least squares support vector machine with tuning based on ameliorated teaching–learning-based optimization. Chemometr. Intell. Lab. Syst. 126: 11–20, https://doi.org/10.1016/j.chemolab.2013.04.012.Search in Google Scholar

Li, G., Niu, P., Ma, Y., Wang, H., and Zhang, W. (2014a). Tuning extreme learning machine by an improved artificial bee colony to model and optimize the boiler efficiency. Knowl. Base Syst. 67: 278–289, https://doi.org/10.1016/j.knosys.2014.04.042.Search in Google Scholar

Li, G., Niu, P., Wang, H., and Liu, Y. (2014b). Least square fast learning network for modeling the combustion efficiency of a 300 WM coal-fired boiler. Neural Netw. 51: 57–66, https://doi.org/10.1016/j.neunet.2013.12.006.Search in Google Scholar PubMed

Li, L.N., Liu, X.F., Yang, F., Xu, W.M., Wang, J.Y., and Shu, R. (2021). A review of artificial neural network based chemometrics applied in laser-induced breakdown spectroscopy analysis. Spectrochim. Acta Part B At. Spectrosc. 180: 1–18, https://doi.org/10.1016/j.sab.2021.106183.Search in Google Scholar

Li, N., Lu, G., Li, X., and Yan, Y. (2016). Prediction of NOx emissions from a biomass fired combustion process based on flame radical imaging and deep learning techniques. Combust. Sci. Technol. 188: 233–246, https://doi.org/10.1080/00102202.2015.1102905.Search in Google Scholar

Li, Q. and Yao, G. (2017). Improved coal combustion optimization model based on load balance and coal qualities. Energy 132: 204–212, https://doi.org/10.1016/j.energy.2017.05.068.Search in Google Scholar

Liang, Z., Rongwong, W., Liu, H., Fu, K., Gao, H., Cao, F., Zhang, R., Sema, T., Henni, A., Sumon, K., et al.. (2015). Recent progress and new developments in post-combustion carbon-capture technology with amine based solvents. Int. J. Greenh. Gas Control 40: 26–54, https://doi.org/10.1016/j.ijggc.2015.06.017.Search in Google Scholar

Liao, P., Li, Y., Wu, X., Wang, M., and Oko, E. (2020). Flexible operation of large-scale coal-fired power plant integrated with solvent-based post-combustion CO2 capture based on neural network inverse control. Int. J. Greenh. Gas Control 95: 1–15, https://doi.org/10.1016/j.ijggc.2020.102985.Search in Google Scholar

Liu, J., Huang, Q., Ulishney, C., and Dumitrescu, C.E. (2021a). Machine learning assisted prediction of exhaust gas temperature of a heavy-duty natural gas spark ignition engine. Appl. Energy 300: 1–12, https://doi.org/10.1016/j.apenergy.2021.117413.Search in Google Scholar

Liu, J., Ulishney, C., and Dumitrescu, C.E. (2021b). Random forest machine learning model for predicting combustion feedback information of a natural gas spark ignition engine. J. Energy Resour. Technol. Trans. ASME 143: 1–7, https://doi.org/10.1115/1.4047761.Search in Google Scholar

Liu, X. and Bansal, R.C. (2014). Integrating multi-objective optimization with computational fluid dynamics to optimize boiler combustion process of a coal fired power plant. Appl. Energy 130: 658–669, https://doi.org/10.1016/j.apenergy.2014.02.069.Search in Google Scholar

Liu, Z. and Reynolds, A.C. (2020). A sequential-quadratic-programming-filter algorithm with a modified stochastic gradient for robust life-cycle optimization problems with nonlinear state constraints. SPE J. 25: 1938–1963, https://doi.org/10.2118/193925-pa.Search in Google Scholar

Liu, Z. and Karimi, I.A. (2020). Gas turbine performance prediction via machine learning. Energy 192: 1–10, https://doi.org/10.1016/j.energy.2019.116627.Search in Google Scholar

Lü, X., Wu, Y., Lian, J., Zhang, Y., Chen, C., Wang, P., and Meng, L. (2020). Energy management of hybrid electric vehicles: a review of energy optimization of fuel cell hybrid power system based on genetic algorithm. Energy Convers. Manag. 205: 1–26.10.1016/j.enconman.2020.112474Search in Google Scholar

Lv, Y., Yang, T., and Liu, J. (2015). An adaptive least squares support vector machine model with a novel update for NOx emission prediction. Chemometr. Intell. Lab. Syst. 145: 103–113, https://doi.org/10.1016/j.chemolab.2015.04.006.Search in Google Scholar

Ma, Y., Wu, L., Guan, Y., and Peng, Z. (2020). The capacity estimation and cycle life prediction of lithium-ion batteries using a new broad extreme learning machine approach. J. Power Sources 476: 1–11, https://doi.org/10.1016/j.jpowsour.2020.228581.Search in Google Scholar

Manisalidis, I., Stavropoulou, E., Stavropoulos, A., and Bezirtzoglou, E. (2020). Environmental and health impacts of air pollution: a review. Front. Public Health 8: 1–13, https://doi.org/10.3389/fpubh.2020.00014.Search in Google Scholar PubMed PubMed Central

Menad, N.A., Hemmati-Sarapardeh, A., Varamesh, A., and Shamshirband, S. (2019). Predicting solubility of CO2 in brine by advanced machine learning systems: application to carbon capture and sequestration. J. CO2 Util. 33: 83–95, https://doi.org/10.1016/j.jcou.2019.05.009.Search in Google Scholar

Mohammadi, K., Ellingwood, K., and Powell, K. (2020a). A novel triple power cycle featuring a gas turbine cycle with supercritical carbon dioxide and organic Rankine cycles: thermoeconomic analysis and optimization. Energy Convers. Manag. 220: 1–22, https://doi.org/10.1016/j.enconman.2020.113123.Search in Google Scholar

Mohammadi, K., Jiang, Y., Borjian, S., and Powell, K. (2020b). Thermo-economic assessment and optimization of a hybrid triple effect absorption chiller and compressor. Sustain. Energy Technol. Assessments 38: 1–17, https://doi.org/10.1016/j.seta.2020.100652.Search in Google Scholar

Mowbray, M., Savage, T., Wu, C., Song, Z., Cho, B.A., Del Rio-Chanona, E.A., and Zhang, D. (2021). Machine learning for biochemical engineering: a review. Biochem. Eng. J. 172: 1–22, https://doi.org/10.1016/j.bej.2021.108054.Search in Google Scholar

Nian, R., Liu, J., and Huang, B. (2020). A review on reinforcement learning: introduction and applications in industrial process control. Comput. Chem. Eng. 139: 1–30, https://doi.org/10.1016/j.compchemeng.2020.106886.Search in Google Scholar

Niu, Y., Kang, J., Li, F., Ge, W., and Zhou, G. (2020). Case-based reasoning based on grey-relational theory for the optimization of boiler combustion systems. ISA Trans. 103: 166–176, https://doi.org/10.1016/j.isatra.2020.03.024.Search in Google Scholar PubMed

Osarogiagbon, A.U., Khan, F., Venkatesan, R., and Gillard, P. (2021). Review and analysis of supervised machine learning algorithms for hazardous events in drilling operations. Process Saf. Environ. Protect. 147: 367–384, https://doi.org/10.1016/j.psep.2020.09.038.Search in Google Scholar

Otchere, D.A., Arbi Ganat, T.O., Gholami, R., and Ridha, S. (2021). Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: comparative analysis of ANN and SVM models. J. Pet. Sci. Eng. 200: 1–20, https://doi.org/10.1016/j.petrol.2020.108182.Search in Google Scholar

Owoyele, O., Pal, P., and Torreira, A.V. (2021). An automated machine learning-genetic algorithm framework with active learning for design optimization. J. Energy Resour. Technol. Trans. ASME 143: 1–10, https://doi.org/10.1115/1.4050489.Search in Google Scholar

Pahlavani, P., Sheikhian, H., and Bigdeli, B. (2020). Evaluation of residential land use compatibilities using a density-based IOWA operator and an ANFIS-based model: a case study of Tehran, Iran. Land Use Pol. 90: 1–15, https://doi.org/10.1016/j.landusepol.2019.104364.Search in Google Scholar

Park, K.S., Seo, Y.C., Lee, S.J., and Lee, J.H. (2008). Emission and speciation of mercury from various combustion sources. Powder Technol. 180: 151–156, https://doi.org/10.1016/j.powtec.2007.03.006.Search in Google Scholar

Perera, A.T.D. and Kamalaruban, P. (2021). Applications of reinforcement learning in energy systems. Renew. Sustain. Energy Rev. 137: 1–22, https://doi.org/10.1016/j.rser.2020.110618.Search in Google Scholar

Pornsing, C., and Watanasungsuit, A. (2016). Steam generating prediction of a biomass boiler using artificial neural network. In: Proc. – 2016 2nd Int. Conf. Control. Autom. Robot. ICCAR 2016, pp. 281–284.10.1109/ICCAR.2016.7486741Search in Google Scholar

Pourrajabian, A., Dehghan, M., and Rahgozar, S. (2021). Genetic algorithms for the design and optimization of horizontal axis wind turbine (HAWT) blades: a continuous approach or a binary one? Sustain. Energy Technol. Assess. 44: 1–10, https://doi.org/10.1016/j.seta.2021.101022.Search in Google Scholar

Probst, D.M., Raju, M., Senecal, P.K., Kodavasal, J., Pal, P., Som, S., Moiz, A.A., and Pei, Y. (2019). Evaluating optimization strategies for engine simulations using machine learning emulators. J. Eng. Gas Turbines Power 141: 1–11, https://doi.org/10.1115/1.4043964.Search in Google Scholar

Rasmussen, C.E. and Williams, C.K.I. (2006). Gaussian processes for machine learning. MIT Press, Cambridge.10.7551/mitpress/3206.001.0001Search in Google Scholar

Roman, N.D., Bre, F., Fachinotti, V.D., and Lamberts, R. (2020). Application and characterization of metamodels based on artificial neural networks for building performance simulation: a systematic review. Energy Build. 217: 1–22, https://doi.org/10.1016/j.enbuild.2020.109972.Search in Google Scholar

Romeo, L.M. and Gareta, R. (2006). Hybrid System for fouling control in biomass boilers. Eng. Appl. Artif. Intell. 19: 915–925, https://doi.org/10.1016/j.engappai.2006.01.019.Search in Google Scholar

Safdarnejad, S.M., Tuttle, J.F., and Powell, K.M. (2019). Dynamic modeling and optimization of a coal-fired utility boiler to forecast and minimize NOx and CO emissions simultaneously. Comput. Chem. Eng. 124: 62–79, https://doi.org/10.1016/j.compchemeng.2019.02.001.Search in Google Scholar

Sakthivel, G., Snehitkumar, B., and Ilangkumaran, M. (2016). Application of fuzzy logic in internal combustion engines to predict the engine performance. Int. J. Ambient Energy 37: 273–283, https://doi.org/10.1080/01430750.2014.952844.Search in Google Scholar

Sansaniwal, S.K., Pal, K., Rosen, M.A., and Tyagi, S.K. (2017). Recent advances in the development of biomass gasification technology: a comprehensive review. Renew. Sustain. Energy Rev. 72: 363–384, https://doi.org/10.1016/j.rser.2017.01.038.Search in Google Scholar

Schulz, E., Speekenbrink, M., and Krause, A. (2018). A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions. J. Math. Psychol. 85: 1–16, https://doi.org/10.1016/j.jmp.2018.03.001.Search in Google Scholar

Shalaby, A., Elkamel, A., Douglas, P.L., Zhu, Q., and Zheng, Q.P. (2021). A machine learning approach for modeling and optimization of a CO2 post-combustion capture unit. Energy 215: 1–8, https://doi.org/10.1016/j.energy.2020.119113.Search in Google Scholar

Shan, S., Cai, X., Li, K., Zhang, Q., Zhou, Z., and Zhang, Y. (2021). Spectral energy characteristics of radiation in oxy-coal combustion for energy utilization. Fuel 289: 1–12, https://doi.org/10.1016/j.fuel.2020.119917.Search in Google Scholar

Sharma, R., Kamble, S.S., Gunasekaran, A., Kumar, V., and Kumar, A. (2020). A systematic literature review on machine learning applications for sustainable agriculture supply chain performance. Comput. Oper. Res. 119: 1–17, https://doi.org/10.1080/13675567.2020.1830049.Search in Google Scholar

Shi, Y., Zhong, W., Chen, X., Yu, A.B., and Li, J. (2019). Combustion optimization of ultra supercritical boiler based on artificial intelligence. Energy 170: 804–817, https://doi.org/10.1016/j.energy.2018.12.172.Search in Google Scholar

Si, F., Romero, C.E., Yao, Z., Schuster, E., Xu, Z., Morey, R.L., and Liebowitz, B.N. (2009). Optimization of coal-fired boiler SCRs based on modified support vector machine models and genetic algorithms. Fuel 88: 806–816, https://doi.org/10.1016/j.fuel.2008.10.038.Search in Google Scholar

Sipöcz, N., Tobiesen, F.A., and Assadi, M. (2011). The use of artificial neural network models for CO2 capture plants. Appl. Energy 88: 2368–2376.10.1016/j.apenergy.2011.01.013Search in Google Scholar

Smrekar, J., Potočnik, P., and Senegačnik, A. (2013). Multi-step-ahead prediction of NOx emissions for a coal-based boiler. Appl. Energy 106: 89–99, https://doi.org/10.1016/j.apenergy.2012.10.056.Search in Google Scholar

Somoano, M.D. (2019). 3 - Minimization of Hg and trace elements during coal combustion and gasification processes. In: Suárez-Ruiz, I., Diez, M. A., and Rubiera, F. (Eds.), New trends in coal conversion. Woodhead Publishing, Cambridge, UK, pp. 59–88.10.1016/B978-0-08-102201-6.00003-0Search in Google Scholar

Song, J., Romero, C.E., Yao, Z., and He, B. (2016). Improved artificial bee colony-based optimization of boiler combustion considering NOx emissions, heat rate and fly ash recycling for on-line applications. Fuel 172: 20–28, https://doi.org/10.1016/j.fuel.2015.12.065.Search in Google Scholar

Song, J., Romero, C.E., Yao, Z., and He, B. (2017). A globally enhanced general regression neural network for on-line multiple emissions prediction of utility boiler. Knowl. Base Syst. 118: 4–14, https://doi.org/10.1016/j.knosys.2016.11.003.Search in Google Scholar

Strušnik, D., Agrež, M., Avsec, J., and Golob, M. (2021). Optimisation of an old 200 MW coal-fired boiler with urea injection through the use of supervised machine learning algorithms to achieve cleaner power generation. J. Clean. Prod. 290: 1–19.10.1016/j.jclepro.2020.125200Search in Google Scholar

Sun, H., Burton, H.V., and Huang, H. (2021). Machine learning applications for building structural design and performance assessment: state-of-the-art review. J. Build. Eng. 33: 1–14, https://doi.org/10.1016/j.jobe.2020.101816.Search in Google Scholar

Sun, S., Cao, Z., Zhu, H., and Zhao, J. (2020). A survey of optimization methods from a machine learning perspective. IEEE Trans. Cybern. 50: 3668–3681, https://doi.org/10.1109/tcyb.2019.2950779.Search in Google Scholar PubMed

Suresh, M.V.J.J., Reddy, K.S., and Kolar, A.K. (2011). ANN-GA based optimization of a high ash coal-fired supercritical power plant. Appl. Energy 88: 4867–4873, https://doi.org/10.1016/j.apenergy.2011.06.029.Search in Google Scholar

Tahmasebi, P., Kamrava, S., Bai, T., and Sahimi, M. (2020). Machine learning in geo- and environmental sciences: from small to large scale. Adv. Water Resour. 142: 1–33, https://doi.org/10.1016/j.advwatres.2020.103619.Search in Google Scholar

Tan, C.K., Wilcox, S.J., and Ward, J. (2006). Use of artificial intelligence techniques for optimisation of co-combustion of coal with biomass. J. Energy Inst. 79: 19–25, https://doi.org/10.1179/174602206x90913.Search in Google Scholar

Tan, P., Xia, J., Zhang, C., Fang, Q., and Chen, G. (2016). Modeling and reduction of NOx emissions for a 700 MW coal-fired boiler with the advanced machine learning method. Energy 94: 672–679, https://doi.org/10.1016/j.energy.2015.11.020.Search in Google Scholar

Tang, Z. and Zhang, Z. (2019). The multi-objective optimization of combustion system operations based on deep data-driven models. Energy 182: 37–47, https://doi.org/10.1016/j.energy.2019.06.051.Search in Google Scholar

Tejedor, M., Woldaregay, A.Z., and Godtliebsen, F. (2020). Reinforcement learning application in diabetes blood glucose control: a systematic review. Artif. Intell. Med. 104: 1–13, https://doi.org/10.1016/j.artmed.2020.101836.Search in Google Scholar PubMed

Theobald, S. (2015). Advancing thermal manufacturing: a technology roadmap to 2020. ASM International, Russell Township, Ohio.Search in Google Scholar

Tóth, P., Garami, A., and Csordás, B. (2017). Image-based deep neural network prediction of the heat output of a step-grate biomass boiler. Appl. Energy 200: 155–169.10.1016/j.apenergy.2017.05.080Search in Google Scholar

Tröltzsch, A. (2016). A sequential quadratic programming algorithm for equality-constrained optimization without derivatives. Opt Lett. 10: 383–399.10.1007/s11590-014-0830-ySearch in Google Scholar

Tunckaya, Y. and Koklukaya, E. (2015). Comparative prediction analysis of 600 MWe coal-fired power plant production rate using statistical and neural-based models. J. Energy Inst. 88: 11–18, https://doi.org/10.1016/j.joei.2014.06.007.Search in Google Scholar

Tursi, A. (2019). A review on biomass: importance, chemistry, classification, and conversion. Biofuel Res. J. 6: 962–979, https://doi.org/10.18331/brj2019.6.2.3.Search in Google Scholar

Tuttle, J.F. and Powell, K.M. (2019). Analysis of a thermal generator’s participation in the western energy imbalance market and the resulting effects on overall performance and emissions. Electr. J. 32: 38–46, https://doi.org/10.1016/j.tej.2019.05.010.Search in Google Scholar

Tuttle, J.F., Vesel, R., Alagarsamy, S., Blackburn, L.D., and Powell, K. (2019). Sustainable NOx emission reduction at a coal-fired power station through the use of online neural network modeling and particle swarm optimization. Control Eng. Pract. 93: 1–13, https://doi.org/10.1016/j.conengprac.2019.104167.Search in Google Scholar

Tuttle, J.F., Blackburn, L.D., and Powell, K.M. (2020). On-line classification of coal combustion quality using nonlinear SVM for improved neural network NOx emission rate prediction. Comput. Chem. Eng. 141: 1–11, https://doi.org/10.1016/j.compchemeng.2020.106990.Search in Google Scholar

Tuttle, J.F., Blackburn, L.D., Andersson, K., and Powell, K.M. (2021). A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling. Appl. Energy 292: 1–34, https://doi.org/10.1016/j.apenergy.2021.116886.Search in Google Scholar

Vapnik, V. (1999). The nature of statistical learning theory. Springer, New York City.10.1007/978-1-4757-3264-1Search in Google Scholar

Vaughan, A. (2015). Adaptive machine learning for modeling and control of non-stationary, near chaotic combustion in real-time, Ph.D. thesis. Ann Arbor, University of Michigan.Search in Google Scholar

Venkata Rao, R. (2016). Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int. J. Ind. Eng. Comput. 7: 19–34, https://doi.org/10.5267/j.ijiec.2015.8.004.Search in Google Scholar

Wang, C., Liu, Y., Zheng, S., and Jiang, A. (2018a). Optimizing combustion of coal fired boilers for reducing NOx emission using Gaussian process. Energy 153: 149–158, https://doi.org/10.1016/j.energy.2018.01.003.Search in Google Scholar

Wang, F., Ma, S., Wang, H., Li, Y., Qin, Z., and Zhang, J. (2018b). A hybrid model integrating improved flower pollination algorithm-based feature selection and improved random forest for NOx emission estimation of coal-fired power plants. Meas. J. Int. Meas. Confed. 125: 303–312, https://doi.org/10.1016/j.measurement.2018.04.069.Search in Google Scholar

Wang, F., Zhang, H., and Zhou, A. (2021). A particle swarm optimization algorithm for mixed-variable optimization problems. Swarm Evol. Comput. 60: 1–12, https://doi.org/10.1016/j.swevo.2020.100808.Search in Google Scholar

Wei, Z., Li, X., Xu, L., and Cheng, Y. (2013). Comparative study of computational intelligence approaches for NOx reduction of coal-fired boiler. Energy 55: 683–692, https://doi.org/10.1016/j.energy.2013.04.007.Search in Google Scholar

Wu, X., Shen, J., Wang, M., and Lee, K.Y. (2020). Intelligent predictive control of large-scale solvent-based CO2 capture plant using artificial neural network and particle swarm optimization. Energy 196: 1–14, https://doi.org/10.1016/j.energy.2020.117070.Search in Google Scholar

Xi, H., Liao, P., and Wu, X. (2021). Simultaneous parametric optimization for design and operation of solvent-based post-combustion carbon capture using particle swarm optimization. Appl. Therm. Eng. 184: 1–14, https://doi.org/10.1016/j.applthermaleng.2020.116287.Search in Google Scholar

Xu, A., Chang, H., Xu, Y., Li, R., Li, X., and Zhao, Y. (2021). Applying artificial neural networks (ANNs) to solve solid waste-related issues: a critical review. Waste Manag. 124: 385–402, https://doi.org/10.1016/j.wasman.2021.02.029.Search in Google Scholar PubMed

Yan, W. (2020). Detecting gas turbine combustor anomalies using semi-supervised anomaly detection with deep representation learning. Cognit. Comput. 12: 398–411, https://doi.org/10.1007/s12559-019-09710-7.Search in Google Scholar

Yang, G., Wang, Y., and Li, X. (2020). Prediction of the NOx emissions from thermal power plant using long-short term memory neural network. Energy 192: 1–13, https://doi.org/10.1016/j.energy.2019.116597.Search in Google Scholar

Yang, X.S. (2010). A new metaheuristic bat-inspired algorithm. Stud. Comput. Intell. 284: 65–74, https://doi.org/10.1007/978-3-642-12538-6_6.Search in Google Scholar

Yang, X.S. (2012). Flower pollination algorithm for global optimization. In: Intl. Conf. on unconventional computing and natural computation, Vol. 7445, pp. 240–249.10.1007/978-3-642-32894-7_27Search in Google Scholar

Yravedra, F.A. and Li, Z. (2021). A complete machine learning approach for predicting lithium-ion cell combustion. Electr. J. 34: 1–10, https://doi.org/10.1016/j.tej.2020.106887.Search in Google Scholar

Yuan, Z., Meng, L., Gu, X., Bai, Y., Cui, H., and Jiang, C. (2021). Prediction of NOx emissions for coal-fired power plants with stacked-generalization ensemble method. Fuel 289: 1–12, https://doi.org/10.1016/j.fuel.2020.119748.Search in Google Scholar

Zhang, L., Wen, J., Li, Y., Chen, J., Ye, Y., Fu, Y., and Livingood, W. (2021). A review of machine learning in building load prediction. Appl. Energy 285: 1–22, https://doi.org/10.1016/j.apenergy.2021.116452.Search in Google Scholar

Zhao, B., Zhang, Z., Jin, J., and Pan, W.-P. (2010). Modeling mercury speciation in combustion flue gases using support vector machine: prediction and evaluation. J. Hazard Mater. 174: 244–250, https://doi.org/10.1016/j.jhazmat.2009.09.042.Search in Google Scholar PubMed

Zhao, R., Wang, Q., Zhao, L., Deng, S., Bian, X., and Liu, L. (2021). Comparative study on energy efficiency of moving-bed adsorption for carbon dioxide capture by two evaluation methods. Sustain. Energy Technol. Assess. 44: 1–11, https://doi.org/10.1016/j.seta.2021.101042.Search in Google Scholar

Zheng, L.-G., Zhou, H., Cen, K.-F., and Wang, C.-L. (2009). A comparative study of optimization algorithms for low NOx combustion modification at a coal-fired utility boiler. Expert Syst. Appl. 36: 2780–2793, https://doi.org/10.1016/j.eswa.2008.01.088.Search in Google Scholar

Zheng, Z., Lin, X., Yang, M., He, Z., Bao, E., Zhang, H., and Tian, Z. (2020). Progress in the application of machine learning in combustion studies. ES Energy Environ. 9: 1–14, https://doi.org/10.30919/esee8c795.Search in Google Scholar

Zhou, L., Song, Y., Ji, W., and Wei, H. (2022). Machine learning for combustion. Energy AI 7: 1–27, https://doi.org/10.1016/j.egyai.2021.100128.Search in Google Scholar

Received: 2022-01-03
Accepted: 2022-06-03
Published Online: 2022-08-12
Published in Print: 2023-08-28

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 13.5.2024 from https://www.degruyter.com/document/doi/10.1515/revce-2021-0107/html
Scroll to top button