Advancements in Machine Learning Modeling of Cofiring Systems: A Mini Review
DOI:
https://doi.org/10.55981/mipi.2023.1735Keywords:
Biomass co-firing, Emission, Literature review, Machine learning, Modeling, Renewable energy, Thermal efficiencyAbstract
Accurate modeling of biomass co-firing systems is essential to enhance renewable energy usage by optimizing efficiency and minimizing harmful emissions. Traditional modeling approaches, such as mathematical models and simulations, have limitations in capturing the complex dynamics and non-linear relationships inherent in co-firing systems. In contrast to traditional modeling, machine learning provides a promising approach by utilizing historical data patterns to create precise prediction models. This paper reviews recent machine learning techniques applied in modeling biomass co-firing systems, focusing specifically on models for predicting thermal efficiency and emissions. The examined studies exhibit machine learning's potential to accurately forecast and enhance thermal efficiency factors like feed water, fuel, and air properties. Deep learning methods, including Deep Neural Networks (DNN) and Artificial Neural Networks (ANN), have shown superior modeling capabilities in optimizing thermal efficiency. Regression tree, random forest, and fuzzy logic algorithms have also proved effective in optimizing thermal energy production and power estimation. Moreover, machine learning algorithms such as Support Vector Machine (SVM), Gaussian process (GP), polynomial regression, and fuzzy logic have demonstrated accurate predictions of emissions, including CO2, NOx, and other pollutants. Challenges related to data availability, model interpretability, and scalability need to be addressed for further advancements in machine learning modeling.
References
Y. Xu, K. Yang, J. Zhou, and G. Zhao, “Coal-biomass co-firing power generation technology: Current status, challenges, and policy implications,” Sustain., vol. 12, no. 9, 2020, doi: 10.3390/su12093692.
R. Pant and D. Bisht, “The Smart Computation of Biomass Co-Firing in Small Scale Power Plants by using Artificial Intelligence based Analysis Model,” 2023. doi: 10.1109/ICICACS57338.2023.10100147.
O. Sedej, E. Mbonimpa, T. Sleight, and J. Slagley, “Artificial Neural Networks and Gradient Boosted Machines Used for Regression to Evaluate Gasification Processes: A Review,” J. Energy Power Technol., vol. 4, no. 3, pp. 1–1, 2022, doi: 10.21926/jept.2203027.
J.-Q. M. Wang, Jian-Guo et al., “A Deep Learning-based Operation Optimization Strategy for BFG / coal Co-firing Boiler,” Proc. 36th Chinese Control Conf., pp. 9720–9724, 2017.
C. Wang, Y. Liu, R. M. Everson, A. A. M. Rahat, and S. Zheng, “Applied Gaussian Process in Optimizing Unburned Carbon Content in Fly Ash for Boiler Combustion,” Math. Probl. Eng., vol. 2017, 2017, doi: 10.1155/2017/6138930.
H. Akbas and G. Özdemir, “An integrated prediction and optimization model of a thermal energy production system in a factory producing furniture components,” Energies, vol. 13, no. 22, Nov. 2020, doi: 10.3390/en13225999.
W. M. Ashraf et al., “Optimization of a 660 MWe supercritical power plant performance⇔a case of industry 4.0 in the data-driven operational management part 1. thermal efficiency,” Energies, vol. 13, no. 21, Oct. 2020, doi: 10.3390/en13215592.
M. Karaçor et al., “Life performance prediction of natural gas combined cycle power plant with intelligent algorithms,” Sustain. Energy Technol. Assessments, vol. 47, no. June, 2021, doi: 10.1016/j.seta.2021.101398.
Z. Z. Han, Y. Z. Huang, J. Li, B. Zhang, M. M. Hossain, and C. L. Xu, “A hybrid deep neural network based prediction of 300 MW coal-fired boiler combustion operation condition,” Sci. China Technol. Sci., vol. 64, no. 10, pp. 2300–2311, 2021, doi: 10.1007/s11431-020-1796-2.
N. Effendy, E. D. Kurniawan, K. Dwiantoro, A. Arif, and N. Muddin, “The prediction of the oxygen content of the flue gas in a gas-fired boiler system using neural networks and random forest,” IAES Int. J. Artif. Intell., vol. 11, no. 3, p. 923, Sep. 2022, doi: 10.11591/ijai.v11.i3.pp923-929.
C. W. Hong and J. Kim, “Exhaust Temperature Prediction for Gas Turbine Performance Estimation by Using Deep Learning,” J. Electr. Eng. Technol., no. 0123456789, 2023, doi: 10.1007/s42835-023-01488-x.
C. Saleh, N. R. Dzakiyullah, and J. B. Nugroho, “Carbon dioxide emission prediction using support vector machine,” in IOP Conference Series: Materials Science and Engineering, Mar. 2016, vol. 114, no. 1. doi: 10.1088/1757-899X/114/1/012148.
C. Wang, Y. Liu, S. Zheng, and A. Jiang, “Optimizing combustion of coal-fired boilers for reducing NOx emission using Gaussian Process,” Energy, vol. 153, pp. 149–158, 2018, doi: 10.1016/j.energy.2018.01.003.
F. Elmaz, Ö. Yücel, and A. Y. Mutlu, “Predictive modeling of biomass gasification with machine learning-based regression methods,” Energy, vol. 191, 2020, doi: 10.1016/j.energy.2019.116541.
V. Kovalnogov, R. Fedorov, V. Klyachkin, D. Generalov, Y. Kuvayskova, and S. Busygin, “Applying the Random Forest Method to Improve Burner Efficiency,” Mathematics, vol. 10, no. 12, Jun. 2022, doi: 10.3390/math10122143.
C. X. Zhezhe Han, Jian Li, Md. Moinul Hossain, Qi Qi, Biao Zhang, “An ensemble deep learning model for exhaust emissions prediction of heavy oil-fired boiler combustion,” Fuel, vol. 308, 2022, doi: https://doi.org/10.1016/j.fuel.2021.121975.
J. Krzywanski et al., “Modelling of SO2 and NOx Emissions from Coal and Biomass Combustion in Air-Firing, Oxyfuel, iG-CLC, and CLOU Conditions by Fuzzy Logic Approach,” Energies, no. x, 2022.
S. Zhou, H. He, L. Zhang, W. Zhao, and F. Wang, “A Data-Driven Method to Monitor Carbon Dioxide Emissions of Coal-Fired Power Plants,” Energies, vol. 16, no. 4, Feb. 2023, doi: 10.3390/en16041646.
S. Ren, S. Wu, and Q. Weng, “Physics-informed machine learning methods for biomass gasification modeling by considering monotonic relationships,” Bioresour. Technol., vol. 369, Feb. 2023, doi: 10.1016/j.biortech.2022.128472.
Z. Wang et al., “NOx emission prediction using a lightweight convolutional neural network for cleaner production in a down-fired boiler,” J. Clean. Prod., vol. 389, no. January, p. 136060, 2023, doi: 10.1016/j.jclepro.2023.136060.
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