Abstract
Monitoring and enforcing the performance of equipment in coal-based thermal power plants play a vital role in operational management. As the coal-based power plant is a nonlinear system involving multiple inputs and multiple outputs, the standard and typical identification methods tend to deviate. This can happen due to factors such as strong coupling, multivariable characteristics, time delays, and noise contamination of onsite data. Due to the availability of distributed control system and advances in data collection technology, huge amounts of data is available for analysis. Past works in this area have underutilized the historical data collected from the physical analysis of power plants. This work has analyzed this data using Feature Selection and Machine Learning modeling techniques to derive meaningful variables and make accurate predictions using them. These predictions can help power plant operators improve boiler efficiency and reduce toxic gas emissions from their plants. This paper presents approaches using empirical modeling based on classification algorithms along with feature selection methods (Pearson Correlation, Random Forest) that are used to predict boiler efficiency. In the present research work, the XGBoost algorithm outperforms Gradient Boosting. In addition to that XGBoost algorithm is trained with 4, 8, and 13 features resulted in an accuracy of 91.98%, 91.13%, and 91.3%. The outcome of our research provides recommendations to the power plant operator for improving the boiler efficiency and hence lowering the quantity of toxic flue gases emitted into the atmosphere.
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References
http://indianpowersector.com/home/power-station/thermal-power-plant/
http://www.chemicalogic.com/Pages/DownloadSteamTabCompanion.html
https://www.eecpowerindia.com/codelibrary/ckeditor/ckfinder/userfiles/files/PADO%20Overview_new.pdf
https://www.electrical4u.com/economiser-in-thermal-power-plant-economiser/
Ahmed ST, Patil KK (2016) An investigative study on motifs extracted features on real time big-data signals. In 2016 International Conference on Emerging Technological Trends (ICETT): 1–4. IEEE
Ahmed ST, Sankar S, Sandhya M (2021) Multi-objective optimal medical data informatics standardization and processing technique for telemedicine via machine learning approach. J Ambient Intell Human Comput 12(5):5349–5358. https://doi.org/10.1007/s12652-020-02016-9
Basha SM et al (2019) Comparative study on performance of document classification using supervised machine learning algorithms: KNIME. Int J Emerg Technol 10.1:148–153
Bhaskaran HA (2016) Influence of flue gas and feed water temperatures on boiler efficiency–an analysis. Int J Innovative Res Sci 5(1):94–109
Bora MK, Nakkeeran S (2014) Performance analysis from the efficiency estimation of coal fired boiler. Int J Adv Res 2.5:561–574
Chu J-Z et al (2003) Constrained optimization of combustion in a simulated coal-fired boiler using artificial neural network model and information analysis☆. Fuel 82.6:693–703
Deshpande P, Warke N, Khandare P, Deshpande V (2012) Thermal power plant analysis using artificial neural network. Nirma University International Conference on Engineering (NUiCONE), IEEE
Gurusingam P et al (2017) Intelligent monitoring system of unburned carbon of fly ash for coal fired power plant boiler. MATEC Web of Conferences. Vol. 131. EDP Sciences
Hall MA (2000) Correlation-based feature selection of discrete and numeric class machine learning. In: ICML '00: Proceedings of the seventeenth international conference on machine learning, pp 359–366
Hu X et al (2020) Multi-objective prediction of coal-fired boiler with a deep hybrid neural networks. Atmos Pollut Res 11.7:1084–1090
John DK, Namee BM, D’Arcy A (2015) Fundamentals of machine learning for predictive data analytics- algorithms, Worked examples and case studies. MIT Press
Kumar SS, Ahmed ST, Vigneshwaran P, Sandeep H, Manjunath Singh H (2021) Two phase cluster validation approach towards measuring cluster quality in unstructured and structured numerical datasets. J Ambient Intell Humaniz Comput 12(7):7581–7594
Kusiak A, Song Z (2006) Combustion efficiency optimization and virtual testing: a data-mining approach. IEEE Trans Industr Inf 2(3):176–184
Li G et al (2019) Circular convolution parallel extreme learning machine for modeling boiler efficiency for a 300 MW CFBB. Soft Comput 23.15:6567–6577
Li G, Niu P (2016) Combustion optimization of a coal-fired boiler with double linear fast learning network. Soft Comput 20(1):149–156
Li G, Niu P, Liu C, Zhang W (2012) Enhanced combination modeling method for combustion efficiency in coal-fired boilers. Appl Soft Comput 12(10):3132–3140
Li G, Niu P, Ma Y, Wang H, Zhang W (2014a) Tuning extreme learning machine by an improved artificial bee colony to model and optimize the boiler efficiency. Knowl-Based Syst 67:278–289
Li G, Niu P, Wang H, Liu Y (2014b) Least square fast learning Network for modeling the combustion efficiency of a 300WM coal-fired boiler. Neural Netw 51:57–66
Li G, Qi X-B, Chan KCC, Chen B (2017) Deep bidirectional learning machine for predicting NO x emissions and boiler efficiency from a coal-fired boiler. Energy Fuels 31.10:11471–11480
Ma Y et al (2021) Three-objective optimization of boiler combustion process based on multi-objective teaching-learning based optimization algorithm and ameliorated extreme learning machine. Mach Learn Appl 5:100082
Niu P et al (2019) A novel flower pollination algorithm for modeling the boiler thermal efficiency. Neural Process Lett 49.2:7li–759
Niu P, Ma Y, Li M, Yan S, Li G (2016) A kind of parameters self-adjusting extreme learning machine. Neural Process Lett 44(3):813–830
Ogilvie T, Swidenbank E, Hogg BW (1998) Use of data mining techniques in the performance monitoring and optimisation of a thermal power plant. In: IEE colloquium on knowledge discovery and data mining (1998/434). IET
Raju VG, Lakshmi KP, Jain VM, Kalidindi A, Padma V (2020) Study the influence of normalization/transformation process on the accuracy of supervised classification. In: 2020 Third international conference on smart systems and inventive technology (ICSSIT). IEEE, pp 729-735
Sabilla SI, Sarno R, Triyana K (2019) Optimizing threshold using pearson correlation for selecting features of electronic nose signals. J Intell Eng Syst 12.6:81–90
Saeys Y, Abeel T, Van de Peer Y (2008) Robust feature selection using ensemble feature selection techniques. In: Joint European conference on machine learning and knowledge discovery in databases. Springer, Berlin
Saptoro A, Vuthaluru HB, Tade MO (2006) Prediction and monitoring of unburnt carbon in fly ash in coal-fired power plant. Proceeding of the International Conference on Modeling and Simulation
Schivley G et al (2015) Identifying/quantifying environmental trade-offs inherent in GHG reduction strategies for coal-fired power. Environ Sci Technol 49.13:7562–7570
Shah S, Adhyaru DM (2011) Boiler efficiency analysis using direct method. In: 2011 Nirma university international conference on engineering. IEEE, pp 1–5
Singh BR, Valsalam SR, Pratheesh H, Sujimon KT, Aditi C (2015) Real time pulverised coal flow soft sensor for thermal power plants using evolutionary computation techniques. ICTACT J Soft Comput 5(2):911–916
Song Z, Kusiak A (2007) Constraint-based control of boiler efficiency: a data-mining approach. IEEE Trans Industr Inf 3(1):73–83
Srinivas GT, Kumar DR, Mohan PVVM, Rao BN (2017) Efficiency of a coal fired boiler in a typical thermal power plant. Am J Mech Indust Eng 2.1:32
Thota S, Mandi RP, Chaudhari S (2018) Survey on intelligent control approaches for prediction of boiler efficiency in thermal power plant. In: 2018 Second international conference on green computing and internet of things (ICGCIoT). IEEE
Valsalam S, Rominus SA, Raja Singh B (2009) Boiler modelling and optimal control of steam temperature in power plants. IFAC Proceed 42.9:125–130
Wang K et al (2016) Environmental co-benefits of energy efficiency improvement in coal-fired power sector: a case study of Henan Province, China. Appl Energy 184:810–819
Wang C et al (2017) Applied Gaussian process in optimizing unburned carbon content in fly ash for boiler combustion. Math Probl Eng 2017:6138930
Xie Q, Fu H, Wang X, Zuo Y (2016) SVR based prediction modeling for carbon content of pulverized coal in primary air pipe of thermal power plant boiler. In: 2016 35th Chinese control conference (CCC). IEEE, pp 2235–2240
Zhao H, Wang P-H (2009) Modeling and optimization of efficiency and NOx emission at a coal-fired utility boiler. In: 2009 Asia-Pacific power and energy engineering conference. IEEE
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Thota, S., Syed, M.B. Analysis of feature selection techniques for prediction of boiler efficiency in case of coal based power plant using real time data. Int J Syst Assur Eng Manag 15, 300–313 (2024). https://doi.org/10.1007/s13198-022-01725-y
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DOI: https://doi.org/10.1007/s13198-022-01725-y