外文翻譯---鍋爐用人工神經(jīng)網(wǎng)絡(luò)模型和信息分析(文件)
【正文】 depends mainly on the air to fuel ratio, and on the distribution of air and fuel to burners at different locations if two or more burners are used. For different fuels and different furnace configurations, the best air to fuel ratio and the best air and fuel distributions are surely different, which may be roughly estimated by analysis but can only be determined accurately by testing runs. As for the control of trace pollutant emissions (in terms of ppm or ppd NOx, SO2, etc.) of a boiler, all of such uncertain factors as fuel positions, plexity of flow and temperature fields in the burning chamber, and the plicated mechanism of chemical reactions make model prediction highly unreliable. The inability of theoretical analysis makes empirical approaches necessary to explore the optimum operation conditions of a boiler. Nardin et al. [2] used fractional factorial design procedure and identified the important factors of NOx reduction in a fluidized bed bustor (FBC) with primary measures and selective noncatalyticreduction. Henryton et al. [3] performed an optimizingcontrol of NOx and SO2 emissions in the FBC process based on static models which were regressed from experimental data. George [4] suggested a boiler test system in order to monitor NOx emissions through optimizing various operation conditions in a prehensive and systematical manner, and claimed that achieving NOx emission reduction targets does not necessarily mean equipment retrofits or poor boiler performance. The capability of artificial neural networks (ANNS) as a universal modeling tool has been widely recognized in the last 20 years, and Cyberpunk [5] showed that ANNS could approximate any arbitrary nonlinear functions. ANNS offer an alternative approach to model process behavior, as they do not require a prior i knowledge of the process phenomena. They learn by extracting impede patterns from data that describe the relationship between the inputs and the outputs in any given process phenomenon. When appropriate inputs are applied to an ANN, the ANN acquires ‘knowledge’ from the environment in a process known as ‘learning’. As a result, the ANN assimilates information that can be recalled later. ANNS are capable of handling plex and nonlinear problems, processing information rapidly and reducing the engineering effort required in model development. ANNS have been successfully applied to a variety of problems such as process fault diagnosis, system identification, pattern recognition, process modeling and control, and statistical time series modeling. Karyological [6] gave a review on the application of ANNS in energy systems.Reif man et al. [7] developed an intelligent emissions controller for fuel gas reburning in coalfired power plant,and in their study, a feedforward neural network (FFN) was used to model the static nonlinear relationships between the distribution of injected natural gas into the upper region of the furnace of a coalfired boiler and the correspondingoxides of nitrogen emissions exiting the furnace. ZHOU et al. [8] used an ANN model and genetic algorithms to optimize low NOx pulverized coal bustion. Other instances of ANNS applied in solving bustion problems include: modeling the temporal evolution of a reduced bustionsystem [9], predicting coal ash fusion temperature [10], predicting coal/char bustion rate [11], conducting model predictive control in thermal power plant [12], etc. It is obvious that
環(huán)評(píng)公示相關(guān)推薦
鄂ICP備17016276號(hào)-1