To solve the problem that the prediction accuracy of coal ash slagging behavior is generally low by single index, a model has been built up for the prediction purpose based on partial least squares (PLS) method and cross-validation theory, which includes four input variables, such as the softening temperature, base-acid ratio, percentage of silicon content and silica-alumina ratio, and one output variable, i.e. the slagging rate. A new concept of isotropic and anisotropic index is proposed, according to which the influence of each index on the slagging behavior is qualitatively analyzed, and subsequently an evaluation criterion is obtained combined with relevant fitting equations. Measurement results show that the proposed PLS model is much more accurate in prediction than that of single index, proving the model to be reasonable and feasible.
Key words
coal ash /
slagging behavior /
partial least squares method /
index /
prediction
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References
[1] CHEN Lijun, WEN Xiaoqiang, WANG Gong, et al. A summary of prediction methods for slagging properties coal-fired boilers[J]. Thermal Power Generation,2006, 35(6): 1-18.
[2] HE Peiao, ZHANG Zhongxiao. Experimental study of China's power coal slagging characteristics[J]. Power Engineering, 1987, 7(2): 1-11.
[3] LI Wenyan, KANG Zhizhong, SONG Zhiping. Development of slagging prediction technique for pulverized coal-fired boiler and application of new techniques in this field[J]. Electric Power,2003, 36(2): 12-15.
[4] 李永兴,陈春元.动力用煤结渣特性综合判别指数的研究[J].热力发电,1994,23(3):36-39.
LI Yongxing, CHEN Chunyuan.Study of the slagging property of the coals of comprehensive discriminant index [J]. Thermal Power Generation,1994,23(3):36-39.
[5] QIU Jianrong, MA Yuyi. Grey clustering judgment on performance of coal[J]. J Huazhong Univ of Sci and Tech,1994,22(3): 52-55.
[6] 冯宝安,撒应禄.电站锅炉用煤常规结渣指标的模糊综合评判[J].锅炉技术,1996,27(5):13-16.
FENG Baoan, SA Yinglu. A study of conventional slagging indicator in power plant based on fuzzy multifactorial decision [J]. Boiler Technology, 1996,27(5):13-16.
[7] FENG Baoan. Fuzzy multifactorial decision for coal slagging property judging [J]. Boiler Technology, 1997,28(3): 21-25.
[8] LIU Boqian, WANG Yuan, LI Jinfeng. Fuzzy analysis of combustion characteristics of Yuanbaoshan lignite [J]. Journal of Northeast China Institute of Electrical Power,1998,18(2): 80-84.
[9] LIU Gehui, ZHENG Chuguang, LIU Yinghui. Application of adaptive fuzzy neural network to the forecast of coal slagging in power plants[J]. J Huazhong Univ of Sci and Tech,2000, 28(3): 108-110.
[10] WANG Binzhong, WU Zhansong, XU Lidong, et al. Forecasting slagging characteristic of coal ash using fuzzy neural network method[J]. J Tsinghua Univ:Sci and Tech,1999, 39(4): 104-107.
[11] XIAO Jun, L Zhenzhong, WANG Jun, et al. A model for the diagnosis of coal ash slag-buildup characteristics by using an improved BP algorithm [J]. Journal of Engineering for Thermal Energy and Power, 2002, 17(3): 271-274.
[12] GUO Jia,ZENG Hancai. Coal slagging characteristics recognition by fuzzy mathematics for coal-fired power plants[J]. Thermal Power Generation, 1994, 23(2): 4-6.
[13] ZHAO Limin. Comprehensive fuzzy assessment of slagging in utility boilers furnaces[J]. Thermal Power Generation, 1998, 27(5): 8-10.
[14] XUE Guisheng, DING Weiming, CHENG Li. Slag characteristic valuation based on supporting vector machine[J]. Energy Research and Utilization, 2008, 20(6): 13-15.
[15] DING Shifei, SHI Zhongzhi, JIN Fengxiang. Information pattern recognition algorithm based onnonlinear iterative PLS[J]. Computer Engineering, 2008, 34(1): 20-22.
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Footnotes
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