Study On Combustion Characteristics Of Coal Jet Flame In High Temperature Air And Low NO_x Combustion Optimization Of Coal-fired Boiler

As a backdrop for the present work, pollution control has and will become one of the most important subjects in environment field and energy field as the energy consumption and environmental preservation increase. NOx is the one of the main pollutants in emissions of coal-fired boilers. In order to meet the increasingly rigorous emission limit, the strategies for NOx control at all levels have currently been a hot research topic. Therefore, the present dissertation includes two subjects, namely combustion characteristics of coal jet in high temperature/low oxygen air and low NOx emission combustion optimization of a coal-fired boiler by using artificial intelligence.High temperature air combustion has been proved to achieve energy savings and pollution reduction on industrial furnaces. However, there are only few reports on applications of this technique to coal combustion. AI based low NOx emissions combustion optimization of coal-fired boilers can reduce effectively NOx emissions. Never the less, it is still necessary to conduct further study on modeling tools and optimization algorithms, which are key parts of combustion optimization.The dissertation consists of two parts. In part one, combustion characteristics of coal jet in high temperature/low oxygen air is investigated. In part two, low NOx combustion optimization of a 300MW dual-furnace coal-fired boiler is conducted by using artificial intelligence. Specific objectives of the dissertation work are as follows.(1) Investigation on the generation of high temperature/low oxygen air. In this work, the hot exhaust gas from the propane premixed flame combusted in the fuel-lean conditions serves as the high temperature/low oxygen air, which will supply enough oxygen and ignition energy to the coal jet. Therefore, a novel flame-stabilized gas burner is designed. In all cases, the minimum averaged coflow temperature is 850K, and the maximum averaged coflow temperature is 1138K. In the tested cases, the approximately homogenous temperature of about 1000～1100K, being dependent on the propane flowrate, is appeared in a region with 100mm height and 40mm radius.In conclusion, the currently-designed porous medium flame-stabilized gas burner can provide high temperature/low oxygen air with the oxygen concentration of 7-9% and the coflow temperature of 1000-1100K. (2) Measurement of flame flicker frequency and flame height based on flame image processingThe flame flicker frequency was measured by using RedLake high speed carema. It is shown that the frequency of the flame root was highest, followed by that of the middle part of the flame, then followed by that of the upper part. The frequency increased with the primary air velocity, the coal flowrate, the particle size of coal and coal rank. On the whole, the frequency increased first and then decreased when the stoichiometry of the coflow flame is increased. The frequency show no significant change with the oxygen concentration in O2/CO2, which serves as the primary air to tansport the pulverized coal.(3) The burnout of carbon and the release characteristics of nitrogen for coal combustion in high temperature air. The coal ash was sampled by using water-cooled probe. Then, the coal ash was analyzed in the laboratory to obtain the ultimate analysis and ash content. The burnout of coal is increased with the increasing oxygen and the decreasing temperature. The remaining rates for both anthracite and bituminous decreased with the oxygen concentration from 10% to 30% when the O2/CO2 mixture was serves as the primary air. However, the influence of O2/CO2 on the anthracite was larger than on the bituminous due to the lower gasification rate between CO2 and C resulted form high ash content.The remaining rate of anthracite varied from 72.521%-77.448%. The release of nitrogen was intensified with the increasing oxygen concentration.(4) Study on the NOx emission at the early stage of the coal jet combustion in the high temperature air. As the primary air was set as air, the effects of the four high temperature air properties on NOx emission with bituminous and the lignite were quite similar. Lower air temperature and higher oxygen concentration resulted in lower NOx emission. However, for anthracite, the trend of NOx emission was different from that of the former coals. Lower air temperature and higher oxygen concentration resulted in higher NOx emission, and the value was 513～767 mg/m3. When CO2 was used as the primary air, the peak value for NOx emission of lignite was achieved at 205 mm, and then this value was decreased, which indicated the NOx at the early combustion stage was reduced in advance. When the primary air was the mixture of O2/CO2, the NOx emission for bituminous at the early combustion stage increased with increasing oxygen concentration in the primary air until the value reached 30%. Further increasing oxygen concentration did not increase the NOx emission any more.The NOx emitted increased from 139.4mg/m3 to 184.5 mg/m3. With increasing oxygen concentration in the O2/CO2, the NOx emission for anthracite first decreased and then increased. The possible reason for this might be that the ignition delay elongated the length of the flame and decreased the flame front temperature. It is found that oxygen-riched combustion can effectively reduce the NOx emission at the early combustion stage. For different coals the magnitude of the NOx emission reduced ranged from 38.78%-59.87%.As air was used as the primary air, finer Shenhua coal resulted in lower NOx emission, a similar trend as that of ordinary coal combustion in air.(5) The establishment of NOx prediction models of a coal-fired boiler. The cross correlation analysis between the operating parameters of a 300MW dual-furnace coal-fired boiler and the NOx emissions is performed. In order to eliminate the potential co-linearity between the operating parameters of the studied boiler, the principal components analysis is performed. The result shows that 18 principal components can explain the 99.999% variance of the 21 operating parameters. It is concluded that the SVR model demonstrates the best prediction producing the mean relative error of 1.59% on the testing subset consisted of 224 cases, followed by neural network model. The computational time for the SVR model is about 164sec, which is only 16.7% of that for GRNN and 3.9% of that for BPNN.(6) Low NOx combustion optimization of a coal-fired boiler. Combined with NOx emission models, Genetic Algorithm (GA), Ant Colony Optimization (ACO), Estimation of Distribution Algorithm (EDA) and Particle Swarm Optimization are respectively employed to search the optimal inputs of the SVR model so as to achieve the minimum NOx emissions for a particular boiler load by regulating the operating parameters of the studied boiler. The computational times for four optimization algorithms are 120.18 sec,120.14 sec,84.68 sec and 29.17sec, respectively.